Air quality monitoring is a critical aspect of urban environmental management, and optimizing the efficiency of monitoring systems is crucial for real-time data analysis and decision-making. This research focuses on leveraging deep learning techniques to enhance the feature selection process in IoT-based air quality monitoring systems. By employing advanced feature selection algorithms, the study aims to identify the most relevant variables that contribute to accurate air quality predictions. A deep learning-based framework is developed to automatically analyze and select the best features from sensor data collected in smart cities. The approach aims to minimize computational complexity, reduce data redundancy, and improve model performance for predictive air quality monitoring. Experimental results demonstrate the effectiveness of the proposed method in achieving high prediction accuracy while optimizing the use of IoT resources.

The growing challenge of climate change has made CO2 emission reduction in urban areas a critical goal for achieving sustainable development. Smart cities, utilizing the Internet of Things (IoT) and advanced machine learning algorithms, offer innovative solutions for monitoring, predicting, and minimizing CO2 emissions. This study focuses on integrating IoT-based sensors for real-time monitoring of environmental variables, combined with machine learning models to optimize energy consumption and reduce emissions across key urban sectors. By employing datadriven approaches, including regression analysis, neural networks, and reinforcement learning, the research proposes optimization strategies for energy usage in transportation, buildings, and industrial activities. The system showcases its potential to significantly reduce emissions while improving the efficiency of urban infrastructure. Performance metrics are provided to assess the effectiveness of the algorithm in predicting CO2 levels and optimizing emission control processes.

Accurate and early diagnosis of cancer is vital for effective treatment and patient survival. Recent advances in deep learning and neural network architectures have revolutionized medical image bioinformatics, offering enhanced precision and speed in cancer detection. This study explores the integration of convolutional neural networks (CNNs) and deep learning models for the classification and analysis of medical images, including MRI, CT, and histopathological data. By leveraging high-dimensional image features and learning complex patterns indicative of malignancies, the proposed approach significantly improves diagnostic accuracy compared to traditional methods. Additionally, the application of bioinformatics tools aids in correlating imaging findings with genomic data, supporting personalized medicine initiatives. The study highlights performance metrics across different datasets, demonstrating the model’s generalizability and clinical relevance.

With rapid urbanization and the growing concern over air pollution, real-time air quality monitoring has become essential for sustainable urban development. This study proposes an Internet of Things (IoT)-based framework for real-time air quality monitoring and optimization within smart city environments. By deploying a network of interconnected, low-cost air quality sensors, the system collects real-time environmental data, including particulate matter (PM2.5, PM10), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) levels. Advanced data analytics powered by machine learning models are applied to analyze spatial and temporal patterns, predict pollution trends, and support decision-making for urban planning and environmental regulation. The integration of cloud computing and edge AI further enhances data processing efficiency and system scalability. The proposed solution demonstrates significant potential in enabling proactive air quality management and supporting health-conscious urban living.

The increasing demand for sustainable and efficient energy solutions in urban environments has driven the integration of intelligent technologies into renewable energy systems. This study presents a comprehensive approach to optimizing renewable energy systems within smart cities by employing machine learning (ML) algorithms and neural network models. The research focuses on real-time energy demand forecasting, load balancing, and predictive maintenance using historical and sensor-based data from solar, wind, and hybrid renewable sources. Deep neural networks and supervised learning models are utilized to enhance energy production efficiency and grid reliability. Through simulations and case studies, the paper demonstrates the significant improvements in operational performance, energy cost reduction, and CO₂ emission mitigation. The findings highlight the critical role of intelligent computational methods in supporting smart energy infrastructure and advancing the transition towards greener urban ecosystems.

Smart agriculture is rapidly evolving with the integration of advanced computational technologies aimed at enhancing crop yield, resource efficiency, and sustainability. This paper investigates the application of quantum algorithms for optimizing potato farming and water resource management in IoT-enabled agricultural systems. By leveraging quantum-inspired techniques such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Machine Learning (QML), the study enhances the efficiency of data processing and decision-making in complex agricultural environments. The proposed framework incorporates real-time data from soil sensors, weather stations, and irrigation systems to predict crop needs, manage irrigation schedules, and minimize water usage without compromising crop productivity. Experimental simulations demonstrate that quantum algorithms outperform classical heuristics in terms of computational speed and optimization quality, offering a transformative approach for sustainable agriculture. This research underscores the potential of quantum computing in addressing pressing challenges in agricultural optimization, particularly in water-scarce regions.

Air pollution is a growing concern in urban environments, significantly impacting public health and environmental sustainability. This study proposes a deep learning-based framework integrated with Internet of Things (IoT) infrastructure for enhanced air quality monitoring in smart city contexts. The system utilizes distributed sensor networks to collect real-time environmental data— such as particulate matter (PM2.5, PM10), nitrogen dioxide (NO₂), and carbon monoxide (CO)— which is then processed using advanced deep neural networks including convolutional neural networks (CNNs) and long short-term memory (LSTM) models. These models improve the accuracy of pollution level predictions, anomaly detection, and spatial-temporal forecasting. Moreover, the framework supports adaptive learning for dynamic environmental conditions and supports decision-making for municipal planning and public health interventions. Results show a marked improvement in detection precision, responsiveness, and scalability, positioning the proposed solution as a pivotal tool in future smart city infrastructure.

In the era of global health challenges, the demand for rapid, accurate, and scalable virus detection systems has grown significantly. This study explores the integration of biosensor technologies with Internet of Things (IoT) infrastructure and machine learning (ML) algorithms to optimize virus detection in real-time. The proposed framework leverages advanced ML techniques—such as decision trees, support vector machines (SVM), and deep learning models—to analyze biosensor data transmitted through IoT networks. These methods enhance the detection accuracy, reduce false positives, and enable timely responses to viral outbreaks. Additionally, edge computing is employed to minimize latency and ensure efficient data processing in resource-constrained environments. The system's performance is evaluated across various biosensor platforms and virus datasets, demonstrating high sensitivity, specificity, and adaptability. The integration of intelligent biosensing and IoT-based ML optimization offers a promising direction for next-generation public health monitoring and epidemic management systems

As urbanization accelerates and environmental concerns intensify, the reduction of carbon dioxide (CO₂) emissions has become a critical objective in the development of smart cities. This research investigates the application of advanced neural network architectures to optimize energy systems for minimizing CO₂ emissions. The proposed model integrates real-time urban energy consumption data with predictive neural networks to enhance energy distribution efficiency, promote renewable energy integration, and reduce reliance on fossil fuels. By employing deep learning techniques, including convolutional and recurrent neural networks, the system forecasts emission patterns, identifies energy inefficiencies, and suggests adaptive control strategies for intelligent energy management. Simulation results across various smart city scenarios indicate significant improvements in emission reduction and energy utilization efficiency, positioning the model as a key enabler in sustainable urban development.

The increasing demand for sustainable and efficient energy systems in smart cities has necessitated the integration of advanced computational techniques for renewable energy optimization. This research explores the combined application of neural networks and quantum algorithms to enhance the efficiency and stability of renewable energy sources, such as solar and wind, within urban infrastructures. Neural networks are employed for dynamic prediction and adaptive control of energy consumption and generation patterns, while quantum algorithms are leveraged to solve complex optimization problems related to grid stability, load forecasting, and resource allocation. The hybrid model aims to improve decision-making speed and accuracy in real-time energy management systems. Experimental evaluations demonstrate superior performance over conventional models in terms of accuracy, scalability, and computational cost, indicating a significant step toward intelligent, sustainable urban energy ecosystems.

With the rapid deployment of Internet of Things (IoT) technologies in smart city environments, ensuring robust and scalable network security has become a critical challenge. Traditional security mechanisms often struggle to adapt to the heterogeneous, dynamic, and resource-constrained nature of IoT systems. This study explores the application of metaheuristic algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—to enhance the security of IoT networks in smart city applications. The proposed approach aims to optimize key security parameters, including intrusion detection, data encryption, and authentication protocols, while maintaining computational efficiency. Simulation results demonstrate the effectiveness of metaheuristic-based models in identifying security threats, minimizing attack surfaces, and adapting to evolving cyber threats in complex urban IoT ecosystems.

The transition toward sustainable agricultural practices necessitates the integration of intelligent technologies that enable efficient resource management and environmentally conscious decisionmaking. This research focuses on optimizing smart agriculture through the deployment of Internet of Things (IoT) technologies and machine learning (ML) techniques. By collecting real-time data on soil conditions, weather patterns, crop health, and irrigation needs, the system applies advanced ML models to predict optimal farming strategies. The proposed framework facilitates sustainable resource utilization, minimizes environmental impact, and enhances crop productivity. Furthermore, the study demonstrates how data-driven insights can support precision agriculture and long-term agricultural resilience in response to climate variability and increasing food demand.

With the increasing demand for sustainable and efficient agricultural practices, the integration of Internet of Things (IoT) technologies with intelligent data-driven methods has become pivotal in modern farming. This study explores the optimization of potato farming through IoT-enabled systems combined with machine learning (ML) and neural network algorithms. Real-time data collected from environmental sensors—measuring soil moisture, temperature, humidity, and nutrient levels—are processed and analyzed using artificial neural networks (ANNs) to enable predictive decision-making in irrigation, fertilization, and pest control. The proposed framework significantly enhances crop yield, reduces resource wastage, and supports adaptive farm management. By leveraging deep learning models and IoT infrastructure, this system aims to revolutionize potato cultivation through precision agriculture, improving both productivity and sustainability.

The escalating concern over air pollution in urban environments necessitates the development of intelligent systems for real-time monitoring and management. This study proposes a comprehensive framework for optimizing air quality monitoring in smart cities by integrating Internet of Things (IoT) technologies with deep learning models. IoT sensors deployed across various urban zones continuously collect data on pollutants such as PM2.5, CO₂, NOx, and O₃. The collected data is processed using advanced deep learning algorithms—particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs)—to enable accurate forecasting, anomaly detection, and spatial-temporal analysis. The system enhances the efficiency and responsiveness of municipal environmental control strategies, contributing to healthier urban living conditions. The integration of AI-driven models with IoT infrastructures not only improves air quality assessment but also facilitates proactive policymaking for sustainable city development.

Early and accurate cancer diagnosis plays a pivotal role in effective treatment planning and patient survival. This study explores the optimization of cancer diagnostic systems by integrating deep learning techniques with neural network models applied to medical imaging data. Advanced convolutional neural networks (CNNs) are employed to automatically extract high-level features from radiological images such as MRI, CT, and histopathological scans. These models are trained to detect, localize, and classify malignant patterns with high precision, significantly reducing the dependence on manual interpretation and inter-observer variability. Furthermore, the paper discusses the integration of transfer learning and hybrid neural architectures to enhance diagnostic accuracy, reduce training time, and improve generalizability across cancer types. The proposed framework demonstrates notable improvements in sensitivity, specificity, and diagnostic throughput, marking a transformative step in AI-powered medical imaging for oncology.

As the global energy landscape shifts toward sustainable practices, the integration of renewable energy sources into smart grid infrastructures has become critical. This paper investigates the deployment of advanced neural network models within IoT-based smart grid systems to enhance the optimization of renewable energy generation, distribution, and consumption. By leveraging real-time data streams from distributed IoT sensors, neural networks can predict energy demand, adapt to supply fluctuations, and automate control mechanisms across the grid. The proposed approach emphasizes the role of deep learning architectures such as LSTM and CNN in handling time-series and spatial data to achieve load balancing, peak shaving, and fault detection. The integration significantly improves energy efficiency, grid stability, and supports the transition to greener urban ecosystems.

The integration of Internet of Things (IoT) technologies within urban smart city frameworks has emerged as a pivotal strategy for optimizing CO₂ emission reductions. By deploying a network of interconnected sensors and devices, cities can monitor real-time data on energy consumption, traffic flow, and environmental conditions. This data-driven approach enables the implementation of adaptive systems, such as intelligent traffic management and energy-efficient building operations, which collectively contribute to significant reductions in greenhouse gas emissions. Moreover, IoT facilitates the seamless integration of renewable energy sources into the urban grid, enhancing sustainability efforts. The adoption of these technologies not only supports environmental objectives but also improves the quality of urban life by promoting cleaner air and more efficient resource utilization.

The global demand for sustainable agriculture necessitates the development of intelligent farming systems capable of optimizing crop yield while conserving resources. This study explores the application of machine learning techniques, particularly neural networks, integrated with Internet of Things (IoT) technologies to enhance potato farming practices. The proposed framework leverages real-time environmental and soil sensor data collected via IoT devices to inform predictive models that guide irrigation, fertilization, and disease management. Neural networks are employed to model complex interactions between agronomic factors and yield outcomes, facilitating adaptive decision-making for farmers. The system demonstrates significant improvements in crop productivity and sustainability, reducing input waste and environmental impact.

The increasing demand for rapid and accurate virus detection in the wake of global health crises has driven innovation in biosensor technologies integrated with Internet of Things (IoT) frameworks. This paper investigates the optimization of biosensor performance using machine learning and signal processing techniques within IoT-based smart healthcare environments. By deploying biosensors capable of real-time data acquisition and transmission, the system enhances early detection capabilities and supports proactive healthcare interventions. Neural networks and data-driven models are utilized to improve signal classification accuracy, reduce false positives, and adapt to varying viral profiles. The proposed system presents a scalable, intelligent solution for timely virus detection, especially in urban and remote settings where healthcare infrastructure may be limited.

Efficient water resource management is critical for ensuring sustainability in modern agriculture, particularly within the context of smart farming systems. This study explores the application of neural network algorithms for optimizing irrigation practices and water usage in smart agriculture. By integrating Internet of Things (IoT) devices with machine learning frameworks, particularly neural networks, the system can monitor environmental parameters—such as soil moisture, temperature, and humidity—in real time. The neural models analyze these inputs to predict optimal irrigation schedules and quantities, reducing water waste while maintaining crop health. Case studies in potato farming demonstrate the effectiveness of this approach in enhancing water-use efficiency and promoting sustainable agricultural practices.

Urban smart city frameworks increasingly rely on renewable energy systems to promote sustainability and reduce carbon emissions. This study investigates the application of deep learning models to optimize the management and integration of renewable energy sources, such as solar and wind power, within smart city infrastructures. By analyzing large-scale real-time data from IoT-enabled energy grids, deep learning algorithms can predict energy demand, optimize energy storage, and enhance load balancing. The proposed approach improves energy efficiency, reduces reliance on non-renewable sources, and supports dynamic decision-making in urban energy management. The findings highlight the potential of AI-driven models to revolutionize energy sustainability in smart urban environments.

The integration of Internet of Things (IoT) technologies and deep learning techniques in smart agriculture has significantly transformed the way crop yield optimization is approached. This research focuses on enhancing potato crop yield by utilizing IoT-based sensors and deep learning algorithms to collect and analyze real-time agricultural data. By monitoring environmental variables such as soil moisture, temperature, and humidity, the proposed system offers predictive insights into crop health and growth patterns. Deep learning models are trained to identify optimal growing conditions and early signs of disease or nutrient deficiencies, allowing for targeted interventions. The results demonstrate a substantial increase in potato yield, resource efficiency, and sustainability, making this approach a promising solution for modern agricultural challenges.

The rapid proliferation of Internet of Things (IoT) devices in smart cities has heightened concerns about network security, data integrity, and system resilience. This research explores the deployment of neural network-based algorithms to optimize IoT network security within urban smart city infrastructures. The study proposes a deep learning framework for real-time anomaly detection, intrusion prevention, and adaptive threat response. By analyzing heterogeneous traffic data across smart city services, the system learns complex threat patterns and dynamically enhances security protocols. Performance evaluation demonstrates significant improvements in detection accuracy, false-positive reduction, and computational efficiency compared to traditional security models. This approach provides a scalable and intelligent solution to fortify IoT ecosystems, ensuring robust and sustainable digital urbanization.

As urban environments face increasing challenges in managing air pollution, the integration of advanced computational paradigms is essential to enhance monitoring and decision-making processes. This study investigates the application of quantum computing for optimizing air quality monitoring systems within Internet of Things (IoT)-enabled smart cities. By leveraging the computational advantages of quantum algorithms—such as quantum annealing and quantum support vector machines—alongside real-time IoT sensor data, the proposed framework aims to improve the speed and accuracy of air pollutant detection, prediction, and spatial-temporal data processing. The hybrid architecture integrates classical and quantum computational resources to support efficient resource allocation, anomaly detection, and adaptive environmental response strategies. The results underscore the transformative potential of quantum computing in reshaping environmental intelligence infrastructures and promoting sustainable urban living.

Smart agriculture has emerged as a transformative approach to enhance crop productivity and resource efficiency through the integration of Internet of Things (IoT) technologies and datadriven techniques. This study focuses on optimizing potato farming by applying data mining algorithms within IoT-enabled smart agriculture systems. Real-time data from soil sensors, weather stations, and crop monitoring devices are collected and analyzed using advanced data mining methods such as decision trees, clustering, and association rule mining. The aim is to identify patterns and correlations that inform optimal irrigation schedules, fertilization strategies, and pest control measures. The integration of these insights into farming operations results in improved yield, reduced resource consumption, and sustainable agricultural practices. The proposed framework demonstrates the potential of combining IoT infrastructure with intelligent data analytics to support precision farming for potato cultivation.

The rapid and accurate detection of viral pathogens is essential for effective public health response, especially in densely populated urban environments. This study presents a novel framework for optimizing virus detection systems by integrating Internet of Things (IoT)-based biosensors with neural network algorithms. The proposed system enables real-time monitoring and early detection by leveraging biosensors that collect biological data, which are then analyzed using deep learning models such as convolutional neural networks (CNNs) for pattern recognition and anomaly classification. The optimization process employs techniques to enhance accuracy, reduce false positives, and ensure low-latency responses. Experimental validation demonstrates significant improvements in detection speed and reliability compared to traditional methods. This interdisciplinary approach offers a scalable and efficient solution for modern health surveillance in smart environments.

The increasing energy demands in urban environments necessitate innovative strategies for achieving energy efficiency in smart cities. This research investigates the integration of machine learning (ML) techniques and metaheuristic algorithms to optimize energy consumption, distribution, and management within smart city infrastructures. By employing predictive models, such as decision trees and support vector machines, alongside optimization strategies like genetic algorithms and particle swarm optimization, the proposed framework dynamically manages energy resources based on real-time data from IoT sensors. Experimental results using simulated smart city datasets demonstrate improvements in load balancing, peak demand prediction, and overall energy savings. The study contributes a hybrid intelligent approach that enhances sustainability and resilience in urban energy systems.

Accurate and early cancer diagnosis is critical for effective treatment planning and improved patient outcomes. This study explores the application of neural network algorithms in optimizing medical image analysis for cancer detection and classification. By leveraging deep learning techniques such as convolutional neural networks (CNNs), the research aims to enhance feature extraction, image segmentation, and tumor classification accuracy in various imaging modalities, including MRI, CT, and histopathology slides. The proposed framework is evaluated on benchmark medical imaging datasets, demonstrating superior performance in terms of sensitivity, specificity, and diagnostic precision. The findings underscore the potential of neural networks in transforming oncological diagnostics by offering automated, scalable, and reliable image interpretation tools.

The transition to renewable energy sources necessitates intelligent energy management systems capable of optimizing generation, distribution, and consumption. This research investigates the integration of deep learning algorithms with Internet of Things (IoT)-enabled smart grid technologies to enhance renewable energy optimization. By leveraging real-time data from distributed sensors and renewable energy sources—such as solar and wind—deep learning models predict energy demand, detect faults, and optimize energy distribution patterns. The study evaluates various deep learning architectures, including convolutional and recurrent neural networks, for their performance in dynamic energy environments. Results indicate significant improvements in energy efficiency, grid reliability, and decision-making processes, thereby supporting sustainable urban infrastructure development.

Air pollution in urban environments presents a significant threat to public health and environmental sustainability. This study explores the application of advanced neural network architectures for real-time air quality monitoring and optimization within smart city frameworks. By integrating Internet of Things (IoT) sensors and deep learning models, the proposed system enables accurate prediction and classification of key pollutants such as PM2.5, NO₂, and CO. The neural networks are trained on multi-source environmental data, allowing for adaptive forecasting and intelligent decision-making in urban planning and traffic regulation. The system demonstrates high predictive accuracy and robustness across diverse urban conditions, offering a scalable solution for environmental management and public health protection in smart cities.

The integration of Internet of Things (IoT) technologies in agriculture is transforming traditional farming into smart agriculture, enabling data-driven decision-making and enhanced crop productivity. This study focuses on the application of IoT systems combined with neural network techniques for optimizing potato crop production. By deploying IoT sensors to monitor soil moisture, temperature, humidity, and other environmental variables in real time, data is continuously collected and analyzed. Neural networks are then used to model the complex relationships between environmental factors and crop yield, facilitating accurate predictions and adaptive responses. The proposed framework not only improves resource efficiency, such as water and fertilizer use, but also supports precision farming practices that contribute to sustainable agricultural development. Experimental results demonstrate significant improvements in potato yield, early disease detection, and optimized irrigation scheduling.

As global energy demands rise and environmental concerns intensify, the optimization of renewable energy systems has become essential for achieving sustainable development goals. This paper presents a comprehensive approach to enhancing the efficiency and reliability of renewable energy systems by integrating Internet of Things (IoT) technologies with deep learning and metaheuristic algorithms. IoT devices are used to collect real-time data on energy production, environmental conditions, and consumption patterns. Deep learning models are applied for accurate forecasting of energy demand and production, enabling dynamic system responses. Metaheuristic algorithms—such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—are employed to optimize energy flow, resource allocation, and storage management. The synergy of these technologies contributes to reducing operational costs, minimizing energy losses, and improving the overall sustainability of energy networks in smart city infrastructures.

Reducing CO2 emissions is a critical challenge for smart cities striving for environmental sustainability. This research proposes the use of machine learning (ML) and neural network solutions to optimize CO2 emission reduction strategies within urban environments. By leveraging data from various city sensors, including traffic flow, energy consumption, air quality, and transportation patterns, advanced ML algorithms and neural networks analyses real-time data to predict emission trends and identify potential areas for improvement. These techniques enable the design of targeted strategies to reduce emissions from key sectors such as transportation, energy, and waste management. The study presents a framework for applying ML models, including supervised and unsupervised learning, to develop optimized emission reduction plans and inform decision-making in urban planning. The implementation of such solutions can significantly contribute to achieving sustainability goals in smart cities while improving air quality and quality of life for residents.

The optimization of potato supply chains in agriculture is crucial for improving efficiency, reducing waste, and ensuring sustainability. This research explores the integration of data mining techniques and IoT technologies to enhance the potato supply chain management process in smart agriculture systems. By deploying IoT sensors across various stages of the supply chain—from production, harvesting, and storage to transportation and retail—real-time data on environmental conditions, crop health, and logistics are continuously monitored and analyzed. Data mining algorithms are then employed to process this vast array of information, uncover patterns, and predict supply chain trends such as demand fluctuations, spoilage risks, and optimal distribution strategies. The optimization aims to streamline operations, reduce costs, minimize losses, and improve the overall efficiency of the potato supply chain. This approach provides valuable insights for farmers, distributors, and retailers, contributing to a more sustainable, responsive, and efficient agricultural system.

The integration of biosensors with IoT networks and machine learning algorithms offers a promising solution for real-time virus detection, particularly in the context of public health monitoring. This study explores the optimization of virus detection systems using biosensors that are connected through IoT networks, coupled with machine learning techniques to enhance diagnostic accuracy and response time. The research focuses on leveraging advanced biosensor technologies for detecting viral biomarkers and transmitting real-time data via IoT networks to centralized processing systems. Machine learning algorithms, including supervised learning and deep learning techniques, are utilized to analyze the sensor data, classify virus-related patterns, and predict outbreaks. The optimization process involves improving the sensitivity and specificity of virus detection, enhancing system reliability, and reducing false positives/negatives. The study demonstrates how the combination of IoT, biosensors, and machine learning can provide an efficient, scalable solution for timely virus detection, crucial for controlling epidemics and ensuring public health safety.

As urbanization increases, the need for sustainable energy solutions in smart cities becomes critical. This study investigates the application of neural networks for optimizing energy systems in smart cities using IoT-based technologies. The research integrates IoT sensors to collect realtime data on energy consumption, renewable energy generation, and environmental factors. Neural network models are then employed to analyze this data and make real-time predictions, enabling dynamic energy management. The proposed approach aims to optimize energy distribution by predicting demand patterns, identifying inefficiencies, and suggesting energy-saving strategies. By leveraging deep learning algorithms, the system can dynamically adjust the energy grid, enhance the integration of renewable energy sources, and reduce carbon footprints. The study highlights the potential of IoT and neural networks to create intelligent energy management systems that contribute to sustainability goals and improve the overall efficiency of smart cities.

Water resource management in agriculture is critical for ensuring sustainability and addressing the growing challenges posed by climate change and population growth. This study presents IoT-based solutions for the real-time optimization of water resources in smart agricultural systems. The research focuses on integrating IoT sensors, data analytics, and machine learning techniques to monitor soil moisture levels, weather conditions, and water usage in agricultural fields. By utilizing smart sensors and automated irrigation systems, the IoT network provides continuous data streams that enable precise water management, ensuring that crops receive the necessary water while minimizing waste. The system uses machine learning algorithms to analyze real-time data, optimize irrigation schedules, and predict water demand, which leads to enhanced crop yield and water conservation. The study demonstrates how IoT technologies can revolutionize agricultural practices by improving efficiency, reducing water consumption, and promoting sustainable farming practices. The proposed solution is scalable and adaptable, offering potential applications in various agricultural settings worldwide.

Climate change adaptation requires efficient decision-making tools capable of processing vast amounts of geographical and environmental data. This study explores the optimization of geoinformation systems (GIS) using quantum algorithms to enhance climate change adaptation strategies. Traditional GIS techniques face limitations in terms of data processing speed and complexity when analyzing large-scale environmental datasets. By leveraging quantum computing, this research proposes novel quantum algorithms that can significantly accelerate the optimization process of GIS models, improving their ability to predict and adapt to climate change impacts. Quantum-enhanced GIS systems utilize quantum machine learning and quantum algorithms for data analysis, pattern recognition, and simulation tasks that are essential for effective climate adaptation planning. The proposed solution aims to optimize resource allocation, land use, and disaster preparedness strategies in response to climate risks. The results highlight the potential of quantum algorithms to revolutionize the field of geo-information systems, providing more accurate, efficient, and scalable tools for climate change mitigation and adaptation.

Air quality monitoring is crucial for public health and environmental sustainability, especially in urban environments. This research investigates the optimization of air quality monitoring systems using Internet of Things (IoT)-based deep learning and neural network techniques. The study combines IoT sensor networks for real-time air quality data collection with advanced machine learning models, specifically deep learning and neural networks, to predict air pollution levels and identify harmful trends. The deep learning models, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are trained to process vast amounts of time-series sensor data to provide accurate forecasts and detect anomalous pollution events. The optimization focuses on improving data accuracy, reducing false alarms, and enhancing the efficiency of air quality prediction models. By integrating these techniques, the system offers actionable insights for policymakers and urban planners to implement timely interventions aimed at improving air quality. The results demonstrate that IoT-based deep learning systems can significantly enhance air quality monitoring and management, leading to healthier urban environments.

The early and accurate diagnosis of cancer is critical for improving patient outcomes. This study explores the use of deep learning algorithms and neural networks to optimize cancer diagnosis through medical imaging techniques, such as MRI, CT scans, and X-rays. By leveraging advanced image processing methods and convolutional neural networks (CNNs), the model is trained to identify and classify malignant tumors with high accuracy. The neural network analyzes complex imaging data, extracting key features that are often difficult for human clinicians to detect. The system also integrates a feature selection process to enhance diagnostic precision by focusing on the most relevant patterns and characteristics in the medical images. The proposed approach aims to assist healthcare professionals in diagnosing cancer more efficiently and effectively, reducing the time required for diagnosis while maintaining high levels of accuracy. The results highlight the potential of deep learning in revolutionizing cancer diagnosis, offering a reliable tool for clinicians to enhance their decision-making process.

The integration of renewable energy sources into smart city infrastructures presents significant challenges, including the need for efficient energy management, grid optimization, and balancing supply with demand. This research explores the application of advanced machine learning techniques to optimize renewable energy utilization in smart city network infrastructures. By utilizing machine learning algorithms such as deep learning, reinforcement learning, and ensemble methods, the study develops predictive models that analyze energy consumption patterns, weather conditions, and renewable energy generation forecasts. These models help optimize energy distribution, storage, and grid operation, ensuring a reliable and sustainable energy supply. The research focuses on enhancing the integration of solar, wind, and other renewable energy sources into smart city grids, reducing energy waste, and minimizing costs. The findings demonstrate the effectiveness of advanced machine learning models in achieving optimal energy efficiency, enhancing grid stability, and contributing to the overall sustainability goals of smart cities.

The optimization of potato farming systems is essential for increasing yield, improving resource management, and ensuring sustainability. This study investigates the integration of Internet of Things (IoT) and neural network-based applications to optimize potato farming processes. The IoT framework collects real-time data from sensors monitoring environmental factors such as soil moisture, temperature, humidity, and crop health. These data are fed into a neural network model, which predicts optimal irrigation schedules, fertilizer usage, and pest control strategies. The neural network is trained to analyze historical crop data and current environmental conditions to make precise recommendations for each stage of the farming cycle. By incorporating machine learning techniques, the system helps farmers make data-driven decisions, ultimately enhancing productivity and reducing waste. The results of this study demonstrate the potential of IoT and neural networks in creating a more efficient, sustainable, and resilient potato farming system.

The proliferation of Internet of Things (IoT) devices in urban smart city environments introduces significant security challenges, including data privacy risks, unauthorized access, and network vulnerabilities. This study explores the application of metaheuristic algorithms to optimize IoT network security in smart cities. By leveraging algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), the research focuses on developing an adaptive security framework that dynamically adjusts to evolving threats in realtime. The proposed system employs metaheuristics to optimize security protocols, enhance encryption methods, and detect anomalous activities within IoT networks. Simulation results demonstrate the effectiveness of these algorithms in improving intrusion detection, minimizing energy consumption, and ensuring the robustness of IoT systems against cyberattacks. This research highlights the potential of metaheuristic optimization techniques in addressing the growing security concerns of IoT networks in smart cities, providing a scalable and efficient solution for safeguarding urban infrastructure and public data

Water resource management is a critical challenge in agriculture, particularly in regions experiencing water scarcity. This study presents an IoT-based framework integrated with machine learning techniques for optimizing water resource usage in smart agriculture. The system uses IoT sensors to collect real-time data on soil moisture, weather conditions, and crop health, while machine learning algorithms, such as Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), analyze the data to predict water requirements and optimize irrigation schedules. By implementing predictive models, the framework enables precision irrigation, ensuring that crops receive the optimal amount of water at the right time, thus reducing water wastage and enhancing crop yield. The results of this study indicate that the integration of IoT and machine learning significantly improves water use efficiency, promotes sustainable agricultural practices, and helps farmers make data-driven decisions for resource management. This approach not only conserves water but also supports the long-term viability of agricultural production in water-constrained environments. 

Air quality monitoring is a critical aspect of urban sustainability, especially in the context of rapidly growing smart cities. This study explores the integration of deep learning techniques and Internet of Things (IoT)-based neural networks for real-time air quality monitoring in urban environments. The research proposes a hybrid approach combining IoT sensor networks with advanced deep learning models, such as Convolutional Neural Networks (CNNs) and Long ShortTerm Memory (LSTM) networks, to predict and optimize air quality indices across various urban zones. The IoT infrastructure collects data from a network of distributed air quality sensors deployed in strategic locations throughout the city. This real-time data is then processed by the neural network models to detect pollution sources, forecast air quality trends, and provide actionable insights for urban planning and public health initiatives. The results demonstrate the potential of this system to provide accurate, real-time air quality monitoring and early warnings about hazardous pollution levels, supporting efforts to reduce environmental risks and enhance the quality of life in smart cities.

Effective management of renewable energy grids is critical for ensuring a stable, sustainable energy supply, especially with the increasing integration of renewable sources such as solar and wind energy. This study investigates feature selection optimization techniques applied to renewable energy grid management through machine learning algorithms. The research focuses on identifying and selecting the most relevant features from large datasets generated by energy sensors, grid controllers, and weather forecasts. By leveraging machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Gradient Boosting, the study optimizes the grid's energy distribution and load balancing while improving grid reliability and efficiency. Feature selection methods such as Recursive Feature Elimination (RFE) and Mutual Information (MI) are used to reduce dimensionality, minimize computational complexity, and enhance the performance of predictive models. The results show that the optimized machine learning models, powered by carefully selected features, significantly improve the forecasting accuracy and operational efficiency of renewable energy grids, leading to better integration of renewable resources and optimized energy consumption.

The integration of biosensors and Internet of Things (IoT) technologies has revolutionized realtime virus detection, offering new capabilities for rapid diagnostics in healthcare settings. This study explores the optimization of biosensor systems for virus detection through the use of neural networks in IoT environments. By applying deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to biosensor data, the research aims to enhance the sensitivity and accuracy of virus detection systems. The IoT framework enables continuous monitoring and data collection from biosensors embedded in wearable devices, air quality monitors, and diagnostic tools. The optimized neural network models process large volumes of real-time data to identify viral infections at an early stage, even in asymptomatic individuals. Experimental results demonstrate that the proposed approach significantly improves detection accuracy, reduces false positives, and accelerates the response time for virus outbreaks. This study offers a promising solution for efficient, scalable, and non-invasive virus detection, contributing to global public health initiatives.

The diagnosis of cancer through medical imaging and bioinformatics has made significant advancements in recent years, but challenges remain in terms of processing power, accuracy, and computational efficiency. This study explores the application of quantum computing techniques to optimize cancer diagnosis, specifically by enhancing medical imaging analysis and bioinformatics workflows. Quantum algorithms, including Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN), are applied to large datasets derived from medical imaging modalities such as MRI and CT scans, as well as genomic data. These quantum algorithms are designed to improve the accuracy of cancer detection, facilitate the analysis of complex genetic patterns, and accelerate computational processes. The results demonstrate that quantum computing outperforms traditional computing methods in terms of processing speed and accuracy, offering substantial improvements in early cancer detection and personalized treatment planning. This research suggests that quantum computing has the potential to revolutionize cancer diagnostics, paving the way for more efficient and precise healthcare solutions.

In the pursuit of optimizing agricultural practices, smart agriculture leverages advanced technologies to enhance crop management efficiency. This study integrates deep learning and metaheuristic algorithms for the optimization of potato crop management in smart agriculture systems. By combining deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), with metaheuristics like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the research aims to develop a comprehensive solution for optimizing irrigation schedules, pest control, nutrient management, and crop yield prediction. IoT sensors are employed to collect real-time data on soil conditions, weather patterns, and plant health, which is then analyzed to inform decision-making processes. Experimental results show that the hybrid approach significantly improves crop yield, reduces resource consumption, and enhances sustainability in potato farming. The proposed methodology offers a data-driven, adaptive solution to address the evolving challenges of modern agriculture.

The integration of Internet of Things (IoT) technologies in smart cities has revolutionized energy management by enabling real-time monitoring and control of energy systems. This study explores the application of machine learning algorithms to optimize IoT-based energy systems in urban environments. The research investigates various machine learning models, such as supervised learning, reinforcement learning, and deep learning, to predict energy consumption patterns, optimize load distribution, and enhance the efficiency of renewable energy integration. By using IoT-enabled sensors to collect real-time data on energy usage, weather conditions, and grid performance, the system dynamically adjusts energy flows, reducing waste and maximizing sustainability. Simulation results demonstrate significant improvements in energy efficiency, cost reduction, and system stability. The proposed machine learning-driven approach provides a scalable, intelligent solution for managing energy systems in smart cities, aligning with sustainability and carbon reduction goals.

Air quality monitoring is essential for the health and sustainability of urban environments, particularly in smart cities striving for optimal resource management and environmental quality. This study investigates the application of deep learning techniques for real-time air quality monitoring in urban settings. By integrating advanced deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the research aims to enhance the accuracy of air pollution forecasting and pollutant concentration predictions. The proposed system utilizes IoT-based sensors deployed throughout smart cities to gather continuous environmental data, which is then processed by the deep learning models to identify patterns, predict pollution levels, and optimize air quality management strategies. Results demonstrate that deep learning models outperform traditional methods in terms of prediction accuracy and response time, providing a powerful tool for ensuring a sustainable urban ecosystem. This approach offers scalable solutions for improving air quality in smart cities, directly contributing to their environmental goals.

The growing demand for efficient and compact antennas in Internet of Things (IoT) applications necessitates advanced optimization techniques to meet performance requirements in complex communication environments. This study explores the application of neural networks for the optimization of antenna design parameters within IoT-based communication systems. By leveraging deep learning models, particularly feedforward and convolutional neural networks, the research aims to predict and optimize antenna characteristics such as gain, bandwidth, and radiation pattern based on a set of design inputs. The proposed methodology is validated through simulation data and experimental results, demonstrating improved convergence speed and design accuracy over traditional heuristic methods. This neural network-based approach offers a scalable and data-driven solution for designing high-performance antennas tailored to the evolving demands of IoT communication infrastructures.

Smart agriculture systems are pivotal in addressing global food security and environmental sustainability, especially in resource-intensive crops like potatoes. This study presents a metaheuristics-based optimization framework for enhancing potato yield and improving water conservation in smart farming environments. The approach integrates IoT-enabled sensing technologies with advanced metaheuristic algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to optimize irrigation scheduling, nutrient distribution, and crop management strategies. Real-time environmental data collected from soil and climate sensors inform decision-making processes that dynamically adapt to changing field conditions. Experimental evaluations demonstrate significant improvements in yield outcomes and water-use efficiency compared to conventional practices. The proposed model provides an intelligent, adaptive solution for sustainable potato cultivation, promoting both agricultural productivity and environmental stewardship.

Efficient water resource management is a cornerstone of sustainable urban development, especially in the context of rapidly expanding smart cities. This study investigates the application of quantum computing techniques to optimize water distribution and usage through IoT-enabled smart city infrastructures. By leveraging the parallelism and computational power of quantum algorithms, the system addresses complex optimization problems that arise in real-time monitoring, predictive analytics, and decision-making processes. The proposed framework integrates IoT sensors for data collection with quantum-enhanced machine learning models to dynamically allocate water resources based on demand patterns, climatic conditions, and system constraints. Simulation results demonstrate significant improvements in response time, resource allocation accuracy, and system scalability. This research highlights the transformative potential of quantum computing in enhancing the sustainability and intelligence of urban water systems.

Timely virus detection in densely populated urban environments is critical for public health management and epidemic prevention. This research explores the integration of biosensors with neural network optimization models within IoT-based infrastructures to enhance real-time virus detection capabilities. The proposed system employs advanced biosensing technologies for continuous environmental monitoring, transmitting data through IoT networks to centralized neural network-based analytics engines. These neural networks are optimized to classify viral threats with high accuracy, utilizing deep learning architectures trained on extensive biological datasets. The approach emphasizes low-latency processing, scalability, and adaptability to emerging viral mutations. Experimental evaluations in simulated urban scenarios reveal improved detection precision and faster response times compared to traditional systems. This fusion of biosensor data with intelligent neural optimization significantly strengthens health surveillance mechanisms in smart cities.

Reducing CO₂ emissions in urban environments is essential for combating climate change and promoting sustainability. This study proposes an optimization approach for smart cities by integrating machine learning algorithms with renewable energy systems to reduce CO₂ emissions. The system utilizes real-time data from IoT sensors to monitor energy consumption and CO₂ levels across various urban sectors. Machine learning models, such as regression analysis and deep learning algorithms, are applied to optimize the deployment of renewable energy sources, including solar, wind, and biomass, to minimize emissions. The optimization process involves smart grid management and demand-response strategies to balance energy supply and demand efficiently. Results demonstrate that the proposed approach significantly reduces CO₂ emissions while enhancing the overall energy efficiency of smart cities, offering a scalable solution for sustainable urban development.

Cancer diagnosis has greatly benefited from advancements in medical imaging and bioinformatics, particularly through the application of deep learning techniques. This study presents an enhanced framework for cancer diagnosis by leveraging deep learning models for medical image analysis and integrating feature selection methods. The approach utilizes convolutional neural networks (CNNs) to extract relevant features from medical images, followed by advanced feature selection techniques to improve the accuracy of the model while reducing computational complexity. By identifying the most significant image features, the system improves the early detection and classification of various cancers, facilitating more accurate diagnoses. Experimental results demonstrate the effectiveness of the proposed method in enhancing cancer detection accuracy and providing potential clinical applications for medical professionals.

Air quality monitoring in urban environments is crucial for maintaining public health and sustainability in smart cities. This study proposes an optimization framework for air quality monitoring systems that integrates neural networks with Internet of Things (IoT) technologies. IoT sensors collect real-time data on pollutants such as PM2.5, PM10, NO₂, and CO₂, which are then processed using deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to predict air quality levels and detect trends. Feature selection and data fusion techniques are employed to enhance the accuracy and efficiency of the models. Experimental results show that the proposed approach improves air quality prediction accuracy and enables proactive responses to air pollution, contributing to smarter urban management.

Sustainable agricultural practices are essential for ensuring food security while minimizing environmental impacts. This study presents an optimization framework for smart potato farming, integrating advanced techniques to enhance crop yield and reduce CO₂ emissions. The framework employs Internet of Things (IoT) sensors to monitor soil moisture, temperature, and nutrient levels in real time. Using machine learning models and metaheuristic algorithms, the system optimizes irrigation, fertilization, and pesticide application to maximize crop production while minimizing resource consumption. Additionally, the integration of renewable energy sources and carbon footprint analysis contributes to reducing the carbon emissions associated with farming practices. Results from field experiments demonstrate the effectiveness of the system in promoting sustainable farming practices and achieving environmental goals.

Optimizing potato crop production is crucial for enhancing agricultural sustainability and efficiency. This study proposes an integrated approach utilizing Internet of Things (IoT) technologies, metaheuristic algorithms, and deep learning techniques for potato crop optimization. IoT sensors are deployed in the field to collect real-time data on soil conditions, weather patterns, and crop health. Metaheuristic algorithms, such as genetic algorithms (GA) and particle swarm optimization (PSO), are applied to optimize irrigation, fertilization, and pest management strategies. Additionally, deep learning models, including convolutional neural networks (CNNs), are used to analyze image data for disease detection and yield prediction. Experimental results demonstrate significant improvements in crop yield and resource usage efficiency, offering a scalable solution for smart agriculture.

Air quality monitoring has become a critical component of urban planning in smart cities, where real-time environmental insights are essential for public health and sustainability. This research presents a machine learning-based framework for optimizing air quality monitoring systems within urban smart city networks. The proposed system integrates data from distributed IoT sensors to model and predict pollutant levels using advanced machine learning algorithms such as random forests, support vector machines, and deep neural networks. Feature selection and data fusion techniques are employed to enhance prediction accuracy and reduce sensor redundancy. Results from case studies in metropolitan areas demonstrate improved air quality forecasting, enabling proactive environmental management and policy interventions.

Accurate cancer diagnosis in bioinformatics relies on identifying critical biomarkers from highdimensional biological data. This study proposes a deep learning-based framework for optimizing feature selection to enhance diagnostic accuracy in cancer prediction. Utilizing autoencoders and convolutional neural networks (CNNs), the model performs dimensionality reduction and extracts salient features from genomic and transcriptomic datasets. Metaheuristic algorithms such as genetic algorithms and particle swarm optimization are integrated to refine feature subsets and prevent overfitting. The proposed approach demonstrates superior performance in classification accuracy and computational efficiency across several benchmark bioinformatics datasets, highlighting its potential for early and precise cancer diagnosis.

Maximizing the efficiency of renewable energy systems is essential for achieving sustainable energy goals in smart environments. This study explores a novel integration of Internet of Things (IoT) frameworks with quantum computing and neural network models to optimize renewable energy efficiency. By collecting real-time environmental and operational data via IoT sensors, the system utilizes quantum algorithms for rapid data processing and neural networks for pattern recognition and predictive modeling. The hybrid model enhances energy flow management, fault detection, and load forecasting across solar and wind energy infrastructures. Experimental simulations demonstrate the proposed method's ability to significantly reduce energy waste and improve system responsiveness, contributing to more reliable and eco-friendly energy management solutions.

The rapid expansion of Internet of Things (IoT) ecosystems has introduced significant security challenges due to their distributed and heterogeneous nature. This study presents a hybrid framework combining machine learning and metaheuristic algorithms to optimize network security protocols in IoT environments. The approach leverages anomaly detection models, such as support vector machines (SVMs) and deep neural networks (DNNs), integrated with metaheuristic techniques like genetic algorithms (GA) and particle swarm optimization (PSO) to dynamically enhance encryption, authentication, and intrusion detection mechanisms. Simulation results on benchmark datasets demonstrate improved threat detection accuracy, reduced false positives, and enhanced protocol efficiency. The framework offers a scalable solution for securing smart IoT infrastructures while maintaining system performance.

Accurate and timely cancer diagnosis is crucial for effective treatment planning and improved patient outcomes. This study investigates the application of advanced neural network algorithms to optimize cancer detection in medical imaging using deep learning techniques. Convolutional neural networks (CNNs) and hybrid deep learning architectures are employed to enhance feature extraction and classification accuracy in various imaging modalities, including MRI, CT, and histopathological images. The proposed models are trained on annotated medical datasets to identify malignant patterns with high sensitivity and specificity. The framework integrates automated feature selection and image preprocessing for enhanced diagnostic performance. Experimental results validate the system's effectiveness in reducing diagnostic errors and supporting clinical decision-making in oncology.

The convergence of environmental health monitoring and infectious disease control is vital in smart urban management. This study proposes an integrated Internet of Things (IoT)-based system for real-time air quality monitoring and virus detection optimization. Utilizing a network of smart sensors, the system continuously measures pollutants such as PM2.5, NO₂, and CO₂, alongside detecting airborne biological agents. Machine learning algorithms are embedded to analyze sensor data, predict pollution trends, and identify potential viral outbreaks. Optimization techniques ensure efficient data processing and energy-aware sensor deployment. The framework enables proactive public health responses and environmental regulation compliance, reinforcing urban resilience and citizen health protection.

Efficient water resource management is a critical component of sustainable agriculture, particularly in regions facing increasing water scarcity. This study presents a deep learning-based approach for optimizing water usage within smart agriculture systems. By integrating sensorgenerated data on soil moisture, weather conditions, and crop type, deep learning models—such as long short-term memory (LSTM) networks—are trained to predict irrigation needs with high precision. The proposed system enables real-time decision-making for automated irrigation control, reducing water wastage and enhancing crop yield. Simulation results demonstrate the model’s effectiveness in adapting to dynamic environmental conditions and improving overall water-use efficiency. This approach offers a scalable and intelligent solution for sustainable agriculture in data-driven smart farming environments.

The growing urbanization and energy demands of modern cities necessitate innovative approaches to mitigate CO₂ emissions and promote environmental sustainability. This study proposes an integrated framework that leverages machine learning and metaheuristic algorithms to optimize CO₂ emission reduction strategies in smart cities. Machine learning models are employed to analyze large-scale urban data, including traffic patterns, energy consumption, and industrial emissions, to identify high-impact intervention points. Metaheuristic algorithms such as genetic algorithms and particle swarm optimization are then used to design and fine-tune policy and infrastructure interventions for maximum emission reduction. The proposed hybrid approach enhances decision-making, supports real-time adaptation, and contributes to the development of greener, more efficient urban environments.

Early and accurate detection of heart disease is vital for effective treatment and patient prognosis. This research presents a neural network-based approach for optimizing the analysis of chest X-ray images to enhance heart disease detection. The proposed system utilizes convolutional neural networks (CNNs) to automatically extract and learn deep features from chest radiographs, enabling high-accuracy classification of pathological conditions. Optimization techniques, including hyperparameter tuning and data augmentation, are applied to improve model performance and generalization. The system is evaluated using publicly available chest X-ray datasets, showing superior diagnostic accuracy compared to traditional methods. This work highlights the potential of deep learning in facilitating cost-effective, non-invasive, and scalable diagnostic tools for cardiovascular healthcare.

The transition to smart cities necessitates advanced approaches for managing renewable energy systems to ensure efficiency, sustainability, and resilience. This study presents a deep learningbased framework for optimizing renewable energy integration within IoT-enabled smart city networks. By leveraging real-time data from distributed IoT sensors monitoring energy consumption, weather patterns, and grid conditions, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are applied to predict demand patterns and optimize energy distribution. The proposed system enhances grid stability, reduces energy waste, and supports dynamic load balancing, thereby facilitating more sustainable urban energy ecosystems. The results demonstrate that deep learning models significantly outperform traditional methods in managing complex, nonlinear energy systems in smart cities.

Sustainable agricultural practices are crucial to enhancing food security while minimizing environmental impact. This study explores the use of data mining techniques integrated with Internet of Things (IoT) technologies for optimizing potato farming in smart agriculture systems. IoT sensors deployed across farming fields collect data on environmental factors such as soil moisture, temperature, humidity, and crop health. This real-time data is processed using advanced data mining algorithms, including clustering, classification, and regression models, to extract actionable insights for better decision-making. The optimization of irrigation schedules, pest control, and nutrient management not only improves potato yield but also conserves water and reduces the use of chemicals. The proposed framework demonstrates the potential for achieving sustainable and efficient potato farming through data-driven solutions in smart agriculture systems.

The growing need for rapid, accurate, and scalable health monitoring systems in response to emerging viruses has led to the development of innovative solutions integrating biosensors and Internet of Things (IoT) technologies. This study presents an IoT-based optimization framework that leverages biosensors for real-time virus detection and health monitoring. IoT-enabled biosensors collect data on various health parameters, such as temperature, heart rate, and viral load, which are then processed using machine learning models to detect infections early. Optimization algorithms are applied to enhance sensor placement, reduce false positives, and improve data transmission efficiency. The system enables continuous monitoring, rapid diagnosis, and timely interventions, offering a powerful tool for disease prevention and management in both clinical and public health settings.

The integration of quantum computing into smart agriculture offers novel solutions for optimizing farming practices and addressing environmental challenges. This study explores the application of quantum computing to optimize potato farming while simultaneously reducing CO₂ emissions. Quantum algorithms are used to model complex agricultural processes, including soil conditions, crop yield predictions, and water usage, allowing for more accurate and efficient resource management. By leveraging quantum-enhanced machine learning techniques, the system identifies optimal farming practices that minimize input costs, enhance crop yields, and reduce carbon footprints. The study demonstrates that quantum computing can significantly improve decisionmaking in smart agriculture, promoting sustainability and CO₂ reduction in agricultural systems.

As urban areas face increasing environmental challenges, the integration of advanced neural networks in smart city infrastructures offers promising solutions for air quality monitoring and energy optimization. This study explores the application of deep learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to enhance air quality prediction and energy consumption management. Real-time data from IoT sensors monitoring pollutants, weather conditions, and energy usage are processed through neural networks to provide accurate air quality forecasts and predict energy demands. The optimization of energy distribution based on these predictions supports the reduction of consumption peaks, enhances energy efficiency, and promotes the use of renewable energy sources. The proposed system improves urban environmental health and sustainability by enabling smarter energy management and cleaner air.

Accurate and early cancer detection is critical for improving patient outcomes, and medical imaging plays a central role in this process. This study proposes a deep learning-based approach for optimizing cancer diagnosis in medical imaging using neural network feature selection techniques. High-dimensional medical imaging data, such as CT scans and MRI, are first processed using advanced neural networks, including Convolutional Neural Networks (CNNs), to extract relevant features. Feature selection techniques, such as Recursive Feature Elimination (RFE) and Genetic Algorithms (GA), are applied to reduce dimensionality and enhance model performance by focusing on the most discriminative features for cancer classification. The optimized system significantly improves diagnostic accuracy, reduces computational complexity, and facilitates the development of efficient clinical decision support tools for cancer detection.

Effective water resource management is critical for achieving sustainability in smart cities, particularly in the face of growing population demands and climate change. This study proposes an IoT-based framework for optimizing water resource management by integrating machine learning algorithms with metaheuristic optimization techniques. Real-time data from IoT sensors—monitoring parameters such as water levels, flow rates, and quality indicators—are processed using machine learning models to predict consumption patterns and detect anomalies. Metaheuristic algorithms, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are employed to dynamically allocate water resources and schedule distribution with maximum efficiency. The proposed system demonstrates improved water usage efficiency, reduced wastage, and enhanced decision-making capabilities for urban water management infrastructures.

The increasing integration of renewable energy sources into power grids demands advanced energy management solutions to ensure sustainability and efficiency. This study presents a machine learning-based optimization framework designed for IoT-enabled renewable energy grid systems. The proposed model utilizes real-time data collected from distributed IoT sensors across solar, wind, and storage infrastructures to forecast energy production and consumption patterns. Various machine learning algorithms—such as Support Vector Regression (SVR), Long ShortTerm Memory (LSTM) networks, and ensemble learning methods—are employed to enhance predictive accuracy and grid stability. Furthermore, optimization techniques are applied to balance energy load, reduce system losses, and improve resource allocation. The results demonstrate significant improvements in energy efficiency and operational resilience, supporting smart grid development and sustainable urban energy strategies.

The growing concern over deteriorating air quality in urban environments necessitates the development of accurate and efficient real-time monitoring systems. This study introduces an optimized framework that combines neural network-based machine learning models with Internet of Things (IoT) infrastructures for continuous air quality assessment. IoT-enabled sensors collect data on pollutants, meteorological factors, and environmental conditions, which are processed by advanced neural networks—including deep feedforward and recurrent architectures—for precise prediction and anomaly detection. The integration of adaptive learning mechanisms allows the system to dynamically adjust to changing atmospheric patterns, improving long-term reliability and responsiveness. Experimental evaluation across various urban settings reveals enhanced predictive accuracy, minimal latency, and scalability, making this approach suitable for deployment in smart city air quality management initiatives.

With the rapid urbanization and increasing carbon emissions in metropolitan areas, there is a growing demand for intelligent solutions that support sustainable urban planning. This study presents a hybrid optimization framework that integrates deep learning models with metaheuristic algorithms to reduce CO₂ emissions in smart cities. The framework analyzes urban mobility patterns, energy consumption, and infrastructure usage through data collected from IoT-enabled sensors. Deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), are employed to forecast emission trends. Simultaneously, metaheuristic algorithms—including Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)—optimize traffic flow, energy allocation, and green infrastructure planning. The proposed system demonstrates significant emission reduction potential and offers strategic insights for policymakers aiming to implement sustainable urban development initiatives.

Rapid and accurate virus detection is crucial for mitigating public health risks, especially in densely populated smart environments. This research proposes an integrated framework that optimizes biosensor technologies using neural networks and IoT-based solutions to enhance realtime virus detection capabilities. Biosensors are embedded in IoT infrastructures to continuously monitor biological signals, environmental parameters, and pathogen presence. Advanced neural network models—including convolutional and recurrent neural networks—are deployed to process biosensor data, identify viral signatures, and reduce detection latency. The system's performance is further enhanced through adaptive calibration algorithms that maintain sensor accuracy under varying environmental conditions. Results demonstrate high detection accuracy, minimal false positives, and robust real-time response, making this framework suitable for deployment in smart healthcare and urban surveillance systems.

Accurate prediction of air quality is essential for public health management and environmental sustainability in urban areas. This study proposes an optimized machine learning framework for air quality prediction, emphasizing feature selection to enhance model accuracy and computational efficiency. Multiple urban datasets comprising meteorological parameters, vehicular emissions, industrial activity, and pollutant concentrations are preprocessed and subjected to advanced feature selection techniques, including Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and metaheuristic-based methods such as Genetic Algorithms (GA). These selected features are then used to train machine learning models including Random Forests, Support Vector Machines (SVM), and Gradient Boosting. Results indicate a significant improvement in prediction accuracy and processing time, supporting proactive environmental management and policy development in smart city contexts.

Efficient management of renewable energy resources is vital for the sustainability of smart cities. This research introduces a data mining-driven approach to optimize energy efficiency across smart city infrastructures integrated with IoT applications. Large-scale data from smart grids, sensor networks, and urban energy consumption patterns are analyzed using classification, clustering, and association rule mining techniques to uncover hidden patterns and predict energy demands. The integration of data mining with real-time IoT inputs facilitates dynamic load balancing, peak demand prediction, and intelligent distribution of renewable energy sources such as solar and wind. Experimental validation demonstrates that the proposed framework significantly enhances system responsiveness, reduces energy waste, and supports strategic decision-making for sustainable urban energy ecosystems.

The integration of artificial intelligence in precision agriculture has significantly enhanced crop yield prediction and resource management, particularly in high-demand crops like potatoes. This study presents a sustainable potato farming framework utilizing advanced neural networks within IoT-based agriculture systems. Real-time data on soil conditions, climate variables, irrigation levels, and crop health are collected via IoT sensors and processed using deep neural network (DNN) architectures, including convolutional neural networks (CNNs) and long short-term memory (LSTM) models. These models predict optimal irrigation scheduling, detect early signs of disease, and recommend nutrient management strategies. The system promotes sustainable practices by minimizing water usage, enhancing soil health, and reducing chemical inputs. Experimental results demonstrate substantial improvements in yield efficiency and environmental sustainability, offering a scalable solution for smart agriculture initiatives.

The proliferation of IoT devices in smart cities introduces significant security challenges, particularly in safeguarding sensitive data and ensuring reliable communication. This study proposes a hybrid framework that integrates metaheuristic algorithms with machine learning models to optimize IoT network security protocols in urban environments. The framework utilizes real-time network traffic data to detect anomalies, predict vulnerabilities, and adapt security policies dynamically. Metaheuristic techniques such as Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are employed to fine-tune machine learning parameters for intrusion detection models, including decision trees, support vector machines (SVM), and neural networks. The proposed system demonstrates high detection accuracy and reduced false positives while maintaining computational efficiency, thereby enhancing the resilience of smart city infrastructures against cyber threats.

Urban infrastructure plays a pivotal role in contributing to greenhouse gas emissions, particularly CO₂, making emission mitigation a priority in smart city development. This research presents a machine learning-driven framework to optimize smart city infrastructure for effective CO₂ emission reduction. By integrating data from traffic systems, energy grids, environmental sensors, and public transport networks, various supervised and unsupervised machine learning models— such as random forests, support vector machines (SVM), and k-means clustering—are employed to identify emission hotspots and inefficiencies in resource utilization. Predictive analytics support real-time decision-making for energy distribution, traffic flow control, and urban planning strategies aimed at minimizing carbon output. The results show that the proposed framework enables intelligent, adaptive control mechanisms that significantly lower emissions and enhance urban sustainability.

Accurate and early diagnosis of cancer is crucial for improving patient outcomes and guiding effective treatment strategies. This study presents an integrated framework that combines deep learning with medical image-based bioinformatics techniques to optimize cancer detection and classification. High-resolution imaging modalities such as MRI, CT, and histopathological slides are processed using convolutional neural networks (CNNs) and autoencoders to extract significant features and reduce noise. These features are then integrated with genomic and proteomic data through bioinformatics pipelines to enhance diagnostic accuracy and biological interpretability. The proposed hybrid model enables precise tumor localization, subtype differentiation, and risk stratification with high sensitivity and specificity. Experimental results demonstrate improved performance over conventional diagnostic systems, supporting the advancement of personalized and data-driven cancer diagnostics.

Smart agriculture systems increasingly rely on intelligent algorithms to enhance productivity and sustainability, particularly in water-intensive crops such as potatoes. This research proposes a metaheuristic optimization framework for improving resource utilization and crop yield in smart potato farming. The system incorporates real-time data from IoT sensors monitoring soil moisture, temperature, humidity, and nutrient levels. Metaheuristic algorithms—including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)— are employed to optimize irrigation schedules, fertilization strategies, and planting decisions. The integration of these algorithms enables adaptive decision-making under dynamic environmental conditions, significantly improving water efficiency and crop performance. Experimental validation on smart farm datasets demonstrates the effectiveness of the proposed approach in minimizing resource waste and maximizing agricultural output, contributing to the broader goals of precision agriculture and environmental conservation.

Urban CO₂ emissions represent a major challenge to global climate sustainability, particularly within the rapidly expanding infrastructure of smart cities. This study explores the application of neural networks to optimize energy consumption and reduce CO₂ emissions through intelligent urban energy management systems. A multilayered data acquisition framework collects energy usage, traffic flow, weather patterns, and environmental sensor data across city sectors. Neural network models—such as multilayer perceptrons (MLPs) and long short-term memory (LSTM) networks—are employed to predict emission trends, identify inefficiencies, and recommend optimization strategies. The system integrates with IoT-based energy infrastructure to enable realtime adaptive control of lighting, heating, and transportation systems. Simulation results indicate a substantial reduction in emissions and improved energy efficiency, providing a scalable and intelligent approach to environmental sustainability in smart urban ecosystems.

The optimization of crop yield is a central challenge in precision agriculture, especially for highdemand crops such as potatoes. This study introduces an IoT-enabled framework that utilizes advanced feature selection techniques to enhance potato yield prediction and decision-making processes in smart agriculture systems. Real-time environmental and soil data—including temperature, humidity, soil pH, and moisture levels—are collected through a network of IoT sensors. These data are then processed using feature selection algorithms such as Recursive Feature Elimination (RFE), Mutual Information, and ReliefF to identify the most influential factors affecting crop productivity. Machine learning models are trained on the selected features to predict optimal farming conditions and support intelligent irrigation, fertilization, and pest control strategies. Results show significant improvements in prediction accuracy and resource efficiency, demonstrating the system’s potential to support sustainable and data-driven agricultural practices.

Efficient water resource management is a critical aspect of sustainable smart city development, particularly in the face of growing urbanization and climate variability. This study presents a novel optimization framework that integrates deep learning models and quantum algorithms to enhance the planning, distribution, and conservation of urban water resources. Utilizing IoT-based sensors, real-time data on consumption, leakage, and environmental conditions are collected and processed using deep neural networks for predictive analytics. Quantum algorithms are employed to solve complex optimization problems related to resource allocation and network design with superior computational efficiency. The hybrid model enables accurate forecasting of water demand, rapid detection of anomalies, and adaptive control strategies, leading to significant improvements in water use efficiency and sustainability outcomes. Experimental validation demonstrates the model’s robustness and potential to inform smart urban water infrastructure.

Geo-information systems (GIS) play a pivotal role in analyzing environmental patterns and supporting decision-making in climate change mitigation strategies. This study proposes a deep learning-based optimization framework to enhance the sustainability and efficiency of GIS applications in environmental monitoring and planning. Leveraging satellite imagery, spatialtemporal datasets, and environmental indicators, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are utilized to model complex climatic phenomena and predict trends such as temperature fluctuations, land-use changes, and greenhouse gas emissions. The integration of deep learning enables high-resolution analysis, automated feature extraction, and adaptive forecasting in dynamic geospatial environments. Results demonstrate improved accuracy in climate risk assessment and resource allocation, offering a data-driven foundation for sustainable development and policy formulation.

The emergence of infectious diseases necessitates rapid, reliable, and scalable virus detection methods. This study presents an integrated biosensor system empowered by Internet of Things (IoT) technology and neural network optimization for real-time virus detection. The proposed framework utilizes advanced biosensors capable of detecting viral biomarkers in biological samples, which are then connected to an IoT network for continuous data transmission. Deep neural networks are trained to analyze biosensor output, classify viral presence, and predict infection patterns with high accuracy. Optimization algorithms are applied to enhance detection sensitivity and reduce false positives. This intelligent biosensing platform enables timely health interventions and efficient disease management, particularly in high-risk urban environments and remote healthcare settings. The system demonstrates significant improvements in detection speed, accuracy, and scalability compared to traditional diagnostic methods.

Air quality management is a vital component of sustainable urban development, particularly in the context of smart cities. This study explores the application of machine learning algorithms in enhancing the performance of air quality monitoring systems through real-time data analytics and predictive modeling. Utilizing IoT-enabled sensors distributed across urban zones, the system collects environmental data such as PM2.5, PM10, NO₂, and CO concentrations. Various machine learning models—including Random Forests, Support Vector Machines (SVM), and Gradient Boosting—are employed to forecast pollution levels, detect anomalies, and support decisionmaking processes for pollution control. The integration of these models facilitates adaptive environmental governance and targeted policy interventions. Experimental results demonstrate the system’s capability to provide accurate, location-specific air quality predictions, thereby enabling more effective environmental planning and public health protection in smart cities.

The integration of renewable energy sources into smart city infrastructures requires efficient grid management strategies to ensure sustainability and energy reliability. This research presents an IoT-enabled framework for optimizing renewable energy grids using metaheuristic algorithms. Real-time data from solar panels, wind turbines, and energy consumption units are collected via IoT sensors and analyzed to manage energy distribution dynamically. Metaheuristic optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are utilized to address the challenges of load balancing, energy storage, and demand forecasting. These algorithms enhance the operational efficiency and resilience of energy systems by identifying optimal configurations for power allocation and grid stability. The proposed framework demonstrates improved energy utilization and reduced dependency on non-renewable sources, contributing to sustainable smart city development.

Efficient antenna design is a critical factor in enhancing communication performance for IoT devices within smart city infrastructures. This study introduces an advanced neural network-based optimization framework aimed at improving antenna design parameters such as gain, directivity, bandwidth, and radiation efficiency. Using supervised learning techniques, the model is trained on a large dataset of antenna configurations and performance metrics, enabling accurate prediction and optimization of antenna behavior. Deep neural networks (DNNs) are employed to explore the design space and identify optimal configurations tailored to IoT communication requirements. The framework supports rapid prototyping and reduces computational complexity in the design process. Experimental evaluations demonstrate that the proposed method achieves superior performance compared to traditional optimization techniques, offering scalable and energyefficient antenna solutions suitable for dense urban environments and next-generation smart city networks.

Smart agriculture systems, driven by Internet of Things (IoT) technologies, offer significant potential for optimizing crop yield and resource management. This study focuses on enhancing potato farming productivity while promoting water conservation through an IoT-based framework. Sensors are deployed across the agricultural field to monitor key variables such as soil moisture, temperature, humidity, and crop growth stages in real time. These data streams are analyzed using intelligent algorithms to optimize irrigation scheduling and nutrient management. Machine learning models are integrated to predict yield outcomes and support data-driven decision-making. The proposed system reduces water waste, enhances resource efficiency, and increases crop output, aligning with sustainable agriculture goals. Results indicate substantial improvements in both water usage efficiency and potato yield, demonstrating the practical value of IoT-driven optimization in modern farming systems.

The integration of quantum computing with neural network models offers a transformative approach to cancer diagnosis, particularly in the analysis of medical imaging data. This study proposes a novel diagnostic framework that leverages quantum computing to optimize deep neural networks for enhanced image classification and tumor detection. Medical imaging datasets, including MRI and CT scans, are processed using hybrid quantum-classical models to improve computational efficiency and diagnostic precision. Quantum machine learning algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Circuits (VQC), are employed to accelerate feature extraction and classification tasks. The neural network models are further refined using quantum-enhanced optimization techniques, leading to faster convergence and improved diagnostic accuracy. Experimental evaluations reveal that the proposed framework significantly outperforms classical models in detecting cancerous patterns with reduced computational overhead, offering promising implications for clinical oncology.

The continuous monitoring and management of CO₂ emissions are essential for developing sustainable and livable smart cities. This study introduces a deep learning-based framework for real-time CO₂ level monitoring across urban environments using IoT sensor networks. Data collected from strategically deployed sensors in traffic zones, industrial areas, and residential neighborhoods are analyzed using advanced deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These models enable accurate prediction and pattern recognition of CO₂ concentration trends, facilitating timely interventions and policy decisions. The integration of deep learning enhances data interpretation, anomaly detection, and system scalability. Results demonstrate the model's effectiveness in providing high-resolution, real-time insights into urban air quality dynamics, supporting environmental sustainability, and informing smart governance strategies.

Efficient water resource management is vital for enhancing crop productivity and promoting sustainability in smart agriculture. This study presents a machine learning-based framework for optimizing water usage in potato farming, integrating real-time IoT sensor data with predictive modeling techniques. Key environmental parameters such as soil moisture, temperature, humidity, and crop growth stages are monitored and analyzed using machine learning algorithms including Decision Trees, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). These models predict irrigation requirements and optimize watering schedules to minimize water waste while maximizing crop yield. The system supports adaptive decision-making in precision farming, improving resource efficiency and contributing to environmentally sustainable practices. The proposed framework demonstrates high accuracy in forecasting irrigation needs and offers a scalable solution for sustainable water management in potato agriculture.

As smart cities increasingly rely on IoT networks for various urban applications, ensuring the security of these networks has become a critical challenge. This study proposes an innovative approach to enhance IoT network security in smart cities using deep learning algorithms and metaheuristics. By analyzing network traffic data from IoT devices, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used to detect anomalies, potential security threats, and intrusions in real-time. In addition, metaheuristic algorithms like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are applied to optimize network configurations, improving the overall defense mechanism of the system. The framework provides proactive security measures, such as dynamic threat detection and response strategies, ensuring the integrity and resilience of IoT systems in smart cities. Experimental results show that the proposed model significantly enhances detection accuracy and minimizes false positives, making it a robust solution for securing IoT networks.

The optimization of CO₂ emission reduction strategies is a critical challenge in both agriculture and urban environments. This study explores the application of machine learning algorithms to reduce CO₂ emissions by optimizing agricultural practices and urban infrastructure management. In smart agriculture, machine learning models analyze data from IoT sensors to optimize irrigation, fertilization, and crop management, reducing energy consumption and greenhouse gas emissions. In smart cities, machine learning techniques are employed to improve traffic flow, energy usage in buildings, and waste management, all contributing to lowering CO₂ emissions. Algorithms such as Random Forests, Support Vector Machines (SVM), and Neural Networks are applied to realtime datasets to predict and optimize emissions in both sectors. Experimental results show that integrating machine learning with smart systems leads to more efficient energy use and resource management, resulting in significant reductions in CO₂ emissions and fostering sustainable development.

Effective water resource management is crucial for ensuring long-term sustainability, especially in water-scarce regions. This study presents an innovative approach that combines neural networks with IoT-based techniques to optimize water resource allocation and usage in urban and agricultural settings. IoT sensors are deployed to monitor real-time data such as water quality, flow rates, soil moisture, and environmental factors. Neural network models, including Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM) networks, are used to analyze the collected data and forecast water demand, detect leaks, and optimize distribution systems. The system provides decision-makers with actionable insights to improve water usage efficiency, reduce waste, and enhance overall resource management. The proposed framework demonstrates enhanced accuracy in predicting water requirements and sustainable water distribution, contributing to the effective management of this precious resource.

Smart agriculture systems, powered by IoT, offer significant potential for optimizing resource usage and increasing crop yield efficiency. This study presents an optimization framework that combines IoT technologies with machine learning and metaheuristics algorithms to enhance agricultural operations. IoT sensors are deployed across agricultural fields to collect real-time data on soil moisture, temperature, humidity, crop health, and environmental factors. Machine learning models, such as Random Forests and Support Vector Machines (SVM), are applied to analyze this data and predict crop growth, irrigation needs, and pest infestations. Additionally, metaheuristic algorithms, including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are used to optimize irrigation schedules, fertilization strategies, and resource allocation. The system dynamically adapts to environmental changes, improving yield predictions, reducing waste, and supporting sustainable farming practices. Experimental results demonstrate the effectiveness of this hybrid framework in increasing crop productivity while minimizing resource consumption.

Accurate cancer detection from medical imaging requires efficient extraction and selection of diagnostic features to improve classification performance and reduce computational complexity. This study introduces an optimized feature selection framework integrated with neural network models for enhancing the accuracy of cancer detection from imaging modalities such as MRI, CT, and mammography. Advanced feature selection techniques—such as mutual information, recursive feature elimination, and principal component analysis—are employed to identify the most discriminative features from high-dimensional image data. These features are then used to train neural network models, including Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs), to classify cancerous and non-cancerous tissues. The proposed method demonstrates improved diagnostic performance in terms of sensitivity, specificity, and overall accuracy, offering a reliable approach for aiding clinical decision-making in oncological imaging.

The transition to renewable energy is essential for building sustainable smart cities, yet optimizing such complex systems remains a major challenge. This study proposes a novel framework that leverages quantum algorithms to optimize the integration, distribution, and management of renewable energy resources. By modeling large-scale energy systems—encompassing solar, wind, and bioenergy sources—quantum-inspired algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are utilized to solve high-dimensional, non-convex optimization problems with greater efficiency than classical methods. Combined with IoT-based monitoring and real-time data analytics, the framework supports predictive load balancing, energy demand forecasting, and system resilience under dynamic urban conditions. Experimental simulations demonstrate the potential of quantum computing to significantly improve operational performance, scalability, and sustainability in smart city energy infrastructures.

The increasing prevalence of air pollution and airborne viruses has prompted the need for intelligent systems capable of real-time monitoring and response. This study presents a dualpurpose deep learning framework for simultaneous air quality control and virus detection using IoT-integrated sensor networks. Environmental and biological data are collected in real time from distributed sensors, including particulate matter levels, volatile organic compounds, temperature, and humidity, as well as pathogen biomarkers. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are employed to process and classify the data, enabling timely identification of hazardous air conditions and potential viral threats. The system is designed to trigger automated responses, such as ventilation control or health alerts, enhancing public health safety. Experimental validations show that the proposed framework achieves high accuracy, low latency, and scalability, making it a robust tool for smart city health and environmental management.

As urbanization accelerates, the reduction of CO₂ emissions in smart cities has become a critical objective for achieving environmental sustainability. This study introduces a hybrid optimization framework that integrates machine learning techniques with metaheuristic algorithms to enhance CO₂ emission reduction strategies. Real-time data from IoT-enabled urban infrastructures— including traffic patterns, energy consumption, and industrial outputs—are analyzed using machine learning models such as Random Forests and Gradient Boosting to forecast emission levels. To identify optimal mitigation strategies, metaheuristics such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are employed. These strategies guide the adaptive control of energy usage, transportation systems, and urban planning policies. The proposed framework demonstrates significant improvements in reducing emissions while maintaining operational efficiency, providing a scalable and intelligent solution for sustainable urban development.

The integration of neural networks into bioinformatics has significantly advanced the capabilities of cancer diagnosis and disease prediction. This study proposes a neural network-based framework for analyzing high-dimensional biological datasets, such as gene expression profiles, protein sequences, and genomic data, to optimize diagnostic accuracy and early disease prediction. Deep learning architectures, including Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), are employed to detect complex, nonlinear patterns associated with various cancer types and other genetic disorders. The model incorporates advanced feature selection methods to reduce data redundancy and improve computational efficiency. Evaluations using benchmark datasets reveal enhanced predictive performance, sensitivity, and specificity when compared to traditional bioinformatics approaches. This intelligent system demonstrates great promise in enabling precision medicine and personalized healthcare solutions.

Enhancing crop yield prediction and resource efficiency is essential in modern smart agriculture. This study presents an integrated approach for optimizing potato crop yield using data mining techniques in conjunction with IoT-based monitoring systems. IoT sensors deployed in agricultural fields collect real-time data on soil moisture, temperature, humidity, and nutrient levels. These data are processed using data mining algorithms such as Decision Trees, K-Means Clustering, and Association Rule Mining to uncover patterns and correlations that impact crop productivity. The system enables precision farming by identifying optimal planting conditions, detecting stress factors, and recommending data-driven irrigation and fertilization strategies. Experimental results demonstrate improved yield forecasting accuracy and reduced input costs, showcasing the potential of intelligent systems to support sustainable potato farming practices.

Air pollution has become a pressing concern in urban environments, necessitating intelligent systems for its prediction and mitigation. This study proposes an integrated framework that leverages machine learning algorithms and IoT-based sensor networks for real-time air quality monitoring, forecasting, and control optimization. IoT devices are deployed across key urban locations to collect environmental data, including concentrations of pollutants (e.g., PM2.5, CO, NO₂), temperature, humidity, and traffic patterns. Machine learning models—such as Support Vector Machines (SVM), Random Forests, and Artificial Neural Networks (ANNs)—are applied to analyze the data, predict air quality index (AQI) levels, and trigger responsive actions such as traffic management or emission reduction protocols. The system provides urban planners with actionable insights for adaptive pollution control, contributing to healthier and more sustainable cities. Empirical evaluations demonstrate the framework’s effectiveness in improving prediction accuracy and facilitating timely interventions.

The advancement of medical imaging technologies has revolutionized cancer diagnosis, yet the complexity and high dimensionality of imaging data pose challenges for accurate and timely detection. This study presents a comprehensive framework that combines advanced deep learning architectures with robust feature selection models to improve diagnostic performance. Convolutional Neural Networks (CNNs) and Deep Residual Networks (ResNets) are employed to extract hierarchical features from various imaging modalities, including MRI, CT, and histopathology images. To reduce redundancy and enhance classification accuracy, feature selection techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and mutual information-based selection are integrated. The optimized feature subsets are then utilized in ensemble classifiers for final diagnosis. Experimental results show superior accuracy, sensitivity, and specificity compared to conventional approaches, highlighting the effectiveness of the proposed system in supporting clinical decision-making for early and precise cancer detection.

Effective water resource management is critical for ensuring sustainability and resilience in smart urban environments. This study proposes an intelligent optimization framework that integrates metaheuristic algorithms and machine learning techniques to enhance water distribution, usage forecasting, and leakage detection in smart cities. IoT-based sensor networks are deployed to collect real-time data on water flow, consumption, and environmental conditions. Machine learning models, such as Support Vector Machines (SVM) and Random Forests, are used for predictive analytics, while metaheuristic algorithms—including Genetic Algorithms (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)—optimize resource allocation and system performance. The hybrid system allows for adaptive, data-driven decisionmaking, reducing waste and improving the overall efficiency of urban water infrastructure. Simulation results demonstrate improved accuracy, reduced response time, and enhanced sustainability outcomes compared to traditional methods.

The integration of renewable energy systems in smart agriculture is essential for enhancing sustainability, reducing carbon footprints, and ensuring efficient resource utilization. This study proposes a neural network-based optimization framework, supported by IoT infrastructure, to manage and enhance renewable energy usage in agricultural operations. IoT devices collect realtime data on solar radiation, wind patterns, soil conditions, and energy consumption across farms. Neural networks, including Recurrent Neural Networks (RNNs) and Deep Feedforward Networks, are applied to predict energy demands, optimize load balancing, and control the distribution of solar and wind-generated power. The system dynamically adapts to environmental and operational changes, ensuring uninterrupted and efficient energy supply to critical agricultural systems such as irrigation, monitoring sensors, and autonomous equipment. Experimental simulations reveal substantial improvements in energy efficiency, system reliability, and operational sustainability. This framework supports the development of intelligent, eco-friendly farming practices powered by clean energy.

Accurate and efficient cancer diagnosis through medical imaging requires the selection of relevant features that enhance classification performance while minimizing computational complexity. This study introduces a hybrid framework that integrates deep learning with metaheuristics-based feature selection techniques to improve diagnostic accuracy in medical image analysis. Convolutional Neural Networks (CNNs) are employed to extract high-dimensional features from imaging modalities such as MRI, CT, and histopathology. To address the challenge of redundant or irrelevant features, metaheuristic algorithms—including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—are used to identify optimal subsets of discriminative features. The selected features are then fed into classification models for final diagnosis. Experimental results demonstrate that the proposed approach significantly improves performance metrics such as accuracy, sensitivity, and specificity compared to conventional methods. This intelligent diagnostic system holds promise for aiding clinicians in early and reliable cancer detection.

Urban air pollution significantly impacts public health and environmental sustainability, necessitating intelligent approaches for its prediction and control. This study presents a deep learning-based framework for optimizing air quality in smart city network systems. Utilizing data collected from distributed IoT-enabled environmental sensors, the system employs advanced models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to forecast pollutant concentrations, detect spatial-temporal pollution patterns, and recommend mitigation strategies. These models analyze diverse variables including meteorological conditions, traffic flow, and industrial activities to generate accurate predictions and support proactive urban planning. The proposed system enhances air quality monitoring, facilitates real-time decision-making, and contributes to sustainable urban management. Empirical evaluations demonstrate significant improvements in prediction accuracy and system responsiveness compared to traditional models.

Rapid and accurate virus detection is critical for effective disease control and public health response. This study explores the integration of biosensors with IoT-based neural network and machine learning models for real-time virus detection and monitoring. Biosensors, embedded within wearable or portable diagnostic devices, continuously capture biological signals such as biomarkers and viral loads. These signals are transmitted via IoT networks to centralized platforms where machine learning and neural network algorithms analyze the data to detect infection patterns, classify viral strains, and predict outbreak risks. The use of models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees enhances diagnostic accuracy, reduces false positives, and supports timely interventions. The proposed intelligent biosensing framework demonstrates significant potential in improving surveillance systems, enabling decentralized healthcare, and supporting global efforts in managing infectious disease threats.

Sustainable agriculture is essential for addressing global food security and environmental challenges, particularly in water-intensive crops such as potatoes. This study presents a data mining-based approach for optimizing potato farming practices and promoting water conservation within smart agriculture systems. Sensor networks integrated into agricultural fields collect realtime data on soil moisture, temperature, humidity, and crop health. Data mining techniques— including clustering, classification, and association rule mining—are applied to extract meaningful patterns that support informed decision-making in irrigation scheduling, nutrient management, and pest control. The results demonstrate how predictive analytics can identify resource-efficient farming strategies that minimize water usage while maximizing crop yield and quality. This intelligent framework contributes to the development of sustainable and precision farming systems, promoting environmental stewardship and agricultural productivity.

The growing complexity of energy systems in smart cities demands efficient, adaptive, and scalable network architectures to ensure optimal energy distribution and utilization. This study explores the integration of Internet of Things (IoT) networks with advanced neural network models for energy management optimization in smart urban environments. IoT devices are employed to collect real-time data on energy consumption, generation, and environmental parameters across residential, commercial, and industrial sectors. Advanced neural networks—including deep and recurrent architectures—are implemented to analyze temporal and spatial energy patterns, predict demand fluctuations, and dynamically adjust resource allocation. The proposed framework enhances the responsiveness and resilience of energy management systems by enabling autonomous decision-making and fault detection. Experimental results highlight significant improvements in energy efficiency, load balancing, and system reliability, demonstrating the potential of neural network-optimized IoT frameworks to support sustainable smart city development.

The increasing integration of renewable energy sources into modern power systems necessitates advanced optimization techniques to manage variability, improve efficiency, and ensure grid stability. This study investigates a hybrid approach that combines deep learning models with quantum computing applications for the optimization of renewable energy systems. Deep learning techniques, particularly Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), are utilized to forecast energy generation and demand based on historical and real-time data from solar, wind, and hybrid sources. Quantum computing, through algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), is applied to solve complex optimization problems involving resource allocation, load balancing, and energy storage management with unprecedented speed and accuracy. The synergy of deep learning and quantum optimization enables real-time, adaptive, and highly scalable energy system management, demonstrating substantial improvements in performance, cost-efficiency, and sustainability across diverse deployment scenarios

Energy efficiency is a cornerstone of sustainable smart city development, requiring intelligent systems capable of managing complex urban energy dynamics. This study presents a machine learning framework integrated with metaheuristic optimization algorithms to enhance energy efficiency across various sectors of smart cities, including transportation, infrastructure, and residential systems. Machine learning models are trained on real-time data collected from IoTenabled devices and sensors to predict energy consumption patterns and identify inefficiencies. Metaheuristic algorithms such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization are employed to optimize energy distribution, reduce peak loads, and prioritize energy-saving interventions. The hybrid approach supports adaptive decision-making and scalable solutions for urban planners and policymakers. Results indicate that the proposed methodology significantly improves energy utilization, reduces environmental impact, and aligns with global sustainability goals.

Accurate and early diagnosis of cancer through medical imaging is critical for effective treatment planning and improved patient outcomes. This study examines the application of neural networks combined with deep learning-based feature selection techniques in enhancing cancer detection from medical images such as MRI, CT, and histopathology scans. Convolutional Neural Networks (CNNs) are employed to automatically extract and select high-level features that are most indicative of malignancy, reducing the need for manual intervention and minimizing diagnostic errors. The integration of feature selection algorithms, including recursive feature elimination and attention mechanisms, improves model interpretability and classification accuracy. The proposed framework demonstrates high precision and recall rates across multiple cancer types, highlighting the effectiveness of deep learning in supporting radiologists and oncologists. This approach offers a  scalable and non-invasive diagnostic solution, promoting more accurate, timely, and personalized cancer care.

As Internet of Things (IoT) devices continue to proliferate, the demand for efficient and compact antenna designs has grown significantly to support reliable wireless communication. This study explores the application of machine learning algorithms in optimizing antenna performance parameters such as gain, bandwidth, radiation pattern, and efficiency for IoT-based applications. By leveraging supervised and unsupervised learning models, including support vector machines, decision trees, and genetic algorithms, the design process is accelerated and guided toward optimal configurations. The methodology involves training models on a dataset comprising various geometric and material properties, allowing predictive analysis and iterative refinement of antenna prototypes. The results demonstrate that machine learning techniques can significantly reduce design time, improve performance outcomes, and enable adaptive tuning in dynamic IoT environments. The integration of intelligent algorithms into antenna design offers a transformative approach to meeting the evolving connectivity needs of smart devices.

The advancement of smart agriculture technologies has opened new possibilities for optimizing crop production and resource management. This study focuses on the development of an IoT-based system for enhancing potato farming through the application of data mining techniques. IoT sensors are deployed to monitor critical agricultural parameters such as soil moisture, temperature, humidity, and nutrient levels in real-time. The collected data is processed using data mining algorithms to uncover hidden patterns and relationships that inform decision-making related to irrigation scheduling, fertilizer application, and pest control. Techniques such as classification, clustering, and association rule mining are employed to generate actionable insights, thereby improving crop yield, reducing resource waste, and minimizing environmental impact. The proposed smart farming framework demonstrates how the integration of IoT and data mining can transform traditional potato cultivation into a data-driven, efficient, and sustainable agricultural practice.

The global transition towards renewable energy sources necessitates the development of intelligent systems capable of managing complex and dynamic energy grids. This study investigates the application of deep learning techniques to optimize the operation and integration of renewable energy within modern power grids. By analyzing large-scale datasets from solar, wind, and other renewable sources, deep learning models—such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are employed for accurate energy demand forecasting, fault detection, and real-time grid balancing. These models enhance decision-making processes in energy distribution and contribute to the stability and reliability of the grid. Furthermore, the integration of deep learning into sustainability practices promotes energy efficiency, reduces dependence on fossil fuels, and supports carbon-neutral goals. The findings indicate that deep learning offers transformative potential for optimizing renewable energy systems and advancing sustainable development initiatives.

As urbanization accelerates, smart cities face mounting challenges related to environmental sustainability, particularly in reducing carbon dioxide (CO₂) emissions. This study presents a machine learning-based framework to optimize emission reduction plans tailored for smart cities. By leveraging real-time environmental data collected from IoT sensors and city infrastructure, various machine learning algorithms—such as decision trees, support vector machines, and ensemble methods—are applied to model emission patterns and forecast pollution levels. These predictive models are then used to identify high-impact intervention strategies, such as traffic flow adjustments, energy-efficient infrastructure upgrades, and policy modifications. The integration of machine learning not only enhances the accuracy of emission forecasting but also enables datadriven planning for sustainable urban development. The results demonstrate the potential of intelligent systems in achieving measurable reductions in CO₂ emissions while supporting longterm ecological goals.

The increasing global threat of viral infections has emphasized the need for effective and timely virus detection systems to safeguard public health. Biosensor-based real-time monitoring systems offer a promising solution by enabling rapid, on-site detection of viruses in biological samples. This study explores the integration of advanced biosensors with real-time monitoring technologies to optimize health safety measures. The proposed system uses various biosensing techniques, such as electrochemical, optical, and mass-based sensors, to detect viral biomarkers and pathogens. Coupled with real-time data processing, these systems can provide immediate results, enabling rapid decision-making and preventive actions. The application of such biosensor-based systems in various settings, including healthcare facilities, airports, and public spaces, can significantly enhance virus surveillance and control efforts. The results suggest that these systems have the potential to revolutionize public health safety by providing timely, accurate, and non-invasive virus detection.

The management of water resources has become an increasingly critical challenge due to the rising demand and climate change impacts. Internet of Things (IoT) technologies provide an effective means of optimizing water resource management by enabling real-time data collection and analysis. This study investigates the integration of neural networks and metaheuristic algorithms for efficient water resource allocation and usage optimization. The approach utilizes IoT-based sensors to collect data on water levels, usage rates, and environmental factors. Neural networks are applied for pattern recognition and prediction, while metaheuristic techniques are employed to optimize resource distribution. The proposed system aims to improve water conservation, reduce waste, and ensure equitable water distribution. The results demonstrate that the combined use of IoT, neural networks, and metaheuristics can significantly enhance water management efficiency, making it adaptable to various real-world scenarios.

The integration of deep learning into medical image-based bioinformatics has significantly advanced cancer diagnosis by enabling precise, automated analysis of complex imaging data. Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated high accuracy in detecting and classifying various cancer types from imaging modalities such as MRI, CT scans, and histopathological images. These models can identify subtle patterns and anomalies that may be overlooked by human observers, facilitating early detection and improving diagnostic outcomes. Furthermore, the application of deep learning enhances the efficiency of diagnostic workflows, reduces the burden on healthcare professionals, and supports personalized treatment planning by providing detailed insights into tumor characteristics. As research progresses, the continued refinement of deep learning algorithms and their integration with bioinformatics tools hold promise for further improving the accuracy and reliability of cancer diagnostics.

The integration of data mining techniques within Internet of Things (IoT)-enabled smart agriculture systems has significantly enhanced potato farming by enabling precise, data-driven decision-making. IoT devices, such as soil moisture sensors, weather stations, and drones, collect real-time data on environmental conditions, which is then analyzed using data mining algorithms to uncover patterns and insights. These insights facilitate optimized irrigation scheduling, pest and disease prediction, and yield forecasting, leading to improved resource utilization and crop productivity. Furthermore, the application of data mining in smart agriculture supports sustainable farming practices by reducing input waste and minimizing environmental impact. By leveraging the synergy between IoT and data mining, potato farmers can achieve higher efficiency, better crop management, and increased profitability.

The integration of metaheuristic algorithms with neural network models has emerged as a potent strategy for optimizing energy consumption in sustainable building designs. Metaheuristic techniques, such as genetic algorithms, particle swarm optimization, and simulated annealing, offer robust solutions for navigating complex, multidimensional design spaces, enabling the identification of optimal configurations for building systems. When combined with neural networks, which excel at modeling nonlinear relationships and predicting energy performance, these hybrid approaches facilitate precise control over heating, ventilation, and air conditioning (HVAC) systems, lighting, and other energy-intensive components. This synergy enhances the ability to forecast energy demands accurately and implement responsive control strategies, thereby reducing energy consumption and improving occupant comfort. The adoption of such integrated optimization frameworks supports the development of energy-efficient buildings, contributing to broader sustainability goals and compliance with stringent energy regulations.

The integration of machine learning (ML) techniques into real-time air quality monitoring systems has significantly enhanced the capability of smart cities to manage environmental health. By employing a variety of sensors, such as gas sensors and environmental monitors, these systems collect comprehensive data on pollutants like CO₂, NH₃, CH₄, LPG, alcohol, and CO. This data is processed using ML algorithms to compute the Air Quality Index (AQI), providing accurate and timely assessments of air quality. The implementation of such ML-driven systems enables proactive interventions, supports public health initiatives, and contributes to the sustainable development of urban environments.

The integration of Internet of Things (IoT) technologies and deep learning algorithms has revolutionized potato crop management by enabling precise, data-driven agricultural practices. IoT devices, including soil moisture sensors, weather stations, and drones, facilitate real-time monitoring of environmental conditions, providing critical data for informed decision-making in irrigation, fertilization, and pest control. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been effectively applied to various stages of the potato production chain, including disease detection, yield prediction, and quality assessment. These techniques help identify potato leaf diseases with high accuracy, enabling timely interventions to mitigate crop losses. Additionally, the models assist in forecasting crop yields by analyzing historical and real-time data, thereby optimizing resource allocation and enhancing productivity. The synergy between IoT and deep learning not only improves the efficiency and sustainability of potato farming but also contributes to food security by maximizing yield and reducing environmental impact.

The integration of neural network-based optimization techniques into antenna design has significantly advanced the development of efficient and adaptable antennas for Internet of Things (IoT) applications and beyond. Deep learning models, particularly convolutional neural networks (CNNs), have been employed to predict and optimize antenna performance metrics such as gain, efficiency, and radiation patterns, enabling the design of antennas that meet specific application requirements. Optimization-oriented methods using deep neural learning have demonstrated the potential to streamline the antenna design process in automated environments. Additionally, machine learning frameworks have been used to swiftly optimize antennas by leveraging surrogate models and intelligent criteria, reducing computational costs while maintaining design accuracy. These advancements underscore the transformative impact of neural network-based optimization in antenna design, facilitating the development of antennas that are not only efficient but also adaptable to the dynamic requirements of modern wireless communication systems.

The integration of quantum algorithms into geo-information systems (GIS) is emerging as a transformative approach to address the complexities of climate change mitigation. Quantum computing offers enhanced computational capabilities that can significantly improve the accuracy and efficiency of climate models by solving complex differential equations and optimizing largescale environmental data processing. For instance, quantum-enhanced machine learning models have demonstrated superior performance in predicting climate-related outcomes, enabling more precise simulations of atmospheric and oceanic processes. Additionally, quantum algorithms facilitate the optimization of renewable energy systems and carbon capture technologies by efficiently analyzing vast datasets to identify optimal configurations and strategies. The application of quantum computing in GIS also supports the development of high-resolution climate models, which are crucial for understanding localized climate impacts and informing targeted mitigation efforts. These advancements underscore the potential of quantum algorithms to revolutionize climate science by providing more accurate predictions and effective solutions for sustainable development.

The integration of Internet of Things (IoT) technologies in renewable energy and smart agriculture has revolutionized operational efficiency and sustainability. In renewable energy systems, IoT devices facilitate real-time monitoring and optimization of energy production, distribution, and storage, enhancing the reliability and efficiency of power grids . However, the proliferation of IoT devices introduces significant security challenges, including vulnerabilities to cyberattacks that can disrupt energy systems and compromise data integrity . In smart agriculture, IoT-enabled sensors and devices enable precision farming by monitoring soil conditions, crop health, and environmental factors, leading to optimized resource utilization and increased crop yields . Yet, these advancements also expose agricultural systems to cybersecurity threats, necessitating robust security measures to protect sensitive data and ensure system resilience . To address these challenges, the implementation of advanced encryption standards, secure communication protocols, and intrusion detection systems is critical. Furthermore, adopting a holistic security framework that encompasses threat modeling, risk assessment, and continuous monitoring can significantly enhance the security posture of IoT networks in both renewable energy and smart agriculture applications.

The integration of deep learning techniques into real-time monitoring systems has significantly enhanced the accuracy and responsiveness of CO₂ and air quality assessments. Advanced models, such as Long Short-Term Memory (LSTM) networks, have been effectively utilized to predict indoor CO₂ concentrations, enabling proactive ventilation strategies to maintain optimal air quality levels citeturn search Hybrid deep learning architectures combining Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) have demonstrated superior performance in forecasting air pollutant concentrations, including PM2.5 and NO₂, by capturing both spatial and temporal dependencies in environmental data citeturn search Furthermore, the deployment of Internet of Things (IoT) devices equipped with deep learning models facilitates continuous, low-cost monitoring of air quality, providing real-time data analysis and alerts for pollution levels citeturn search These advancements contribute to the development of intelligent environmental monitoring systems capable of supporting public health initiatives and informing policy decisions.

Advanced neural network applications have significantly enhanced bioinformatics approaches for cancer detection and disease prediction. Deep learning models, particularly convolutional neural networks (CNNs) and graph convolutional networks (GCNs), have demonstrated high accuracy in analyzing complex biological data such as gene expression profiles and histopathological images. For instance, a study employing a 4-layer GCN on whole slide images of gastric and colon adenocarcinomas achieved improved survival prediction outcomes, surpassing traditional CNN models. Additionally, integrating noncoding RNA biomarkers with deep learning neural networks has enabled the discrimination of multiple cancer types with high accuracy, facilitating early detection and personalized treatment strategies. These advancements underscore the potential of neural network-based models in transforming cancer diagnostics and prognostics by providing more precise and individualized patient care.

The integration of biosensor technology with neural network optimization models has significantly improved the detection of viruses and airborne pathogens. Neural networks trained on large and diverse datasets enhance the precision of biosensors by reducing false positives and improving sensitivity. Advanced techniques, such as convolutional neural networks (CNNs) combined with generative adversarial networks (GANs), are used to analyse biosensor outputs like surfaceenhanced Raman scattering (SERS) spectra, enabling rapid and accurate pathogen identification. Optimization models such as mayfly-optimized CNNs (MOCNN) further enhance signal processing and classification accuracy. These advancements offer real-time, efficient, and scalable solutions for monitoring public health threats in diverse environments.

The integration of machine learning (ML) techniques into smart agriculture has significantly enhanced potato yield prediction and contributed to CO₂ emission reduction. Advanced ML models, such as Random Forest and Gradient Boosting, have demonstrated high accuracy in forecasting potato yields by analyzing diverse datasets, including soil properties, weather conditions, and remote sensing imagery citeturn search These predictive capabilities enable farmers to make informed decisions regarding fertilization and irrigation, optimizing resource use and minimizing environmental impact. Furthermore, the application of ML in precision agriculture facilitates site-specific management practices, leading to improved crop performance and reduced greenhouse gas emissions citeturn search By leveraging data driven insights, smart agriculture systems can enhance productivity while promoting environmental sustainability in potato farming.

The integration of deep learning and optimization algorithms is revolutionizing energy consumption forecasting in smart cities, enabling more accurate predictions and efficient energy management. Advanced models such as Long Short-Term Memory (LSTM) networks, Transformer-based architectures, and hybrid approaches combining Seasonal Autoregressive Integrated Moving Average (SARIMA) with Grey Wolf Optimization (GWO) have demonstrated superior performance in capturing complex temporal patterns and seasonal fluctuations in energy usage. For instance, a study employing a GWO-SARIMA-LSTM model achieved a 15% reduction in prediction error, highlighting the effectiveness of hybrid models in enhancing forecasting accuracy citeturn academia19. These methodologies not only improve the reliability of energy demand predictions but also support the development of sustainable and resilient urban infrastructures by facilitating proactive energy management strategies.

The convergence of machine learning (ML) and metaheuristic algorithms is revolutionizing the security landscape of Internet of Things (IoT) networks by enabling adaptive, efficient, and scalable intrusion detection systems (IDS). ML classifiers, particularly deep learning models like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have demonstrated high accuracy in identifying complex attack patterns within IoT traffic. To enhance these models, metaheuristic optimization techniques such as Firefly Algorithm, Salp Swarm Optimization, and Grey Wolf Optimization are employed to fine-tune hyperparameters and optimize feature selection, thereby improving detection rates and reducing false positives. For instance, a hybrid approach combining Firefly and Salp Swarm Optimization with deep CNNs has shown improved performance in intrusion detection tasks. Additionally, integrating self-attention mechanisms with deep learning and metaheuristic optimization has led to the development of intelligent cybersecurity systems capable of real-time threat detection and response. These advancements underscore the potential of combining ML and metaheuristic algorithms to fortify IoT networks against evolving cyber threats.

The integration of machine learning (ML) and neural network techniques has significantly enhanced cancer diagnosis by optimizing feature selection in medical imaging. Advanced models like D-Cube utilize diffusion models combined with contrastive learning to extract robust hyperfeatures, improving classification performance even in scenarios with data imbalance and limited samples . Similarly, the application of random projection algorithms in conjunction with support vector machines (SVMs) has demonstrated improved accuracy in breast lesion classification by reducing feature dimensionality and enhancing model robustness . These methodologies underscore the critical role of effective feature selection in developing reliable and efficient diagnostic tools, facilitating early detection and personalized treatment planning in oncology. 

The integration of quantum-inspired algorithms and metaheuristic optimization techniques is revolutionizing renewable energy management in smart cities. By emulating quantum computing principles, these algorithms—such as Quantum Particle Swarm Optimization (QPSO) and Quantum Approximate Optimization Algorithm (QAOA)—enhance the efficiency of energy distribution systems. They address complex, multi-objective challenges like minimizing operational costs and reducing environmental emissions in grid-connected microgrids. The application of these advanced computational methods facilitates real-time decision-making and adaptive control in dynamic urban energy environments, contributing to the development of sustainable and resilient smart city infrastructures.

Feature selection is a critical step in bioinformatics, aiming to identify the most informative variables from high-dimensional biological datasets to enhance predictive modeling and interpretability. Machine learning techniques, including filter, wrapper, and embedded methods, have been widely employed to assess feature relevance and redundancy, thereby improving model performance and reducing computational complexity. Neural network-based approaches, such as deep learning models, offer advanced capabilities in capturing complex, non-linear relationships among features, facilitating more accurate classification and prediction tasks in genomics and proteomics. Recent advancements have introduced hybrid models that integrate machine learning algorithms with neural networks, leveraging the strengths of both to optimize feature selection processes. These integrative approaches have demonstrated improved accuracy and robustness in various bioinformatics applications, including disease diagnosis and biomarker discovery.

Quantum computing is emerging as a transformative technology in optimizing energy consumption within sustainable water resource management systems. By leveraging quantum algorithms such as the Variational Quantum Eigensolver (VQE), researchers have developed hybrid quantum-classical approaches to enhance water flow control, particularly in systems like irrigation canals and pumping stations. These methods aim to minimize energy costs associated with water distribution while adhering to operational constraints, offering improvements over traditional optimization techniques. The integration of quantum computing in this domain not only enhances computational efficiency but also contributes to the development of more sustainable and resilient water management infrastructures.

The integration of neural networks in chest X-ray analysis has significantly advanced the early detection of heart diseases and viral infections, including COVID-19. Convolutional Neural Networks (CNNs) have demonstrated high accuracy in identifying cardiovascular anomalies and viral pneumonia by analyzing radiographic patterns. For instance, a study developed a deep learning model capable of predicting a patient's 10-year risk of heart disease using chest X-rays, achieving accuracy comparable to traditional risk assessment methods that require blood tests and blood pressure measurements. citeturn news24 Similarly, CNN-based models have been employed to detect COVID-19 from chest X-rays, providing rapid and reliable screening tools that can assist in managing pandemics. citeturn search These advancements highlight the potential of neural network-based approaches in enhancing diagnostic accuracy and efficiency in medical imaging.

The integration of deep learning (DL) with Internet of Things (IoT) technologies in renewable energy and smart city frameworks is transforming urban infrastructure by enabling intelligent, data-driven energy management. DL algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to analyze sensor data for accurate energy demand forecasting, system optimization, and fault detection. Furthermore, deep reinforcement learning (DRL) is employed to implement adaptive control strategies for smart grids, enhancing energy distribution and minimizing wastage. These technologies support the efficient integration of renewable energy sources into urban energy systems, fostering sustainable and resilient smart cities. 

Metaheuristic optimization techniques have emerged as powerful tools in bioinformatics, particularly for enhancing cancer diagnosis through medical image processing. By integrating algorithms such as Grey Wolf Optimizer, Cuckoo Search, and Particle Swarm Optimization with imaging modalities like MRI and ultrasound, researchers have developed advanced computeraided diagnosis (CAD) systems. These systems effectively address challenges in feature selection, parameter tuning, and image segmentation, leading to improved classification accuracy and reduced computational costs. For instance, the combination of wavelet neural networks with Grey Wolf Optimization has demonstrated superior performance in breast cancer diagnosis by optimizing neural network parameters and enhancing image quality through preprocessing techniques. Similarly, the application of K-means clustering enhanced by Grey Wolf and Cuckoo Search optimizers has shown promise in automating MRI image clustering, facilitating more accurate tumor detection. Moreover, the integration of deep learning models with metaheuristic algorithms has further refined the segmentation and classification processes, contributing to more precise and reliable cancer diagnostics. These advancements underscore the significant role of metaheuristic optimization in improving the efficacy of bioinformatics applications in oncology.

The integration of Internet of Things (IOT) devices and deep learning algorithms has significantly advanced precision agriculture by enabling real-time monitoring, predictive analytics, and intelligent decision-making. This research explores how deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can analyze vast datasets collected by IOT sensors to optimize crop yields, detect diseases, and manage resources efficiently. It also proposes strategies that combine optimization methods with deep learning architectures to enhance data transmission efficiency and energy utilization in smart farming environments. The findings underscore the transformative potential of integrating IOT and deep learning in agriculture, leading to more sustainable and productive farming practices.