According to statistical data from international organizations, the rising global population is intensifying the several critical challenges particularly in smart cities including air pollution, carbon emissions, traffic congestion, and healthcare infrastructure. Researchers have concluded that these challenges present significant barriers to meeting the United Nations’ sustainable development goals (SDGs) by 2030, hindering efforts toward a sustainable future and enhancing overall quality of life. Recent studies have concluded that the enabling technologies of Industry 4.0 have the potential to overcome the aforementioned challenges and establish sustainable infrastructure in smart cities. Edge computing and deep learning are two transformative technologies that have shown substantial outcomes in meeting the targets of SDG 3: Good health and well-being, SDG 9: Industry, Innovation, Infrastructure and SDG 11: Sustainable cities and communities. This study highlight the importance of sustainability, with specific focus on SDG goals 3.8, 3.9, 9.1, 9.4, 11.3, and 11.6, providing a technical comparative analysis of the integration of deep learning and edge computing technologies in healthcare, unmanned aerial vehicles (UAVs,) and other smart city applications. The study also investigates the challenges associated with various parameters and provides recommendations for future advancement, with plans to explore additional SDGs in future research.

Deforestation is a significant contributor to global greenhouse gas emissions, underscoring the need for effective forest conservation and management strategies. Developing such strategies requires a thorough understanding of the primary drivers of forest loss. However, the complexity of these factors, combined with the requisite skill set for accurate identification, poses considerable challenges for data collection. This study introduces a novel deep learning-based approach, termed Deep Transformation Forest Detection (DTFD), which utilizes vision transformers equipped with a self-attention mechanism. This innovative method enhances the modeling of contextual and spatial relationships in satellite imagery while facilitating efficient processing without relying on convolutions. This capability is particularly beneficial for heterogeneous and binary classification tasks. The self-attention mechanism allows for the assignment of varying weights to input data, thereby improving the identification of areas at risk of deforestation adjacent to forested regions. The results achieved by DTFD demonstrate exceptional performance compared to state-of-the-art methods across multiple datasets. Notably, the findings reveal significant changes in forest cover and environmental dynamics, with DTFD attaining superior metrics, including accuracy (95.64%), precision (95.55%), F1 score (93.74%), recall (94.83%), and Intersection over Union (IoU) (94.31%). This research contributes to the monitoring of climate change impacts, rapid urbanization, and natural disasters, with a specific emphasis on urban forests and their interactions with urban environmental changes.

Panic disorder (PD) is considered one of the destructive ailments, with various individuals experiencing a critical functional disorder. As the range of remission for PD is achieved only between 20% and 50% with the help of regular pharmacotherapy, modern solutions are expected to deal with this issue. By taking the advantage of Internet of Things (IoT), a novel IoT-inspired behaviour monitoring framework is proposed for the analysis of panic disorder in a particular context. A quantum probability-inspired quantification measure is calculated to determine the scale of health irregularity. In addition, Temporal Data Mining (TDM) is performed for the formulation of temporal data granules to measure Individual Health Index (IHI) by utilising the Multi-scaled Gated Recurrent Unit (M-GRU) technique of deep learning. Moreover, a two-phased alert generation approach is proposed for notifying the current health condition of an individual to the concerned caretaker or medical specialist for assistive or medical services. In the comparative analysis, the proposed framework has outperformed the state-of-the-art approaches by achieving a considerable classification accuracy of 96.89% for event determination and 94.14% accuracy for health severity determination. Similarly, a considerable improvement with respect to Specificity, Sensitivity, and F-measure has been observed for the proposed framework.

Recently, deep learning and blockchain technologies have gained successful attention due to the high potential of generating accurate decisions and data security, respectively. The data provenances characteristics such as transparency, traceability, and trustworthiness are provided by the vast majority of centralized server-based deep learning approaches. This article examines the advantages of combining deep learning algorithms with blockchain technology. In addition, the most effective strategy for combining these two technologies to achieve the best result is identified through the most recent state-of-the-art literature. In this manner, the article is divided into seven thematic taxonomies based on the literature review: applications of deep learning and blockchain, deep learning techniques, protocols, domains, types of blockchain, and datasets. We have outlined the advantages and disadvantages of blockchain-based deep learning frameworks to facilitate insightful discussions.

Deep learning (DL) algorithms have become increasingly popular in recent years for remote sensing applications, particularly in the field of change detection. DL has proven to be successful in automatically identifying changes in satellite images with varying resolutions. The integration of DL with remote sensing has not only facilitated the identification of global and regional changes but has also been a valuable resource for the scientific community. Researchers have developed numerous approaches for change detection, and the proposed work provides a summary of the most recent ones. Additionally, it introduces the common DL techniques used for detecting changes in satellite photos. The meta-analysis conducted in this article serves two purposes. Firstly, it tracks the evolution of change detection in DL investigations, highlighting the advancements made in this field. Secondly, it utilizes powerful DL-based change detection algorithms to determine the best strategy for monitoring changes at different resolutions. Furthermore, the proposed work thoroughly analyzes the performance of several DL approaches used for change detection. It discusses the strengths and limitations of these approaches, providing insights into their effectiveness and areas for improvement. The article also discusses future directions for DL-based change detection, emphasizing the need for further research and development in this area.

Smart agriculture is a new sector that integrates cutting-edge technologies for transforming conventional farming methods into sustainable farming methods, such as increasing crop yields, lower expenses, and conserving natural resources. Machine learning (ML) and deep learning (DL) are two significant techniques for smart agriculture that can be used to analyze enormous volumes of data and extract significant insights to enhance agricultural practices. In this context, ML and DL may be utilized for a number of tasks, including crop yield prediction, disease and pest detection, weather pattern monitoring, and irrigation and fertilization management. The proposed chapter investigates the utilization of ML and DL in smart agriculture and highlights some of the most promising uses of these technologies. The study addresses the obstacles and potential of adopting ML and DL in agriculture, such as data quality, privacy problems, and the requirement for specialized hardware and software. The study also looks at some of the most important developments in smart agriculture, including the usage of sensors, drones, and other IoT devices, as well as the integration of ML and DL with other technologies like precision farming and robotics. Overall, we believe that ML and DL have the ability to transform the way we produce food and manage our natural resources by empowering farmers to make better decisions, decrease waste, and boost production.

Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to prevent the diseases from spreading, minimize the need for chemical pesticides and fertilizers, increase apple quality and production, and preserve the healthy growth of apple varieties. To overcome such challenges, different Deep Learning (DL) approaches have been developed to early detect apple leaf diseases. In this paper, the data from 2010 to 2024 has been taken for analysis, and it has been observed that many of the researchers have utilized different types of datasets for disease detection. Moreover, Deep Learning (DL) and Machine Learning (ML) have been mostly utilized for the detection and identification of apple leaf Alternaria diseases. It has also been observed from the previous work that Support Vector Machines (SVM), Random Forests (RF), XGBoost, and many more are the most common approaches utilized by the researchers. On the other hand, DenseNet, MobileNet, Convolutional Neural Network (CNN), and Vision Transformer are the deep learning approaches utilized by the researchers. Furthermore, we have also given a brief analysis of each approach along with a comparative analysis such as lightweight CNNs and Attention-based mechanisms, Transfer Learning (TL), Localization techniques, Vision Transformer (ViT), and Severity estimation techniques. Emphasizing their methods, datasets, performance metrics, and real-world applications. This study explores the proposed models’ approaches, feature selection and extraction techniques, data capturing conditions, accuracy, types of datasets used in the experiments, and their resources. Our research findings indicate that although DL approaches have significant potential for improving disease management in agriculture. There is a crucial need for a more scalable, robust, and flexible solution to handle numerous agricultural conditions and disease complexities. By methodically and comprehensively analyzing the collected data, this study aims to facilitate valuable resources for researchers aiming to design, develop, and implement DL-based systems for apple leaf disease detection and identification, ultimately contributing to sustainable agriculture and improved food security.

Inland water sources like lakes, rivers, and streams are important for the environment and human well-being. Monitoring these water sources is essential to ensure that they remain healthy and productive. This paper presents a study of deep learning-based inland water image classification using neural networks through satellite. The objective of the study is to develop VGG-16 neural network architecture that can be used to accurately distinguish normal images from water images. To assess the performance of the proposed network, several performance metrics are employed. The performance of the neural network is compared to existing methods to ascertain the efficacy of the proposed network. The results of the study show that the proposed neural network architecture is capable of accurately distinguishing normal images from water images, thus demonstrating its potential for successful implementation in real-world applications.

Since the development of smart healthcare services, different solutions have been developed in the field of healthcare to increase the life expectancy of the patient by reducing the cost of healthcare. Digital Twin (DT) is considered one of the most promising technologies and a game changer in the field of healthcare. DT is generating a virtual imitation of a physical object that mimics the status of an event by changing the information in real time. In this article, a smart context-aware physical activity monitoring framework is developed by combining different advanced techniques such as IoT, DT, FoT, CoT, and Blockchain to maintain the sensitiveness of the healthcare domain. In the proposed study, the physical movements of an elder are analyzed by utilizing the sequential data processing capability of deep learning to detect irregular physical events. In addition, the proposed framework can keep the data of an individual secured by applying progressed security highlights of blockchain. The proposed solution effectively analyzed an irregular event of an individual with considerable accuracy in real time. The calculated outcomes have shown the effectiveness of DT with smart healthcare solutions that would help to develop effective medical services by bringing patients and medical care experts together. Furthermore, the performance of the proposed solution is measured with respect to irregular event recognition, model training and testing, rate of latency, and data processing cost. In this manner, a case study defines the effectiveness of the proposed methodology in the smart healthcare industry.

As water is considered one of the essential assets of nature, the recognition of the availability of water at a specific location can help government bodies to take necessary action toward water conservation. Monitoring water from satellite images is considered one of the most difficult areas of pattern recognition. In this manner, a novel multi-level feature fusion approach is proposed to predict the pattern of water concerning a specific location to analyze the scale and availability. The proposed framework can access the spatial features from sentinel-2 images by utilizing the concept of structural learning. For evaluating the prediction performance, the calculated outcomes are compared with the traditional and modern pattern recognition approaches. It has been observed that the proposed approach is more robust in terms of pattern analysis as compared to the state-of-the-art approaches. Moreover, the performance of the proposed approach is evaluated on different training and testing ratios such as 70:30, 75:25, and 80:20. In this manner, the calculated outcomes define the pattern recognition efficiency of the proposed approach over the state-of-the-art approaches by achieving 94.51% of accuracy.

Monitoring a specific area to analyze a continuous change has become more accessible by using optical images in remote sensing technology. However, several natural and artificial aspects such as fog and air pollution make it difficult to extract correct geometric information. To overcome the limitation of optical images, Synthetic Aperture Radar (SAR) images can be used to access more accurate information with respect to the targeted area. In this manner, optical and SAR images can be utilized together to detect the scale of change even in bad weather conditions. To process optical and SAR images, an image translation process-oriented Deep Adaptation-based Change Detection Technique (DACDT) is proposed. An optimized U-Net++ model is proposed that helps to improve the global and regional impacts of the images. Moreover, a multi-scale loss function is utilized to access the features of different dimensions. In this manner, the final change maps are generated by transferring the features of optical images to the SAR images for better change analysis. The prediction performance of the proposed approach is evaluated on four different datasets such as Gloucester I, Shuguang Village, Gloucester-II, and California. The calculated outcomes define the prediction performance of the proposed solution by registering the accuracy of 98.67%, 99.77%, 97.68%, and 98.87%, respectively.

The focus of this chapter is to provide brief knowledge about the concept and advantages of integrating Big Data and blockchain technology. As we are focusing on the blockchain and Big Data, it is suitable to introduce Big Data before exploring its interactions with blockchain. The blockchain technology is introduced and then the interaction between blockchain technology and Big Data is focused, in order to gain a clear understanding of how blockchain technology is used for Big Data. Thereafter, the different applications of blockchain and Big Data are explored.

In India, agribusiness is directly dependent on the precise monitoring of paddy areas to take considerable supportive actions toward food security. For this, satellite-based data is considered one of the effective solutions. The goal of this study is to design an intelligent framework to determine the crop area by using satellite data that is easily available. In this article, a Multi-resolution Deep Neural Network (MR-DNN) is proposed to determine rice fields by performing multi-streaming classification. The task of prediction is performed on Landsat 8 satellite images with high spatial resolution. The prediction performance of the proposed model is justified by comparing the calculated outcomes from a few selected methods. The proposed model has achieved the highest prediction performance in terms of the F1 score with the accuracy of 95.40% and 95.12% for Punjab and West-Bengal dataset as compared to the selected models, such as DeepLabV3+, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Light-Gradient Boosting Method (LGBM), eXtreme Gradient Boosting (XGBoost), Spectral, and Threshold. In this manner, the empirical evaluation defines the prediction performance of the proposed model over the visual interpretation of the maps as well as seasonal impacts.

Diarrhea is one of the most common infectious diseases that affect people of all ages and is a serious public health concern around the world. The main causes of diarrhea include food quality, water, indoor meteorological, and outdoor meteorological conditions. In this study, a dew computing-assisted smart monitoring framework is developed to evaluate the relationship among the health, indoor meteorological, and food factors of an individual to predict the cause of diarrhea with the scale of severity. Smart sensors are utilized at the physical layer to collect the targeted parameters of health, indoor meteorological, and food of the individual. The captured events are classified at the cyber layer by utilizing the Probabilistic Weighted-Naïve Bayes (PW-NB) classification approach for quantifying abnormal health events. Furthermore, a Multi-scale Gated Recurrent Unit (M-GRU) is suggested to obtain the scale of severity by analyzing the correlation between irregular health, food, and environmental events. In this manner, the proposed model M-GRU has achieved a high precision value of (93.26 %), whereas, LSTM, RNN, SVM achieved the precision value of (89.13 %), (90.43 %), (88.23 %), respectively. In addition, the precision value of the PW-NB is (97.15 %), which is also higher as compared to KNN (93.25 %) and DT (96.91 %). The outcome of the proposed solutions is shown the higher Precision values on dew computing and cloud computing. Moreover, a comparative analysis defines the prediction effectiveness of the proposed solution over several other decision-making solutions with regards to event classification, severity determination, monitoring stability, and prediction efficiency.

Recent advancements in remote sensing technologies, as well as high-resolution satellite images, have opened up new avenues for comprehending the earth's surfaces. However, owing to the significant unpredictability in satellite data, satellite images categorization is a difficult task. Availability of the satellite dataset is a challenging task in the field of remote sensing. To overcome this challenge a novel sentinel-2 image dataset is proposed. Two different techniques for categorizing a large-scale dataset containing various types of land-use and land-cover surfaces are proposed and compared for this goal. In this article, an enhanced version of ResNet50 has been proposed to predict the multiple classes from sentinel 2 images. Furthermore, the outcome of ResNet50 is compared with traditional (shallow) machine learning models and deep learning models to check the working efficiency of the proposed approach. The shallow approach had the best F1-score of 0.87, while the deep approach ResNet50 achieved the best F1-score of 0.924. It has been realized from the outcome that the deep learning approaches are most robust than the machine learning approach in terms of classifying the multi-label satellite images classification.

Waterbody identification from satellite images in an automated manner is one of the difficult tasks in the domain of Remote Sensing (RS). In recent years, several image processing approaches have been developed to process RGB or multispectral images to analyze the availability of land, water prediction, object detection, climate change, LULC, and many others. In this study, a Multi-data Fusion Network (MDFN) is developed to extract the sources of water by utilizing Sentinel-2 satellite images. The spatial features are extracted by proposed model from RS images by comprising multiple structural learning-assisted feature fusion layers for water resource prediction. To justify the prediction performance, the calculated outcomes of developed solution are correlated with the other approaches such as DeepLabv3+, VGG, NDWI, SegNet, DenseNet, and ResNet. The calculated outcomes define the prediction superiority over the other models by registering the high value of Precision, F1-score, Recall, and IoU with the value of 0.958%, 0.928%, 0.899%, and 0.874%, respectively.

Satellite images taken on the earth's surface are analyzed to identify the spatial and temporal changes that have occurred naturally or manmade. Real-time prediction of change provides an understanding related to the land cover, environmental changes, habitat fragmentation, coastal alteration, urban sprawl, etc. In the current study, various digital change detection approaches and their constituent methods are presented. It was found that (i) change vector analysis method provides better accuracy among the algebra-based change detection approach, (ii) discrete wavelet transformation is better among transformation techniques, (iii) considering the artificial neural network and fuzzy-based approaches to analyze the prediction performance over the traditional state-of-the-art approaches, (iv) analyzing the promising outcomes generated by deep learning techniques for difference analysis related to the images captured at a different instance of time. The brief outlines of different change detection approaches are discussed in this study and addressed the need for improvement in the methods that are developed for the detection of a change in the remote sensing community.

In the context of climate change, the extraction of accurate information on natural resources becomes necessary and is considered one of the most challenging tasks in the field of remote sensing. The identification of water resources has achieved considerable attention in the field of remote sensing to deal with the problem of water scarcity. In the proposed study, a novel Multi-layered Data Integration Technique (MDIT) is proposed for the identification of water resources from satellite imagery. To evaluate the patterns, Deep Convolutional Restrictive Model (DCRM) is proposed to extract deep hierarchical features from the satellite images. Furthermore, the DCRM model is calculating the relationship between the features to evaluate the meaningful patterns. Moreover, Spatial Inferred Features (SIF) and Deep Sparse Auto-encoder (DSA) modules are utilized in MDIT to improve the inferences between the spatial features and to calculate the non-direct relationship between the extracted features. To evaluate the performance, the prediction efficiency of the proposed solution is compared with different state-of-the-art conventional and deep learning approaches such as Normalized Difference Water Index (NDWI), Residual Neural Network (ResNet), Visual Geometry Group (VGG), DeepLab V3, Densely Connected Convolutional Network (DenseNet), and Semantic Segmentation Network (SegNet). The proposed solution outperformed all the state-of-the-art approaches by achieving a higher precision of 0.945% for the extraction of water resources from low-resolution satellite imagery.