The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges.

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future.

Early Alzheimer’s disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer’s disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer’s and non-Alzheimer’s cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer’s disease with a high degree of accuracy, which paves the way for early diagnosis and treatment. In addition, an interpretability study is carried out in order to provide light on the decision-making process underlying the model. This helps to contribute to a better understanding of the characteristics and biomarkers that play a role in the identification of Alzheimer’s disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer’s disease identification. It does this by harnessing the capabilities of hybrid deep learning models and sophisticated sampling approaches to improve clinical decision support and, as a result, eventually improve patient outcomes.

In this paper it is proposed to separate the ECG signal taken from skin electrodes located on a pregnant woman's body into Maternal Electro Cardiogram signal and Fetal Electro Cardiogram signal. Blind Source Separation is the technique used for separating these source signals. ICA is applied on the mixed signals and the separated signals are reconstructed using wavelet reconstruction. Comparison results show that Lifting Wavelet Transformation and FASTICA algorithm produces the best SNR value of 11.39 for maternal and 10.10 for fetal Electro Cardio Gram signals.

Blind source separation (BSS) is used in many fields of signal and image processing. It is used for separating a set of source signals from mixed signals without the aid of information about the source signals or the mixing process. The paper mainly focuses on (1) Separation of maternal and fetal ECG signal (2) Performance measure of various BSS and JBSS algorithm in term of SIR and execution time. Various BSS algorithm like ICA, FASTICA, JADE and JBSS algorithms like MCCA, SOBI, JBSS_SOS, JBSS_CUM4 are used for blindly separating the source signals. And the impacts on separation of maternal and fetal signal are examined. The simulations are conducted in MATLAB using Non-Invasive ECG of pregnant women from PhysioNet database. The JBSS_CUM4 algorithm shows better performance with the SIR value of 29.58dB for MECG and 7.44dB for FECG.

Social distancing is necessary to reduce community socializing in a pandemic situation like Covid-19. Keeping an appropriate physical distance and reducing mass gatherings in places like schools, malls, religious places can reduce the transmission of the virus among a community. During pandemic situations, it becomes hard for a person to follow the necessary rules for social distancing when the schedule for purchasing necessary articles is very short and places are over-crowded. The proposed smart monitoring system consists of a new smart wearable prototype of a compact and low-cost electronic device, based on human detection and proximity distance functions, to estimate the social distance (SD) between people and issue a notification when the social distance is less than a predefined threshold value.