Hyperspectral imaging has emerged as an effective powerful tool in plentiful military, environmental, and civil applications over the last three decades. The modern remote sensing approaches are adequate for covering huge earth surfaces with phenomenal temporal, spectral, and spatial resolutions. These features make HSI more effective in various applications of remote sensing depending upon the physical estimation of identical material identification and manifold composite surfaces having accomplished spectral resolutions. Recently, HSI has attained immense significance in the research on safety and quality assessment of food, medical analysis, and agriculture applications. This review focuses on HSI fundamentals and its applications like safety and quality assessment of food, medical analysis, agriculture, water resources, plant stress identification, weed & crop discrimination, and flood management. Various investigators have promising solutions for automatic systems depending upon HSI. Future research may use this review as a baseline and future advancement analysis.

Agriculture is the ultimate imperative and primary source of origin to furnish domestic income for multifarious countries. The disease caused in plants due to various pathogens like viruses, fungi, and bacteria is liable for considerable monetary losses in the agriculture corporation across the world. The security of crops concerning quality and quantity is crucial to monitor disease in plants. Thus, recognition of plant disease is essential. The plant disease syndrome is noticeable in distinct parts of plants. Nonetheless, commonly the infection is detected in distinct leaves of plants. Computer vision, deep learning, few-shot learning, and soft computing techniques are utilized by various investigators to automatically identify the disease in plants via leaf images. These techniques also benefit farmers in achieving expeditious and appropriate actions to avoid a reduction in the quality and quantity of crops. The application of these techniques in the recognition of disease can avert the disadvantage of origin by a factious selection of disease features, extraction of features, and boost the speed of technology and efficiency of research. Also, certain molecular techniques have been established to prevent and mitigate the pathogenic threat. Hence, this review helps the investigator to automatically detect disease in plants using machine learning, deep learning and few shot learning and provide certain diagnosis techniques to prevent disease. Moreover, some of the future works in the classification of disease are also discussed.

Long Term Evolution (LTE) brings to the theory of advanced leading-edge technologies which guarantees ubiquitous broadband access. As a result, the continuous increment in the number of user terminals (UT) and their expectation leads to the importance of managing and updating the networks as per the expectation of users. In this paper, the concept of self-tuning is used for adjusting the service priority factor in the scheduling algorithms to allocate the resource blocks (RBs) in order to give the appropriate Quality of Service (QoS) based on Quality of Experience (QoE). To access QoE-aware, a QoE model is created by using the Artificial Neural Network (ANN) algorithm to estimate the QoE score by using the QoS parameters. We propose the Particle Genetic Algorithm (PGA) to find the optimal parameter of service priority factors, and the proposed algorithm works efficiently by increasing the average QoE of the network along with maintaining the QoE threshold for each of the multiservice. The detailed comparison is presented between the proposed and reference algorithms to highlight the significance of the proposed algorithm and the simulation and analytical results show that the proposed algorithm outperforms the existing ones in terms of improving the allocation of resources under the environment of limited resources.

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.

Recent studies have shown the potential of the Data-Efficient Image Transformer (DeiT)-based transfer learning method in speech/image recognition and classification utilizing models pre-trained on image datasets. However, the use of DeiT models, especially those pre-trained on image datasets, has not yet been explored for Valvular Heart Disease (VHD) detection. This paper proposes a transfer learning methodology using the DeiT model pre-trained on image datasets for VHD classification. Additionally, we introduce a hybrid Convolution-DeiT (Conv-DeiT) architecture to further improve classification performance. The Conv-DeiT framework integrates a convolutional block with a Squeeze-and-Excitation (SE) attention mechanism to enhance the channel and spatial information within the input features before processing by the DeiT model. The proposed models were assessed using the Heart Sound Murmur (HSM) database, accessible on GitHub. Experimental results show that the DeiT-based transfer learning approach achieved an overall accuracy of 97.44%. Moreover, our Conv-DeiT method outperformed the DeiT-based transfer learning with an impressive overall accuracy of 99.44%. This study indicates the effectiveness of transfer learning using DeiT models pre-trained on image datasets for heart sound classification. Specifically, our hybrid Conv-DeiT method, which combines the convolutional block and the SE-attention mechanism, demonstrates significant advantages in this context.