Elderly falls are a major concerning threat resulting in over 1.5–2 million elderly people experiencing severe injuries and 1 million deaths yearly. Falls experienced by Elderly people may lead to a long‐term negative impact on their physical and psychological health conditions. Major healthcare research had focused on this lately to detect and prevent the fall. In this work, an Artificial Intelligence (AI) edge computing based wearable device is designed and developed for detection and prevention of fall of elderly people. Further, the various deep learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) are utilized for activity recognition of elderly. Also, the CNN-LSTM, RNN-LSTM and GRU-LSTM with and without attention layer respectively are utilized and the performance metrics are analyzed to find the best deep learning model. Furthermore, the three different hardware boards such as Jetson Nano developer board, Raspberry PI 3 and 4 are utilized as an AI edge computing device and the best deep learning model is implemented and the computation time is evaluated. Results demonstrate that the CNN-LSTM with attention layer exhibits the accuracy, recall, precision and F1_Score of 97%, 98%, 98% and 0.98 respectively which is better when compared to other deep learning models. Also, the computation time of NVIDIA Jetson Nano is less when compared to other edge computing devices. This work appears to be of high societal relevance since the proposed wearable device can be used to monitor the activity of elderly and prevents the elderly falls which improve the quality of life of elderly people.

Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

In recent years, Electric Vehicles are becoming more popular. The pollution level in the atmosphere can be effectively minimized by using Electric vehicles for large-scale transportation. A battery station is required for continuous operation; however, the Photovoltaic-based OFF grid charging station can only operate during the day. Therefore, the three-port converters have started to arise from a number of current EV charging station developments. In this study, a unique PWM and Phase Shift Controller are proposed to reduce switching losses and to improve reliability. In addition, for Maximum PowerPoint Tracking, a Fuzzy is added to the PV system. Furthermore, an appropriate interleaved boost converter topology is used to create the various charging voltages required for EV and battery stations. The proposed topology is simulated, and the hardware prototype has been created and tested. The result shows that the proposed topology has a better efficiency than the traditional converters.Article Highlights. A dual composite charging station for electric vehicle charging in environment friendly manner. Optimization of power electronics required in Electric Vehicle charging stations. Maximum utilization of battery for cyclic charging from charger and discharging through Electric Vehicles.

In recent years, the demand for efficient and sustainable agricultural practices has been leveraged, leading to smart farming practices. These practices aim to enhance agricultural processes and productivity and minimize resource waste. One of the crucial challenges faced by farmers is the uneven distribution of soil humidity and pH across their agricultural lands. Further, the irregularity in soil moisture content and pH can lead to poor crop performance, water wastage, and increased resource utilization. In this work, an Internet of Things-based smart sensor node was developed, which consists of humidity and pH sensors to ensure the efficient management of water and soil conditions across an entire farm. Also, an array of humidity and pH sensors were placed across the farm, and these units worked independently as they have their own controller and battery unit. The developed device was integrated with a solar cell, which charged the battery. Further, the data acquired from these sensors were wirelessly transmitted to the base station, and it gathered the information of each unit, including their humidity levels, pH values, signal strength, and energy supply. This information was processed at the base station, and a graphical overview of the farm with the acquired information was represented, which provides farmers with real-view insight to identify areas with poor humidity and pH conditions. These data were transmitted to an IoT cloud, offering the farmer the ability to monitor their farm from a remote location. In cases where humidity levels dropped drastically and remained unchecked for more than two hours, the system triggered an alert. This mechanism makes sure that farmers are notified of potential issues, allowing them to prevent crop damage and optimize their resource usage.