Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper.

Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.