Abstract The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments.

Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. Meanwhile, deep learning has been successfully implemented to classify and predict dementia disease. However, the accuracy of MRI image classification is low. This paper aims to increase the accuracy and reduce the processing time of classification through Deep Learning Architecture by using Elastic Net Regularisation in Feature Selection. The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularisation. Initially, the MRI images are fed into CNN for features extraction through convolutional layers alternate with pooling layers, and then through a fully connected layer. After that, the features extracted are subjected to Principle Component Analysis (PCA) and Elastic Net Regularisation for feature selection. Finally, the selected features are used as an input to Extreme Machine Learning (EML) for the classification of MRI images. The result shows that the accuracy of the proposed solution is better than the current system. In addition to that, the proposed method has improved the classification accuracy by 5% on average and reduced the processing time by 30 ~ 40 seconds on average. The proposed system is focused on improving the accuracy and processing time of MCI converters/non-converters classification. It consists of features extraction, feature selection, and classification using CNN, FreeSurfer, PCA, Elastic Net, and Extreme Machine Learning. Finally, this study enhances the accuracy and the processing time by using Elastic Net Regularisation, which provides important selected features for classification.

Abstract Recently, deep learning using convolutional neural network (CNN) has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a patch‐based classifier (PBC) along with deep learning architecture. The proposed system consists of a deep convolutional neural network that helps in enhancing and increasing the accuracy of the classification process. This is done by the use of the PBC. CNN has completely different layers where images are first fed through convolutional layers using hyperbolic tangent function together with the max‐pooling layer, drop out layers, and SoftMax function for classification. Further, the output obtained is fed to a PBC that consists of patch‐wise classification output followed by majority voting. The results are obtained throughout the classification stage for breast cancer images that are collected from breast‐histology datasets. The proposed solution improves the accuracy of classification whether or not the images had normal, benign, in‐situ, or invasive carcinoma from 87% to 94% with a decrease in processing time from 0.45 to 0.2 s on average. The proposed solution focused on increasing the accuracy of classifying cancer in the breast by enhancing the image contrast and reducing the vanishing gradient. Finally, this solution for the implementation of the contrast limited adaptive histogram equalization technique and modified tangent function helps in increasing the accuracy.

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile devices. A mobile device roams sensing fields and collects data from sensors through a short transmission range. Multihop communication is used to improve data gathering by reducing the tour length of the mobile device. In this paper we study the trade-off between energy saving and data gathering latency in wireless sensor networks. In particular, we examine the balance between the relay hop count and the tour length of a mobile Base Station (BS). We propose two heuristic algorithms, Adjacent Tree-Bounded Hop Algorithm (AT-BHA) and Farthest Node First-Bounded Hop Algorithm (FNF-BHA), to reduce energy consumption of sensor nodes. The proposed algorithms select groups of Collection Trees (CTs) and a subset of Collection Location (CL) sensor nodes to buffer and forward data to the mobile BS when it arrives. Each CL node receives sensing data from its CT nodes within bounded hop count. Extensive experiments by simulation are conducted to evaluate the performance of the proposed algorithms against another heuristic. We demonstrate that the proposed algorithms outperform the existing work with the mean of the length of mobile BS tour.

In a mobile ad hoc network, where nodes are deployed without any wired infrastructure and communicate via multihop wireless links, the network topology is based on the nodes' locations and transmission ranges. The nodes communicate through wireless links, with each node acting as a relay when necessary to allow multihop communications. The network topology can have a major impact on network performance. We consider the impact of number and placement of neighbours on mobile network performance. Specifically, we consider how neighbour node placement affects the network overhead and routing delay. We develop an analytical model, verified by simulations, which shows widely varying performance depending on source node speed and, to a lesser extent, number of neighbour nodes.

Prolonging network lifetime is one of the most important design objectives in energy-constrained wireless sensor net-works (WSNs). Using a mobile instead of a static base sta-tion (BS) to reduce or alleviate the non-uniform energy con-sumption among sensor nodes is an efficient mechanism to prolong the network lifetime. In this paper, we deal with the problem of prolonging network lifetime in data gathering by employing a mobile BS. To achieve that, we devise a novel clustering-based heuristic algorithm for finding a trajectory of the mobile BS that strikes the trade-off between the traf-fic load among sensor nodes and the tour time constraint of the mobile BS. We also conduct experiments by simulations to evaluate the performance of the proposed algorithm. The experimental results show that the use of clustering in con-junction with a mobile BS for data gathering can prolong network lifetime significantly.