An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

Clustering is considered as one of the most prominent solutions to preserve theenergy in the wireless sensor networks. However, for optimal clustering, anenergy efficient cluster head selection is quite important. Improper selectionofcluster heads(CHs) consumes high energy compared to other sensor nodesdue to the transmission of data packets between the cluster members and thesink node. Thereby, it reduces the network lifetime and performance of thenetwork. In order to overcome the issues, we propose a novelcluster headselection approach usinggrey wolf optimization algorithm(GWO) namelyGWO-CH which considers the residual energy, intra-cluster and sink distance.In addition to that, we formulated an objective function and weight parametersfor anefficient cluster head selection and cluster formation. The proposedalgorithm is tested in different wireless sensor network scenarios by varyingthe number of sensor nodes and cluster heads. The observed results conveythat the proposed algorithm outperforms in terms of achieving better networkperformance compare to other algorithms

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown

Data for: Role of gender on academic performance based on different parameters: Data from secondary school education

Academic performances and personal habits of higher secondary school students data were collected from high school education institutions located at Guntur, Andhra Pradesh, India in the academic year of 2017-18. The dataset of 1116 records, each with 9 parameters: mother and father education, the impact of advisor, time spent on study after school, time spent on sports, time spent with mobile per day, the impact of health problems, goal, time spent on yoga or physical exercise.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

An innovative framework for the recognition of human activity in smart healthcare

Health services are one of the most difficult aspects of the massive influx of people into urban centers. With global digitization, especially in healthcare, the ability to collect, receive and present data has become a top priority. Currently, many applications in the healthcare business use Machine Learning to give individualized therapies, which will be more dynamic and efficient once personal health and predictive analytics are combined.  Furthermore, machine learning-based tools aid in the treatment of patients starting at the ground level, with clinical practice diagnosis and suggestions. Massive amounts of data may be collected from smart devices in our digital age. Human activity recognition is a classification problem in which data is used to identify events performed by humans. This data is categorized and analyzed to discover patterns in the data that may be used to create future predictions. In theory, activity detection can provide significant societal benefits. Activity recognition and pattern discovery are two aspects of human activity comprehension. The first is concerned with detecting human activities accurately using a specified activity model