HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution

Artificial Intelligence, Sensors and Vital Health Signs: A Review

Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions