Narrow Results

In the recent past, numerous frameworks have been designed to take decision support from samples for analyzing ECG signal data classification with wearable devices to prevent health risks in sports. As various frameworks permit a distinctive set of results, assessing the framework’s classification control in examination with other order frameworks or in correlation with human specialists is hard. The order precision is generally utilized as a measure of classification execution in this research. A novel hybrid Improved Monkey-based search (IMS) and support vector machine (SVM) technique have been designed and developed in this research for the health risk identification in ECGs. It incorporates handling of noise, extraction of signals, rule-based beat classification, and sliding window arrangement using a wearable device for the sportsperson. It can be executed continuously and can give clarifications to the analytic choices, and maximum scores have been acquired in terms of sensitivity and specificity (98.1% and 98.5% correspondingly using collective accuracy gross information, and 98.8% using aggregate average statistics, which has been shown in this research. Finally, experimental analysis has exposed that the hybrid Improved Monkey-based search (IMS) and support vector machine (SVM) technique achieve high precision (99.01%) in analyses of the heart rate for the sportsperson.

Intake of heavy metals from contaminated agricultural products represents a significant pathway for human exposure. Banglish village in the Comilla district is one of the most devastated arsenic (As) contaminated areas in Bangladesh. This study focus whether As is solely responsible for causing arsenicosis or whether some other heavy metals have a synergistic effect on the toxicity of As. The study sampled various leafy and non-leafy vegetables and groundwaters were analyzed by using the Proton Induced X-ray Emission (PIXE) method. The results revealed that both the vegetables and the groundwater were highly contaminated with As and lead (Pb), although the contents in the vegetables and the groundwater varied depending on species and tube wells. As and Pb concentrations in the edible part of all tested vegetables and in groundwater exceeded the permissible intake levels of the Food and Agriculture Organization (FAO) and the World Health Organization (WHO). The findings inferred that the inhabitants of the study area are experiencing health risks resulting from the intake of As and Pb, and that Pb might have a synergistic role with As by aggravating the arsenicosis. The potential health risks due to Pb is being reported for the first time in Bangladesh.

A two-person conflict is analyzed, where a water company and a community are the players and water supply and health risk constitute the payoff functions. An aquifer's response to different pumping policies is estimated at points of interest by a computational neural network, which was trained using simulation data from MODFLOW, a large-scale complicated finite difference model. Using the weighting method, the Pareto frontier is first determined, and four particular resolution methodologies are applied. The numerical results are analyzed and compared.