Gun identification from gunshot audios for secure public places using transformer learning.

Increased mass shootings and terrorist activities severely impact society mentally and physically. Development of real-time and cost-effective automated weapon detection systems increases a sense of safety in public. Most of the previously proposed methods were vision-based. They visually analyze the presence of a gun in a camera frame. This research focuses on gun-type (rifle, handgun, none) detection based on the audio of its shot. Mel-frequency-based audio features have been used. We compared both convolution-based and fully self-attention-based (transformers) architectures. We found transformer architecture generalizes better on audio features. Experimental results using the proposed transformer methodology on audio clips of gunshots show classification accuracy of 93.87%, with training loss and validation loss of 0.2509 and 0.1991, respectively. Based on experiments, we are convinced that our model can effectively be used as both a standalone system and in association with visual gun-detection systems for better security.

Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials.

Waste management is a critical problem for every country, whether it is developed or developing. Selecting and managing waste are a critical part of preserving the environment and maximizing resource efficiency. In addition to reducing trash and disposal, reusable items are predicted to be of great benefit since they lessen our dependence on raw materials. The usage of compostable trash may be expanded outside fertilizers and dung after the metallic, chemicals, and glass items have been recycled. After a good scrubbing, the glass may be broken down and remelted to create new items. Reusing waste items via garbage recovery is one of the best methods to do so. This document outlines the steps that must be taken to maximize the use of garbage. This work describes a reusable industrial robot arm for grasping and sorting things depending on the resources they contain. Gripping, motion control, and object material categorization are all integrated into a full-automation, reusable system architecture in this study. LeNet also was adjusted to classify garbage into cartons and plastics using an artificial intelligent technique, with the use of a customized LeNet model. Movement in terms of moving the robot in the most efficient way possible, the robot's grabbing, and categorization were incorporated into the movement design process. The system's grabbing and object categorization success rates and computation time are calculated as metrics for evaluation.

Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.

Applying Dynamic Systems to Social Media by Using Controlling Stability.

This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When lim t ⟶ ∞ Y - α X = 0 , where Y and X are the state vectors of the drive and response systems, respectively, and Wan (α = ∓1)), the two systems' hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach.

E-Health System Characteristics, Medical Performance, and Healthcare Quality at Jordan's Health Centers.

This research explores how e-health systems' features (information quality, quality of the system, usability perceived, and perceived usefulness) contribute to improving medical personnel performance in medical centers, patient care, and physician-patient interactions in Jordan. The objective is to evaluate a single integrated model consisting of the technology acceptance model. This study used the logical research method and approach. A collection of data from 212 medical personnel working in 19 healthcare facilities throughout Jordan were gathered. To analyze the data collected and test the hypotheses of the research, a partially square/structural equation modeling method has been employed. The study found that the health information system (HIS) information quality has a direct and indirect beneficial effect on the performance of the staff, beneficial effects on patient care alone, and only favorable, indirect effects on the doctor-patient relationship. On the contrary, system quality was shown to influence directly and indirectly and to have a direct and indirect beneficial effect both on the connection between doctors and patients. Remember that the HIS has accessibility, speed, and mistake detection and avoids error issues. These shortcomings are suggested to be rectified in conjunction with improved user perception towards easy usage and utilization of the system.

Effect of Six-Minute Walk Test and Incremental Exercise on Inspiratory Capacity, Ventilatory Constraints, Breathlessness and Exercise Performance in Sedentary Male Smokers without Airway Obstruction.

We investigated physiological responses and exercise capacity in sedentary young smokers during sub-maximal and maximal test and its impact on dyspnea and exercise intolerance. Fifty sedentary male smokers and non-smokers (age: 24 ± 1 years., weight: 71 ± 9 kg, height: 177.3 ± 4.8 cm, body mass index: 22.6 ± 2.5 kg/m2) underwent two visits with pulmonary function tests, breathing pattern, and inspiratory capacity measurement at rest and during sub-maximal and maximal exercise. Smokers show reduced exercise capacity during six minutes walk test (6-MWT) with decreased walked distance (p < 0.001) and inspiratory capacity (p < 0.05). During cardiopulmonary exercise test (CPET), smokers had higher minute ventilation VE for a given submaximal intensity (p < 0.05) and lower minute ventilation at maximal exercise (p < 0.001). End expiratory lung volume was significantly lower in sedentary smokers at rest (p < 0.05), at ventilatory threshold during exercise (p < 0.05), but not during peak exercise. End inspiratory lung volume was significantly lower in smokers at rest (p < 0.05) and ventilatory threshold (p < 0.05). Cigarette smoking alters lung function during submaximal and maximal exercise. This alteration is manifested by the development of dynamic hyperinflation contributing to exercise capacity limitation.

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

The Dynamic Movement of Disaster Management Systems Based on Vehicle Networks and Applied on the Healthcare System.

In order to save human life and assets, the emergency management system (DMS) requires roving rescue teams to respond promptly and effectively. Installation and restoration of appropriate communication infrastructure are important for reducing the effect of disasters and enabling and coordinating information flow among relief teams working in the region. This paper describes a data collection system based on vehicular cloud network services that incorporates the advantages of both architectures of vehicular ad hoc networks (VANETs) with the cloud to establish vehicular cloud networks (VCNs). Vehicles in the current plan perform tasks like monitoring the environment, gathering data, and transmitting data to the control center depending on their positions and instructions. To build a disaster management system, the proposed system uses hybrid wireless networking, which includes both a central system and ad hoc networks. The implementation results show that the suggested system is more dependable and efficient; even light density is improved in terms of reachability with few hops. Furthermore, as compared to the existing system, the suggested system has a lower end-to-end delay and a higher packet delivery ratio.

Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E-App.

Lifestyle influences morbidity and mortality rates in the world. Physical activity, a healthy weight, and a healthy diet are key preventative health behaviours that help reduce the risk of developing type 2 diabetes and its complications, such as cardiovascular disease. A healthy lifestyle has been shown to prevent or delay chronic diseases and their complications, but few people follow all recommended self-management behaviours. This work seeks to improve knowledge of factors affecting type 2 diabetes self-management and prevention through lifestyle changes. This paper describes the design, development, and testing of a diabetes self-management mobile app. The app tracked dietary consumption and health data. Bluetooth movement data from a pair of wearable insole devices are used to track carbohydrate intake, blood glucose, medication adherence, and physical activity. Two machine learning models were constructed to recognise sitting and standing. The SVM and decision tree models were 86% accurate for these tasks. The decision tree model is used in a real-time activity classification app. It is exciting to see more and more mobile health self-management apps being used to treat chronic diseases.

A Survey Research on the Willingness of Gynecologists to Employ Mobile Health Applications.

In gynecological care, mHealth (mobile health) technology may play an important role. Medical professionals' willingness to use this technology is the key to its acceptance. Most doctors utilize mobile health technology; however, there is still room for improvement in the use of mHealth. Gynecologists were asked to participate in this research to see how open they were to use mobile health technologies. In this descriptive-analytical investigation, the researchers determined the average scores for each variable. The overall mean for preparedness to embrace mobile medical technology is 1.8 out of 2, as shown in Table 1. When it came to their desire to embrace mobile health technology, doctors' years of experience correlated negatively with their age. According to our findings, the amount of interest in mobile health technology is high. Patients' private information must be protected throughout the usage of this technology though. Mobile health technology may effectively reach patients in remote areas, but it is not a substitute for face-to-face encounters with medical professionals.

Mechanism of Dyspnea during Exercise in Children with Corrected Congenital Heart Disease.

This study will evaluate cardiorespiratory and peripheral muscle function and their relationship with subjective dyspnea threshold after the surgical correction of congenital heart disease in children. Thirteen children with surgically repaired congenital heart disease were recruited. Each participant performed an incremental exercise test on a cycle ergometer until exhaustion. Gas exchanges were continuously sampled to measure the maximal aerobic parameters and ventilatory thresholds. The functional capacity of the subjects was assessed with a 6 min walk test. At the end of the exercise test, isokinetic Cybex Norm was used to evaluate the strength and endurance of the knee extensor muscle in the leg. Dyspnea was subjectively scored with a visual analog scale during the last 15 s of each exercise step. Oxygen consumption measured at the dyspnea score/VO2 relationship located at the dyspnea threshold, at which dyspnea suddenly increased. Results: The maximal and submaximal values of the parameters describing the exercise and the peripheral muscular performances were: VO2 Peak: 33.8 ± 8.9 mL·min-1·kg-1; HR: 174 ± 9 b·min-1; VEmax: 65.68 ± 15.9 L·min-1; P max: 117 ± 27 W; maximal voluntary isometric force MVIF: 120.8 ± 41.9 N/m; and time to exhaustion Tlim: 53 ± 21 s. Oxygen consumption measured at the dyspnea threshold was related to VO2 Peak (R2 = 0.74; p < 0.01), Tlim (R2 = 0.78; p < 0.01), and the distance achieved during the 6MWT (R2 = 0.57; p < 0.05). Compared to the theoretical maximal values for the power output, VO2, and HR, the surgical correction did not repair the exercise performance. After the surgical correction of congenital heart disease, exercise performance was impeded by alterations of the cardiorespiratory function and peripheral local factors. A subjective evaluation of the dyspnea threshold is a reliable criterion that allows the prediction of exercise capacity in subjects suffering from congenital heart disease.