TracWork: An On-Field Employee Tracking System

The internet has transformed the world into a global village, benefiting our society as a whole and empowering people in a variety of ways. Many mobile applications are becoming a part of people's daily lives and assisting them in their jobs or daily routines, thanks in part to the phenomenal growth of Internet usage over the last 21 years. Previous research has found a scarcity of high-quality apps that cover all bases. This project's primary goals are to combine fragmented market systems into a product capable of performing functions such as tracking an employee's on-field movement using GPS; assisting employees in navigating to their next destination; maintaining and improving productivity levels using indicators such as battery status; current and past location; and so on. We highlight previous work and how we learned to extract a model that harmonises current systems while also improving quality of life in this study. We investigated numerous approaches, methods, and procedures before applying them to the development of the system

PSYCARIA - EMOTION DETECTOR FOR A PSYCHIATRIST

Every person will experience stress around the world, some healthy, called EUSTRESS and some unpleasant, named DISTRESS. Good pressure and stress promote success. Stress damages people's lives and health and causes various diseases. On the other hand, psychiatrists have a hard time treating their patients owing to a lack of time. They need innovative and intelligent equipment to treat their patients. We prepared a device that can detect a person's POSITIVE and NEGATIVE emotions through a smartwatch and a gadget that can sense body temperature, respiration, and heart rate. After witnessing these parameters, it can store the results on a website depending on the patient's condition. For example, the psychiatrist observed one patient for at least seven days regarding the days' results stored on a website. After seven days, the report is generated. The goal of psychiatrists in keeping their patients for seven days is to assess their emotional health and determine if they need to adjust their treatment. This system detects eight positive and negative emotions through heartbeat, respiratory, and body temperature sensors. These sensors are incorporated by utilizing machine learning. Web-based apps interpret sensor readings. Psychiatrists will analyze and report the website's results

SHIFAYAAB – Centralized Platform For Vaccination Program

Vaccinations are very essential for the prevention of harmful diseases. However, the implementation rate of vaccination varies in different parts of the world. Many countries struggle to achieve the maximum immunization ratio due to their vaccination practices and methodologies. However, the authors have developed a solution to strengthen the vaccination procedure. SHIFAYAAB, Proposed Methodology in this paper provides a centralized platform for different healthcare organizations and hospitals, working on various vaccination programs. The idea is to collectively provide a centralized database for the vaccination programs by integrating the platform with the healthcare organizations and hospitals, to enhance and improve the vaccination procedure for the workers as well as the public. SHIFAYAAB proposes automation of the vaccination procedure by replacing the old school vaccine schedule card-reports with autonomous system[1]generated microplans. It will assemble the vaccination records and provide a user-friendly platform for the vaccinators to carry out the vaccination process. It will also provide children parents a platform to keep track of their vaccination progress by monitoring their microplan along with regular notification reminders from the platform

A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual support, healthcare, and security. Human-Computer Interaction (HCI) is a field that has been combining AI and human-computer engagement over the past several years in order to create an interactive intelligent system for user interaction. AI, in conjunction with HCI, is being used in a variety of fields by employing various algorithms and employing HCI to provide transparency to the user, allowing them to trust the machine. The comprehensive examination of both the areas of AI and HCI, as well as their subfields, has been explored in this work. The main goal of this article was to discover a point of intersection between the two fields. The understanding of Explainable Artificial Intelligence (XAI), which is a linking point of HCI and XAI, was gained through a literature review conducted in this research. The literature survey encompassed themes identified in the literature (such as XAI and its areas, major XAI aims, and XAI problems and challenges). The study’s other major focus was on the use of AI, HCI, and XAI in healthcare. The poll also addressed the shortcomings in XAI in healthcare, as well as the field’s future potential. As a result, the literature indicates that XAI in healthcare is still a novel subject that has to be explored more in the future

A novel deep learning technique to detect electricity theft in smart grids using AlexNet

Electricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper-parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre-processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1-score, up to 86%, 89%, 86%, and 84%, respectivel