VOLUME 13, ISSUE 10, OCTOBER 2024

Abstract: Myriad challenges exist in the healthcare sector, including personnel shortages, increasing costs, the need for timely decision-making, big data, competition, lack of proper infrastructure, and many others. While the sector is growing, knowledge management is emerging as a critical field that will assist in dealing with these existing and emerging challenges to improve outcomes and performance. A functional knowledge management framework is expected to improve productivity, quality, and patient care by offering the correct information to the right people at the right time. This paper analyzes the function of knowledge management in the healthcare sector, accentuating the methods through which it enhances clinical outcomes. The study provides insights into the best practices and challenges that healthcare organizations have experienced in adopting knowledge management strategies successfully through evaluating the literature in the domain and identifying various information-sharing techniques. The outcomes from this systematic review of literature point to benefits that can be drawn through the proper use of knowledge management in reducing errors in medication, improving collaboration, and supporting knowledge sharing.

Keywords: Knowledge management, clinical outcomes, healthcare organizations, knowledge sharing.

Abstract: Digital technologies have changed the way organisations and educational institutions communicate and collaborate. Video conferencing and virtual meetings have become indispensable tools for remote communication, especially following the COVID-19 pandemic, which restricted physical meetings and in-person schooling worldwide. This research explored the motivation, adoption, and impact of video conferencing and virtual meetings in organizations in developing countries. A desk review was done to build on peer reviewed existing knowledge, identify factors for adoption, motivation, impact of digital technologies in the current literature and provide a comprehensive overview of the research topic. The findings revealed that, while video conferencing provided flexibility and is cost savings, challenges like limited infrastructure, financial constraints, internet reliability and digital literacy hinder its full functionality. Improvements in digital infrastructure and increased internet accessibility help to facilitate the adoption of video conferencing and online meetings. The benefit is significant, with improved collaboration, lower travel expenses, and streamlined processes. There is need to emphasize the significance of deliberate investment in technology to foster long-term growth in developing countries.

Keywords: Video conferencing, virtual meetings, motivation, adoption, impact, developing countries

Keywords: Cloud Storage, Next.js, Firebase, Clerk Authentication, Full-stack Web Development, File Management.

Abstract: In present scenario, people are buried up in a heavy work culture, as everyone is engaged with busy schedules, and hectic tasks which make them deviate from family life. If any issues encounter unexpectedly, it distracts them and makes them choose over the work they have to accomplish primarily. It is important to manage both professional and family life. In such circumstances, every one of us would have fantasized about a kind of house which doesn’t have any leaks in pipes, if it doesn’t have any mess in fixing a furniture and a kind of house which never face any maintenance issues and every one of us have thought that a life would be much better if no point of issue arises in getting a service at your door step and if there is no mess in bargaining a labor for home service. In such situation’s E-Commerce plays a vital role in today’s life as it has so many advantages in our life because it makes convenient in daily life of the people.

Keywords: Urban ease, home services, customizable packages, AI-based recommendation, secure payment gateways, user experience, operational efficiency

Abstract: Text recognition for E-Commerce boosts search engine performance, facilitates product discovery, enhances user experience, lets you create personalized recommendations, and simplifies inventory control. We’ve developed a novel Ensemble SVM Multinomial Naive Bayes Approach in our research project that is specifically designed to detect e-commerce text. Our dataset had four different classes: books, electronics, household, and clothing and accessories. It contained 50,425 numeric values. Our impressive training accuracy of 99.83% and validation accuracy of 98.35% were attained by applying this state-of- the-art model. The precision of e-commerce text detection has advanced significantly with this accomplishment. We truly believe that our detection technology is capable of what it does. In our opinion, it offers a sound and useful approach to the analysis of text related to online commerce. In addition, we anticipate its integration serving as a cornerstone of future e-commerce sections, promising improved functionality and precision, which excites us about its future potential to refine the e-commerce scene. Index Terms: component, formatting, style, styling, insert.

Abstract: Electronic Health Records (EHRs) are valuable for predicting patient outcomes and hospital resource demands. However, EHR data's vast scale and intricate structure present significant challenges for traditional predictive models. This paper introduces a dual prediction machine learning computational model that simultaneously forecasts patient outcomes and hospital resource utilization with improved accuracy. Using a hybrid approach that combines machine learning techniques such as artificial neural networks and support vector machines. The model effectively addresses the scale and complexity of EHR data by managing the data volume and variable relationships. Designed for concurrent execution, this model allows real-time outcome and resource predictions to run, providing high availability and reliable decision support. Trained on historical EHR data and validated for accuracy and adaptability, the model continuously learns from new patient data, accepting changes in patient demographics and hospital practices. This research has substantial implications for healthcare, enabling hospitals to make real-time predictions for resource allocation and patient care. Additionally, it can reduce costs by identifying high-risk patients early, allowing for pre-emptive interventions. This study advances EHR-based decision-making and care delivery by leveraging a concurrent model structure.

Keywords: Electronic Health Records, Machine Learning, Computational Model, Support Vector Machine

Abstract: The coping of physical and non physical education students measure through the The Ways of Coping-Revised (WOC-R) Scale was used and it was developed from a study of the ways of coping college students used to deal with an examination. It included 66-items in the questionnaire asking about the cognitive and behavioural strategies that students used to deal with the internal and/ or external demands of a stressful situation encountered, which were referred to as academic stress in the current study. Items were rated by a 4-point Likert scale. There are eight subscales including Problem-focused coping, Wishful thinking, Detachment , Seeking social support, Focusing on the positive, Self-blame, Tension reduction, and Keep to self. The purpose of the study was to find out the differences of coping Skill between physical and non physical education students. Total 300 physical education and 300 other students selected for the study and their age ranged between 18-30years. This study involves a cross sectional, comparative study of physical and non-physical education students. The research design of the study is to descriptive research design. The study depends mainly on primary source of data. The data was collected through respondents in physical and non-physical education students the Instructions was given to the respondent before filling the questionnaires. The findings of the study reveals that Non - Physical Education Students incur significantly low Self-blame coping as compared to Physical Education Students . The findings of the study reveals that Non - Physical Education Students incur significantly low Keep Coping (Combine Sample ) as compared to Physical Education Students

Abstract: This paper reveals on the accessibility challenges of Registration Self-Service Machines (RSSMs) are increasingly prevalent in public spaces like museums, yet their accessibility for individuals with disabilities remains a significant challenge. Prior research has highlighted general accessibility issues in kiosk design, but specific challenges faced by disabled persons using RSSMs in museum contexts, such as navigating complex registration processes and accessing exhibit information, are under-explored. This paper investigates the accessibility barriers of current RSSMs for users with diverse disabilities, focusing on limitations in usability, privacy, security, and the lack of universal design principles. We propose and evaluate a novel inclusive interaction design framework incorporating tactile interfaces, auditory feedback, gesture recognition, and voice control, tailored to the specific needs of museum visitors with disabilities. Our user testing with 30 participants across Visual impairment, Physical disability, learning disability, Hearing impairment- Absence of visual cues or transcripts for audio instructions excludes individuals with hearing impairments from accessing crucial information during the museums visiting process. demonstrated significant improvements compared to traditional RSSM designs, including a 30% reduction in task completion time, a 20% decrease in error rates, and a 40% increase in user satisfaction. These findings underscore the effectiveness of our inclusive design framework in promoting accessibility and inclusivity in museum technology. Future research will explore personalized interfaces using machine learning, integration with assistive technologies like screen readers, and seamless connectivity with personal devices such as smartphones and wearables to further enhance the museum experience for all visitors. In essence, the lack of universal design principles in current RSSMs creates a disabling environment by failing to accommodate the diverse needs and abilities of all users. prioritizing accessibility and incorporating inclusive design features, we can ensure that technology empowers rather than excludes individuals with disabilities.

Keywords: Interaction design; Universal design; Inclusive design; Disabilities; Ergonomics

Abstract: This review paper explores the current advancements in intrusion detection and anomaly detection systems specifically designed for Internet of Things (IoT) environments, focusing on the integration of machine learning techniques. As IoT devices proliferate, so do the associated security vulnerabilities, necessitating robust detection mechanisms to safeguard sensitive data and maintain system integrity. The paper synthesizes findings from various studies, highlighting the efficacy of hybrid models, supervised and unsupervised learning algorithms, and their applications in addressing diverse security challenges. Key outcomes demonstrate significant improvements in detection accuracy and efficiency; however, challenges such as adaptability to evolving threats, scalability, and real-world deployment persist. The review underscores the need for adaptive algorithms, federated learning approaches, and lightweight solutions tailored for resource-constrained devices. Furthermore, it emphasizes the importance of collaboration across sectors to drive research forward. Ultimately, this paper aims to provide insights into future research directions that can enhance the security landscape of IoT systems, contributing to the development of more resilient cybersecurity frameworks.

Keywords: Internet of Things (IoT), Intrusion Detection Systems (IDS), Anomaly Detection, Cybersecurity, Real-time Monitoring, Adaptive Algorithms, Federated Learning, Data Privacy.

Abstract: Secure voting using Ethereum blockchain is a secure, transparent and tamper-proof way of conducting online voting. It is a decentralized application built on the Ethereum blockchain network, which allows participants to cast their votes and view the voting results without the need for intermediaries. In this system, votes are recorded on the blockchain, making it impossible for anyone to manipulate or alter the results. The use of smart contracts ensures that the voting process is automated, transparent, and secure. The use of the blockchain technology and the implementation of a decentralized system provide a reliable and cost-effective solution for conducting trustworthy and fair elections.

Keywords: Ethereum blockchain, online voting, decentralized application, smart contracts

Abstract: In the past years, thousands of fitness enthusiasts are seeking solutions to having an effective and personalized workout. Presently, most of the users experience a bad posture during some exercises that results in pain or reduced outcome. To overcome this challenge, the "AI-Based Workout Guide" is a technology-driven solution which uses computer vision and AI to give real-time posture correction along with rep counting. The project uses AI and machine learning algorithms to assess the motion of users' bodies while executing a workout. Captured through a camera or smartphone, the system will compare this posture against predefined models for the best techniques to do the exercises. If there is an incorrect posture, it will always give instant feedback as to what adjustments are needed. Another system feature is that the number of repetitions is automatically counted, so there's no manual counting involved and even more concentration on the proper form by the user. The model has been trained on the dataset of various exercise postures, including squats, push-ups, and lunges. Computer vision libraries OpenPose or Mediapipe are used to detect key landmarks, such as joint angles and alignment. In real time, the system evaluates these landmarks in order to provide an accurate posture assessment and count of the rep. In the end, this would help the users improve their workout efficiency, reduce the chances of injury, and achieve fitness goals in a better manner. It's an accessible, scalable AI-based workout guide, from which follows that it can easily be adapted into any mobile or web application. Its applicability extends to novices and more experienced fitness enthusiasts. This project shows how technology may transform personal training in fitness: a combination of advanced AI techniques with a practical solution for the application of fitness.

Keywords: AI, Computer Vision, Workout Guide, Pose Estimation, MediaPipe, OpenCV, Exercise Form Correction.

Abstract: Health insurance fraud has been one of the biggest financial and operational headaches in recent years, running into billions annually, which creates upward pressure on the premium paid by the policyholder. Artificial intelligence and machine learning can create new avenues for combating fraud by employing real-time detection systems to identify and react to suspicious claims with unprecedented accuracy and speed. This paper explores the opportunities AI presents in transforming fraud detection within health insurance, focusing on both technical advancements and potential roadblocks. Real-time AI systems bring opportunities for automated and continuous monitoring, allowing insurers to assess fraud risk more efficiently and enabling proactive fraud prevention measures that ultimately reduce operational costs. An insurer requires sophisticated computing architecture, rapid processing capabilities of data, and powerful integration frameworks of data for the effective application of such systems. There are many computational challenges where high-speed processing is crucial, along with efficient handling of data and not losing model transparency. Moreover, compliance to HIPAA makes insurers undertake strict security measures for preventing unauthorized disclosure of health data. Findings suggest that real-time AI fraud detection could facilitate the prevention of fraud while accelerating the process of examining claims and significantly reducing costs. In fact, ongoing challenges that include regulatory compliance, computations, and keeping pace with fraud tactics in evolution argue for a balanced approach for the deployment of AI by health insurance.

Keywords: Fraud-detection, health insurance, AI, compliance, data-driven decision making

Abstract: In recent years, combating financial crimes such as money laundering has become increasingly complex due to the sophisticated techniques employed by criminals. Anti-Money Laundering (AML) guard systems, traditionally reliant on rule-based mechanisms, have faced significant challenges, particularly in terms of generating high false-positive rates and failing to detect novel laundering patterns. The emergence of Generative Artificial Intelligence (GenAI) offers a transformative solution by integrating advanced techniques such as deep learning, pattern recognition, and natural language processing (NLP) to address these issues. This paper explores how GenAI-powered AML systems can enhance the detection of financial crimes through superior pattern recognition, anomaly detection, and real-time data analysis. Specifically, it highlights the role of deep learning models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in detecting suspicious activity, as well as the use of NLP in processing unstructured textual data for AML compliance. Moreover, we delve into the reduction of false positives, which remains a persistent issue in traditional systems, and the challenges posed by ethical considerations and privacy concerns. As GenAI continues to evolve, its application in AML guard systems holds promise for significantly improving the detection of money laundering activities while ensuring compliance with regulatory frameworks.

Keywords: Generative AI, Anti-Money Laundering, Pattern Recognition, Anomaly Detection, Deep Learning, Generative Adversarial Networks, Natural Language Processing, False Positives, Compliance, Financial Crime, Privacy Concerns, Ethical AI.

Abstract: Chronic Kidney Disease (CKD) presents a considerable public health challenge, often detected at advanced stages when intervention is less effective. This study conducts a correlation-based analysis of essential biomarkers for predicting Chronic Kidney Disease (CKD), focusing on indicators like serum creatinine, blood urea, albumin, haemoglobin and blood pressure. Utilizing correlation matrix analysis, the study identifies positive and negative correlations among these biomarkers, revealing key associations with CKD progression. The correlation analysis revealed strong positive relationships between CKD stages and biomarkers such as haemoglobin (0.77) and specific gravity (0.73), both critical for assessing disease progression. Conversely, negative correlations, such as between serum creatinine and sodium (-0.69) and albumin and CKD class (-0.63), highlight electrolyte imbalances and kidney damage markers that commonly manifest in advanced CKD. The findings highlight biomarkers with high predictive value, contributing to enhanced early detection and risk assessment. These findings underscore the predictive value of key biomarkers, providing insights for refining machine learning models and enhancing CKD diagnosis and management strategies for early intervention and personalized treatment. This analysis supports the refinement of machine learning models and aids in developing more effective CKD diagnosis and management strategies.

Keywords: Chronic Kidney Disease (CKD), Machine Learning, Biomarkers.

Abstract: The aim of the project is to give information of real estate (bungalow, flats), instead of visiting construction side. Application is constructed out of services like Project information services, Upcoming Project Information service, Project construction side information service. In this project, user information service is open to all application so that information distribution is possible. It will be an Android app, which will have complete information about the real estate such as projects, EMI process, Upcoming Projects, Etc It will also have a notification system for Upcoming events. This application will be of great use to Customer and construction companies, which will not cause much congestion in the construction side, which will make the whole process peaceful. And Customer will get complete information of Projects sitting at Home.

Keywords: Real Estate, Android App, Application, Java, SQLite, Bungalow, Building.

Abstract: This paper examines the potential of blockchain technology to transform land ownership management by creating a secure, transparent, and efficient system for recording and transferring property rights. Blockchain’s key features, including immutable records and decentralized validation, enhance the integrity and reliability of ownership data, reducing the risks of fraud and disputes. By centralizing historical ownership information, blockchain streamlines title searches, lowers transaction costs, and improves data accessibility, especially for marginalized communities seeking to establish or protect their land rights. The integration of blockchain with Geographic Information Systems (GIS) further enhances data clarity and spatial accuracy. The paper additionally discusses the broader outcomes for dispute resolution, global standardization, and innovations such as multi-party ownership and simplified inheritance processes. While blockchain-based land registries face legal and technological obstacles, their ability to update property management systems and eliminate fraud makes them a promising alternative for the future. Keyword: Blockchain technology, Blockchain-based land registries, Land Administration, Smart Contracts.

Abstract: Roles of Amygdala Scripts, Hippocampus, Mind, Brain, and MGBA in Stress, Health, Longevity and Life Quality of Humans are very important. Mind, like a CEO of a company, can bring success or destruction to human health because it is the decision maker.

Abstract: AI is found in many different places. Your smart phone relies on it, as do digital assistants, chatbots, and social media platforms. Many household devices, like robotic vacuum cleaners and security systems, also use AI. Classic examples include autonomous navigation systems and robotics.

Keywords: Artificial Intelligence ,Humans and AI, Sourcing, Screening.

Abstract: In the face of rapidly advancing cybersecurity threats, Intrusion Detection and Prevention System (IDPS) have established themselves as critical tools for warding off harmful activities against a network. Based on this consideration, this review tracks the development and impact of Machine Learning and Deep Learning strategies as associated with IDPS, focusing particularly on their ability to enhance detection performance. We have Surveyed various Intrusion Detection and Prevention System Datasets for assessing their effectiveness in detecting network intrusions. More importantly, it focuses on critical datasets and talks about the pros associated with them, such as better detection capability and their flexibility toward ever-evolving threats, but failed to fight some limitations like increased computational complexity and complex real-time traffic management. This survey gives an overview of the evolution and effectiveness of "Machine Learning and Deep Learning" techniques in advancing IDPS, addressing major concerns over issues of scalability, false positive rates, accuracy, Recall, Precision, F1 Score and overall system efficiency, with an aim to improve the fairness and reliability of intrusion detection and prevention system mechanisms.

Keywords: Intrusion Detection and Prevention System, Machine Learning, Deep Learning, Network Security, Random Forest, Support Vector Machine, Convolutional Neural Networks, Cybersecurity, Anomaly Detection, False Positives, Real-time Traffic, Scalability, Detection Accuracy.

Abstract: The rapid expansion of educational institutions and digital technology has led to a growing need for efficient communication and engagement platforms within campuses. However, most existing student engagement platforms either cater to specific platforms (Android or iOS) or lack the comprehensive functionality needed for seamless student-faculty interaction. The primary objective of this survey is to explore the development of a cross-platform mobile and web application aimed at enhancing student engagement by integrating essential campus services into a unified interface. This survey examines the potential development of a cross-platform mobile and web application designed to enhance student engagement by providing an intuitive and user-friendly interface for students and faculty to interact, share information, manage events, and engage in academic discussions. The key features include a centralized forum for student discussions, a real-time notification system for event updates, a file-sharing module for academic resources, and an integrated calendar for academic and extracurricular activities. The study highlights gaps in existing platforms, such as platform dependency, lack of real-time engagement, and suboptimal user experiences. To address these challenges, the envisioned application aims to incorporate real-time data synchronization, push notifications, and an optimized interface tailored to the diverse needs of students and faculty across multiple platforms, including Android, iOS, and Web.

Keywords: Machine Learning, Recommendation systems, Cross Platform, hybrid recommendation, personalization, open platform, scalability, campus services, service-oriented architecture.

Abstract: This describes the development of an application designed to assist low-literacy rural communities to handle their bill and paper safely and efficiently. To make its functionality work, the application features a very robust login mechanism that avails itself of the fingerprint authentication mode for confirmed access. After the customers authenticate themselves, they can use the camera of their device to scan critical documents like utility bills, PAN cards, and Aadhar cards. The application uses NLP techniques, particularly that of SpaCy, to analyze the retrieved text and provide critical insights derived after using the Optical Character Recognition (OCR) technology to pull the text from these scanned images. This enables non-English-speaking users to access the application through transliteration options in Hindi, Kannada, Telugu, and Tamil together with the text to be read. The application will transform the words into speech by using Google Text-to-Speech (gTTS), depending on the needs of the user as it is unlikely that the audio would be in any other language The app has a reminder alert about payments which are soon to be overdue with respect to bills, so one may not suffer from late fines and scams. This makes the application easy to use, accessible, and full of an inclusive approach in order to improve the management of documents and bills for the vulnerable communities of the rural fraternity.

Keywords: Rural development, fraud, extract text, insights, OCR, NLP, multi lingual support, text-to-speech, multi-language translation, document access, , camera, gTTS, notification reminder.

Abstract: In today's rapidly changing world, personal and professional development is crucial for individuals to navigate their careers successfully. This abstract explores the concept of a personalized system based career guidance system that empowers individuals to reflect on their skills, interests, and goals. The system incorporates intelligent recommendation mechanisms to guide users toward suitable career paths and skill development opportunities. By leveraging advanced algorithms and machine learning techniques, this system aims to provide tailored suggestions, enabling users to enhance their skillsets and make informed decisions about their careers. Limbic mate system works on integration of facial-based emotion recognition, user interest-based recommendation systems, asynchronous chat rooms, and personal diaries represents a multifaceted approach to enhancing human-computer interaction and emotional well-being. Leveraging advanced facial recognition technology, the system accurately detects and interprets users' emotions, enabling a more empathetic and responsive user experience. Concurrently, a recommendation system analyses users' interests and preferences, delivering personalized content and suggestions, fostering engagement and satisfaction. The asynchronous chat room feature provides users with a flexible platform for communication, transcending geographical boundaries and time zones, facilitating meaningful interactions at their convenience. Additionally, the incorporation of a personal diary feature offers users a private space to reflect, express emotions, and track personal growth, promoting emotional catharsis and self- awareness. This holistic integration not only enriches digital interactions but also nurtures users' emotional intelligence, fostering a more empathetic and fulfilling online environment.

Keywords: Limbic Mate, Emotion Detection, Personal Dairy, Chatroom, Asynchronous system, Recommendation System, Area Of Interest.

Abstract: In response to the growing threat of deepfake technology, which can deceive and manipulate individuals, leading to identity theft, financial fraud, and political manipulation. This paper proposes a deepfakes detection model using a deep learning framework and image processing techniques. The proposed utilizes the ResNeXt architecture as a powerful feature extractor to capture intricate patterns and discriminative features from input images or video frames. Additionally, the LSTM architecture is employed to handle temporal dependencies, enabling the model to analyze the temporal coherence and consistency of video sequences. By leveraging ResNeXt and LSTM, the model achieves enhanced accuracy and robustness in detecting deepfake content.

Keywords: LSTM- long short term memory, cnn- convolution neural network.

Abstract: In light of the escalating incidents of violence and harassment against women globally, there is an urgent need for innovative solutions that enhance personal safety. The Smart Women Protection System aims to develop a cutting-edge wearable or portable device that harnesses advanced technology to empower women in unsafe situations. This multifaceted system integrates various self-defence mechanisms including a remotely activated pepper spray, an electroshock module, a high-decibel alarm, and real-time GPS tracking with live streaming capabilities into a single compact device. The system combines hardware components, software algorithms, and open-source technologies to create a holistic safety solution. By enabling women to protect themselves proactively and communicate effectively during emergencies, this project aspires to foster a sense of security, independence, and confidence in women's daily lives.

Keywords: ESP32 Microcontroller, GSM, GPS, Arduino Nano, Wearable Device, Self-Defence.

Abstract: This paper presents Galaxy Ride, a game that combines elements of design, development, and potential contributions to both the gaming and scientific sectors. Galaxy Ride invites players to embark on interstellar journeys through procedurally generated galaxies, focusing on exploration, resource management, and strategic decision-making. Players can choose between peaceful exploration or engaging with hostile factions, allowing for diverse gameplay experiences. The game is enriched by a deep storyline and additional side missions, which delve into ancient civilizations and cosmic enigmas, adding to both its educational appeal and narrative depth. Drawing inspiration from real-world astronomy and theoretical physics, Galaxy Ride emphasizes scientific discovery, increasing its educational value. This paper explores how Galaxy Ride nurtures a curiosity for space exploration while contributing to both entertainment and educational goals.

Keywords: Space exploration, Interstellar travel, Resource management, Strategic gameplay.

Abstract: The optimization of ZnO is nanostructures for enhanced the photocatalytic hydrogen generation focuses on improving the efficiency of hydrogen production through photocatalysis. ZnO, a widely studied photocatalyst, offers advantages such as high stability and a broad bandgap. This study explores various strategies to optimize ZnO nanostructures, including size and morphology control, doping, and composite formation. By fine-tuning these parameters, we aim to enhance light absorption, increase surface area, and improve charge carrier dynamics. The results is indicate the significant improvements in photocatalytic activity, with optimized ZnO nanostructures demonstrating enhanced hydrogen generation rates under visible light. This optimization approach contributes to advancing green energy technologies and sustainable hydrogen production.

Keywords: ZnO Nanostructures, Photocatalysis, Hydrogen Generation, Semiconductor Materials,Doping Techniques, Green Energy.