World Wide Web Research Articles

Community Crime Reporting and Alert System

The Community Crime Reporting and Alert System is a web-based application designed to facilitate real-time crime reporting and awareness through modern web technologies. Built using HTML, CSS, JavaScript (React.js or Vue.js for frontend), and Node.js with Express.js for backend, the system ensures a seamless user experience. It employs MongoDB or MySQL as the database to store user reports, authentication details, and crime statistics securely. The system supports user authentication (JWT or OAuth 2.0), allowing registered users to report crimes while also providing an option for anonymous submissions. Reported incidents include key details such as crime type, description, date, time, and location, which are stored in the database and visualized on an interactive Google Maps API integration.

Code optimization of contraction operation in agglomeration-based node importance analysis

Networks are used for modeling different systems like chemical systems, neural networks, social systems or the Internet, and the World Wide Web. A communication network consists of nodes and links connecting these nodes. Damage to an important node in the network often leads to damage to the entire network. Therefore, it is very important to determine the critical nodes of the networks. When the network is really complex like the World Wide Web or neural networks, determining important nodes of the network becomes impossible for humans. In this paper, an advanced novel algorithmic method that uses agglomeration-based node contraction is proposed to solve this problem. After the design of the algorithm, it has been tested on randomly produced connected undirected networks and its correctness has been proven by comparing it with the brute-force method. The calculation of the shortest path after each contraction in the original algorithm is accelerated by a dynamic method. Then, the algorithm is tested on computers with different configurations. Statistics collected from these tests are also presented in this paper.

Student Result Management System

The Student Result Management System (SRMS) is a web-based application designed to automate and streamline the process of managing and publishing student results efficiently. Traditional result management methods rely heavily on manual data entry, extensive paperwork, and time- consuming verification processes, which often lead to errors and inconsistencies. Our system provides an efficient, secure, and automated approach to handling student records, reducing administrative workload while improving accuracy and accessibility. By integrating a centralized database, the system ensures easy storage, retrieval, and management of academic results. The system is designed to cater to the needs of administrators, faculty, and students by providing role-based access control. Administrators can efficiently manage student data, input marks, and generate reports, while faculty members can update subject-specific scores and analyze student performance. Students can securely access their results online without the need for physical mark sheets, ensuring transparency and convenience. The RMS also includes an automated grading system, which calculates grades based on predefined criteria, minimizing human intervention and ensuring fairness in evaluation. The implementation of robust security measures, including authentication and encryption, ensures data privacy and protection against unauthorized access. Additionally, the RMS provides historical data analysis, allowing institutions to track academic trends and improve decision-making processes. With a user-friendly interface and responsive design, the system ensures seamless access across various devices, improving the overall user experience. Developed using modern web technologies, the Student Result Management System are designed for scalability and adaptability. It can be customized to fit the specific requirements of different educational institutions, making it a versatile solution for schools, colleges, and universities. By digitizing the result management process, this system significantly reduces paperwork, minimizes human errors, enhances operational efficiency, and promotes a more transparent and accessible.

Use of web and mobile device technologies in the management of childhood asthma: a systematic review and meta-analysis.

The objective of this review is to assess the use of support tools for children with asthma, based on web and mobile device technologies, and their impact on asthma control. This is a systematic review conducted in accordance with the PRISMA guidelines and the Joanna Briggs Institute (JBI) Manual. The research question defined by the PICO strategy was: ''What are the effects of web-based and mobile device support tools on asthma control in children?'' The search was conducted in the Medline (via PubMed), SciELO, and Embase databases between October and December 2023, with completion in July 2024. The systematic review analyzed 388 articles and selected 4 studies on technologies for managing asthma in children. The studies showed that mobile apps and electronic monitoring improve asthma control, treatment adherence, and caregivers' quality of life. The meta-analysis showed a mean increase in Asthma Control Test (ACT) scores of 2.73 (95 % CI: 1.95, 3.51) with P < 0.0001, indicating a significant improvement in asthma control scores, highlighting the effectiveness of these technologies. This study demonstrates that digital tools, such as web technologies and mobile devices, can significantly improve the management of childhood asthma, as reflected by an increase in Asthma Control Test (ACT) scores. Despite limitations, the findings are promising. Future research is needed to strengthen the evidence and guide clinical practice in pediatric asthma management.

PG Dissertation Management System

The PG Dissertation Management System is an innovative web-based platform designed to streamline the dissertation process for postgraduate students. By integrating multiple functionalities into a cohesive environment, the system enhances communication, collaboration, and efficiency between students and faculty members. A core feature of the system is its HTML5 Faculty Appointment Scheduling Module, which utilizes modern web technologies such as HTML5, CSS3, and JavaScript alongside the powerful DayPilot library. This scheduling module allows students to seamlessly book appointments with their academic supervisors. The user-friendly calendar interface displays available time slots using distinct color codes: blue for free slots, orange for waiting confirmations, and green for confirmed meetings. This visual representation aids students in navigating their scheduling options while maintaining clear communication with faculty. The platform also integrates a robust Document Management Module, enabling efficient upload and management of essential dissertation documentation, including proposals, drafts, and progress reports. This centralized repository promotes organization and facilitates easy access to critical assignment materials. Additionally, the Dashboard Module acts as a central hub for students, showcasing vital information about their dissertation progress, including milestone tracking for proposal submissions and reviews. The Topic Recommendation System further assists students in selecting research areas that align with their interests and academic strengths. Furthermore, the incorporation of an AI chatbot accessible via the dashboard provides immediate responses to common inquiries, enriching the learning environment. In summary, the PG Dissertation Management System represents a comprehensive solution that fosters collaboration and enhances the educational journey for both students and faculty, ultimately improving dissertation management processes and academic outcomes. By mechanizing existing workflows and building a supportive community, the platform ensures that postgraduate students have the tools and assistance they need to succeed in their academic endeavors. Keywords— Dashboard Layout and Structure , Event Handling Guides , API Documentation , Responsive Design Techniques , Version Control Tools , Google Meeting , Generative AI Key and Deployment and Hosting Platforms.

Fortune Spin Wheel Game

The Fortune Spin Wheel Game is a dynamic, web-based application designed to replicate the excitement of traditional spinning wheels found in game shows, events, and educational setups. Developed using web technologies such as HTML, CSS, and JavaScript, this game integrates randomness and interactive elements to engage users across multiple devices. This report details the development process, challenges, solutions, and potential future enhancements of the game. . Key Words: Game, Web Game, Mobile App, Fortune Spin Wheel Game, Wheel

AI Healthcare Chatbot

The Healthcare Chatbot is an AI-powered web-based system designed to assist users in diagnosing diseases based on symptoms and providing medical guidance. Built using machine learning models such as Decision Tree and SVM, the chatbot predicts potential diseases based on user input and offers personalized health recommendations. Additionally, the system features a diabetes prediction module that assesses risk based on user-provided health metrics. To further enhance healthcare accessibility, the chatbot integrates alternative medicine suggestions, offering holistic treatment options alongside conventional medical advice. The platform leverages RapiDoc for seamless API documentation and interaction, ensuring a user-friendly experience. Developed using advanced web technologies, the Healthcare Chatbot streamlines symptom analysis, encourages early diagnosis, and promotes well-informed healthcare decisions, ultimately contributing to improved patient awareness and proactive health management..

A Review of Online Home Services: Innovations, Challenges, and Future Enhancements

The traditional approach to booking household services has long been plagued by inefficiencies, miscommunication, and trust issues, leading to delays, unreliable service providers, and a lack of transparency in transactions. With the rapid advancement of digital platforms, home service booking has undergone a significant transformation, integrating modern technologies to streamline processes, enhance service reliability, and improve user experience. This paper presents a comprehensive review of four key studies on home service applications, analysing their methodologies, features, strengths, and limitations. It explores the evolution of digital home service platforms, emphasizing crucial challenges such as service provider authentication, seamless user experience, real-time scheduling, and secure transactions. Furthermore, the study examines innovative solutions such as AI-driven recommendation systems for personalized service matching, blockchain-based authentication for enhanced security, and mobile-friendly designs for accessibility across diverse user demographics. The integration of predictive analytics and intelligent automation is also discussed, highlighting how these advancements can optimize operational efficiency and elevate customer satisfaction. Additionally, this paper delves into the role of cloud computing in ensuring scalable and reliable service delivery, along with the potential of IoT-enabled smart home integrations for proactive maintenance and monitoring. By evaluating existing frameworks and emerging technologies, this research provides insights into how modern web technologies can further refine home service applications, ensuring efficiency, security, and enhanced user engagement. The findings serve as a foundation for future innovations in the field, paving the way for smarter, more responsive, and highly efficient home service solutions. Keywords— Home Services, Service Booking, Web Application, Digital Platform, Real-time Scheduling, AI in Home Services, Blockchain Authentication, User Experience, Secure Transactions, Cloud Computing, IoT in Home Services, Predictive Analytics.

Online Voting System using Blocks

This paper presents the development and implementation of an Online Voting System, designed to enhance the security, accessibility, and efficiency of electoral processes. The system leverages web technologies to provide a secure, user-friendly platform for electronic voting, addressing key challenges such as voter authentication, data integrity, and result accuracy. The methodology includes database-driven user verification, encryption mechanisms for data security, and a tamper-proof audit trail. The proposed system aims to eliminate traditional paper-based voting inefficiencies, reduce manual errors, and provide real-time vote counting. The results indicate that the system significantly improves voter participation and ensures a transparent and fraud-resistant electoral process. The study highlights the importance of digital solutions in modernizing democratic participation and suggests future enhancements for scalability and integration with biometric authentication. Key Words: Online Voting, Electronic Elections, Cybersecurity, Digital Democracy, Authentication, Secure Voting.

A digital corpus for exploring the lute music of John Dowland (1563-1626)

This article outlines a case for the building of a digital corpus of lute music of the period covering the sources of the works of John Dowland (1563-1626), made feasible by the existence of a large number of works in informal encodings by enthusiasts for distribution via the world wide web. Editorial work needs to be done on the basic texts, but the extra effort is likely to be less than that which would be demanded by a similarly comprehensive encoding initiative for keyboard music of the same period. Dowland’s works are very widely spread in the manuscript and printed lute tablatures of his time, but relatively few pieces come from sources that are truly ‘close’ to the composer; most are transmitted in versions that sometimes vary significantly in detail. Dowland travelled widely in Europe before receiving his long-awaited English court appointment in 1612; several pieces exist solely in continental sources, and some of these are stylistically distinct from his early repertory. This article advocates the building of a corpus of relevant lute music which would allow a digital-humanities, computer-assisted approach to problems of attribution and style analysis.

Product Based Sentiment Analysis for text and Image Using Machine Learning

With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.

Panorama das Abordagens de Integração de Dados

In a context marked by the growing complexity of information management, characterized by system diversity and interoperability demands, this article presents an overview of data integration approaches in Brazilian scientific production in Information Science, suggesting a categorization of these strategies. This article aims to map and analyze data integration approaches in Brazilian scientific production in Information Science, proposing a categorization of these approaches. The methodology is applied and exploratory, based on bibliographic research, with analysis of 33 articles selected from the Reference Database of Journal Articles in Information Science (Brapci). The results reveal three main categories: Integration of Systems and Technologies, Semantic and Ontological Integration, and Integration of Heterogeneous Data and Information. The first category highlights technical interoperability and metadata standardization, especially in digital libraries. The second focuses on the use of ontologies and Semantic Web technologies to overcome semantic barriers. The third addresses strategies for dealing with data heterogeneity in specific contexts, such as scientific research and cultural heritage. It is concluded that Brazilian scientific production has evolved from a technical approach to a more semantic and contextualized perspective, aiming to overcome challenges such as technological heterogeneity, data fragmentation, and standardization complexity.

Facetrace Attendance

It’s a system designed to introduce modernization in attendance management by using facial recognition. Existing attendance systems which include manual registers or RFID cards are highly erroneous, fraud-prone and inefficient. This project offers a contactless, automated, accurate attendance management. System created with the use of the Django framework is a scalable and flexible system that integrates cutting-edge facial recognition libraries like OpenCV or similar tools. The system captures a live video stream from a camera, detects them in the real-time work and matches them with the pre-registered database of users. Attendance of the person is automatically recorded in the DB once the match is confirmed. Source code of the project is modular and well-documented, it can be easily modified and extended by developers. The project presents real practical use of machine learning and AI principles in solving real-life tasks. It demonstrates how machine learning can be used in web technologies to increase the effectiveness and productivity of everyday activities.

Assessing the user experience of marker-based 3D WebAR applications using user experience questionnaire

Marker-based 3D web-based augmented reality (WebAR) applications are an emerging field that merges web technologies with augmented reality. WebAR has gained popularity because of its ability to provide users with a reliable and autonomous platform. Yet, a limited investigation has verified its application and user perspective on its ability to function. This study is designed to evaluate the user experiences of marker-based 3D WebAR applications using the user experience questionnaire (UEQ). This study assesses various elements of the user experience, including attractiveness, clarity, engagement, efficiency, and innovation, utilizing the UEQ. This study aims to analyze user perceptions and interaction patterns thoroughly to get useful insights into the usability and user satisfaction aspects of marker-based 3D WebAR apps. The findings reveal that the WebAR app is both appealing and efficient, instilling confidence in its users. This underscores the pivotal role of user experience in shaping the effectiveness and reception of WebAR applications. This research has the potential to influence the creation of more user-focused and engaging marker-based 3D WebAR experiences, improving user engagement and immersion in web-based augmented reality environments.

Spellbound Kannada: Harnessing Conditional Generative Adversarial Networks for Transformative Word Suggestion Systems in Kannada Language Processing

INTRODUCTION: The advancement of a word suggestion system model is driven by the need to enhance user interaction and efficiency in digital communication. Hence, the word suggestion system helps minimize typographical errors and spelling mistakes. Therefore, various traditional methods are used to suggest words to sentences; however, these traditional models are extremely time consuming, prone to errors and tedious. METHODS: Owing to these factors, the present paper focuses on developing a Kannada word suggestion system using cGAN (Conditional Generative Adversarial Networks), as this system is designed to significantly enhance user interaction by offering predictive text suggestions in the Kannada language. RESULTS: The training dataset, which resides on AWS S3, comprises a comprehensive collection of Kannada texts utilized for both training and validation purposes. Furthermore, the implementation of the model leverages the TensorFlow and keras framework, specifically employing long short-term memory (LSTM) networks for effective sequence prediction and generation. LSTMs are particularly advantageous in NLP processing because they can capture long-term dependencies within sequential data. To facilitate user interaction, a web-based interface has been developed using Flask, enabling users to input initial characters and receive dynamically generated Kannada word suggestions. CONCLUSION: This paper not only delves into the application of cGANs within the realm of NLP but also illustrates practical deployment strategies utilizing cloud services and modern web technologies. Overall, the proposed approaches demonstrate the potential of the cGAN in enhancing the user experience through intelligent text prediction systems tailored for the Kannada language.

Design and Realisation of Digital Non-Legacy Based on Web Technology

With the rapid development of computer network technology, web system has been widely used with its unique display method. Based on B/s architecture, this paper proposes a web - based display system for intangible cultural heritage. Firstly, the collaborative instruction sharing and interaction between the client and server side is achieved through B/S architecture, and the XML data transmission format and Ajaxpolling mechanism are adopted to achieve the network sharing and transmission of 3D model data. It also implements an interactive interface design based on Java Web technology to facilitate users' social interaction, and the interactive interface achieves the expected goal in all functions, with excellent security and practicality, and can provide a good virtual interactive platform for online communication for the society

SkillTech: A Modern LMS for Personalized Learning and Career Growth

The rapid evolution of digital learning platforms has revolutionized education making it more accessible and interactive. However, traditional Learning Management Systems (LMS) often lack seamless career integration and personalized guidance limiting their effectiveness. This paper presents SkillTech an innovative full-stack platform that integrates an LMS with a job portal and an AI-driven chatbot to enhance learning outcomes and career opportunities. SkillTech offers interactive course modules, skill-based job recommendations bridging the gap between education and employment. The chatbot provides intelligent assistance guiding users through course selections and career pathways. The platform leverages modern web technologies including MongoDB, Express.js, React, and Node.js ensuring scalability and efficiency. By combining education, career services and AI-driven mentorship in a single ecosystem. SkillTech aims to redefine online learning and job placement fostering a more comprehensive digital learning experience. Keywords— LMS, Job Portal, AI Chatbot, Full-Stack Development, Online Learning, Career Integration

AI Voice Assistance for Smarter Business Analysis

This paper presents the development and implementation of an AI-powered voice assistance tool designed for smarter business analysis, with a focus on SEO auditing. The tool integrates voice command capabilities using the Web Speech API, combined with advanced data visualization and reporting features. Built with modern web technologies such as React 18, TypeScript, and Tailwind CSS, the tool offers a user-friendly interface for analysing website performance. The paper discusses the core features, technologies used, and the workflow of the tool, emphasizing its potential to revolutionize business analysis through voice-enabled interactions. The tool’s ability to generate interactive visual reports and comprehensive PDF downloads via voice commands highlights its innovative approach to simplifying complex data analysis. By leveraging voice interaction and realtime data visualization, the project demonstrates how voice-assisted technologies can enhance accessibility, efficiency, and decision- making in business analysis workflows. This research paves the way for future advancements in AI-driven business tools. Keywords— AI voice assistance, business analysis, SEO auditing, Web Speech API, data visualization, React, TypeScript.

БОЄЗДАТНІСТЬ УЧАСНИКА БОЙОВИХ ДІЙ ТА ЧИННИКИ ЇЇ ФОРМУВАННЯ

The hypothesis of the study was to assume that a soldier's combat capability depends on his psychological state, which is determined by many factors. We called these factors combat capability components. The combat capability of military personnel affects the effectiveness of military operations. Human beings remain the central figure in war. Human nature, as evidenced by cultural monuments of different eras, has been unchanged for many millennia. This makes it possible to use the results of studies of previous wars, especially in the current situation of a lack of scientific information about the well-being of servicemen on the front line, their experiences, thoughts, attitudes towards war, and the impact of an extreme situation of contact combat on the psyche of a soldier. The objective of the study was to examine the factors of formation of combat capability components. It is clear that the quality of the army's functioning as an integral system is a prerequisite for the formation of combat capability. It determines the level of satisfaction of the basic needs of servicemen in terms of ensuring the conditions of their physical existence, management and resource support for offensive and defensive operations, and the moral and psychological climate in units. We have identified and studied such components of combat capability as socio-psychological, motivational, psychophysiological, psychological and psychiatric. The study was theoretical in nature and involved the generalisation of data obtained by different researchers in different historical periods and testimonies of qualified experts and participants in military operations available on the World Wide Web. The study was based on the works of the British military historian R. Holmes (Holmes, Richard. 1985; 2011). As a result of the study, conclusions were drawn on the ways to build combat capability. The need to strengthen small groups as basic units of the army is emphasised and the need for rotation is substantiated.

CodeGeek: A Next-Generation Coding Platform with AI- Driven Feedback

As the demand for online coding platforms continues to rise, developers require efficient and feature-rich environments for coding practice and problem-solving. CodeGeek is a web-based coding platform designed to enhance programming education and competitive coding by offering real-time code execution, an AI- powered coding assistant, and a comprehensive problem repository. This platform is built on the MERN stack, leveraging MongoDB for data storage, Express.js for backend functionality, React.js for the user interface, and Node.js for server-side execution. To enhance the coding experience, it integrates Monaco Editor for interactive coding and supports multiple programming languages via the Judge0 API [1][2][12][13]. With CodeGeek, users can write, test, and optimize their code against predefined test cases, ensuring accuracy and efficiency. The AI-driven assistant provides contextual hints, debugging support, and performance insights, helping users improve their coding skills. Looking ahead, planned enhancements include coding contests, broader language support, and a diverse range of programming challenges to foster a dynamic learning environment. By integrating modern web technologies and AI-powered assistance, CodeGeek strives to create an engaging and intelligent coding platform tailored to students, developers, and competitive programmers, enhancing both learning and problem-solving efficiency.