Category Archives: Uncategorized

Abstract-The demand for robust authentication systems has risen significantly as cyberattacks become increasingly sophisticated. Current authentication mechanisms, such as textual passwords, biometrics, and graphical systems, each have unique vulnerabilities. This research explores the concept of a 3D password system, which integrates various authentication schemes into a virtual 3D environment to enhance security. The system allows users to interact with objects in a 3D space, forming unique and complex passwords based on sequences of interactions. This paper elaborates on the system’s design, implementation, and potential applications in critical and non-critical systems. Detailed analyses reveal that the 3D password provides superior resistance to timing attacks, brute force attempts, and well-studied schemes, while maintaining user-friendliness. Future research avenues include the incorporation of AR/VR and IoT technologies to further expand the utility of the 3D password system.

Abstract-Phishing attacks remain a prevalent cybersecurity threat, exploiting vulnerabilities in digital platforms to compromise sensitive user data. This paper introduces a novel machine learning-based framework for phishing URL detection, combining advanced feature engineering techniques and classification algorithms. By integrating lexical attributes, WHOIS data, and ranking metrics like PageRank and Alexa Rank, our approach enhances detection accuracy and minimizes false positives. Experimental results demonstrate superior performance across classifiers, achieving an accuracy of 99.8% using Support Vector Machines. The framework’s modular design ensures adaptability to evolving phishing tactics and scalability for enterprise deployment. This research lays the foundation for future advancements in AI-driven cybersecurity solutions.

Abstract-The plight of homeless children remains one of the most urgent global challenges, with millions of vulnerable children deprived of basic human rights such as shelter, healthcare, and education. Despite the rapid advancement of technology, child welfare systems in many developing countries still face significant hurdles, marked by inefficiencies and fragmented services. This paper proposes an innovative AI-driven system for adoption and rehabilitation that aims to address these systemic challenges holistically. By harnessing cutting-edge artificial intelligence (AI) algorithms, the system streamlines the adoption process, delivers personalized healthcare recommendations, and optimizes resource allocation for child welfare organizations. Through the integration of predictive analytics, data-driven decision-making, and a robust ethical framework, the system ensures transparency, fairness, and scalability. Early simulations and case studies highlight the transformative potential of AI in enhancing adoption success rates and improving healthcare outcomes for homeless children. The findings emphasize the system’s ability to drive meaningful improvements in global child welfare efforts, offering a scalable, ethical solution that can have a lasting impact on vulnerable children worldwide.

Abstract-Virtual meetings have become an essential mode of communication in contemporary professional environments. However, the fast-paced nature of virtual meetings undermines the ability to remember critical information accurately as even making notes is an imperfect mundane task, manual note-taking is both time- consuming and error-prone, often resulting in overlooked decisions and action items. SummrAIze is an AI-powered meeting assistant designed to address these challenges by automating the transcription, [1]summarization, and extraction of actionable insights during virtual meetings on platforms like Google Meet and Microsoft Teams. Using advanced machine learning algorithms, SummrAIze produces real-time summaries, highlights key points, and identifies action items, enabling participants to engage fully in discussions without sacrificing documentation accuracy. Integrated with productivity tools, SummrAIze not only reduces manual effort but also ensures that all essential information is recorded and accessible, enhancing team collaboration and workflow continuity. This paper presents the design, methodology, and potential impact of SummrAIze, a tool that redefines productivity in the context of virtual meetings.

Abstract-With the advent of the “e-” revolution starting in 2000, the issue of cyber security, cyber-attacks and cyber threats which included domains, but not e-business, e-government, e-; commerce etc. only occurred because for the issue of cybersecurity in e- learning is under-explored, the aim of this paper is to present methods that focus on monitoring cybersecurity issues related to e- learning processes on. In addition, this article aims to present some good examples of cybersecurity management strategies in e- learning and cybersecurity trends in this area.[2] This paper will present possibilities for increasing information security and cyber- security awareness in education and e-learning that will inspire future cybersecurity professionals to navigate their career path.[3].

Abstract-The cost of commuting to and from school is a burden for many people, especially in urban areas. While ride-hailing services are popular worldwide, most students face issues with accessibility and convenience. The aim of this work is to create and use fairShare. A web platform that allows students to connect and share rides, thereby reducing transportation costs and reducing the environment around them. Users can register, post trips,and compete with other students using the same route. Early tests of the platform have shown that it reduces student travel costs and provides a good user experience. The platform also promotes sustainable practices for students. fairShare demonstrates the potential of student-friendly carsharing to reduce transportation costs and improve social interaction. The platform has the ability to measure a broader and more effective way for students to take action.

Abstract-Employing generative adversarial networks, specifically with regard to cycle consistency loss and mask vectors, mainly concentrates on the colorization of Synthetic Aperture Radar (SAR). Most SAR imagery is devoid of chromatic information. Contemporary deep learning techniques are the predominant approach for SAR colorization. The methodology proposed herein employs a multidomain cycle-consistency generative adversarial network (MC-GAN). It enhances performance through the integration of a mask vector and cycle-consistency loss. The approach does not necessitate the availability of paired SAR-optical imagery. The multidomain classification loss contributes to the precision of the color output. The methodology has been evaluated using the SEN1-2 dataset for urban and terrain areas.

Abstract-This study focuses on the design and analysis of a hollow shaft for an EV go-kart, optimizing weight reduction and structural integrity. Using SolidWorks for design and ANSYS for Finite Element Analysis (FEA), the shaft’s performance under mechanical stresses and cyclic loads was evaluated. Results demonstrated significant weight savings while maintaining strength, rigidity, and durability, enhancing the go-kart’s efficiency and reliability. This work highlights the potential of hollow shafts in improving EV performance through lightweight design.

Abstract-The challenges of urban areas with ever-increasing traffic congestion, emergency response, and maintaining road safety are the basis of this paper. The ITMS proposed in this paper treats optimization of timings at the traffic signals based on real-time vehicle counts, along with the detection of emergency vehicles and accidents, as its prime mandate. To achieve these objectives of optimal traffic management, advanced technologies, such as sensor detectors, algorithms for processing data, and communicating networks, were adopted. With simulations and evaluations, the ITMS holds great promise in enhancing traffic flow efficiency as well as reducing congestion while shortening emergency vehicle response times vis-a-vis fixed-time signal control. The research performed here addresses the development of more sustainable and resilient urban transportation systems.

Abstract-Artificial Intelligence (AI) has become a pivotal technology for automating complex processes, while Soft Computing provides innovative ways to manage imprecise and uncertain data. By combining the two, hybrid systems leverage the strengths of AI’s precision and Soft Computing’s adaptability. This paper delves into the principles behind these hybrid models, emphasizing their use in healthcare, autonomous systems, finance, and smart cities. It also highlights the challenges of scalability and interpretability and outlines potential research directions, including integrating quantum computing and promoting explainable models.