Authors: Mohammad Atiullah Ansari

Abstract: Shop Gara is a modern digital platform designed to facilitate seamless cross-border trade for Nepalese businesses and consumers. The platform streamlines international procurement and sales by offering secure transactions, efficient logistics, and transparent trade procedures. This paper presents the design, development, testing, and future scope of Shop Gara — a scalable, reliable, and efficient cross-border e-commerce platform. It also highlights the impact of digiti- zation on Nepal’s international trade landscape.

DOI: https://doi.org/10.5281/zenodo.19661712

Published by: vikaspatanker

Authors: Mohit Japee, Parthi Soni

Abstract: Modern enterprise networks generate a large volume of security events, making it difficult for security analysts to identify critical threats in real time. Traditional rule-based detection mechanisms often fail to detect advanced and evolving cyber attacks. Artificial Intelligence (AI) and Machine Learning (ML) techniques have shown promising capabilities in analyzing large-scale security data and predicting potential cyber threats. This research proposes an AI-based cyber threat prediction framework designed to enhance threat detection and decision-making in enterprise environments. The framework focuses on log analysis, anomaly detection, and threat prediction using machine learning techniques. The study highlights the potential of predictive analytics in improving proactive cybersecurity strategies and reducing response time in security operations centers (SOCs). The proposed framework is conceptual and aims to provide a cost-effective and scalable approach for organizations adopting intelligent cybersecurity solutions.

DOI: https://doi.org/10.5281/zenodo.19675247

 

Published by: vikaspatanker

Authors: Yash Sakhareliya

Abstract: Auction systems have become increasingly popular as the uptake of e-commerce grows globally. However, conventional auction systems may be inflexible and unable to accommodate several bidders simultaneously. To address these issues, AuctionOasis provides a modular and comprehensive web platform that incorporates real-time bid processing, auction management, and secure participation of users. The platform is developed using Node.js, Express.js, MongoDB, and EJS for front-end rendering.Further, the system is planned to implement the Socket.io technology for conducting group live bidding and chatting. This document discusses the motivation behind developing AuctionOasis, its architectural framework, design aspects, implementation process, and future directions.

Published by: vikaspatanker

Authors: Mahesh Saini, Dinesh Cholkar

Abstract: The rapid evolution of mobile computing has fundamentally transformed how humans interact with technology. This paper presents an AI-Integrated Android and Mobile Development Framework (AI-AMDF) that leverages machine learning, cross-platform development tools, and intelligent UI/UX systems to deliver high-performance, adaptive mobile applications. The proposed framework dynamically optimizes app behavior, battery usage, and user experience based on real-time device analytics and user interaction patterns. Results demonstrate a 38% improvement in app performance metrics and a 31% reduction in development time-to-deployment compared to conventional mobile development approaches.

Published by: vikaspatanker

Authors: Drbrindhas, Ms. P.Abirami In, Mr. Ajay.R, Mr. Anbarasan.R, Mr. Rishihesh .M.M, Mr.Safwan.S, Mr.Sriram.V

Abstract: This paper presents the design and implementation of a Smart Playlist Generator using Affective Computing — a real-time, AI-driven music recommendation system that personalizes playlists based on the user's emotional state. The system integrates three core components: (1) a Facial Emotion Recognition (FER) module built on OpenCV and Convolutional Neural Networks (CNNs) that classifies emotions in real time from webcam input, (2) a Natural Language Processing (NLP) module that supports Thanglish (Tamil- English transliterated) text commands for conversational interaction, and (3) a Spotify Web API integration that maps detected emotions to audio features such as valence, energy, and tempo to generate context-aware playlists. The system achieves an emotion recognition accuracy of 87– 90%, Thanglish command interpretation accuracy exceeding 90%, and a playlist-mood alignment rate of 85–90%, with an end-to-end latency of approximately 3 seconds. The architecture leverages HTML/CSS/JavaScript for the frontend, Node.js with Express for the backend, Firebase for data persistence, and Python-based AI modules for emotion and language processing. Experimental results confirm the viability of affective computing for dynamic, personalized music delivery, and the system demonstrates significant potential for next- generation human-computer interaction in multimedia platforms.

DOI: https://doi.org/10.5281/zenodo.19659822

 

Published by: vikaspatanker

Authors: Aarthi R, Ranjith S, Subash P, Surya T

Abstract: The Hospital-Based Smart Hematology Analyzer with Cancer Risk Alert is an advanced system designed to automate blood analysis while providing early cancer risk detection for organs such as the brain, lung, and skin. The system integrates a deep learning algorithm, InceptionV3, to analyse blood smear images and identify abnormal cell patterns indicative of potential malignancies. High-resolution images captured through an optical sensor are pre-processed and fed into the algorithm for feature extraction and classification. The hardware architecture includes a microcontroller interfaced with sensors and a display unit, interconnected through UDP communication to ensure fast, reliable, and real-time data transfer within the hospital network. The analyser automatically computes hematology parameters such as RBC, WBC, haemoglobin levels, and platelet count, while the AI module evaluates potential cancer risk based on morphological anomalies. Alerts and reports are generated for medical staff if any abnormal patterns are detected, facilitating prompt medical intervention. The working flow begins with blood sample collection, followed by automated slide preparation, image acquisition, and pre-processing. The processed images are analysed by the InceptionV3 model, which classifies the results and calculates risk levels. Data is transmitted via UDP to a central monitoring system for visualization, record keeping, and further evaluation by doctors. This system emphasizes automation, real-time analysis, and predictive diagnostics, aiming to reduce manual errors, accelerate clinical decision-making, and improve early cancer detection. It provides a cost-effective, intelligent, and scalable solution for hospital-based patient care.

Published by: vikaspatanker

Authors: Vansh Nigam, Mr. Pankaj Lalwani

Abstract: Artificial Intelligence (AI) is no longer just a futuristic concept; it has quietly become a part of our daily lives, influencing the way people search, shop, and interact with brands. From personalized recommendations on e-commerce platforms to virtual assistants answering queries in real time, AI has started to reshape how consumers make decisions. This research paper focuses on understanding the impact of AI on consumer behaviour, looking beyond the technology itself to explore how it changes trust, buying patterns, loyalty, and expectations. The study examines how AI creates value by offering convenience and personalization—consumers now expect brands to “know them” and provide solutions tailored to their needs. At the same time, it highlights challenges such as privacy concerns, over-reliance on algorithms, and the risk of losing the human touch in brand–consumer relationships. By analysing existing studies, market practices, and consumer perceptions, this paper aims to bridge the gap between technological advancement and human psychology. Ultimately, the research argues that AI is not just influencing consumer choices but also shaping a new kind of consumer—more informed, more connected, and more demanding. Businesses that can balance AI-driven efficiency with ethical responsibility and genuine human engagement will be the ones to build lasting trust in the age of intelligent technology.

DOI: https://doi.org/10.5281/zenodo.19658824

Published by: vikaspatanker

Authors: Dr. Ch. Venkata Krishna Reddy, B. Varun Tej, T. Prabhas, G. Vishnu Charan

Abstract: Accidents caused by fire result in severe damage to life and property, especially in hazardous and hard-to-reach areas. In order to minimize human risk and increase the efficiency of firefighting, a Fire Fighting Robot with ESP32 Camera is proposed and implemented. In this system, the Arduino Uno board is used as a primary controller. The ESP32-CAM is used for live video streaming through a web page for the user. The robot is designed to operate in two modes: manual mode and automatic mode. The modes are selected through a web page. In manual mode, the user controls the robot’s movement and views the live video feed. The ultrasonic sensor is used in manual mode for obstacle detection. Four flame sensors are used to detect fire. Once the fire is detected, the robot moves towards the fire source. A DC water pump is used to spray water on the fire and extinguish it. The robot’s movement is controlled using DC motors driven by an L298 motor driver. A servo motor is used for direction control of the water pump. A buzzer is used for alarm generation when the fire is detected. The robot is powered using a battery supply regulated using an LM2596 voltage regulator module. This project is a simple and cost-effective way of remote fire detection and firefighting using robotics and wireless monitoring techniques. It is useful for industrial areas, warehouses, and places where human access is hazardous.

DOI: https://doi.org/10.5281/zenodo.19658551

Published by: vikaspatanker

Authors: Anto Maurin Lisha L, Muthu M, Sadeesh P, Tamilarasan S

Abstract: Liver diseases such as fatty liver, cysts, and tumors require early and accurate diagnosis to improve patient outcomes. Ultrasound imaging is widely used due to its non-invasive and cost-effective nature; however, its heterogeneous characteristics, including speckle noise, low contrast, and variability across devices, make diagnosis challenging. This paper proposes a deep learning-based approach for the classification of liver diseases using heterogeneous ultrasound images. The system employs pre-processing techniques such as noise reduction, normalization, and contrast enhancement to improve image quality. A YOLO-based architecture integrated with convolutional neural networks is used for feature extraction and simultaneous detection and classification of liver abnormalities. Experimental results show that the proposed model achieves improved accuracy and robustness compared to conventional methods. The system supports real-time analysis and can assist clinicians in reliable and efficient liver disease diagnosis.

Published by: vikaspatanker

Authors: Pooja Sharma

Abstract: Network virtualization has emerged as a transformative technology in modern networking by enabling the abstraction of physical network resources into flexible, scalable, and programmable virtual networks. It allows multiple virtual networks to coexist on a shared physical infrastructure, improving resource utilization, isolation, and management efficiency. This review explores key network virtualization technologies, including Software-Defined Networking (SDN), Network Function Virtualization (NFV), and virtual overlay networks. It examines how these technologies decouple network control from hardware, enabling dynamic configuration, automated provisioning, and improved scalability in cloud and data center environments. The study also discusses the role of network virtualization in supporting cloud computing, IoT, and 5G networks. Furthermore, it highlights critical challenges such as performance overhead, security concerns, interoperability issues, and orchestration complexity. Emerging trends such as intent-based networking, edge virtualization, and AI-driven network management are also analyzed. The findings emphasize that network virtualization significantly enhances flexibility, efficiency, and scalability in modern network infrastructures.

DOI:

Published by: vikaspatanker