Category Archives: Uncategorized

Authors: Assistant Professor Dr. Gurpreet Singh

Abstract: In a MANET number of devices or mobiles are connected to each other with wireless medium. This network is a temporary network. In the MANET, there is not any centralized device which control all netwrok. MANET using dynamic topology. The Sybil attack is characterized as a malicious node misguidedly taking on various MANET . A Malicious device acts as though it's anything but a bigger number of nodes, for instance by mimicking different devices or basically by asserting bogus MANET. In this,a terrible device present more than one character in MANET. So it is not much safe Network. The attacker are easily attacks on the MANET. Consequently, Security is an essential worry to give ensured correspondence between nodes in impromptu organizations and shots at having the weaknesses are additionally more. In this paper we complete analysis and classification of the various Sybil attack techniques and decline the network performance and throughput.

DOI: https://zenodo.org/records/20042738

Published by: vikaspatanker

Authors: Jay Pradip Deshmukh, Siddhi Kadam, Prajakta Badhe, Professor Snehal Awatade, Professor Bhagyashri Ramchandra Gunjal

Abstract: The use of Generative AI has started changing how software is developed today. Many tools are now available that help developers write code faster or fix errors, but most of them only support specific tasks. They do not handle the complete software development process from In this paper, we propose a system called JellyfishAI ,which aims to automate the full software development lifecycle. The idea is simple — a user can describe their requirements in normal language, and the system will generate a complete, ready-to-use application. It combines different technologies like natural language processing, code generation models, testing, and deployment into one system. Technology is advancing very fast, and modern applications need to be scalable, reliable, and developed quickly. Because of this, traditional development methods are under a lot of pressure. Usually, software development includes many steps like requirement analysis, design, coding, testing, and deployment. These steps often require manual work and skilled developers, which makes the process slow and costly. It can also lead to mistakes and inconsistent results. With the introduction of Generative AI, things have started to improve. These systems can understand user input and generate code, documentation, and even design ideas. They help developers save time by assisting in coding and debugging tasks. However, these tools still have limitations. Most of them only focus on one part of development, like code suggestions or bug fixing, and do not provide a complete solution. Another issue is that developers still need to use multiple tools separately and connect them manually. This makes the workflow complicated and less efficient. Also, the code generated by AI tools often needs to be checked by humans to ensure it is correct, secure, and follows proper standards. Even though CI/CD pipelines help automate deployment, they mostly follow fixed rules and do not have intelligent decision-making capabilities. Similarly, testing tools can find errors but cannot automatically improve the quality of the code. Because of this, there is a need for a system that can intelligently manage all stages of development together. Today’s software systems are also becoming more complex, and there is a growing need to build applications quickly. Startups and companies want to turn their ideas into working products as fast as possible. However, depending on skilled developers and manual work often slows things down. This becomes even more difficult when dealing with large systems that require proper coordination and integration. To solve these problems, we introduce JellyfishAI, which is designed to automate the entire development process. Unlike existing tools, this system brings everything together in one place. It can understand user requirements, generate code, test it, check for errors, and deploy the application automatically. The system is built using multiple layers that include language processing, AI-based code generation, validation, and deployment. This ensures that the generated code is not only functional but also tested and ready to use. The system also improves over time using feedback and learning mechanisms. JellyfishAI also focuses on important factors like security, scalability, and maintainability. It includes features to detect vulnerabilities, manage dependencies, and improve performance, so that the final application meets industry standards. This system can change how software development is done. By automating repetitive tasks, developers can focus more on designing and solving problems instead of doing routine work. It also makes it possible for people without strong technical skills to build applications. In conclusion, AI has the potential to improve software development by making it faster, cheaper, and more efficient. However, to achieve full automation, we need systems that combine all stages of development in one place. JellyfishAI is an attempt to do that by providing a complete, end-to-end solution for building software automatically.

DOI: https://zenodo.org/records/20042299

Published by: vikaspatanker

Authors: Aaryan Kansal, Aditya Garg, Rajat Takkar

 

 

Abstract: Modern software systems must handle unpredictable user loads while maintaining performance and reliability. This paper proposes an AI-based load testing framework using asynchronous load generation, machine learning models, anomaly detection, and digital twin simulation. The system predicts latency, detects anomalies, and estimates failure thresholds, enabling proactive performance optimization for high-traffic applications.

DOI:

 

 

Published by: vikaspatanker

Authors: Dr. Anand Singh Rajawat, Mayur Devare, Vaibhav Ingale, Mohit Deshmukh, Prathmesh Ingle

Abstract: Traditional attendance marking is a labor-intensive process prone to human error and proxy attendance. This paper presents an automated Smart Attendance Tracking System (SATS) that utilizes Quick Response (QR) Code technology for instant identification. The system works by generating unique encrypted QR codes for each student, which are then scanned using a standard high-definition webcam. Built on a Java-based technical stack with a MySQL backend, the system processes image data to decode information in real-time. Experimental results demonstrate that the system can process an individual scan in less than 0.8 seconds with 100% accuracy in standard lighting. By eliminating physical registers and dedicated scanners, this system offers a cost-effective and highly scalable solution for educational institutions.

DOI: https://doi.org/10.5281/zenodo.20038072

Published by: vikaspatanker

Authors: Sufiyan Ansari, Arhaan Shaikh, Usaid Khairdi, Moaiz Kazi

Abstract: Text classification has numerous applications in real world scenarios. Emotion detection from text is one of the vital tasks within natural language processing, which has gained significant attention of researchers over the years. In this study, emotions are detected and classified for better human–computer interaction, sentiment analysis, health management, and smart chattingbots. A number of deep learning models are developed and outperform the traditional models for the classification. Con- volutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid of CNN and LSTM are used for emotion classification. Furthermore, two traditional machine learning ap- proaches including Logistic Regression and Naive Bayes are also implemented for comparison purpose. Preprocessing is a very important step for a good model. Text normalization, stop words removal, tokenization, and padding are used for data preparation. Word embeddings, specifically pre-trained word2vec, are used to capture the semantic relationship of text features learned from deep learning models. The performance evaluation of these models is done using accuracy, precision, recall, F1-score, and confusion matrix. The experimental results show that the deep learning models have outperformed the traditional models. The hybrid CNN-LSTM model achieved the best results to classify emotions in multi-class problem.

Published by: vikaspatanker

Authors: B. Anief, M. Sakthivanitha

Abstract: Contemporary higher education institutions operate a fragmented portfolio of digital tools — separate learning management systems, manual attendance registers, paper-based hostel outpass forms, and WhatsApp-group announcements — that impose coordination overhead on students and staff while producing no integrated data trail for institutional analytics. This paper presents the design, implementation, and evaluation of the Smart Campus Engagement System (SCES), a cloud-deployed, role-aware web platform that unifies nine functional modules — user management, AI-assisted learning, attendance and academics, hostel and outpass management, events and activities, communication and alerts, complaints and feedback, campus services, and analytics — within a single authenticated interface. The system is implemented using a Next.js 14 frontend, a FastAPI Python backend, a PostgreSQL cloud database, and a Groq API–powered LLaMA-3.3-70B language model for an AI assistant. Containerised deployment via Docker Compose supports horizontal scaling. System testing across eight functional scenarios at up to 200 concurrent users demonstrates API response times below 900 ms and a peak-load error rate of 2.8%. Security testing confirms resistance to SQL injection, JWT tampering, cross-site scripting, and unauthorised role escalation. Comparative analysis against four published smart campus systems confirms that the proposed implementation is the only system combining LLM-based AI assistance, geolocation-verified attendance, digital outpass workflow, and real-time push notifications in a single unified deployment. The system establishes a replicable, open-architecture blueprint for next-generation campus digitalisation.

DOI: https://doi.org/10.5281/zenodo.20037223

Published by: vikaspatanker

Authors: Dr. K.N.Kazi, Miss. Kolekar Akshata Bharat, Miss. Adling Snehal Bhagwat, Mr. Chorage Mahesh Santosh

Abstract: Automation plays a vital role in modern industries by improving efficiency, accuracy, and productivity. This project presents the design and development of a Packing Controlled Robotic Arm using Arduino. The system is designed to perform automated pick-and-place operations for packing applications in small-scale industries. The robotic arm is controlled using Arduino Nano, with servo motors providing movement to each joint and a gripper mechanism handling objects. Joy Sticks are used to detect the presence of items for packing, while Arduino coordinates motion control through pre-programmed instructions. The developed system aims to reduce manual labor, minimize errors, and provide a cost-effective automation solution. The prototype demonstrates the potential of using simple, low-cost components for effective packaging automation in educational and industrial setups.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Rohit Dhamale, Prathamesh Sonawane.

Abstract: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare. It has the potential to significantly improve the accuracy and efficiency of disease diagnosis and treatment. One of the most important applications of AI in healthcare is early disease detection. Early detection allows medical professionals to identify diseases in their initial stages, enabling timely treatment and improving patient survival rates. AI technologies such as machine learning, deep learning, natural language processing, and predictive analytics can analyze large volumes of medical data quickly and accurately. These systems can process electronic health records, laboratory reports, medical images, and patient history to identify patterns that indicate the early onset of diseases. AI-based systems assist doctors by providing data-driven insights and predictions that help in clinical decision-making. This research paper explores the role of artificial intelligence in early disease detection and its impact on modern healthcare systems. The study examines different AI technologies used in disease diagnosis, the methodology used to implement these systems, and the challenges associated with AI integration in healthcare environments. Furthermore, the paper discusses the benefits of AI-driven diagnostic systems in improving healthcare efficiency, reducing medical errors, and enhancing patient outcomes.

DOI: https://doi.org/10.5281/zenodo.20036666

Published by: vikaspatanker

Authors: Prof. Rahul D. Ingle, Prof. Rohan B. Kokate, Jyoti Ramesh Lanjewar

Abstract: The rapid advancement of digital technology has significantly transformed financial transactions, leading to the widespread adoption of cashless payment systems. Among these, QR code-based payment systems have emerged as one of the most convenient and efficient methods for conducting fast and contactless transactions. However, despite their growing popularity, these systems still face critical challenges such as transaction fraud, unauthorized access, phishing attacks, and security vulnerabilities. To overcome these limitations, there is a need to integrate intelligent technologies that can enhance both security and user experience. This project presents the design and development of a QR Based Online Payment System for Enhanced Convenience Using Machine Learning (ML). The primary objective of the system is to provide a secure, fast, and user-friendly digital payment platform that allows users to make payments simply by scanning QR codes. The system eliminates the need for physical cash, card swiping, or manual bank details entry, thereby reducing transaction complexity and improving efficiency. A key feature of the proposed system is the integration of Machine Learning-based fraud detection mechanisms. The ML model continuously analyzes transaction patterns, user behavior, device information, and payment history to identify unusual or suspicious activities. By using classification and anomaly detection techniques, the system can detect potential fraud in real time and prevent unauthorized transactions before they are completed. This enhances the overall trust and reliability of the payment platform. The system also includes essential modules such as secure user authentication, dynamic QR code generation, transaction processing, payment history tracking, and notification services. Each transaction is securely encrypted and stored in a centralized database to ensure data integrity and confidentiality. The platform is designed using modern web technologies to ensure scalability, responsiveness, and compatibility across multiple devices. From a functional perspective, the system supports both users and merchants, enabling seamless peer-to-merchant and peer to peer payments. Merchants can generate unique QR codes linked to their accounts, while users can scan and complete payments instantly. The inclusion of real-time alerts and dashboards helps users track their financial activities efficiently.

DOI: https://doi.org/10.5281/zenodo.20036097

Published by: vikaspatanker

Authors: Sony V Hovale, Manu K C, Naresh Patel, Pavithra B, Shradha G Vernekar

Abstract: Early detection and classification of oral lesions are essential for the prevention of oral cancers, and yet, manual diagnosis is still a challenge due to variations in the appearance of lesions, quality of images, and limited clinical datasets. This research explores the use of diffusion models, a recent class of generative models renowned for their stable training and high-fidelity reconstruction, to improve the automatic classification of oral lesion images. The proposed system includes dataset collection from open-source platform kaggle, preprocessing of dataset, a diffusion- based denoising and feature extraction pipeline, and finally, a classification stage to categorize the normal, precancerous, and cancerous lesions. By leveraging the forward and reverse process of diffusion, the model improves the clarity of the images and effectively extracts discriminative features, mitigating problems of noise, imbalance, and low-quality clinical images. In a deep learning approach combining CNN-based classification with the concept of enhancement provided by diffusion mechanisms, the generalization performance is boosted. The system will be evaluated based on accuracy, precision, recall, and F1-score, and the results provide promising improvements compared to the state-of-the-art traditional deep learning methods. This paper has found that diffusion models provide a robust, scalable, and clinically valuable pipeline for early oral lesion detection, with strong potential to be deployed in real-world diagnostic pipelines and future research on medical imaging and we obtained a very good accuracy i.e., 96% while training the model. This paper establishes the diffusion model as a promising approach for medical image analysis, particularly in the early detection and classification of oral lesions, paving the way for future research and clinical applications in healthcare.

DOI: http://doi.org/

Published by: vikaspatanker