Category Archives: Uncategorized

Authors: Mrs. N. Nikhitha, Malla Sudarsan Sai Sunny, Guthula Surya Sindhu, Neelapalli Sri Durga Abhilasha, Govada Pavan Sai, Palisetti Siva Ram

Abstract: The increasing rate of criminal activities and the limitations of existing police complaint systems highlight the need for a more transparent, secure, and efficient method for managing complaints and First Information Reports (FIRs). In many cases, complaints remain unreported or are not officially registered due to procedural delays, corruption, or lack of proper documentation systems. Although online portals such as the Crime and Criminal Tracking Network and Systems (CCTNS) have been introduced, they still operate on centralized architectures that may suffer from issues such as single points of failure, limited transparency, and vulnerability to data tampering. To address these challenges, this study proposes a blockchain-based police complaint management system designed to provide secure, decentralized, and tamper-proof storage of complaint records. In the proposed system, complaint details and FIR records are encrypted and stored using the InterPlanetary File System (IPFS), while the corresponding hash values are recorded on a blockchain network to ensure immutability and data integrity. The decentralized nature of blockchain technology ensures that complaint records cannot be altered or deleted without network consensus, thereby preventing unauthorized modifications and enhancing system transparency. Additionally, timestamped blockchain entries provide verifiable proof of complaint submission, enabling citizens to demonstrate that their complaint was officially recorded even if authorities deny receiving it. By integrating blockchain with distributed file storage technologies, the proposed system enhances trust between citizens and law enforcement agencies while ensuring secure and transparent management of police complaints. The framework also supports the broader goals of e-governance by improving accountability, data security, and accessibility in public service systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.189

Published by: vikaspatanker

Authors: Mrs. Ch. Veera Gayatri, Palivela Geethasri, Kothapalli Venkannababu, Madhavarapu Chandhra Sekhar Sri Sai, A Lakshmi Chinmayi, Anupoju Sainadh

Abstract: The rapid growth of e-commerce platforms has increased the need for intelligent pricing strategies that can adapt to continuously changing market conditions. Traditional pricing methods used in online retail are often static and rely heavily on historical data analysis, making them inefficient in responding to dynamic market factors such as customer demand, competitor pricing, and seasonal trends. In modern digital marketplaces, businesses generate large volumes of transactional and behavioural data, which creates opportunities for applying machine learning techniques to improve pricing decisions. This study proposes a machine learning-enabled business intelligence framework for dynamic pricing optimization in e-commerce environments. The proposed system integrates data preprocessing, predictive modelling, and business intelligence analytics to support real-time pricing decisions. During the preprocessing stage, historical pricing data, market trends, competitor price information, and customer behavior patterns are collected and processed to improve data quality and consistency. Support Vector Machine (SVM) is employed as the primary machine learning algorithm due to its ability to handle complex and non-linear relationships within large datasets. The business intelligence component of the framework enables efficient data visualization, monitoring, and analysis of market conditions through interactive dashboards and analytical tools. This integration allows businesses to combine predictive insights from machine learning with data-driven business intelligence reports to determine optimal pricing strategies. The proposed system dynamically adjusts product prices by analyzing multiple influencing factors such as demand fluctuations, competitor behavior, and customer purchasing patterns. Experimental evaluation demonstrates that the integration of machine learning and business intelligence significantly improves pricing accuracy, market responsiveness, and decision-making efficiency. By enabling automated and adaptive pricing strategies, the proposed framework helps businesses maximize revenue, enhance competitiveness, and respond effectively to rapidly changing e-commerce environments.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.190

Published by: vikaspatanker

Authors: Mr. K. Srikanth, Beeraka Sharmila, Puppala Madhuri Lakshmi, Darla Ratan Abhishek, Pitchuka Veerababu, Seeram Jaya Venkata Somesh

Abstract: Pneumonia is a significant respiratory disease and one of the top causes of illness and death around the world, especially among children and the elderly. Timely and accurate diagnosis is essential for effective treatment and better patient outcomes. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), have shown impressive results in medical image analysis by automatically identifying important patterns in complex image data. This project introduces a real-time pneumonia identification system that combines CNN-based classification with Explainable Artificial Intelligence (XAI) techniques to improve diagnosis accuracy and model clarity. The proposed system processes digitized chest X-ray images through an efficient preprocessing pipeline. This includes noise removal, image normalization, and background feature consideration before sending the images to a trained deep learning model. The ensemble model merges two strong CNN architectures, VGG16 and ResNet50, and uses their complementary feature extraction abilities to boost classification performance. The model classifies Bacterial Pneumonia, Viral Pneumonia, and normal cases, providing clearer clinical insights. Experimental results show high accuracy, strong sensitivity, and real-time inference capability. This allows for pneumonia detection within seconds, which is vital in clinical settings that need quick diagnoses. To tackle the black-box issue of deep learning models, Explainable AI techniques like Grad-CAM++ (Gradient-weighted Class Activation Mapping++) and Score-CAM are used to visualize the key lung areas that affect the model’s predictions. The system also offers confidence scores with visual explanations, enhancing understanding and aiding clinical decision-making. Overall, the proposed CNN and XAI framework offers an efficient, clear, and clinically helpful solution for automated pneumonia detection. The system has strong potential to assist radiologists, boost diagnostic confidence, and contribute to early disease detection and improved patient care.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.191

Published by: vikaspatanker

Authors: Mrs. N. V. S. Sowjanya, Chavvakula Lasyavalli, Sunkara Vijay Kishore, Kammakatla Shreya, Yerra Sai Rajesh, Chelli Tarun Teja

Abstract: Maritime surveillance plays a crucial role in ensuring the safety, security, and regulation of activities in open sea environments. One of the major challenges faced by maritime authorities is the detection of vessels that intentionally disable their Automatic Identification System (AIS) transponders to conceal illegal activities such as unauthorized fishing, smuggling, or unauthorized entry into restricted maritime zones. AIS messages transmitted by ships are widely used for monitoring vessel trajectories; however, missing AIS signals may occur due to multiple reasons including satellite reception limitations, weather disturbances, or intentional shutdown of AIS devices. Distinguishing between these scenarios becomes difficult when dealing with massive volumes of satellite AIS data. This study proposes an intelligent deep learning framework for detecting intentional AIS shutdown events using self-supervised learning techniques. The proposed approach processes large-scale AIS datasets collected from satellite-based maritime surveillance systems and extracts trajectory-based features such as vessel position, speed, time intervals between messages, and movement patterns. A transformer-based deep learning architecture is used to analyse sequential AIS message data and predict whether a new AIS message is expected within a specific time window. By comparing the predicted results with the actual observations, the system identifies abnormal missing AIS receptions that may indicate intentional signal shutdown. The self-supervised learning approach allows the model to generate pseudo-labels from unlabelled AIS data, eliminating the need for manually labelled datasets. Experimental analysis demonstrates that the proposed framework can process millions of AIS messages in near real-time while achieving high prediction accuracy in detecting abnormal vessel behaviour. The integration of deep learning techniques improves the reliability and scalability of maritime surveillance systems, enabling authorities to identify suspicious vessel activities more efficiently. This framework contributes to enhancing maritime security, improving monitoring capabilities in open sea environments, and supporting timely detection of illegal maritime operations

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.192

Published by: vikaspatanker

Authors: Mr. M. V. Rajesh, Balla Aarathisree, S S V Sumanvitha Palivela, Nagala Bhavya Pragna, Kamalesh Chitra, Taneti Ritesh

Abstract: The rapid growth of encrypted internet protocols such as HTTP/3 and QUIC has significantly improved communication speed and security on modern networks. However, these protocols also introduce new challenges for network security, particularly in detecting Distributed Denial of Service (DDoS) traffic flood attacks. Traditional monitoring techniques rely on packet inspection, which becomes difficult when network traffic is encrypted. This study proposes an intelligent machine learning framework for detecting QUIC-based traffic flood attacks in encrypted HTTP/3 network environments. The proposed system analyses network flow behaviour rather than packet content, enabling effective detection even when traffic payloads are encrypted. To build the detection model, network traffic data are captured and processed into flow-based features such as packet rate, packet size distribution, inter-arrival time, and connection statistics. Data preprocessing techniques are applied to prepare the dataset for machine learning training. Multiple classification algorithms including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest are implemented and evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC–AUC. Experimental results demonstrate that the Random Forest classifier achieves the highest detection accuracy and provides reliable performance for distinguishing between normal and malicious QUIC traffic patterns. To improve transparency and interpretability of the prediction process, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME are incorporated into the framework. These methods highlight the most influential network features contributing to attack detection and help security analysts understand the reasoning behind model predictions. The proposed framework enhances the reliability of encrypted traffic monitoring, improves early detection of QUIC traffic flood attacks, and contributes to strengthening the security of next-generation web communication protocols.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.193

Published by: vikaspatanker

Authors: Prasanna R, Anbarasan M, Nithish Murugesan, Gokulapriyan S, Dr. Rajini S

Abstract: Digital event management faces significant challenges due to the increasing demand for virtual and hybrid events and the lack of integrated platforms for managing them efficiently. Event organizers often rely on multiple disconnected systems for handling registrations, scheduling, live streaming, and participant engagement, which leads to operational complexity and inconsistent user experiences. This project proposes an intelligent and automated digital event management system that uses modern web technologies, cloud services, and real-time communication modules to manage and streamline event operations. The system employs a centralized web-based platform integrated with user interfaces for real-time event coordination using scalable frontend and backend frameworks. A role-based access control mechanism and automated workflow system manage activities such as registration, ticketing, session scheduling, and live interaction. The platform integrates engagement tools including chat, polls, and Q&A features to enhance user participation. This automated approach improves event efficiency, reduces manual coordination effort, minimizes system fragmentation, and supports the growing demand for scalable and user-centric digital event solutions. Digital event management, Web application, Virtual events, Cloud computing, Real-time systems, User engagement, Event analytics, Automated workflow, Scalable platforms, Hybrid events

Published by: vikaspatanker

Authors: Dr. Viji R, Director, Prof. (Dr.) Vellayan Srinivasan, Dr.V.O.Kavitha

Abstract: Employee training is a crucial human resource management practice that enhances employee skills, knowledge, and competencies, thereby improving organizational productivity. This study examines the impact of employee training on organizational productivity through an empirical analysis of 100 employees from different departments in selected organizations. Primary data were collected using a structured questionnaire and analyzed using percentage analysis, mean scores, correlation, and regression techniques. The findings reveal that effective employee training significantly improves employee performance, job efficiency, and overall productivity. The study concludes that systematic and continuous training programs are essential for organizational growth and competitiveness.

DOI: https://doi.org/10.5281/zenodo.19590742

Published by: vikaspatanker

Authors: Assistant Professor H. M. Pawar, Pravin Chavan, Kumar Borole, Rahul Vispute, Sona Bhalerao

Abstract: The rapid adoption of electric vehicles (EVs) has created a growing demand for efficient, sustainable, and convenient charging infrastructure. This paper proposes an IoT-based solar-powered wireless power transfer (WPT) system integrated into roadways for dynamic charging of electric vehicles. The system utilizes photovoltaic panels installed alongside or beneath road surfaces to harness solar energy, which is stored and managed using intelligent energy storage systems. Wireless power transfer is achieved through inductive coupling between transmitter coils embedded in the road and receiver coils mounted on EVs, enabling real-time charging while the vehicle is in motion. The integration of Internet of Things (IoT) technology allows for continuous monitoring, control, and optimization of energy distribution, traffic conditions, and system performance. Sensors and communication modules collect real-time data, which is processed to ensure efficient power delivery and load balancing. This approach minimizes range anxiety, reduces dependency on stationary charging stations, and enhances energy efficiency by utilizing renewable energy sources. The proposed system presents a scalable, eco-friendly, and smart solution for future transportation infrastructure, contributing to reduced carbon emissions and sustainable urban mobility.

DOI: http://doi.org/10.5281/zenodo.379

Published by: vikaspatanker

Authors: Aditya Phad, Shubajit Avachite, Sujal Jadhav, Rugved Gunvante

Abstract: An online auction system developed using Python provides a digital platform where users can buy and sell items through competitive bidding. The project utilizes Python for backend logic, ensuring efficient handling of user data, bids, and auction processes. It typically includes features like user registration, login authentication, and item listing. Sellers can upload product details, while buyers can place bids in real time. The system manages bid validation to ensure only higher bids are accepted. A timer mechanism is implemented to automatically close auctions after a specified duration. The highest bidder at the end of the auction is declared the winner. Data is often stored using databases such as SQLite or MySQL for persistence. The project emphasizes security, fairness, and user-friendly interaction. Overall, it demonstrates practical a pplication of Python in building scalable web-based systems.

DOI: https://doi.org/10.5281/zenodo.19589768

Published by: vikaspatanker

Authors: Shardul Prakash Jangam

Abstract: Achieving self-reliance (Atmanirbhar) in India’s electric vehicle (EV) sector mandates a strategic shift from reliance on imported lithium-ion (Li-ion) technology toward alternative chemistries utilizing domestically abundant raw materials. The Sodium-ion Battery (SIB) is identified as the leading alternative technology capable of ensuring immediate raw material independence by leveraging domestic reserves of sodium, iron, and manganese. SIBs are ideally suited for the cost-sensitive, short-range segments of India's market, specifically electric two-wheeler (e-2W) and three-wheeler (e-3W) segments.1 While Li-ion currently holds a cost advantage, scaled SIB production is projected to achieve cost leadership, potentially reaching $40-$50 per kilowatt-hour (kWh) at the pack level by the end of the decade.4 SIBs also offer superior safety and temperature tolerance, critical for mass EV adoption and Battery Energy Storage Systems (BESS).2 The primary recommendation is the rapid utilization of the 5 GWh capacity earmarked under the Production-Linked Incentive (PLI) Niche ACC scheme to commercialize SIBs, focusing especially on establishing a completely indigenous supply chain for the hard carbon anode using domestic biomass.7 Successful localization is projected to capture nearly 80% of the estimated $300 billion domestic EV battery market by 2030, generating substantial employment and revenue.

DOI: https://doi.org/10.5281/zenodo.19589549

Published by: vikaspatanker