Daily Archives: April 7, 2026

Authors: Mr.V.Prem Kumar, Manyam Teja Siva Ganesh Goud, Kothapalli Vennela Sri Sai Bhargavi, Yeluri V N S S P Teja, Vedagiri Yuva Sai Suresh, Dara Teja

Abstract: Traffic violations have become a major cause of road accidents and fatalities in many countries, particularly in densely populated urban areas. Common violations such as red-light jumping, triple riding on two-wheelers, and reckless driving significantly increase the risk of road accidents. Traditional traffic monitoring systems rely heavily on manual observation by traffic police or limited sensor-based systems, which are inefficient, time-consuming, and prone to human errors. To address these challenges, intelligent traffic monitoring solutions based on computer vision and deep learning have gained significant attention. This paper proposes a deep learning-based automated traffic violation detection system using the YOLOv7 object detection model. The proposed system processes video streams obtained from roadside surveillance cameras and analyses them frame-by-frame to detect different traffic violations. The YOLOv7 model is employed to identify vehicles and generate bounding boxes around detected objects. A predefined threshold line is used to determine whether a vehicle crosses the traffic signal during a red light, thereby identifying signal violations. Additionally, the system detects over boarding or triple riding on two-wheelers by analysing the number of riders detected within a single vehicle bounding box. The system uses publicly available datasets such as the MS COCO dataset for vehicle detection and a custom annotated dataset for over boarding detection. The model is trained and evaluated using performance metrics including precision, recall, F-measure, and mean Average Precision (mAP). Experimental results demonstrate that the proposed model effectively detects multiple traffic violations with high accuracy while maintaining efficient real-time performance. The proposed approach provides a cost-effective, automated, and scalable traffic monitoring solution that can assist traffic authorities in improving road safety and reducing the workload associated with manual monitoring systems. The system can be integrated with existing smart city surveillance infrastructures to enhance intelligent transportation management and law enforcement.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.148

Published by: vikaspatanker

Authors: Mrs.K.Ganga Devi Bhavani, Bonam Geetha Chitti Jyothi, Siravapu Santhi Kumari, Karri Manikanta Sai, Alluri Sri Akshay Satya Srinivas, Thella Aditya

Abstract: The spread of fake news has become a significant concern in today’s society, as misleading information can easily damage reputations and lives. To address this issue, researchers have developed fake news detection systems using machine learning techniques. The identification of fake news is rapidly gaining traction and is increasingly being adopted by various industries, either for their own use or to offer as a service to others. Machine learning (ML) and deep learning (DL) are two prominent approaches employed to determine the authenticity of news. There are various methods available for detecting false news through both ML and DL techniques. This paper presents a comprehensive analysis of fake news detection using machine learning approaches. Upon thorough examination, it was found that several ML and DL algorithms have been applied in this domain, with the Support Vector Machine (SVM) being the most commonly used ML method, and Long Short-Term Memory (LSTM) being the most widely applied DL technique.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.147

Published by: vikaspatanker

Authors: Ms.G.Naga Rani, Mangipudi V N S Sekhar Sarma, Khandavalli V V Lakshmi Srirama Karthik, Malla Karthik, Pabbineedi Vanshika, Ventru Hemanth Kumar

Abstract: The banking sector plays a vital role in the global financial system by providing loans to individuals and businesses for various purposes. While loans generate significant revenue through interest, there is always a risk that borrowers may fail to repay the loan, resulting in financial losses for lending institutions. Therefore, accurately predicting the risk level associated with a loan application is an important task for banks and financial organizations. Traditional loan approval processes rely heavily on manual analysis of customer information, which can be time-consuming and prone to human bias. With the advancement of machine learning techniques, automated systems can now analyse large amounts of financial data to support more efficient and accurate loan approval decisions. This study proposes a machine learning-based loan risk prediction system that analyses customer personal and financial attributes to determine the likelihood of loan default. The dataset used for this study contains multiple features commonly included in loan applications, such as credit history, checking account status, loan amount, employment status, and age of the applicant. Data preprocessing techniques including outlier removal, categorical encoding, and feature scaling are applied to prepare the dataset for model training. Several machine learning algorithms are implemented and compared, including Decision Tree, Random Forest, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Naive Bayes, and a Stacking Ensemble model. The models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrate that ensemble-based approaches provide improved predictive performance compared to individual machine learning models. The proposed system can assist financial institutions in making faster and more reliable loan approval decisions by identifying high-risk applicants before granting loans. By leveraging machine learning techniques, the system enhances the efficiency of credit risk assessment and supports more effective financial decision-making in the banking industry.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.146

Published by: vikaspatanker

Authors: Poosarla Durga Bhavani, Shaik Roshan Jameer, Vasamsetti Monika Durga Satya Vani, Shaik Ubaid Ahamad, Purushottapatnapu Ravi Sai Krishna, Mr. A. V. Sudhakar Rao

Abstract: Finding a rental or a tenant is often a difficult task. The market is messy, with listings spread across many sites, often outdated, and communication is slow. "Rent Ease" is an all-in-one digital platform built to fix these problems, providing a single, reliable hub that makes renting simpler for everyone. This platform integrates modern technologies such as React.js for frontend development, FastAPI for backend services, and MongoDB for data storage. Key features include real-time chat communication between users and owners, Google Maps integration for accurate location tracking, and an AI-powered module that automatically generates property descriptions to enhance listing quality. Additionally, the system provides filtering options, detailed property views, and a rating and review mechanism to ensure transparency and better decision- making. This system improves user experience, reduces communication gaps, and provides reliable property information, making it a comprehensive solution for both property owners and tenants. Our main goal is to modernize the rental experience. By leveraging technology to connect owners and tenants directly, Rent Ease makes the process faster, more transparent, and significantly less of a hassle, creating.

DOI: https://doi.org/10.5281/zenodo.19451659

 

Published by: vikaspatanker

Authors: Piyush Pawar, Ayush Jagdale, Yuvraj More, Gunesh Padmukhe, Prof. Meshram A.G

Abstract: The Gesture Vocalizer is a smart assistive communication system developed to help speech- impaired and physically challenged individuals convey messages using hand gestures. The system employs gesture-detection sensors such as flex sensors or accelerometers to recognize predefined hand movements. These gestures are processed by a microcontroller, which converts them into corresponding voice outputs through a speaker or mobile application. The device enables real-time communication without the need for verbal speech, making it highly useful in daily interactions, hospitals, and emergency situations. Users can customize gesture-to- message mappings, improving flexibility and usability. By combining sensor technology, embedded systems, and voice output, the Gesture Vocalizer enhances independence, accessibility, and social interaction for differently-abled individuals.

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Published by: vikaspatanker

Authors: Ishita Shinde, Tanushri Jadhav, Apurva Ransing, Pritesh Patil, Dr. Mrunal Pathak

Abstract: The SkillBridge app aims to link professionals from a variety of industries with people who wish to acquire practical skills. Traditional learning approaches occasionally fall short of offering real-time mentoring and hands-on experience in today's quickly changing digital world. SkillBridge fills the need of developing a platform where students can find mentors, access skill-based resources and work together on learning opportunities. The software encourages knowledge sharing, community-driven learning, and skill development across a range of professions. Through the use of technology, SkillBridge assists professionals, students, and lifelong learners in increasing the effectiveness, accessibility, and interactivity of skill learning.

DOI: https://doi.org/10.5281/zenodo.19449850

Published by: vikaspatanker

Authors: Pranjal Mahendra Bhosale, Revati Machindra Wahul

 

 

Abstract: Cyber bullying is an increasing issue on all online platforms, especially targeting teenagers and young people. Conventional machine learning algorithms fail to perform well in identifying subtle or context-related abusive language. Recent developments in Natural Language Processing (NLP), specifically the transformer model BERT, have demonstrated immense potential in text classification. However, the computational requirements of the full-sized BERT model make it impractical for real-time applications or mobile-based solutions. Proposed in this research is a fast and light cyberbullying detection system based on compact BERT variants like DistilBERT and TinyBERT,CNN,LSTM. These models preserve the language understanding abilities of the original BERT model but with far fewer parameters and computational costs. The model is then fine-tuned on labeled datasets with content related to cyberbullying, and particular emphasis is placed on handling the class imbalance problem through methods such as Focal Loss. Through this process, the model is able to achieve performance metrics that are comparable to those of the full-sized BERT models.

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Published by: vikaspatanker

Authors: Dr. Barkha

Abstract: Dengue fever is an emerging mosquito-borne viral disease and a significant global public health concern, particularly in tropical and subtropical regions. The present study investigates the dynamics of dengue fever in Bareilly district, Uttar Pradesh, India, with special emphasis on epidemiological patterns, environmental factors, and clinical manifestations. The study was conducted during the peak transmission period from August to October 2014 through surveys of ten hospitals and pathology laboratories. Data were collected on suspected and confirmed dengue cases, including variables such as age, sex, symptoms, and diagnostic methods. Blood samples were analyzed using ELISA, rapid diagnostic kits, and microscopy. The results revealed a considerable number of dengue cases across all age groups, with both males and females equally affected. Common clinical features included high fever and thrombocytopenia (low platelet count), while mortality remained below 1% due to timely medical intervention. Environmental and socio-economic factors such as rapid urbanization, poor waste management, water stagnation, and favorable climatic conditions (temperature, humidity, and rainfall) were identified as major contributors to dengue transmission. Comparative analysis with the 2010 nationwide.

DOI: https://doi.org/10.5281/zenodo.19448692

Published by: vikaspatanker