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

Authors: Swapnil Wagh, Ruchi Sharma, Ankit Temurnikar

Abstract: Plant diseases are also known to place huge burden on food security structure and agriculture to the global world; it is approximated that all plant diseases development costs a giant (an estimated 220 billion/year). To address this, the computer vision -specific and deep learning based automated disease detection systems are expandingly viewed as rather interesting as an option instead of the traditional forms of diagnosing that involve a significant amount of new employees . However, the literature screening is saturated with models that have been alleged to be super high in accuracy with regard to classifications when they are under some form of controlled conditions in the laboratory that must in no way imply any trustworthy depiction that they can be relayed over the situation in the real field. It can be said that such discrepancy in performance can stress the idea that there is a dire necessity to carry out more related and stiffer analysis of existing measures of data mining and optimization. This article has such an experimental alloy of which the plant disease variable models can be detected multi faceted in, which is discussed in detail on three axes parametric axis, combinatorial axis, computational axis. The rate of model performances to the hyperparameter options enshrined in the parametric assessment that may also be the optimizers are called counting. The combinatorial work involves the study of connections pertaining to the utility of various Convolutional Neural Network Convolutional designs, as well as the use of spectacular measures of data augmentation and fold up learning methods. The computational verification provided is a practical test of the feasibility of the model, comparison of statistics on the training time, model complexity, and speed of inference. According to the opinion that our experimental findings indicate, our individual models (as well as our EfficientNet) that come with the highest classification performance of about above 98 percent accuracy would always be the best trade off between accuracy and efficiency whereas ensemble models would adopt a combination of soft voting as the best trade off prerogative. The paper further estimates the radical performance augmentation with the generative data augmentation models against the conventional geometric transformations to apply the models in the truly competitive use. The primary accomplishment of this project is the system, which surpasses those pathetic signs of precision and rests upon the familiarization of scientists and performers with how to create, alter, and put to practical practice the scaleable, resilient, and effective plant disease detection methods used in the enhancement of the designated work in the agricultural forerunners.

DOI: https://doi.org/10.5281/zenodo.19444915

 

 

Published by: vikaspatanker

Authors: Ghanshyam G.Lihankar, Sarvesh D.Tak, Akhilesh M.Bhagat, Dhiraj R.Gedam, S V..Raut, D G..Ingle, R S.Durge

Abstract: This research presents the design and development of an integrated web-based travel planning platform combined with an AI-driven chat assistant to improve the efficiency and convenience of travel planning. The system is developed to provide intelligent recommendations, automate itinerary creation, and assist users in making better travel decisions. The paper describes each phase of development, including requirement analysis, system architecture design, module integration, and implementation. The proposed system enables users to interact with the platform through a conversational AI assistant that understands user preferences, travel interests, and budget constraints. Based on user inputs, the system generates personalized travel plans, suggests suitable destinations, recommends accommodations, and provides relevant travel information.

DOI:

 

 

Published by: vikaspatanker

Authors: Srinithi R T, Tisya Chellapandian, Venisha K, Dr. Sumathi V P, Dr. Sumathi V P

Abstract: This research presents a hospital assistance framework that uses AI to enable smooth communication between patients and reception staff. The system recognizes Indian Sign Language (ISL) in real-time and translates speech and text. This helps guide patients effectively without needing a human interpreter. The framework allows for two-way communication: patients use ISL gestures, which are translated into text or voice for the receptionist. In turn, the receptionist's responses convert back into ISL animations displayed to the patient. The model uses Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) architectures, along with a Connectionist Temporal Classification (CTC) decoder for aligning sequences. The preprocessing pipeline uses MediaPipe and OpenCV to extract hand landmarks and reduce noise. A dataset with healthcare-related gestures, such as “doctor,” “appointment,” “medicine,” and “wait,” trained the model. The system operates fully on software and does not require specialized hardware. This solution offers an efficient and accessible way for guiding patients through hospital services, ensuring inclusivity and improving communication at the reception desk.

DOI: https://doi.org/10.5281/zenodo.19444073

 

 

Published by: vikaspatanker

Authors: Gyara Monika, M. Suchith Reddy, Macha Sujith, Thakur Akshat Singh

Abstract: The project proposes a Gesture-Based Presentation Controller that allows users to control slides using hand gestures through a webcam, eliminating the need for keyboards or remote controllers. It uses computer vision and hand- tracking techniques to detect real-time hand movements and identify key landmarks such as finger positions and hand orientation. Predefined gestures are recognized by analyzing spatial relationships between fingers and joints, ensuring accurate interpretation of user actions. Recognized gestures are mapped to presentation commands like next/previous slide, slideshow control, and pointer movement by simulating keyboard and mouse inputs. The system includes gesture stabilization mechanisms to improve accuracy and is lightweight, cost-effective, and suitable for classrooms, corporate meetings, and professional presentations.

DOI: https://doi.org/10.5281/zenodo.19443936

 

 

Published by: vikaspatanker