Volume 7, Issue 2

2026 : Volume 7 Issue 2

Title A MACHINE LEARNING FRAMEWORK FOR EARLY PREDICTION OF BRAIN STROKE USING CLINICAL ATTRIBUTES
Authors 1. B NANDANA KUMAR,2. P.PARVATHI,3. L.VINOD,4. M.KRISHNA NAGA SAI,5.M.SRIDEVI
Affiliation 1. Asstistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19692936
Abstract Stroke remains one of the leading causes of mortality and long-term disability worldwide, necessitating early detection for timely clinical intervention and improved patient outcomes. Inspired by methodologies used in ADMET based drug side-effect prediction—where functional group patterns and engineered descriptors facilitate early risk assessment—this study proposes an explainable machine learning framework for predicting stroke occurrence based on clinical, demographic, and lifestyle factors. The curated dataset incorporates key attributes such as age, hypertension status, history of heart disease, average glucose level, smoking behavior, and body mass index (BMI).
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Title SMART CRIME ANALYTICS AND HIGH-RISK ZONE FORE CASTING SYSTEM USING HISTORICAL CASE RECORDS AND GEO VISUALIZATION
Authors 1.Dr. A Ramamurthy. 2.K. Devi Chandrakala, 3.M. Hemalatha, 4.P. Karunkar,5.P. Murali Krishna
Affiliation 1. Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19641260
Abstract The Smart Crime Analytics and High-Risk Zone Forecasting System is an intelligent data analytics platform developed using Streamlit, Pandas, and Folium to analyze historical crime datasets and forecast high-risk areas through geo-visualization and temporal trend analysis. The system enables users to upload crime records, apply city and crime-type filters, and visualize spatial crime patterns via interactive heatmaps and marker clusters.
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Title HOSPITAL READMISSION PREDICTION SYSTEM BASED ON ELECTRONIC HEALTH RECORDS (EHR) DATA
Authors 1.M.P.V. HARIKA, 2.M. RANI, 3.K. SUSHMASRI, 4.P. MANIKANTA SOBHANADRI, 5.M. PRASHANTH
Affiliation 1. Asst.Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19641382
Abstract Predicting ICU readmissions is critical for improving healthcare outcomes and reducing costs. This study employs a data-driven approach to analyze electronic health records (EHRs) and predict ICU re admissions using preprocessing techniques such as age mapping, normalization, and binary encoding. The dataset includes key patient attributes such as demographics, medical history, and treatment data. A robust preprocessing pipeline ensures clean and normalized inputs for predictive modelling. This approach enables the effective handling of missing values, categorical variables, and feature scaling. By transforming raw EHR data into a structured format, the study lays the groundwork for advanced machine learning models to enhance predictive accuracy and improve patient management
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Title ADVANCED DEMAND FORECASTING FOR RETAIL SUPPLY CHAIN MANAGEMENT USING DATA SCIENCE AND MACHINE LEARNING INVENTORY OPTIMIZATION
Authors 1.P. Anjaneya, 2.Junnutala Tarun Achari ,3.Adiboyina Sujana, 4.Kamsetty Harsha Vardhan
Affiliation 1. Asst.Professor ,Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA. 2,3,4,5.Student ,Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19641568
Abstract This project presents an interactive Streamlit-based web application that enables advanced demand forecasting for retail supply chain management using a pre trained XGBoost machine learning model. The main goal is to optimize inventory by accurately predicting daily item-level sales based on features like store ID, item ID, and date attributes (year, month, day, day of week). Users can upload a CSV file containing test data, and the application performs real-time sales predictions using the trained model. The app provides rich visual analytics including monthly sales trends, store and item-specific breakdowns, and actual vs. predicted comparison charts. It also highlights the month with the highest sales and calculates metrics such as Root Mean Square Error (RMSE) when actual sales data is available. Users can download the forecast results as a CSV file for further analysis. The application uses data preprocessing, Seaborn and Matplotlib for plotting, and joblib for loading the XGBoost model efficiently. Through an intuitive interface, it empowers retailers to make data-driven inventory decisions, reduce overstock or understock issues, and enhance operational efficiency
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Title AI Based Brain Stroke Prediction Using MRI Images and Vision Transformer Models
Authors 1.Mr.L BUJII BABU,2.K.SUSHMA,3. P.HARSHITHA,4. M.AKHILESH4,5. K.SAROJA
Affiliation 1. Asst.Professor ,Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
2,3,4,5. Student,Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19641761
Abstract Stroke is a severe neurological condition that requires early and accurate diagnosis to improve patient outcomes. This paper presents an AI-based brain stroke classification system using Vision Transformers (ViT-B/16) to classify brain MRI scans into Normal and Haemorrhagic categories. The dataset is preprocessed and augmented using techniques including random horizontal flip, rotation, and colour jitter to enhance model generalisation. We compare three architectures — Convolutional Neural Networks (CNN), ResNet-18, and Vision Transformer (ViT) — finding that ViT significantly outperforms both baselines. The ViT-B/16 model achieves 97.59% accuracy, surpassing VGG-16 (90%), ResNet-50 (87%), InceptionV3 (82%), and VGG-19 (81%), with precision, recall, and F1-scores all exceeding 0.96.
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Title A DATA DRIVEN APPROACH TO RANSOMWARE DETECTION WITH MACHINE LEARNING
Authors 1.J. PRIYANKA , 2.K. PAPARAO, 3.K. UJJAYINI,4. P. DIVYA,5. N.N S MANIKANTA
Affiliation 1. Asst.Professor , Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
2,3,4,5. Student , Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19641885
Abstract Ransomware attacks represent a growing cybersecurity threat, affecting individuals and organizations by compromising data integrity, causing financial losses, and damaging reputations [1]. Early and accurate detection of ransomware is essential to mitigate these risks. This study presents a data-driven machine learning approach for ransomware detection using a dataset of 138,047 executable file records.
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Title AI-POWERED BOOK RECOMMENDATION FRAMEWORK USING NATURAL LANGUAGE PROCESSING
Authors 1.K.TV SUBBA RAO,2.K. ROHITH SATYA,3. P. JYOTHSNA,4. K. LAKSHMI JYOTHI,5 P. SRI SAI MOHAN VARMA
Affiliation 1.Asst.Professor ,Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
2,3,4,5. Asst.Professor ,Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19642073
Abstract The exponential growth of digital reading platforms has resulted in the availability of millions of books online, posing a significant challenge for users in identifying content aligned with their interests. Conventional recommendation systems, primarily based on collaborative filtering and user ratings, often encounter critical limitations such as the cold-start problem and data sparsity, which adversely affect their performance and scalability.To address these challenges, this paper presents an AI-powered content-based book recommendation framework that leverages Natural Language Processing (NLP) techniques to generate accurate and meaningful recommendations using only textual descriptions of books. The proposed system employs Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for measuring semantic relationships between book descriptions.The framework incorporates comprehensive Exploratory Data Analysis (EDA) and robust text preprocessing techniques, including tokenization, stop-word removal, and normalization, to enhance data quality and representation. The processed textual data is transformed into high-dimensional vector space representations, enabling efficient similarity computation and retrieval of relevant books.Furthermore, the system integrates semantic topic modeling to improve diversity and mitigate over-specialization in recommendations. An interactive Streamlit-based web application is developed to provide real-time user interaction and recommendation
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Title Customer Churn Prediction And Retention Strategy Optimization For Subscription-Based Services Using Behavioural Data Analytics And Machine Learning Models
Authors 1.Mr. K S R PRASAD,2.KOLLI LEELA SAI SRAVYA,3.KONDURI NIKHILESH KRISHNA,4.KATTA RAMA SWATHI
Affiliation 1. Assistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4.Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19689885
Abstract This project presents a user-friendly Customer Churn Prediction and Retention Dashboard developed using Streamlit. The tool enables business users and analysts to upload customer datasets in CSV format, preprocess the data, and apply multiple machine learning models to predict customer churn. Supported models include Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, XGBoost, LightGBM, and CatBoost. The dataset is automatically encoded and scaled for compatibility with the models. Once trained, the selected model predicts customer churn and evaluates performance using metrics such as accuracy score, classification report, and confusion matrix.
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Title Personalized Itinerary Planning & Dynamic Pricing
Authors 1.CH.VENKATA REDDY, R.2.CHIRU VIGNESH KUMAR, 3.U. VATSALA ANJANA MAHESWARI, V.4.DEEVEN RAJU,5.K.JAI SAI VINAY
Affiliation 1. Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19689914
Abstract This project presents a web-based AI-powered travel itinerary planner, built as a Streamlit application. It demonstrates an end-to-end implementation of a generative AI workflow, primarily utilizing the LangChain orchestration framework and a Large Language Model (LLM) accessed through the Groq API. The application's architecture is structured to facilitate a clear user interaction flow. The Streamlit front-end provides an intuitive graphical user interface (GUI) where users can input travel parameters such as destination, trip duration, interests, and preferred travel style. This user input is then used to dynamically populate a ChatPromptTemplate from the LangChain library.
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Title Disaster Response AI: Resilience Net Forecaster – Predictive Hazard Mapping & Resource Allocation
Authors 1.K.T.V. Subba Rao,2. V. Devi Durga Bhavani,3. S. Jagadeesh,4. T. Gopala Ramanjaneyulu,5.V. Alekhya
Affiliation 1. Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19689961
Abstract Disaster management systems worldwide have historically operated reactively, mobilizing resources only after catastrophic events have already unfolded. ResilienceNet Forecaster addresses this critical gap by presenting an AI-driven Disaster Risk Management (DRM) platform that integrates Machine Learning (ML), Explainable Artificial Intelligence (XAI), geospatial hazard mapping, and intelligent resource allocation into a unified, deployable system.
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Title SPORTS ANALYTICS AI: PLAYER PERFORMANCE PREDICTION AND STRATEGY OPTIMIZATION
Authors 1. M. BHARGAVI,2. U. VIDHYA SAGAR,3. S. JAYASRI,4.T.R.V.S. SATISH,5. R. TULASI RAM
Affiliation 1. Asistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19689987
Abstract Athlon Predict Pro is an advanced sports analytics platform designed for cricket, leveraging machine learning techniques to predict player performance and optimize team strategies. The system processes historical One-Day International (ODI) match data extracted from YAML files, generating structured datasets for batsmen and bowlers that include both aggregate statistics (e.g., batting average, strike rate, centuries) and match-level contextual features (e.g., runs scored, wickets taken, venue, and opposition).The platform employs state-of-the-art machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machines (SVM) to forecast player performance metrics, including batsman runs and bowler wickets.
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Title Smart Coding Interview Preparation Portal with Live Code Execution
Authors 1.B NANDANA KUMAR,2. T. LAKSHMI PRASANNA,3. M. MADHU BABU,4. Y. JHANSI,5. Y. JISHNU PHANI VARMA
Affiliation 1. Asistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19690020
Abstract Our project, Smart Coding Interview Preparation Portal with Live Code Execution, is developed to address the limitations of traditional interview preparation platforms. Existing systems mainly provide static coding questions without integrated real-time code execution, forcing users to switch to external IDEs for compilation and testing, which reduces practice efficiency.
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Title AI-Based Osteoporosis Detection Using Clinical Bone Densitometry Data and Deep Learning Techniques
Authors 1.V.NAVYA DEVI,2. V. YASWANTH,3.T. DEVIKA SAROJINI, 4.P. PRABHU KALYAN,5.S. PRASANTH KUMAR
Affiliation 1. Asistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4,5 . Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19690156
Abstract Osteoporosis is a progressive bone disease characterized by decreased bone mineral density (BMD) and an increased risk of fractures, especially among elderly individuals and postmenopausal women. Early detection is crucial to prevent severe complications and improve quality of life. In this project, we propose an AI-based Osteoporosis Detection System that analyzes both clinical patient data and Dual-Energy X-ray Absorptiometry (DXA) images to accurately classify bone health status into three categories: Normal, Osteopenia, and Osteoporosis. The methodology incorporates multiple machine learning models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, LightGBM, and a Multilayer Perceptron (MLP), trained using clinical attributes such as BMD, T-score, age group, and height. Additionally, a Convolutional Neural Network (CNN) is used to analyze DXA scan images for supportive prediction.
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Title An Ai-Powered Predictive Health Nexus for Proactive Disease Identification and Personalized Patient Outcome Forecasting
Authors 1.A Ramamurthy1,2.T.S.S.K. GAYATHRI, 3.K. KRUPA MAYUDU,4 S. SHARUN KUMAR
Affiliation 1.Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4. Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19690215
Abstract Heart disease is a leading cause of mortality worldwide, making early detection essential. This project presents a Smart Heart Disease Prediction System, an AI-powered web application that predicts the likelihood of heart disease using machine learning based o n clinical parameters such as age, sex, chest pain type, blood pressure, cholesterol, fasting blood sugar, ECG results, maximum heart rate, exercise-induced angina, old peak, and ST slope.
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Title Smart Crop Recommendation and Yield Prediction System Using Machine Learning
Authors 1.G. V. S. Sriram, 2.Y. M. Mahalakshmi Kondalamma, 3.S. Mounika Divya Sri,4. T. Srinivasa Rao,5. V. Chaitanya Ganesh
Affiliation 1.Assistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4. Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19695338
Abstract Our project, Smart Crop Recommendation and Yield Prediction System Using Machine Learning, is developed to address the key limitations of traditional agricultural decision-making practices. Existing approaches primarily rely on farmer experience, regional advisories, and static guidelines that lack personalization, accuracy, and real-time adaptability. Farmers struggle to determine the most suitable crops for their specific soil and environmental conditions, leading to poor crop selection, resource wastage, and reduced productivity. To overcome these challenges, the proposed system leverages advanced Machine Learning algorithms — specifically Light Gradient Boosting Machine (LightGBM) and Random Forest — to analyze soil parameters (Nitrogen, Phosphorus, Potassium, and pH) and environmental factors (temperature, humidity, and rainfall), providing accurate and data-driven crop recommendations. The system performs multi-class classification on structured agricultural datasets and incorporates feature importance analysis to improve transparency.
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Title Identification of Psychological Stress from Speech Signal Using Deep Learning Algorithm
Authors 1. B. Supraja, 2.T. Krupa Kiran,3. S. Naga Lakshmi Bhavani, 4.T. Durga Devi,5. R. Prasanth
Affiliation 1.Assistant Professor, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.

2,3,4. Student, Department of Computer Science & Engineering, DNR College of Engineering & Technology, Balusumudi, Bhimavaram -534 202, W.G. Dist , Andhra Pradesh, INDIA.
DOI 10.5281/zenodo.19695480
Abstract Our project, Smart Crop Recommendation and Yield Prediction System Using Machine Learning, is developed to address the key limitations of traditional agricultural decision-making practices. Existing approaches primarily rely on farmer experience, regional advisories, and static guidelines that lack personalization, accuracy, and real-time adaptability. Farmers struggle to determine the most suitable crops for their specific soil and environmental conditions, leading to poor crop selection, resource wastage, and reduced productivity. To overcome these challenges, the proposed system leverages advanced Machine Learning algorithms — specifically Light Gradient Boosting Machine (LightGBM) and Random Forest — to analyze soil parameters (Nitrogen, Phosphorus, Potassium, and pH) and environmental factors (temperature, humidity, and rainfall), providing accurate and data-driven crop recommendations. The system performs multi-class classification on structured agricultural datasets and incorporates feature importance analysis to improve transparency.
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Title RACH procedure in 4G technology
Authors 1.V.Sreedhar babu, 2.G. Sanjeevarayudu, 3.B. Venkateswaramma
Affiliation 1.Teach lead, mail id: sridhar.vankam@gmail.com.
2.Associate professor in ECE Dept.Gouthami institute of technology and management for women, Peddasettipalle, Proddatur,Mail id: sanjeev.rayudu9@gmail.com
3.Associate professor in ECE Dept.Gouthami institute of technology and management for women, peddasettypalle, Proddatur , Mail id: venkateswamma8@gmaol.com
DOI 10.5281/zenodo.19733159
Abstract Our project, Smart Crop Recommendation and Yield Prediction System Using Machine Learning, is developed to address the key limitations of traditional agricultural decision-making practices. Existing approaches primarily rely on farmer experience, regional advisories, and static guidelines that lack personalization, accuracy, and real-time adaptability. Farmers struggle to determine the most suitable crops for their specific soil and environmental conditions, leading to poor crop selection, resource wastage, and reduced productivity. To overcome these challenges, the proposed system leverages advanced Machine Learning algorithms — specifically Light Gradient Boosting Machine (LightGBM) and Random Forest — to analyze soil parameters (Nitrogen, Phosphorus, Potassium, and pH) and environmental factors (temperature, humidity, and rainfall), providing accurate and data-driven crop recommendations. The system performs multi-class classification on structured agricultural datasets and incorporates feature importance analysis to improve transparency.
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Title Technology Development in Online Grocery Shopping: From Shopping Services to Virtual Reality
Authors 1. Kancherla Venkateswara Reddy, 2. Kandula Semanth Siva Sri Krishna, 3. Kankipati Uday Kumar, 4. Kathothi Navya, 5. Kalipindi Kanaka Teja
Affiliation 1,2,3,4,5. B. Tech CSE Student, Department of CSE, Sir C R Reddy College of Engineering, Eluru, A.P., India.
DOI 10.64264/ijisea/0723
Abstract rapid advancement of digital technologies has significantly transformed traditional grocery shopping methods into modern, efficient online platforms. This paper presents the design and development of a web-based Online Grocery Shopping System that enables customers to conveniently browse grocery products, manage shopping carts, and place orders through an intuitive interface. The proposed system is developed using the Python Django framework for backend processing, HTML and CSS for the user interface, and SQLite for database management. The system incorporates essential modules including user authentication, product management, shopping cart functionality, wish list management, and order processing, all accessible through a centralized administrative panel.
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Title Real-Time Network Security Monitoring and Auto-Mitigating Firewall System Using Python and IPTables
Authors 1. K. Karthik, 2. K. Lokesh, 3. K. Naidu, 4. K. Geethasri, 5. K. Krishna
Affiliation 1,2,3,4,5. B. Tech CSE Students, Dept. of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0778
Abstract As the internet-based services and online communication grow at an alarming pace, computer networks are becoming more susceptible to cybercrime, including Distributed Denial-of-Service (DDoS) attacks, unauthorized access attempts, and traffic surges. The conventional network security systems are majorly based on the use of fixed firewall settings and manual monitoring systems that tend to lead to a sluggish reaction to the changing threats. Consequently, companies need automated security systems that can identify and respond to malicious actions on a real-time basis.
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Title Stock Market Price Prediction and Trend Analysis Dashboard Using Machine Learning and Power BI
Authors 1. M. Kusuma, 2. M. Subhash Chandra, 3. L. Aditya Soma Sekhar, 4. K. Chiranjeevi, 5. L. Gnanaswaraj
Affiliation 1,2,3,4,5 B. Tech CSE Students, Dept. of CSE, Sir C R Reddy College of Engineering, Eluru, India.
DOI 10.64264/ijisea/0725
Abstract Our project, Smart Crop Recommendation and Yield Prediction System Using Machine Learning, is developed to address the key limitations of traditional agricultural decision-making practices. Existing approaches primarily rely on farmer experience, regional advisories, and static guidelines that lack personalization, accuracy, and real-time adaptability. Farmers struggle to determine the most suitable crops for their specific soil and environmental conditions, leading to poor crop selection, resource wastage, and reduced productivity. To overcome these challenges, the proposed system leverages advanced Machine Learning algorithms — specifically Light Gradient Boosting Machine (LightGBM) and Random Forest — to analyze soil parameters (Nitrogen, Phosphorus, Potassium, and pH) and environmental factors (temperature, humidity, and rainfall), providing accurate and data-driven crop recommendations. The system performs multi-class classification on structured agricultural datasets and incorporates feature importance analysis to improve transparency.
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Title AI-Based Augmented Reality Smart Shopping Assistant
Authors 1. M. Sai Surya, 2. M. Prasanna Kumari, 3. M. Lakshmi Meghana, 4. M. Sai Karthik, 5. M. Raju
Affiliation 1,2,3,4,5 B. Tech CSE Students, Dept. of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI DOI: 10.64264/ijisea/0727
Abstract The fast-changing nature of e-commerce sites has greatly changed the retail sector as it allows people to shop easily at a distance. Nevertheless, a common drawback of online clothing shopping is the inefficient possibility to touch and wear clothes prior to the purchase decision. This usually creates confusion, dissatisfaction and high product returns. In order to overcome this problem, this study suggests an Artificial Intelligence (AI)-powered Augmented Reality (AR) Smart Shopping Assistant that offers a real-time virtual try-on experience with computer vision and web-based technologies.
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Title Credit Risk Assessment and Decision Support System with Comprehensive Visualization Using Python and Power BI
Authors 1. M. Adarsh Kumar, 2. M. Niharika, 3. M. Bhavitha, 4. M. Venkata Kavya Sri, 5. M. Veda Sai Prakash Raja, 6. V. Pranav
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6 Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0728
Abstract Credit risk assessment is really important in finance because it affects how banks decide on loans and manage risks to stay stable. With all the data piling up and people borrowing in more complicated ways, the old methods just do not cut it anymore for getting quick, accurate info. This project is about building a system that uses machine learning and some visualization tools to fix those issues. It pulls together different parts to predict if someone might default on a loan or not. I used Python for most of it, like Pandas to clean up the data and handle missing stuff, then Scikit-learn to build the models.
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Title Online Court Booking and Management System for Legal Firms
Authors 1. M. Pavani Surekha, 2. M. Ganesh, 3. M. Chaitanya Sai Phanidhar, 4. M. Mindhi Vara Shyam Sai, 5. M. Jabivullah, 6. Dr. K. Sreenu
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6 Associate Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0729
Abstract Our project, Smart Crop Recommendation and Yield Prediction System Using Machine Learning, is developed to address the key limitations of traditional agricultural decision-making practices. Existing approaches primarily rely on farmer experience, regional advisories, and static guidelines that lack personalization, accuracy, and real-time adaptability. Farmers struggle to determine the most suitable crops for their specific soil and environmental conditions, leading to poor crop selection, resource wastage, and reduced productivity. To overcome these challenges, the proposed system leverages advanced Machine Learning algorithms — specifically Light Gradient Boosting Machine (LightGBM) and Random Forest — to analyze soil parameters (Nitrogen, Phosphorus, Potassium, and pH) and environmental factors (temperature, humidity, and rainfall), providing accurate and data-driven crop recommendations. The system performs multi-class classification on structured agricultural datasets and incorporates feature importance analysis to improve transparency.
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Title Intrusion Detection and Prevention using Snort and Python for Detecting DDOS attack and DNS Flood
Authors 1. M. Haritha, 2. M. Nithin Venkat Sai, 3. M. Sree Rishik, 4. M. Venkata Sriram, 5. M. S. Siva Srinivas, 6. S. V. V. S. Kumar
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6 Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0730
Abstract With the rapid expansion of Internet-based applications and services, network infrastructures have become increasingly vulnerable to Distributed Denial of Service (DDoS) attacks, particularly DNS flood attacks, which exploit the DNS protocol to exhaust server and network resources. Traditional intrusion detection mechanisms often rely on static signatures or centralized architectures, making them less effective against evolving and high-rate attack patterns.
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Title Real-Time Anomaly Detection in IoT Sensor Data via Deep Learning and Deployment with Python Frameworks
Authors 1. M. Yogeswar, 2. M. Hemanth, 3. M. Srikanth, 4. M. Harshitha, 5. N. Venkata Maruthi Sai Ram Chandu, 6. K. Budda Vara Prasad
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6 Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0731
Abstract The rapid proliferation of Internet of Things (IoT) devices has generated massive volumes of time-series sensor data that require continuous, intelligent monitoring. This project presents a comprehensive real-time anomaly detection system for IoT sensor data leveraging Long Short-Term Memory (LSTM) Autoencoder neural networks. The system processes multivariate sensor streams — including temperature, humidity, air quality, light intensity, and loudness — and identifies deviations from learned normal patterns using reconstruction error thresholding.
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Title A Workforce Harmony Predictor System for Proactive Employee Churn Identification and Strategic Retention Intervention
Authors 1. N. Bhaskar, 2. N. Pavan Sai, 3. N. Yagna Harshitha, 4. N. D. S. N. V. G. Varalakshmi, 5. N. Anusha
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0732
Abstract Employee attrition has become a serious concern for many organizations, as it directly affects productivity, costs, and overall stability. Most traditional HR approaches depend on methods like exit interviews and surveys, which only provide insights after employees decide to leave. Because of this, organizations often struggle to take preventive actions.
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Title Automated Fraud Detection in Transactions Data Leveraging Machine Learning Pipelines
Authors 1. P. Shahanaz, 2. P. Lakshmi Prasad, 3. P. Rana Prathap, 4. P. Rukmini, 5. P. Abhinav Sai, 6. Dr. K. Sreenu
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6 Associate Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0733
Abstract This paper presents an end-to-end machine learning pipeline for detecting fraudulent financial transactions in large-scale, highly imbalanced datasets. The study utilizes a synthetic dataset comprising over 6.3 million transactions, where fraudulent instances account for only 0.13% of the total data.Exploratory Data Analysis (EDA) is conducted to identify underlying fraud patterns, revealing that fraudulent activities are predominantly associated with CASH_OUT and TRANSFER transaction types, high transaction amounts, and specific temporal behavior.
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Title AI POWERED INTERVIEW PREPARATION WITH CODE EXECUTION FEATURE
Authors 1. T. Rakshita, 2. T. Anil Kumar, 3. T. Sunanda, 4. T. Adithya Vardhan, 5. T. Bhargavi Prasanna, 6. Ch. Srinivas
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0734
Abstract The growing need of effective and individualized interview preparation tools has led to the emergence of AI-based learning systems. This project introduces AI-run Interview Preparation Portal that is combined with real-time code activation option with the help of the JDoodle API.
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Title Advanced Demand Forecasting Models for Retail Supply Chain Management Using Data Science and Machine Learning for Inventory Optimization
Authors 1. T. Yaswanth, 2. T. Navitha, 3. U. Jnana Sai Samkeerthi, 4. V. Sai Sowmika, 5. V. Dileep Kumar, 6. K. Ramya Krishna
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0735
Abstract The retail industry faces increasing challenges in matching supply with demand due to evolving consumer behaviors, market volatility, and supply chain disruptions. While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions.
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Title INTERACTIVE SALES PERFORMANCE DASHBOARD WITH MACHINE LEARNING FORECASTING USING POWER BI AND PYTHON
Authors 1. Y. Harshit, 2. Y. Thanvisha, 3. Y. V. Manikanta, 4. A. Gangothr, 5. S. Nateesha, 6. M. Krishna
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0736
Abstract The Sales Performance Dashboard is an advanced business intelligence solution designed to provide comprehensive analytics and visualization for automobile sales data. In today's competitive automotive market, organizations require real-time insights into their sales performance across multiple dimensions including dealer regions, vehicle body styles, temporal trends, and individual model performance.
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Title Real Time Social Media Sentiment Analysis with Deep Learning
Authors 1. S. Meghana, 2. Sd. Tabassum, 3. T. Mythri, 4. T. Yashoda, 5. U. Ramu, 6. E. S. Ekambareesh
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0737
Abstract This project presents a scalable solution for multi-class sentiment analysis of Twitter data. It classifies tweets into five categories: Extremely Negative, Negative, Neutral, Positive, and Extremely Positive. The system is built using FastAPI for backend API development. A deep learning model is developed using TensorFlow for accurate sentiment prediction. The model is trained on a large dataset of tweets. NLP techniques like text cleaning, normalization, and tokenization are applied. Sequence padding is used to make inputs suitable for the neural network. A softmax layer enables multi-class classification. The trained model is integrated into a FastAPI service for real-time predictions. This project demonstrates a complete workflow from data processing to deployment.
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Title A Scalable Web-Based Attendance Management System with Integrated Analytics for Academic Decision Support
Authors 1. V. Deepak Jaya Ram Yadav, 2. V. Madhu Sudhan, 3. V. Harsha Vardhan, 4. Y. Naveen, 5. B. Venkata Satya Umesh
Affiliation 1,2,3,4,5 B. Tech CSE (AIDS) Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0738
Abstract In educational institutions, effective attendance management is essential for tracking student involvement and assisting with academic decision-making. Conventional manual attendance tracking techniques are frequently laborious, prone to mistakes, and unable to yield insightful data. The design and implementation of a scalable web-based attendance management system with analytical capabilities integrated to improve accuracy and efficiency are presented in this work. The suggested system is built using a multi-tier design, in which a relational database guarantees structured data storage, the backend manages business logic using a lightweight web framework, and the frontend interface facilitates user interaction.
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Title Design and Development of a Secure and Scalable E-Commerce Web Application
Authors 1. P. Sampath Rama Krishna, 2. M. Purna Nagendra Reddy, 3. T. Hemanth, 4. Y. Lakshmi Narasimha, 5. M. Vamsi Kumar, 6. P. Veera Venkata Anurosh, 7. K. Budda Vara Prasad
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0739
Abstract The rapid growth of digital commerce has created a demand for secure, scalable, and efficient e-commerce platforms that can support diverse user needs while maintaining high performance and data integrity. This project presents the design and development of a full-stack e-commerce web application named Nexacart, aimed at delivering a robust, user-friendly, and secure platform for online transactions.
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Title Explainable Retention Intelligence Framework for Telecom Customer Attrition Prediction Using Ensemble Learning and Interactive Business Analytics
Authors 1. J. Rakesh, 2. K. Akash, 3. K. Vasanthi, 4. K. Naga Anjali Devi, 5. K. Sai Kiran, 6. S. Mohan Babu Chowdary
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0740
Abstract Customer attrition prediction is a critical challenge in the telecommunications industry due to its direct impact on customer retention and organizational revenue. Traditional churn analysis approaches primarily rely on descriptive statistics and reactive strategies, which are insufficient for identifying high-risk customers at an early stage. This paper proposes an explainable retention intelligence framework for telecom customer attrition prediction using ensemble learning techniques and interactive business intelligence visualization.
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Title End-To-End Predictive Analytics Pipeline For Customer Behaviour Using Python And Neural Networks
Authors 1. L. Asritha, 2. M. Poojitha, 3. M. Srinivasa Rao, 4. N. Satya Sai, 5. N. Suresh, 6. S. Lakshmi Vijetha
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0741
Abstract The aim of this project, titled “End-to-End Predictive Analytics Pipeline for Customer Behaviour Using Python and Neural Networks”, is to develop a system that predicts customer behaviour and satisfaction using advanced machine learning techniques. In the existing system, organizations rely on traditional statistical methods and manual analysis to evaluate customer data. However, these approaches are time-consuming and fail to effectively capture complex patterns in large and dynamic datasets.
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Title Intelligent Customer Segmentation And Lifetime Value Prediction Using Rfm Analysis And Machine Learning with KPI Dashboard Visualization
Authors 1. S. Lokesh, 2. S. Rohith, 3. T. Deepika, 4. T. Poorna Venkata Sri Sai, 5. U. Helda Jessie, 6. V. Valli Gayathri
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
6. Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0742
Abstract In today's competitive business environment, understanding customer behavior and predicting customer value are essential for effective marketing and business growth. This project focuses on Customer Segmentation and Customer Lifetime Value (CLTV) Prediction using data modelling techniques. The system analyzes customer transactional data using Recency, Frequency, and Monetary (RFM) analysis to evaluate customer purchasing behavior.
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Title Personalized E-Commerce Recommendation Engine Powered And Data Science To Enhance Customer Engagement And Sales
Authors 1. J. Sree Vani, 2. J. Sivani, 3. D. Joe Shalem Victor, 4. K. Carmel Keerthana, 5. K. Joy Joshua
Affiliation 1,2,3,4,5 B. Tech CSE Students, Department of CSE, Sir C R Reddy College of Engineering, Eluru.
DOI 10.64264/ijisea/0743
Abstract In the contemporary digital economy, the spread of product options has posed a big problem upon consumers, which has been referred to as information overload. This study introduces a powerful Personalized E-Commerce Recommendation Engine that aims at reducing decision fatigue and improving user experience. The system leverages a hybrid mathematical method, which is mostly based on Singular Value Decomposition (SVD) in a collaborative filtering system.
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Title CONTROL OF SQUIRREL CAGE INDUCTION MOTOR USING CONVENTIONAL CONTROLLERS AND FUZZY LOGIC
Authors 1.Alladi Mydhili, 2.Gentem Charan, 3.Konda Purna Kumar, 4.Duvva Gopal,5.N.Chaitanya
Affiliation 1,2,3,4 .UG Students ,Department of Electrical & Electronics Engineering, RVR&JC College Of Engineering,Chowdavaram, Guntur, Andhra Pradesh.
5.Associate Professor,Department of Electrical & Electronics Engineering , RVR&JC College Of Engineering,Chowdavaram, Guntur, Andhra Pradesh
DOI 10.64264/ijisea/0744
Abstract Induction motors, particularly squirrel cage induction motors, are widely used in industrial applications due to their robustness, low cost, simple construction, and minimal maintenance requirements. However, achieving precise and efficient speed control of these motors remains a significant challenge because of their nonlinear characteristics, parameter variations, and sensitivity to external disturbances such as load changes. Conventional control techniques like Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers are commonly employed, but their performance is limited due to fixed gain parameters and poor adaptability under dynamic conditions. This work presents an advanced control strategy for improving the speed control performance of a three-phase squirrel cage induction motor using a hybrid Fuzzy-PID controller integrated with vector (field-oriented) control.
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