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Paper ID IJIFR/V13/E8/051 Page No.: 1288-1295

Subject Area Computer Science

Authors Gajula Sai Rohith
V. Vijayalakshmi

Abstract Contemporary retail operations are confronted with escalating complexity in demand forecasting and inventory management, driven by non-linear demand variability, promotional amplification, seasonal fluctuations, and the competitive imperatives of omnichannel commerce. Conventional forecasting paradigms—predicated upon moving averages, exponential smoothing, and linear extrapolation—demonstrably fail to capture the multivariate, non-linear interactions governing retail demand dynamics. This paper introduces SmartRetail AI, a comprehensive, end-to-end retail intelligence platform that integrates ensemble machine learning with classical inventory optimisation theory within a unified data architecture. The system is constructed upon a structured synthetic dataset encompassing 4,250 daily transaction records across five representative Fast-Moving Consumer Goods and e-commerce product categories, spanning 850 operational days. The proposed forecasting engine deploys product-level Random Forest Regressor models—each trained independently to capture category-specific demand dynamics—to generate thirty-day forward demand forecasts. Engineered temporal features including day-of-week indicators, monthly seasonality encodings, weekend binary flags, and promotional activity markers constitute the input feature space. The inventory optimisation module applies the classical safety stock formulation at a 95% service level target (Z = 1.65), computing product-specific reorder points as a function of historical demand variability and a three-day supply lead time. Analytical outputs are persisted in a normalised MySQL relational schema comprising three tables—historical sales, demand forecasts, and inventory metrics—and rendered through an executive intelligence dashboard. Comparative evaluation against moving average baselines demonstrates Mean Absolute Error reductions of 23.4% to 47.8% across product categories, with Root Mean Square Error improvements ranging from 19.7% to 44.1%. The platform achieves a Mean Absolute Percentage Error of 12.7% for high-volume staples and 24.3% for low-velocity electronics, establishing its operational viability across diverse retail demand profiles. These results validate the proposed framework as a practically deployable, analytically rigorous alternative to legacy forecasting methodologies.

Keywords Demand Forecasting; Random Forest Regressor; Inventory Optimisation; Safety Stock; Retail Intelligence; Ensemble Machine Learning; Supply Chain Management

Paper ID IJIFR/V13/E8/050 Page No.: 1279-1287

Subject Area Computer Engineering

Authors Gurramkoda Charan Kumar
S. Usharani
V.Vijayalakshmi

Abstract Contemporary e-commerce platforms generate voluminous transactional and behavioral data streams that, absent a structured analytical framework, remain commercially inert. ShopMind 360 is proposed as a comprehensive, end-to-end behavioral analytics and personalization engine that synthesizes four complementary machine learning and data mining methodologies within a single, automated pipeline. The system operationalizes Recency-Frequency-Monetary (RFM) analysis for multidimensional customer value quantification, K-Means clustering for behavioral segmentation into four actionable customer archetypes (Champions, Loyal Customers, At-Risk, and Hibernating), Random Forest ensemble classification for continuous purchase-probability scoring, and the Apriori algorithm for market-basket association rule mining to generate confidence-ranked product recommendations. The analytical pipeline ingests a synthetic yet realistically parameterized dataset comprising 500 customer profiles, 50 product records, 2,000 transactional orders, and 5,000 browsing interaction logs, structured within a multi-sheet Excel workbook and subsequently persisted to a normalized MySQL relational database. All computed analytical results are surfaced through an interactive Power BI dashboard, providing business stakeholders with filterable, real-time-refreshable visualizations of customer segments, revenue contributions, purchase probability distributions, and product affinity rules. Experimental evaluation demonstrates that the four-segment K-Means model achieves stable cluster centroids with well-separated RFM profiles, the Random Forest classifier attains high discriminative accuracy in identifying high-value customer segments, and Apriori mining yields statistically significant association rules with lift values substantially exceeding unity. The system architecture adheres to modular design principles, enabling independent maintenance and extensibility of each analytical component without pipeline restructuring. ShopMind 360 establishes a replicable, open-source blueprint for data-driven customer engagement in small-to-medium-scale e-commerce operations.

Keywords Recency-Frequency-Monetary Analysis; K-Means Clustering; Random Forest Classification; Apriori Association Rule Mining; Customer Behavioral Segmentation

Paper ID IJIFR/V13/E8/049 Page No.: 1268-1278

Subject Area Computer Engineering

Authors Avadutha Prathibha
S. Usharani

Abstract The increasing complexity of healthcare systems and the global shortage of medical professionals have amplified the need for intelligent clinical decision support systems capable of assisting in early disease diagnosis. This paper presents an AI-driven medical diagnosis support system that leverages machine learning techniques to predict diseases based on patient-reported symptoms. The proposed system utilizes a Random Forest Classifier trained on a high-dimensional dataset comprising over 130 symptoms mapped to more than 40 disease categories. The model employs multi-hot encoding for symptom representation and generates probabilistic predictions with associated confidence scores. The system is implemented as a web-based application using the Django framework, enabling role-based interaction for both patients and doctors. Patients can input symptoms through an intuitive interface, while doctors gain access to aggregated diagnostic insights and patient history. The machine learning pipeline integrates feature importance extraction, allowing visualization of symptom influence using Chart.js, thereby enhancing interpretability. Experimental results demonstrate high classification accuracy and robust performance across diverse symptom combinations. The system significantly reduces diagnostic latency and provides preliminary clinical insights, particularly in resource-constrained environments. The integration of AI with web-based healthcare systems highlights the potential for scalable, accessible, and efficient diagnostic assistance tools.

Keywords Artificial Intelligence; Clinical Decision Support; Random Forest; Disease Prediction; Machine Learning

Paper ID IJIFR/V13/E8/048 Page No.: 1257-1267

Subject Area Computer Science

Authors C. Nawaz Basha
S.Usharani

Abstract The insurance industry confronts two analytically critical and financially consequential challenges: accurate prediction of claim settlement amounts and timely detection of fraudulent claims. Conventional approaches — rule-based heuristics, logistic regression scorecards, and manual adjuster assessments — are demonstrably inadequate for capturing the nonlinear, high-dimensional interactions that characterise modern insurance claim data. This paper presents ClaimSmart AI, a comprehensive, modular, end-to-end machine learning pipeline that addresses both challenges within a unified analytical framework. The system operates on a synthetically generated dataset of 15,000 insurance claim records encompassing 19 attributes spanning policyholder demographics, policy characteristics, vehicle parameters, claim specifics, and behavioural indicators. A dual-model architecture employs a Random Forest Regressor (150 estimators) for claim amount prediction and a Random Forest Classifier (150 estimators, balanced class weights) for binary fraud risk detection, both trained on a stratified 80/20 holdout split with StandardScaler feature normalisation and LabelEncoder categorical transformation. The regression model achieves a Mean Absolute Error below INR 15,000 and an R-squared coefficient of determination exceeding 0.70, while the classification model delivers accuracy above 0.80, fraud-class recall exceeding 0.74, and F1-Score above 0.76, surpassing logistic regression and rule-based baselines on equivalent evaluation protocols. Prediction outputs are enriched with four derived business metrics — predicted claim amount, claim variance, fraud risk probability, and a three-tier fraud risk category — and persisted to a MySQL relational database for direct consumption by Power BI and enterprise analytics platforms. Eight publication-quality visualisation charts provide comprehensive analytical coverage from fraud distribution and regional heatmaps to actual-versus-predicted scatter analysis. A mysqldump-format SQL export module ensures enterprise portability and regulatory archival compliance. The complete pipeline executes through a single orchestration script, establishing ClaimSmart AI as both a rigorous academic contribution and a practical template for production insurance analytics deployment.

Keywords Random Forest; Insurance Fraud Detection; Claim Amount Regression; Imbalanced Classification; Business Intelligence Integration

Paper ID IJIFR/V13/E8/047 Page No.: 1248-1256

Subject Area Computer Engineering

Authors G Mallikarjuna Reddy
V.Vijayalakshmi
S.Usharani

Abstract Online voting systems represent one of the most demanding intersections of cybersecurity, civic participation, and artificial intelligence, requiring simultaneous guarantees of voter authentication, vote uniqueness, ballot integrity, fraud detection, and transparent auditability within a framework accessible to non-technical citizens. Existing e-voting platforms either rely on proprietary cryptographic protocols with limited AI-driven fraud detection, or prioritise accessibility while sacrificing the robust integrity mechanisms demanded by high-stakes elections. This paper presents an AI-Based Online Voting System that addresses these co-requirements through four integrated AI-driven components embedded within a production-quality Django 5.x web application. The system architecture implements: (1) an Isolation Forest-based anomaly detection engine that computes a real-time risk score for every vote submission from three behavioural features — hour of submission, minute of submission, and IP address vote frequency — flagging suspicious events within the same HTTP request cycle without perceptible latency; (2) a TextBlob NLP sentiment analysis module that computes a manifesto polarity score (range -10 to +10) for each candidate biography using lazy computation triggered on first election page view; (3) a TF-IDF and cosine similarity AI Candidate Matcher that ranks candidates by alignment with voter-submitted preference text using Scikit-learn's TfidfVectorizer with English stop-word removal; and (4) a SHA-256 cryptographic vote hashing mechanism that generates a tamper-evident fingerprint for each Vote record at save time. A multi-role architecture distinguishes Registered Voters, Election Administrators, and Audit Officers, with role enforcement through Django's @login_required and @user_passes_test decorators. Vote uniqueness is guaranteed through dual-layer enforcement: application-level duplicate checking and database-level unique_together constraints on the (voter, election) pair. Empirical evaluation on a 500-vote simulation dataset demonstrates a fraud detection precision of 91.2%, a false positive rate of 4.3%, a candidate matching accuracy of 89.7% against expert preference alignment, and a mean end-to-end vote processing latency of 87 milliseconds on consumer CPU hardware. The system is implemented entirely on open-source technologies, providing a reproducible reference architecture for AI-enhanced civic technology.

Keywords Online voting system; Isolation Forest; anomaly detection; TF-IDF candidate matching; NLP sentiment analysis; SHA-256 vote integrity; Django; electoral security

Paper ID IJIFR/V13/E8/046 Page No.: 1240-1246

Subject Area Computer Science

Authors Madde Sanghavi
B. Shireesha

Abstract The global burden of lifestyle-related diseases including obesity, type-2 diabetes, and cardiovascular conditions has established personalized nutrition as a healthcare necessity. Conventional dietary guidance, grounded in population-averaged standards such as Recommended Dietary Allowances, fails to account for individual metabolic variation. NutriGen AI addresses this limitation through a machine learning-powered personalized nutrition recommendation engine. The system employs a Random Forest Regressor trained on one thousand synthetic user profiles to predict Total Daily Energy Expenditure (TDEE), a hybrid filtering recommendation architecture combining K-Nearest Neighbors content-based filtering with Truncated Singular Value Decomposition collaborative filtering to produce meal suggestions, and goal-oriented macro-nutrient distribution logic for weight loss, maintenance, and muscle gain objectives. Delivered through a Flask RESTful backend and a glassmorphism-styled HTML5/CSS3/JavaScript frontend with Chart.js visualizations, the system democratizes access to personalized nutritional guidance using entirely open-source technologies. Experimental evaluation demonstrates effective TDEE estimation and nutritionally aligned meal recommendations across diverse user profiles.

Keywords Personalized Nutrition, Machine Learning, Random Forest Regressor, Hybrid Recommendation System, KNN, Collaborative Filtering, TDEE Estimation, Flask, Health Informatics

Paper ID IJIFR/V13/E8/045 Page No.: 1231-1239

Subject Area Computer Engineering

Authors Mutra Rekha
B. Shireesha

Abstract Smart manufacturing (Industry 4.0) demands real-time predictive intelligence to eliminate reactive quality management. QualityPredict AI is a comprehensive, end-to-end machine learning platform for smart factories that predicts continuous product quality scores and classifies manufacturing defects by analyzing production telemetry including temperature, pressure, mechanical vibration, machine rotational speed, ambient humidity, and machine age. The system is trained on a synthetic dataset of ten thousand manufacturing records generated to replicate real-world industrial conditions. Three ensemble regression algorithms — Random Forest Regressor, LightGBM Regressor, and Gradient Boosting Regressor — are comparatively evaluated for quality score prediction, while a Random Forest Classifier provides binary defect detection with calibrated probability scores. Feature importance analysis reveals mechanical vibration as the dominant quality predictor (˜63% variance explained), followed by machine age (˜22%) and operating temperature (˜8%). The best regression model achieves an R² score exceeding 0.88 on a held-out 20% test split, and the defect classifier achieves accuracy above 0.90. Four professional analytical visualizations communicate findings to production managers and quality engineers. The complete pipeline from data generation through model training, visualization, and live prediction simulation is implemented in modular Python, with all model artifacts serialized via Joblib for production deployment.

Keywords Manufacturing Quality Prediction, Industry 4.0, Random Forest, LightGBM, Gradient Boosting, Defect Classification, Feature Importance, Smart Manufacturing, Statistical Process Control, Predictive Quality Management

Paper ID IJIFR/V13/E8/042 Page No.: 1222-1230

Subject Area Computer Science

Authors K. Aparna
V. Vijayalakshmi

Abstract Campus placement plays a vital role in connecting engineering students with employers, but many institutions still rely on manual methods such as spreadsheets and emails, leading to inefficiencies. The Intelligent College Placement Management Platform is a full-stack web application developed using the Django framework to automate and streamline the entire placement process.The system supports three user roles: students, recruiters, and placement officers. Students can create profiles, upload resumes, apply for jobs, and track application status. Recruiters can post job openings, review applications, and issue offer letters. Placement officers manage the system through a centralized dashboard that provides real-time insights into placement activities.A key feature of the platform is the simulated AI-based matching engine, which uses natural language processing techniques to compare student skills with job requirements and generate a match score. This improves decision-making for both students and recruiters. The system also enforces placement policies such as the One-Student-One-Offer rule.Built using Python, Django, SQLite, and Bootstrap, the platform ensures efficient, transparent, and scalable placement management, improving coordination and reducing manual effort in campus recruitment processes

Keywords Campus Placement System, Django Web Application, Placement Management, AI Matching Engine, Natural Language Processing (NLP), Resume Screening, Job Recommendation, Student Recruitment, Skill Matching, Full Stack Development

Paper ID IJIFR/V13/E8/041 Page No.: 1214-1221

Subject Area Computer Engineering

Authors Kadiri Bhuvaneswari
V. Vijayalakshmi

Abstract The contemporary recruitment landscape is characterised by an exponentially growing volume of applications that overwhelm keyword-based Applicant Tracking Systems (ATS), which treat language as a bag of isolated tokens and systematically fail to recognise semantically equivalent competency descriptions. This paper presents ResumeMatch Pro AI, a full-stack intelligent recruitment support system that addresses these representational inadequacies by deploying Sentence-BERT (SBERT), specifically the all-MiniLM-L6-v2 pre-trained transformer model, to encode both resume and job description documents into 384-dimensional dense semantic vector representations. Cosine similarity computed between these embeddings yields a holistic semantic match score that is robust to paraphrase, synonymy, and terminological variation — failure modes that fundamentally undermine conventional lexical matching. The system further employs the spaCy natural language processing library in conjunction with a PhraseMatcher-based skill extractor to perform fine-grained skill gap analysis, enumerating precisely which required competencies are present in the candidate profile and which are absent, thereby transforming an abstract score into an actionable decision-support artefact. The architecture follows a clean two-tier client-server separation: a FastAPI backend exposes RESTful endpoints for single-candidate matching and multi-candidate ranking, whilst a React/Vite frontend renders match results through circular gauge visualisations, colour-coded skill tags, and animated result panels designed for non-technical recruiters. Experimental evaluation using representative professional domain test cases demonstrates that the SBERT-based approach correctly resolves synonym ambiguities — crediting a candidate describing experience in 'statistical learning' against a role requiring 'machine learning' — where keyword systems assign a zero-overlap score. The system achieves single-request response times of one to three seconds on CPU-only infrastructure, confirming practical deployability. The proposed framework demonstrates that the combination of transformer-based holistic semantic embeddings with explicit rule-based skill extraction yields a recruitment tool that is simultaneously more accurate, more transparent, and more actionable than the lexical-matching status quo.

Keywords Sentence-BERT; Semantic Resume Matching; Recruitment Automation; Cosine Similarity; Skill Gap Analysis

Paper ID IJIFR/V13/E8/040 Page No.: 1206-1213

Subject Area Computer Science

Authors Jeripiti Reddy Prasad
V.Vijayalakshmi

Abstract The exponential growth of digital financial transactions in India has precipitated a commensurate escalation in sophisticated payment fraud, necessitating intelligent, adaptive detection systems capable of operating at scale. This paper presents SecurePay Shield, a comprehensive, end-to-end machine learning pipeline engineered for real-time identification of fraudulent transactions within the Indian digital payment ecosystem. The proposed system addresses three principal challenges endemic to fraud detection: severe class imbalance, high-dimensional heterogeneous feature spaces, and the requirement for probabilistic, interpretable risk assessments amenable to regulatory scrutiny. The architecture employs an ensemble learning strategy integrating three complementary algorithms: a Random Forest Classifier (200 estimators), a Gradient Boosting Classifier (150 estimators), and an Isolation Forest anomaly detection model. A domain-specific feature engineering pipeline transforms 23 raw transaction attributes into a 35-dimensional feature space, computing composite risk indicators including the Risk_Composite score, IP_Risk_Score, and Velocity_Score, which collectively emerge as the most discriminative predictors of fraudulent activity. Class imbalance is mitigated through the application of the Synthetic Minority Over-sampling Technique (SMOTE), yielding a balanced training corpus of 22,560 instances. The Random Forest model, selected as the production deployment candidate, achieves an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 1.0000 and an F1-Score of 1.0000 on the held-out test partition, with five-fold stratified cross-validation yielding a mean F1 of 0.9978 (±0.0021), confirming model robustness and generalizability. Predictions and analytical artifacts are persisted in a structured MySQL database comprising five normalized tables, and operational insights are surfaced through a four-page Microsoft Power BI dashboard supporting real-time fraud monitoring. The system is demonstrated on a synthetic dataset of 15,000 Indian financial transactions and evaluated against 2,000 prospectively generated records, achieving a holistic prediction corpus of 17,000 transactions with a four-tier risk stratification (Critical, High, Medium, Low).

Keywords Fraud Detection; Ensemble Learning; SMOTE; Random Forest; Digital Payment Security; Risk Scoring; Feature Engineering







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The International Journal of Informative & Futuristic Research (IJIFR) is a multidisciplinary peer-reviewed Online open access research journal published monthly. It delivers multidisciplinary platform in order to have extreme, accurate, genuine, brainstorming, speculative, intellectual discussion and which has the visualization to understand, comprehend industrial experiences that describes significant advances of changing global scenarios. All the Authors will get Hardcopy of Certificates for Publication free of cost. IJIFR is dedicated to increasing the depth of the subject across disciplines with the ultimate aim of expanding knowledge of the subject. The journal follows a Blind-Review Peer Review System in order to bring in a high-quality intellectual platform for researchers across the world thereby bringing in total transparency in its journal review system. Authors are solicited to contribute by submitting articles that illustrate high quality theoretical and experimental research results, projects, case studies, reviewed work, analytical and simulation models, technical notes and industrial experiences that describe significant advances in research area. IJIFR provides an opportunity to present the innovative and constructive ideas and the outcome of the on-going research in all the areas of research studies in context of changing global scenarios. This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.


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The journal welcomes the researcher and authors from all parts of the world to provide their latest outstanding developments and state-of-the-art research work for the publications of high quality papers having research results either experimental or projected application in their related fields. IJIFR publishes original materials concerned with the theoretical underpinnings, efficacious application, and potential for evolving technology integration in a global range at all edification levels. It aims to guide the society to formulate and reinvent education, and to be the cutting-edge of knowledge, modernization, erudition, and innovation. Papers submitted for publication are selected & peer reviewed through full double - blind international refereeing process to ensure inventiveness, uniqueness, originality, relevance, and readability. Our reviewers are highly qualified academics and industrialists experts who ensure that only quality research should be published by IJIFR Journals. Articles submitted to the journal should meet international standards and must not be under consideration for publication elsewhere.


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