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Paper ID IJIFR/V13/E8/031 Page No.: 1138-1147

Subject Area Computer Engineering

Authors Karamala Sai Pravallika
V. Vijayalakshmi

Abstract Financial markets are driven by both quantitative fundamentals and qualitative information flows — news articles, analyst commentary, press releases, and regulatory announcements — that collectively shape market participant sentiment and govern short-term price dynamics. Translating unstructured financial text into structured predictive signals constitutes a central challenge in computational finance. This paper presents FinSent AI, an end-to-end, modular machine learning pipeline that ingests live financial news from RSS feeds, applies FinBERT — a BERT-based transformer language model pre-trained on financial text corpora — to derive continuous daily sentiment scores, and combines those scores with classical technical price features to train classification models predicting next-day stock price directional movement. The system is demonstrated using Apple Inc. (AAPL) as the primary ticker, sourcing historical OHLCV data via the Yahoo Finance API and news headlines from Yahoo Finance and Reuters RSS feeds. Text preprocessing eliminates noise including URLs, punctuation, and stopwords, after which FinBERT classifies each article as positive, negative, or neutral with an associated confidence score. Daily aggregation of per-article signed sentiment scores — computed as the product of polarity label and model confidence — yields a continuous sentiment signal temporally aligned to the stock price time series. Feature engineering yields a seven-dimensional input matrix comprising closing price, trading volume, daily return, five-day and ten-day moving averages, five-day rolling return volatility, and the daily sentiment score. Two ensemble classifiers — Random Forest and XGBoost — are trained on an 80/20 chronological train-test split to prevent data leakage. The superior model is deployed through an interactive four-tab Streamlit web dashboard delivering stock and sentiment visualization, prediction overlays, news browsing, and real-time next-day directional forecasts with confidence scores. The complete architecture is reproducible, extensible to any tradeable ticker, and readily deployable on cloud infrastructure.

Keywords Financial Sentiment Analysis; FinBERT; Ensemble Learning; Stock Price Prediction; Natural Language Processing

Paper ID IJIFR/V13/E8/030 Page No.: 1129-1137

Subject Area Computer Engineering

Authors Akula Tharun
Dr. S. Usharani

Abstract Hospital overcrowding, rooted in a systemic mismatch between highly dynamic patient demand and coarse-grained resource allocation practices, represents a critical patient safety and operational efficiency challenge in contemporary acute-care environments. Existing hospital management systems are predominantly retrospective in orientation, recording historical events without providing the predictive intelligence necessary for proactive resource deployment. MediFlow Optimizer addresses this fundamental gap through the design and implementation of a four-tier, machine learning-augmented hospital resource and patient flow intelligence platform. The proposed system integrates an Excel-based data acquisition layer, a Python-driven analytics engine employing a Random Forest Regressor (100 estimators) trained on temporal features—hour of day, day of week, and weekend indicator—extracted from historical admission records, a MySQL relational database persistence layer comprising three primary tables and three pre-aggregated analytical views, and a Microsoft Power BI interactive dashboard layer delivering operational intelligence to hospital administrators and clinical quality managers. The Random Forest model generates hourly patient inflow forecasts at seven-day granularity with a Mean Absolute Error (MAE) of approximately 0.85 patients per hour, a Root Mean Squared Error (RMSE) of 1.20, and a coefficient of determination (R²) of 0.78, outperforming baseline Linear Regression, standalone Decision Tree, and Gradient Boosting alternatives across all three evaluation metrics. Each forecasted hourly period is accompanied by peak-status classification (Normal, High, or Critical Peak) and actionable resource recommendations specifying appropriate clinical staffing ratios and bed deployment targets. The system further computes and tracks four daily key performance indicators—average patient wait time, total admission volume, high-severity case count, and bed utilization rate—presented through a custom dark-themed Power BI dashboard designed for operational deployment in clinical monitoring environments. The complete platform is implemented exclusively with open-source technologies, rendering it reproducible and accessible to healthcare organizations without investment in proprietary analytical infrastructure.

Keywords Hospital Patient Flow Prediction; Random Forest Regressor; Healthcare Resource Allocation; Business Intelligence Dashboard; Time-Series Forecasting

Paper ID IJIFR/V13/E8/029 Page No.: 1122-1128

Subject Area Computer Engineering

Authors Bajanthri Reddy Kishore
Dr. S. Usharani

Abstract Contemporary power systems are subject to increasingly volatile consumption patterns driven by urbanization, industrialization, and the proliferation of smart devices, exposing the limitations of conventional statistical forecasting methodologies. This paper presents PowerPulse Analytics, an end-to-end intelligent energy demand forecasting and load optimization framework that integrates ensemble machine learning with a structured data pipeline and interactive visualization. The system employs a Random Forest Regressor trained on a multi-dimensional dataset encompassing temporal attributes, regional consumer segmentation, ambient environmental variables (temperature and humidity), and holiday indicators to generate hourly energy consumption forecasts over rolling seven-day horizons. The architecture is organized into five functional layers: Data Acquisition, Feature Engineering and Preprocessing, Machine Learning Inference, Persistent Storage via a MySQL relational database, and Decision-Support Visualization through a Power BI dashboard. Feature engineering transforms raw timestamps into discriminative temporal signals including hour-of-day, day-of-week, and month, which are critical for capturing diurnal and seasonal consumption cycles. Experimental validation demonstrates that the Random Forest model achieves superior predictive accuracy compared to baseline statistical methods, with a Mean Absolute Error (MAE) below 4.2 kWh and an R² coefficient exceeding 0.91 across residential, commercial, and industrial consumer segments. The automated, modular pipeline architecture ensures reproducibility, scalability, and seamless integration with relational database infrastructure. The proposed framework provides utility operators with actionable decision-support intelligence to proactively mitigate demand spikes, reduce grid imbalances, and optimize resource allocation. Results demonstrate that machine learning-driven forecasting constitutes a substantively superior alternative to conventional heuristics, establishing a scalable blueprint for smart grid energy management systems.

Keywords Energy Demand Forecasting; Random Forest Regressor; Smart Grid Optimization; Temporal Feature Engineering; Decision-Support Analytics

Paper ID IJIFR/V13/E8/028 Page No.: 1116-1121

Subject Area Computer Engineering

Authors Chakali Gireesh
Dr. S. Usharani

Abstract The persistent challenge of banking customer churn imposes substantial revenue attrition on financial institutions operating within hyper-competitive digital environments. This paper presents Retain360, a comprehensive end-to-end analytical platform that addresses this challenge through the systematic integration of ensemble classification, survival analysis, and explainable artificial intelligence (XAI). The system is trained and evaluated on the IBM Banking Customer Churn Dataset, encompassing demographic, transactional, and service-usage attributes for 7,043 customer records. A Random Forest classifier, trained with class-balanced weighting and hyperparameter-optimised via Grid Search Cross-Validation, achieves an F1-Score of approximately 0.62 and a ROC-AUC of 0.85 on the held-out test partition — demonstrating discriminative capability substantially exceeding random baselines and competitive with state-of-the-art benchmarks. The survival analysis component employs the lifelines library to fit Kaplan-Meier survival curves and a Cox Proportional Hazards (Cox PH) model, enabling the derivation of individualised hazard functions and survival probability trajectories over customer tenure. These temporally-resolved risk profiles underpin a personalised Customer Lifetime Value (CLTV) estimator that translates survival-derived expected tenure into quantitative revenue projections. Model interpretability is achieved through a tri-layer explainability framework comprising Permutation Importance for global feature ranking, Partial Dependence Plots (PDPs) for marginal effect visualisation, and SHAP (SHapley Additive exPlanations) force plots for instance-level prediction attribution. The complete system is operationalised as a Flask-based web application delivering real-time churn probability scores, risk gauges, SHAP explanations, and survival visualisations through a form-driven interface accessible to non-technical banking professionals. Retain360 thus bridges the methodological gap between academic machine learning research and actionable, production-grade customer retention intelligence.

Keywords Customer Churn Prediction; Random Forest; Cox Proportional Hazards; SHAP Explainability; Customer Lifetime Value

Paper ID IJIFR/V13/E8/027 Page No.: 1108-1115

Subject Area Computer Engineering

Authors A. Pramod Kumar
Dr. S. Usharani

Abstract Accurate crop yield prediction is a critical component in ensuring sustainable agricultural development and efficient resource utilization. Traditional estimation methods, primarily based on manual surveys and empirical observations, are often limited by scalability, time constraints, and their inability to model complex interactions among agronomic variables. This paper presents AgroYield AI, an interpretable machine learning framework for crop yield prediction using multi-dimensional agricultural data. The proposed system leverages a Random Forest regression model trained on diverse features including crop type, seasonal variations, geographical location, rainfall, fertilizer usage, and pesticide application. A robust preprocessing pipeline incorporating categorical encoding and feature normalization is employed to enhance predictive performance. The system integrates predictive modeling with an interactive visualization interface developed using Streamlit and D3.js, enabling real-time data exploration and decision support. Experimental evaluation demonstrates that the proposed model achieves high predictive accuracy and strong generalization capability compared to conventional regression approaches. Furthermore, feature importance analysis provides transparency and interpretability, allowing stakeholders to understand the influence of key agricultural parameters on yield outcomes. The proposed framework offers a scalable and practical solution for data-driven agricultural decision-making, supporting farmers, researchers, and policymakers.

Keywords Crop Yield Prediction; Machine Learning; Random Forest; Precision Agriculture; Data Analytics

Paper ID IJIFR/V13/E8/026 Page No.: 1099-1107

Subject Area Computer Engineering

Authors Bonasi Anusha
Dr. S. Usharani

Abstract In the modern legal and corporate environment, contract review remains a critical yet resource-intensive task, often requiring significant time, expertise, and manual effort. Traditional approaches to reviewing legal documents are prone to human error, inconsistencies, and inefficiencies, especially when dealing with large volumes of contracts. This paper presents ContractRisk Analyzer AI, an intelligent system designed to automate the identification and classification of legal risk within contract documents using Natural Language Processing (NLP) techniques. The proposed system processes legal contracts in both PDF and plain-text formats, extracting and segmenting the content into individual clauses using advanced sentence tokenization methods. Each clause is evaluated against a structured legal risk knowledge base categorized into High, Medium, and Low risk levels. The classification process is driven by a rule-based NLP engine that ensures transparency by identifying the exact phrases responsible for risk detection. A weighted scoring mechanism aggregates clause-level risks to generate an overall document risk percentage, providing a clear and interpretable assessment. Furthermore, the system integrates an intelligent recommendation module that offers actionable insights based on detected risks, assisting users in making informed legal decisions. Implemented as a web-based application using the Flask framework, the system provides an intuitive user interface with interactive visualizations, including a dynamic risk gauge and clause-level analysis tables. The proposed solution demonstrates the effectiveness of explainable AI in the legal domain, offering a scalable, accessible, and efficient alternative to traditional contract review processes. It significantly reduces review time while maintaining analytical accuracy, making it valuable for legal professionals, organizations, and academic users.

Keywords Contract Analysis; Natural Language Processing; Legal Risk Assessment; Clause Classification; Explainable AI

Paper ID IJIFR/V13/E8/025 Page No.: 1086-1098

Subject Area Computer Engineering

Authors Kodavati Mounika
B. Shreesha
Dr. Usha Rani

Abstract Credit risk assessment plays a vital role in ensuring the financial stability and sustainability of lending institutions. Traditional credit scoring methods, primarily based on statistical models such as logistic regression, often fail to capture complex, non-linear relationships inherent in borrower data. This limitation results in reduced predictive performance, especially in dynamic financial environments. To address these challenges, this paper presents CreditShield AI, an explainable machine learning-based loan default risk prediction system designed to enhance accuracy, transparency, and reliability in credit decision-making.The proposed system leverages the Give Me Some Credit dataset, comprising over 150,000 borrower records with multiple financial and behavioral attributes. A structured data pipeline is developed, including data preprocessing, missing value imputation, outlier detection, feature scaling, and exploratory data analysis. Robust statistical techniques such as median imputation and percentile-based winsorization are applied to ensure data quality and consistency. Furthermore, the system adopts Robust Scaler normalization to mitigate the impact of extreme values.A key contribution of this work is the emphasis on explainable AI, ensuring that model predictions can be interpreted in compliance with financial regulatory standards. The system is designed to integrate advanced machine learning models and interpretability techniques such as SHAP and LIME in future stages. The proposed framework not only improves prediction capability but also promotes fairness, transparency, and responsible AI practices in financial risk management.

Keywords Credit Risk Prediction; Machine Learning; Loan Default; Explainable AI; Financial Analytics

Paper ID IJIFR/V13/E8/024 Page No.: 1070-1085

Subject Area Computer Engineering

Authors K. Bhavani Sankar
V.Vijayalakshmi
Dr. Usha Rani

Abstract Natural disasters such as earthquakes, floods, hurricanes, and wildfires cause extensive damage to infrastructure and human life, making rapid and accurate damage assessment a critical requirement for effective disaster response. Traditional ground-based assessment techniques are time-consuming, risky, and limited in spatial coverage, which delays emergency decision-making processes. To address these limitations, this paper presents DisasterVision AI, an automated satellite imagery analysis system that leverages deep learning for large-scale building damage assessment. The proposed system utilizes a modified Single Shot MultiBox Detector (SSD) with a VGG-16 backbone, enhanced to process six-channel input by combining pre-disaster and post-disaster satellite images. This dual-input architecture enables the model to learn visual differences between temporal image pairs, improving damage detection accuracy. The model is trained using the xView2 dataset, which provides annotated satellite imagery with four damage categories: no-damage, minor-damage, major-damage, and destroyed. The system incorporates advanced training techniques including data augmentation using Albumentations, OneCycle learning rate scheduling, and AdamW optimization for efficient convergence. Performance evaluation is conducted using Mean Average Precision (mAP) metrics across multiple IoU thresholds. Additionally, Non-Maximum Suppression (NMS) is applied for refining detection outputs. Experimental results demonstrate that DisasterVision AI provides fast, scalable, and reliable damage assessment, making it a valuable tool for disaster management authorities and emergency response teams.

Keywords Disaster Damage Assessment; Deep Learning; Satellite Imagery; Object Detection; SSD; xView2 Dataset

Paper ID IJIFR/V13/E8/023 Page No.: 1062-1069

Subject Area Computer Application & Engineering

Authors Sirisani Lavanya
M. Gowthami

Abstract Urban traffic congestion is a critical infrastructure challenge facing modern cities as vehicle populations expand and urban density increases. Conventional fixed-timing traffic signal systems are incapable of adapting to the stochastic and dynamic nature of real-world traffic flows, resulting in wasted green-light time, queue buildup, increased vehicle emissions, and emergency response delays. This paper presents TrafficOpt RL, an end-to-end adaptive traffic signal optimization system that applies the Deep Q-Network (DQN) algorithm to learn intelligent signaling policies at urban intersections through iterative simulation experience. The system is built on a custom Gymnasium-compatible simulation environment modeling a four-way intersection with stochastic Poisson vehicle arrivals. The DQN agent, implemented via the Stable-Baselines3 framework, utilizes experience replay, target network stabilization, and epsilon-greedy exploration to converge on policies minimizing aggregate vehicle waiting times and maximizing intersection throughput. All training metrics and simulation data are persistently stored in a MySQL relational database through automated callback logging, enabling systematic performance analysis. Evaluation via direct comparison against a fixed-timing baseline demonstrates measurable superiority of the reinforcement learning approach across three performance dimensions: average vehicle waiting time, total throughput, and composite efficiency score. Three analytical visualizations are generated to communicate system performance. TrafficOpt RL constitutes a practical proof-of-concept for deep reinforcement learning integration into intelligent transportation systems and smart city infrastructure.

Keywords Deep Reinforcement Learning; Traffic Signal Optimization; Deep Q-Network; Intelligent Transportation Systems; Adaptive Control

Paper ID IJIFR/V13/E8/012 Page No.: 1036-1041

Subject Area Law

Authors S K Sahil

Abstract Section 69 of the Bharatiya Nyaya Sanhita, 2023 introduces a new offence addressing sexual intercourse obtained through deceitful means, particularly false promises of marriage and other inducements. While the provision aims to strengthen the legal framework for protecting women from sexual exploitation, it raises significant concerns regarding its interpretation and practical application. The section suffers from vague and ambiguous language, lack of clarity in defining “consent” and “deceitful means,” and the absence of comprehensive safeguards against misuse. It also reflects a genderbiased approach by recognizing only women as victims, thereby excluding men and LGBTQ+ individuals from its protection.This study adopts a doctrinal methodology, relying on primary sources such as statutory provisions and judicial decisions along with secondary sources including books, research articles, journals and credible electronic resources. Analytical, descriptive and exploratory approaches are used to critically examine the scope, limitationsand implications of the provision.The findings reveal that Section 69 of BNS, despite its progressive intent, creates legal uncertainty, difficulty in proving intention and potential for misuse, which may lead to inconsistent judicial outcomes. The study concludes that there is an urgent need for clearer judicial interpretation, precise legislative drafting, and balanced safeguards to ensure that the provision achieves its intended objective without compromising fairness and justice.

Keywords Free Consent, Deceitful Means, False Promise of Marriage, Misuse of Law, Gender Biasness, Misuse of Law







About IJIFR

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 International Journal of Informative & Futuristic Research (IJIFR) special issue welcomes proposals for new and recurring National Conferences, International Conferences, National Seminars, Workshops conducted by colleges, universities, engineering & management institutes etc. The first aim is to provide opportunities for academics from a range of disciplines and countries to share their research both through the conference podium and IJIFR double-blind refereed publications. Proposals will be selected to ensure the conference program offers a comprehensive, non-commercial, objective, and diverse treatment of issues related to the core concepts of the subject’s related to title, IT Organizational Domains, and IT Hot Topics. Attention will be given to diversity of institutions, presenters, and geographic location. It is one of the excellent services offered by IJIFR that is uniquely intended to support the researchers and conference organizers. IJIFR provides conference organizers a privileged platform for the publishing of research work presented in conference proceedings. The journal is deliberated to disseminate scientific & basic research and to establish long term International collaborations and partnerships with academic communities and conference organizers. We invite you to submit proposals on any topic related to the broad set of research and application areas covered the by IJIFR. The Conference examines the concept of diversity as a positive aspect of a global world and globalised society. Presenting at conferences is an efficient and exciting forum in which you can share your research and findings.


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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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The editors ensure that this journal will be regularly published, widely read and circulated, have high impact and attract an adequate supply of high-quality papers from an international range of authors worldwide. Any selected referee who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the editor and excuse himself from the review process. Any manuscripts received for review must be treated as confidential documents. They must not be shown to or discussed with others except as authorized by the editor. An editor should evaluate manuscripts for their intellectual content without regard to race, gender, sexual orientation, religious belief, ethnic origin, citizenship, or political philosophy of the authors. Double blind reviews will be executed to ensure that biases in the process of evaluating manuscripts.


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Submission of paper to this journal proceeds totally online and you will be guided stepwise through the creation and uploading of your files. All correspondence, including notification of the Editor's decision and requests for revision, takes place by e-mail removing the need for a paper trail. Clearly indicate who will handle correspondence at all stages of refereeing and publication, also post-publication. Ensure that phone numbers (with country and area code) are provided in addition to the e-mail address and the complete postal address. Contact details must be kept up to date by the corresponding author. Those papers that do not reach the required standards of quality and rigor demanded by the journals, in terms of theoretical framework and methodology, will not be accepted for publication. Full papers should be submitted electronically via the ijifr website i.e.www.ijifr.org or by directly mailing to the editor at [email protected] or [email protected] in accordance with the author’s guidelines and paper format of this journal. The entire paper should be created in one document in Word format (.DOC or .DOCX). The first page is the title page, showing the full title, author‘s name, position, affiliation, and present address. Also, include an e-mail address for editorial correspondence. If there is more than one author, please indicate with an asterisk (*) the author who should receive correspondence.


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IJIFR APRIL 2026 EDITION (CONTINUOUS 153 EDITION)
VOLUME 13, ISSUE 8, APRIL 2026
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