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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/E8/010 Page No.: 1042-1050

Subject Area Humanities (English)

Authors Dr. Vaishali Kiran Ghadyalji

Abstract Human nature is fundamentally malevolent as it is very natural for human beings to be selfish and narcissists. Humans are born with greed, envy and jealousy; and these innate feelings make them indulge into different kinds of immoral activities. On the contrary, their benevolence originates from the cognizant and constant imbibing of moral values. Many philosophers and scholars right from Aristotle to Thomas Hobbs, Charls Darwin, Sigmund Freud, Peter Muris, Herald Merckelbach, Henry Otgaar, David Coady, Lee Besham and a plethora have tried to explain and restate this fact. This paper is an attempt to explore the inexorable and unfortunate element of malevolence in human beings by juxtaposing the supposed villainous characters- Manthara from Ramayana and Shakuni from Mahabharata- with an analysis of their influential endevours to inspire their target audience to act according to their philosophy. Manthara, hunchbacked and old, was the confidante attendant and favourite maid of Queen Kaikeyi in the Indian epic Ramayana who instigated her to convince King Dasharatha to coronate Bharata in the place of Lord Rama and ask for fourteen years of exile to Lord Rama. Shakuni was the king of Gandhar and brother of Gandhari, the queen of Dhritrashtra and Hastinapur and the mother of hundred Kauravas in another Indian epic Mahabharata. Shakuni is delineated as one of the exceptionally intelligent characters in the epic but a very scheming one. This article endeavours to compare these two characters possessing malevolent traits, trying to establish that malevolence is not grounded in particular gender, position, era or such related aspects; and the malevolence of both of these characters does enclose certain grey shades.

Keywords human malevolence, scheming, disability, manipulation, malicious counsel


Paper ID IJIFR/V13/E8/007 Page No.: 1051-1061

Subject Area HRM

Authors Ms.Liza Alex
Prof.(Dr.)Mini Joseph
CA Reshma Rachel Kuruvilla

Abstract Purpose The objective of this article is to look into how competitive advantage and green banking practices associate with one another in the Indian banking industry. It considerably looks over how sustainable performance functions as a mediator in this relationship. This study (Siddik et al., 2024) aims to make clear how ecologically responsible actions result in strategic advantages for banks (Gunawan et al., 2022) by investigating the degree to which sustainable performance explains the impact of green banking practices on competitive advantage. Design/methodology/approach With the aim of examining the connection between eco-friendly banking practices and competitive advantage in the Indian banks, this quantitative study uses structural equation modeling with SmartPLS 4.0, with a focus on sustainable performance as a mediator. Convenient sampling will be used to gather the primary dataset from Indian private banks. The causal relationship between GBPs and CA will be assessed using SEM, and the mediating function of SP in this relationship will also be ascertained. Findings According to the analysis, the banking industry's competitive advantage is increased by putting green banking practices into practice. Additionally, the relationship between green practices and competitive advantage is strengthened by sustainable performance, indicating that green initiatives are more purposively effective when banks demonstrate strong sustainable performance. According to the partial mediation finding, banks gain from green banking practices in two ways: they directly increase their competitive advantage and improve sustainable performance (Siddik et al., 2024). Originality/value By learning the circumstances in which green banking practices improve a company's competitiveness, this study contributes to the expanding corpus of research on sustainable banking. It spotlights the significance of sustainable performance as a strategic tool for enhancing the advantages of green banking as well as a desired result (Siddik et al., 2024).

Keywords Green banking practices, sustainable performance, competitive advantage, mediation analysis, Indian banking sector.


Paper ID IJIFR/V13/E8/005 Page No.: 1028-1035

Subject Area Computer Engineering

Authors G.Yarasi
V. Lakshmi Narasimhan
D. Rammohan

Abstract This study presents a comparative evaluation of four deep learning architectures—CNN, ResNet50, VGG16, and InceptionV3—for chilli growth stage recognition and leaf disease classification. The models were assessed to determine their effectiveness in accurately distinguishing developmental stages and identifying disease conditions from image data. Experimental results demonstrate that transfer learning substantially improves classification performance across all architectures. Among the evaluated models, ResNet50 consistently achieved superior accuracy and overall performance in both growth stage and disease classification tasks. These findings highlight the effectiveness of deep transfer learning approaches in agricultural image analysis. Future research will focus on validating model robustness under real-field environmental conditions, designing lightweight architectures for mobile and edge deployment, and expanding the framework to support multi-crop classification systems for broader agricultural applications.

Keywords Chilli Image Classification, ResNet50, VGG16 & InceptionV3 Algorithms, Comparative Analytics


Paper ID IJIFR/V13/E8/001 Page No.: 1021-1027

Subject Area English

Authors Dr Revathy Menon

Abstract Robinson Jeffers, a distinctive figure in twentieth-century American poetry, articulates a radical ecopoetic vision that challenges anthropocentric worldviews through his philosophy of ‘inhumanism’. His work reimagines the human-nature relationship by displacing the human as the central measure of value and emphasizing interdependence within a vast cosmic and geological continuum. Through vivid poetic landscapes shaped by the Californian coast, Jeffers critiques modern humanism and aligns with deep ecological principles, advocating an ethic of humility, reverence, and detachment. His vision foregrounds the autonomy and agency of the nonhuman world, crafting a poetics that is at once tragic, philosophical, and urgently relevant in the context of contemporary ecological crisis.

Keywords : Inhumanism, Humanism, Ecopoetics, Anthropocentrism, Deep Ecology


Paper ID IJIFR/V13/E7/031 Page No.: 1006-1014

Subject Area Medical AI

Authors S.Parthasarathi
V.Lakshmi Narasimhan
D.Rammohan

Abstract Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition affecting over 300 million people globally and remains significantly underdiagnosed due to reliance on traditional spirometry-based screening. This paper presents a comparative study of three state-of-the-art gradient boosting algorithms—XGBoost, LightGBM, and CatBoost—for automated COPD severity classification on real clinical tabular data, benchmarked against a 1D Convolutional Neural Network (CNN) baseline. Additionally, a Stacking Ensemble combining all three gradient boosting models via a Logistic Regression meta-learner is proposed as a novel hybrid approach. Experiments are conducted on the Kaggle COPD Student Dataset comprising 101 patients and 19 clinical features. Results demonstrate that gradient boosting models substantially outperform the CNN baseline, with XGBoost achieving the highest accuracy (90.48%) and F1-score (89.76%), while CatBoost attains a near-perfect AUC-ROC of 99.31%. The Stacking Ensemble achieves competitive performance (85.71% accuracy, 97.9% AUC-ROC), confirming ensemble combination as a viable research direction. SHAP analysis identifies FEV1PRED, FEV1, and FVCPRED as the most influential clinical predictors, consistent with established GOLD severity guidelines.

Keywords COPD severity Classification, XGBoost, Light BGM, CatBoost, Convolutional Neural Network, SHAP, Explainable AI








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.


Focus & Scope

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.


All articles published Open Access will be immediately and permanently free for everyone to read and download. Manuscripts should follow the style of the journal and are subject to both review and editing. IJIFR is multidisciplinary in nature so the topics are not limited to the list that is available. IJIFR will generally publish the research papers in the field as follows:


SOCIAL SCIENCE AND HUMANITIES, SOCIOLOGY, SOCIAL WELFARE, ANTHROPOLOGY, RELIGIOUS STUDIES, VISUAL ARTS, POLITICAL, CULTURAL ASPECTS OF DEVELOPMENT, TOURISM MANAGEMENT, PUBLIC ADMINISTRATION, PSYCHOLOGY, PHILOSOPHY, POLITICAL SCIENCE, HISTORY, EDUCATION, WOMEN STUDIES, BUSINESS AND MARKETING, ECONOMICS, FINANCIAL DEVELOPMENT, ACCOUNTING, BANKING, MANAGEMENT, HUMAN RESOURCES, SCIENCE AND ENGINEERING, TECHNOLOGY AND INNOVATION, ENVIRONMENTAL STUDIES, CLIMATE CHANGE, AGRICULTURAL, RURAL DEVELOPMENT, URBAN STUDIES, BIOTECHNOLOGY, HOTEL AND TOURISM, ENTREPRENEURSHIP DEVELOPMENT, BUSINESS ETHICS, DEVELOPMENT STUDIES, ASTRONOMY AND ASTROPHYSICS, CHEMISTRY, EARTH AND ATMOSPHERIC SCIENCES, PHYSICS, BIOLOGY IN GENERAL, AGRICULTURE, BIOPHYSICS AND BIOCHEMISTRY, BOTANY, ENVIRONMENTAL SCIENCE, FORESTRY, GENETICS, HORTICULTURE, HUSBANDRY, NEUROSCIENCE, ZOOLOGY, COMPUTER SCIENCE, ENGINEERING, ROBOTICS AND AUTOMATION, MATERIALS SCIENCE, MATHEMATICS, MECHANICS, STATISTICS, HEALTH CARE & PUBLIC HEALTH, NUTRITION AND FOOD SCIENCE, PHARMACEUTICAL SCIENCES ETC.


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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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Important Dates
IJIFR APRIL 2026 EDITION (CONTINUOUS 153 EDITION)
VOLUME 13, ISSUE 8, APRIL 2026
FINAL ORIGINAL PAPER SUBMISSION TILL
27-APRIL-2026
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WITH IN 3-7 DAYS AFTER AUTHENTIC REVIEWED PROCESS
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