Publication Status
IJIFR © 2025

Licensed under CC BY-NC-SA 4.0

CC BY NC SA
Today
Tuesday, April 21, 2026 04:23 PM
You are visitor Number:
265441
Recently Publish
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


Paper ID IJIFR/V13/E8/039 Page No.: 1197-1205

Subject Area Computer Engineering

Authors Koppala Jagadeesh
V.Vijayalakshmi
Dr.S.Usharani

Abstract The insurance industry processes millions of claims annually through predominantly manual, paper-based workflows that impose substantial administrative overhead, systemic processing delays, and significant vulnerability to fraudulent submissions. These inefficiencies translate directly into customer dissatisfaction, escalating operational costs, and regulatory compliance risk. Despite incremental digitisation efforts in recent decades, the majority of mid-tier and small-scale insurance operators continue to rely on manual adjudication processes that lack systematic validation mechanisms, consistent decision frameworks, and real-time transparency for policyholders. This paper presents an AI-Driven Insurance Claim Processing System, a comprehensive web-based platform developed using the Django 4.x framework, Python 3.10, and SQLite — engineered to automate and orchestrate the complete lifecycle of insurance claim management. The proposed system implements a three-tier Model-View-Template (MVT) architecture encompassing a responsive presentation layer, a rule-based application logic engine, and a relational data persistence layer. Seven functionally decomposed modules address user authentication, role-based access control, policy management, automated claim validation, administrative adjudication, real-time status notification, and database lifecycle management. The core contribution of the system is a deterministic automated validation engine that evaluates each submitted claim against two critical conditions — policy coverage limit adherence and policy temporal validity — eliminating ineligible claims at the point of submission without human intervention. Validated claims are routed to an administrative dashboard providing centralised oversight, statistical summaries, and structured approval workflows. Empirical evaluation on a simulated dataset of 500 claims demonstrates a claim processing time reduction of 74.3% relative to a manual baseline, a validation accuracy of 99.6%, and a false positive rejection rate of 0.4%. The system's modular Django architecture ensures extensibility toward future integration of machine learning-based fraud detection, OCR-driven document processing, and cloud-scale PostgreSQL deployment.

Keywords Insurance claim processing; Django MVT architecture; automated validation; rule-based classification; role-based access control; digital workflow automation; claim adjudication


Paper ID IJIFR/V13/E8/038 Page No.: 1189-1196

Subject Area Computer Engineering

Authors Kondakavali Vani
V.Vijayalakshmi
Dr.S.Usharani

Abstract Construction sites consistently rank among the most hazardous occupational environments worldwide, with head injuries from falling or flying objects identified as a primary contributor to construction fatalities in every major market. Despite the universal regulatory mandate for safety helmet usage, non-compliance remains pervasive owing to the practical impossibility of maintaining continuous manual supervision across large, complex sites. Traditional automated monitoring approaches based on conventional computer vision techniques have demonstrated insufficient accuracy for reliable deployment in the visually complex conditions typical of active construction environments, while commercial AI-based platforms impose subscription costs prohibitive to small and medium contractors. This paper presents SafeHelmet Vision AI, a deep learning-based industrial safety monitoring system designed to automate helmet compliance detection at construction sites and related industrial workplaces. The proposed system employs a YOLOv8n (nano) object detection model trained via transfer learning from COCO-pretrained weights on a domain-specific dataset of 4,200 annotated construction site images encompassing 9,800 helmet and 8,200 worker bounding box instances. Training was conducted for 100 epochs with AdamW optimisation (lr0 = 0.001), comprehensive data augmentation including mosaic, HSV perturbation, random flip, rotation (±10°), and scale variation (±50%), yielding a validation mean Average Precision at IoU = 0.50 (mAP50) of 97.8%, a precision of 95.2%, and a recall of 94.9%. The trained model is integrated into a Streamlit web application that accepts uploaded construction site images in JPG, PNG, or BMP formats and returns annotated detection results with bounding boxes, class labels, and confidence scores within a mean inference latency of 165 milliseconds on standard CPU hardware. An automated safety compliance assessment engine evaluates helmet-to-person count ratios and generates colour-coded violation alerts. Comparative evaluation demonstrates a 27.2 percentage-point precision advantage and a 33.9 percentage-point recall advantage over a traditional Haar cascade baseline. The complete system requires no client-side installation and is deployable to Streamlit Cloud from a GitHub repository with a single configuration step, making enterprise-grade safety monitoring accessible to safety personnel without specialised technical training.

Keywords Safety helmet detection; YOLOv8; construction site safety; personal protective equipment; transfer learning; real-time object detection; Streamlit deployment


Paper ID IJIFR/V13/E8/037 Page No.: 1180-1188

Subject Area Computer Science

Authors Pudu Bhargava
S. Manjunath Reddy

Abstract Employee attrition is a major challenge for organizations, leading to productivity loss, higher recruitment costs, and disruption in operations. This paper presents an Employee Performance and Attrition Prediction System that combines HR management with machine learning and generative AI. The system is built using Django with SQLite as the database and uses Python libraries such as Scikit-learn for prediction, Openpyxl for data export, and Google Gemini API for generating explanations. It stores structured employee data including performance, attendance, and evaluations for analysis. A classification model predicts attrition risk based on factors like performance rating, projects completed, and salary. To improve interpretability, a generative AI module explains predictions in simple HR-friendly language. Overall, the system improves data management, enhances prediction accuracy, and provides an easy-to-understand, scalable solution for workforce analysis and decision-making.

Keywords Seismic Forecasting; LSTM; ARIMA; Time Series Analysis; Disaster Management


Paper ID IJIFR/V13/E8/035 Page No.: 1172-1179

Subject Area Computer Science

Authors Patnam Jayasree
Manjunath Reddy
Dr. S. Usharani

Abstract Earthquakes are among the most devastating natural disasters due to their sudden occurrence and the severe damage they cause to human life and infrastructure. Traditional seismic monitoring systems primarily focus on real-time detection and post-event analysis, offering limited capability for forecasting future seismic activity. This paper presents SeismoPredict AI, an intelligent seismic activity forecasting system that leverages both deep learning and statistical approaches to analyze historical earthquake data and predict future trends. The proposed system integrates Long Short-Term Memory (LSTM) networks to capture complex non-linear temporal dependencies and Autoregressive Integrated Moving Average (ARIMA) models to provide stable and interpretable time-series forecasts.The system is designed with a user-friendly interface using Streamlit, enabling users to upload datasets, visualize seismic patterns, train predictive models, and generate forecasts without requiring advanced technical expertise. Additionally, the system incorporates automated risk classification and alert generation mechanisms to support early warning and disaster preparedness. Experimental analysis demonstrates that the hybrid LSTM–ARIMA approach improves prediction reliability and trend consistency compared to individual models. The proposed system serves as an effective decision-support tool for researchers, policymakers, and disaster management authorities by providing meaningful insights into seismic activity trends. Although precise earthquake prediction remains inherently uncertain, the system contributes to proactive risk assessment and enhances preparedness strategies through data-driven forecasting

Keywords Seismic Forecasting; LSTM; ARIMA; Time Series Analysis; Disaster Management


Paper ID IJIFR/V13/E8/034 Page No.: 1163-1171

Subject Area Computer Engineering

Authors Aluganti Vishnu Priya
M.Gowthami

Abstract DermAssist AI is an advanced artificial intelligence-powered dermatological diagnostic assistance system that leverages deep learning and computer vision to analyse skin lesion images and provide preliminary diagnostic insights for a wide spectrum of common skin conditions. The system is built upon a Convolutional Neural Network (CNN) architecture enhanced with transfer learning from a pre-trained EfficientNetB3 model, trained on the HAM10000 dataset containing over ten thousand labelled dermatoscopic images spanning seven diagnostic categories: Melanocytic nevi, Melanoma, Benign keratosis-like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, and Dermatofibroma. The complete data science and software engineering lifecycle is implemented, encompassing systematic data preprocessing, augmentation, model training, performance evaluation using medical-grade metrics (AUC, sensitivity, specificity), and deployment as an interactive Flask web application. An explainability layer using Gradient-weighted Class Activation Mapping (Grad-CAM) highlights the specific image regions most influential in the diagnostic prediction. The system achieves a macro-averaged AUC of 0.889 across all seven classes, demonstrating strong generalisation capability. DermAssist AI represents a meaningful contribution to the democratisation of dermatological care through artificial intelligence.

Keywords Skin Disease Detection, Deep Learning, EfficientNet, Transfer Learning, Grad-CAM, HAM10000, Dermatology AI, Flask Deployment, Medical Image Analysis, CNN








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.


Special Issue

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.


Editorial Policy

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.


Paper Submission

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.


Important Dates
Important Dates For Every Issue
Last Date of Paper Submission : 20th of Every Month
Acceptance Notification : Within 7-10 days after paper submission
Final Paper Acceptance Notification up to: 27th of Every Month
Online Publication of Papers: 30th of Every Month
Important Dates
IJIFR APRIL 2026 EDITION (CONTINUOUS 153 EDITION)
VOLUME 13, ISSUE 8, APRIL 2026
FINAL ORIGINAL PAPER SUBMISSION TILL
27-APRIL-2026
ACCEPTANCE NOTIFICATION
WITH IN 3-7 DAYS AFTER AUTHENTIC REVIEWED PROCESS
PAPER ID ACKNOWLEDGEMENT
ONLY AFTER ONLINE SUBMISSION
Join Editorial Board
Announcement
Scholarly Open Access, Peer-reviewed, and Refereed Journal, Impact factor 8.057 , AI-Powered Research Tool , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI), Plagiarism Reports Generation, Scientific & Analytic Review System
Nominal Processing & Publishing Charges (Includes online publication, Indexing & Abstracting in various online repositories, hard copy of certificate to each author, hard copy of Paper& letter of acceptance in journal cover.
All manuscripts are reviewed in fairness based on the intellectual content of the paper regardless of gender, race, ethnicity, religion, citizenry nor political values of author(s).the IJIFR provides free access to research information to the international community without financial, legal or technical barriers. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and must not be under consideration for publication elsewhere. All the accepted papers of the journal will be processed for indexing into different citation databases that track citation frequency/data for each paper. Contributions will therefore be welcomed from practitioners, researchers, scholars and professional experts working in private, public and other organizations or industries.
High Impact Factor Journal with publication of more than 4200+ satisfied authors worldwide. IJIFR is indexed by Scientific Journal Impact Factor : 6.051, Index Copernicus Value(Icv): 6.62, Academia, Internet Archive, Techrepublic, Citeactor, Scribd, Jstor, World Cat, Road, Google Scholar, Slideshare, Jour Informatics, Genamics, Biblioteca, Scientific Research Indexing, LABII, ISRA, I2OR, Journal Index. Net, Newjour, Figshare, Citesser X , Open Access Journals, References*,Research Info, OAJI Indexing, E LIS, Genamics
IJIFR is approved by UGC & National Institute of Science Communication and Informational Resources (NISCIR), Delhi, India. Nominal Processing & Publishing Charges (Includes online publication, Indexing & Abstracting in various online repositories, hard copy of certificate to each author, hard copy of letter of acceptance (Original Papers Only) with additional benefits.
Authors are advised to submit authentic research work only.
Please feel free to contact us at [email protected] or [email protected]