International Journal of Research,Exploration & Knowledge
Promoting quality research and knowledge sharing for a better tomorrow. An international, peer-reviewed, open-access journal promoting research, exploration, and knowledge across its defined academic areas.
Publication Ethics & Integrity
IJREK follows documented publication ethics, research integrity, plagiarism prevention, and peer-review practices.
Ethical Standards
Global Accessibility
IJREK aims to make published research openly accessible to readers and researchers worldwide.
Worldwide Access
Open Access
CC BY 4.0 universal accessibility ensuring that your scholarly publications can be read, cited, redistributed, and built upon without paywalls or barriers.
Creative Commons CC-BY
Editorial & Review Timeline
Initial technical screening is targeted within 24–48 hours, followed by double-anonymized evaluation by two independent expert reviewers.
Target: 24–48h ScreeningPublished Articles & Current Issue
Peer-reviewed scholarly investigations selected for high empirical impact and policy relevance.
Early Cancer Detection using AI
Cancer remains a major global health challenge, and outcomes are often improved when disease is identified at an early stage. Artificial intelligence (AI) can support screening and clinical decision-making by analysing medical images, pathology slides, laboratory measurements, and structured clinical records. This paper reviews a conceptual framework for AI-assisted early cancer detection, focusing on data preparation, model development, clinical validation, explainability, and safe integration into healthcare workflows. It outlines how machine-learning and deep-learning methods may help identify suspicious patterns and prioritize cases for expert review. The proposed workflow emphasizes that AI output is decision support rather than a replacement for qualified clinicians. Data quality, population representativeness, privacy, bias, and external validation are treated as core requirements. The paper also presents an evaluation plan using sensitivity, specificity, precision, F1-score, calibration, and subgroup analysis. No clinical trial or original patient-data experiment is claimed; the framework is intended as a structured basis for future implementation and validation.
Design and Evaluation of a Mobile-Friendly College Information
College websites are important sources of information about courses, examinations, notices, contact details, and student services. However, a website that is difficult to navigate on a small screen or that does not follow accessibility principles can create barriers for students, including people who use assistive technologies. This study proposes the design and evaluation of a mobile-friendly college information portal using usability testing and accessibility checks. The objective is to determine whether a redesigned interface helps users complete common information-seeking tasks more successfully and with fewer errors, while also improving conformance with selected accessibility practices. A lightweight prototype will be developed using HTML, CSS, and JavaScript, with responsive layouts and accessible navigation, headings, labels, contrast, and form controls. The evaluation will compare an initial interface with an improved version using predefined tasks such as locating course information, finding an examination notice, and submitting an enquiry. Measures will include task completion rate, task time, user errors, automated accessibility findings, and perceived usability measured with the System Usability Scale. At the time of this draft, implementation and user testing have not been completed; therefore, no empirical results are claimed. The final paper will report observed measurements, discuss limitations, and identify practical design recommendations based on the evidence collected. The intended contribution is a reproducible, low-cost evaluation procedure for improving the usability and accessibility of college information portals.
Predictive Mechine maintenace using AI&ML
Unexpected machine failures can interrupt production, increase repair expenses, and create safety risks. Predictive machine maintenance uses sensor data, Artificial Intelligence (AI), and Machine Learning (ML) to estimate when equipment may develop a fault. Instead of servicing every machine at fixed intervals or waiting for a breakdown, the system continuously monitors parameters such as vibration, temperature, pressure, current, and operating hours. Data is cleaned and analysed, and a trained ML model identifies abnormal behaviour or predicts the remaining useful life of a component. The system can then generate an alert so that maintenance is planned before failure occurs. This paper explains the concept, working process, major technologies, model design, applications, benefits, limitations, and a practical implementation framework. It also discusses data quality, model evaluation, cybersecurity, and human verification. Predictive maintenance can improve equipment availability and maintenance planning, but its results depend on representative data, suitable sensors, and regular model monitoring.
User experience and trust in AI powered virtual assistant
AI-powered virtual assistants are increasingly used for information retrieval, task support, customer service, and everyday digital interaction. Their usefulness depends not only on technical performance but also on the experience they provide and the trust users place in their responses. This paper presents a conceptual study of user experience and trust in AI-powered virtual assistants. It examines factors such as perceived usefulness, ease of use, response quality, transparency, privacy, reliability, and the handling of errors. A proposed research framework connects these factors with user satisfaction, trust, and intention to continue using an assistant. The paper outlines a survey-based methodology using a structured questionnaire and proposes descriptive and relationship-based analysis. As this is a proposed study, no participant data or statistical findings are claimed. The framework highlights the importance of clear communication, privacy protection, consistent performance, and appropriate disclosure of AI limitations for developing trustworthy virtual assistants.
Explainable Machine Learning-Based Student Academic Performance Prediction
Student academic performance prediction can help educational institutions identify learners who may benefit from timely, targeted support. Machine-learning models can combine carefully selected indicators such as prior assessment results, attendance, assignment completion and learning-platform engagement to estimate likely performance. However, a prediction that cannot be explained may be difficult for teachers and students to trust, and may conceal data quality problems or unfair patterns. This paper presents a conceptual framework for explainable machine-learning-based student academic performance prediction. The proposed workflow includes data governance, preprocessing, feature engineering, baseline and advanced model comparison, interpretable explanations, fairness checks, and human-led intervention. Candidate models include logistic regression, decision trees, random forests and gradient-boosted trees, with SHAP- or permutation-based explanations where appropriate. Evaluation should use held-out data, class-sensitive metrics, calibration and subgroup analysis rather than accuracy alone. The framework is designed to support—not replace—professional educational judgement. No original dataset or experimental result is claimed; model performance must be established through a documented local study.
AI-Powered Phishing Detection
Phishing is a social-engineering threat in which attackers impersonate trusted organisations or individuals to persuade users to disclose information, open harmful attachments, or visit deceptive websites. Conventional filters can be challenged by newly created domains, changing message patterns, and carefully crafted text. This paper proposes an AI-Powered Phishing Detection system that analyses selected email, URL, and message features to estimate whether an item may be suspicious. The workflow includes input validation, feature extraction, machine-learning classification, risk scoring, explanation of influential indicators, and a user-facing warning or review action. Candidate features include URL structure, domain signals, message wording, sender metadata, and selected email-header indicators where available. The system is a defensive screening aid, not a guarantee that a message is safe or malicious. The paper describes architecture, workflow, implementation, evaluation, applications, and limitations. Diagrams and chart values are illustrative, not experimental results.
Aims & Scholarly Scope
IJREK solicits original research contributions, structured systematic reviews, and critical methodological discourse across fifteen specialized technology domains.
Artificial Intelligence & Machine Learning
Deep learning architectures, neural networks, computer vision, natural language processing, reinforcement learning, generative models, and trustworthy AI.
Explore Track ScopeCybersecurity & Information Security
Network defense, zero-trust architectures, cryptography, intrusion detection, software vulnerability mitigation, and privacy preservation.
Explore Track ScopeCloud Computing & Distributed Systems
Cloud-native architectures, distributed consensus, serverless frameworks, edge-to-cloud computing, and resilient distributed data systems.
Explore Track ScopeData Science & Big Data
Big data analytics, data-driven modeling, predictive algorithms, data management architectures, and large-scale computational statistics.
Explore Track ScopeInternet of Things & Embedded Systems
Industrial IoT architectures, wireless sensor networks, real-time embedded computing, cyber-physical systems, and edge telemetry.
Explore Track ScopeRobotics, Automation & Emerging Tech
Autonomous robotic systems, intelligent automation, control engineering, quantum computing paradigms, and novel applied computing.
Explore Track ScopeEditorial Board Spotlight
Guided by an editorial team and peer reviewers supporting the journal's scholarly review and publication process.
Dr. Sachin Bere
IJREK
Mr. Bhosale S. S.
IJREK
Mrs. Waghmode K. H.
IJREK
Mrs. Dhage T. S.
IJREK
Why Publish With IJREK?
We partner with authors to maximize research visibility, ensure fair evaluations, and protect intellectual ownership.
Unrestricted Global Scholarly Dissemination
Published articles are openly accessible under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Readers access, cite, and reference IJREK publications without subscription paywalls or institutional lockouts.
- Crossref DOI — Active (Prefix: 10.68722)
- Google Scholar — In Progress
- DOAJ Application — Planned