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.
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IJREK follows documented publication ethics, research integrity, plagiarism prevention, and peer-review practices.
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IJREK aims to make published research openly accessible to readers and researchers worldwide.
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CC BY 4.0 universal accessibility ensuring that your scholarly publications can be read, cited, redistributed, and built upon without paywalls or barriers.
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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.
Agentic Retrieval-Augmented Generation (RAG): An Adaptive Framework for Evidence-Grounded Question Answering
Retrieval-Augmented Generation (RAG) connects a large language model (LLM) to external knowledge sources so that responses can be based on evidence beyond the model’s parameters. Conventional RAG pipelines commonly follow a mostly fixed sequence: retrieve a small set of passages, place them in a prompt, and generate an answer. This approach can be inadequate for complex questions that require multiple searches, tool use, source comparison, or correction when the first retrieval is weak. This paper proposes a modular Agentic RAG framework in which specialized agents coordinate query planning, retrieval, tool selection, evidence grading, answer generation, and self-reflection. The system maintains a bounded feedback loop: if retrieved evidence is insufficient, the planner revises the query or selects an additional source; if evidence is adequate, a generator produces a response with citations and a confidence assessment. The paper synthesizes relevant work on RAG, reasoning-and-acting agents, self-reflection, corrective retrieval, and graph-based retrieval. It also presents an evaluation framework covering answer quality, retrieval and grounding quality, efficiency, and ablation tests. The included diagrams illustrate the proposed architecture, the contrast with single-pass RAG, and the evaluation workflow. Because no implementation or benchmark run is reported here, performance charts are explicitly conceptual and the evaluation plan is a proposal rather than a claim of measured improvement. The contribution is a practical, auditable design blueprint for building and evaluating Agentic RAG systems.
Blockchain-Based Secure Academic Credential Verification
Academic credential fraud, manual verification delays, fragmented institutional databases, and limited interoperability motivate trustworthy digital credential systems. This paper proposes a permissioned blockchain architecture for issuing, storing, and verifying academic credentials while minimizing exposure of personally identifiable information. The design combines digital signatures, cryptographic hashes, a permissioned ledger, encrypted off-chain storage, issuer authorization, and an explicit revocation lifecycle. The paper presents the complete issuer–holder–verifier workflow, threat model, privacy considerations, implementation architecture, experimental methodology, evaluation metrics, security analysis, limitations, and future scope. Numerical experimental results are intentionally not fabricated; the evaluation section defines a reproducible procedure for obtaining them.
Privacy_Preserving_ML_Sensitive_Data
Machine learning increasingly relies on sensitive data, creating risks of unauthorized disclosure, inference, memorization, and misuse. This paper proposes a layered privacy-preserving machine-learning framework combining federated learning, differential privacy, secure aggregation, and optional encrypted computation. Raw records remain at participating organizations while protected model updates are aggregated for global learning. The paper presents the threat model, formal privacy concepts, system architecture, training workflow, attack analysis, experimental setup, utility and privacy metrics, comparative analysis, limitations, and future research directions. Numerical experimental results are intentionally not fabricated; the paper defines a reproducible evaluation procedure for generating valid measurements.
Deep Learning-Based IoT Anomaly Detection
The scale and heterogeneity of Internet-of-Things deployments create a large attack surface and generate complex network behavior. This paper proposes a deep-learning anomaly-detection framework that learns representations of IoT traffic and identifies deviations from expected behavior. The design uses preprocessing, temporal representation learning, anomaly scoring, and an alert aggregation layer. Evaluation is defined around precision, recall, F1-score, false-positive rate, and detection latency. Illustrative performance values are synthetic and included only as a formatting example.
An AI-Based Personalized Learning System
Personalized learning requires more than predicting whether a learner will pass or fail. A useful learning system must connect performance evidence to topic-level diagnosis, an appropriate level of challenge, and recommendations that a learner can understand and act upon. This paper proposes an AI-based personalized learning framework that combines student-performance prediction, topic-wise weakness detection, recommendation rules, and an explainability layer. The framework is designed to integrate previous examination marks, quiz scores, assignment performance, attendance, number of attempts, learning-activity completion, and time-on-task when these variables are available. For empirical benchmarking, the verified UCI Student Performance dataset is selected as a reproducible public baseline. The UCI repository reports 649 instances and 33 attributes; the dataset supports classification and regression and contains final grade G3 together with prior-period grades and school, demographic, social, and study-related variables. Because the public dataset does not contain genuine LMS event logs or topic-tagged assessment records, it is used only for the performance-prediction component. Topic-level recommendation is specified as a deployable framework requiring richer course/LMS data. Five supervised models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine—are proposed for controlled comparison using stratified cross-validation and Accuracy, Precision, Recall, F1-score, ROC-AUC, and confusion matrices. Recommendation quality is to be evaluated separately with Precision@K, Recall@K, and NDCG@K after implementation. No experimental accuracy or recommendation score is reported in this manuscript because the dataset could not be downloaded into the execution environment and no completed experiment is being represented as performed. The main contribution is therefore a reproducible research design that explicitly links prediction to actionable, explainable learning recommendations while identifying the data requirements and evaluation boundaries of the public benchmark.
AI-Based Hospital Management System: A Modular Framework for Operational Intelligence and Decision Support
Hospitals manage interconnected activities including patient registration, appointment scheduling, clinical documentation, laboratory coordination, pharmacy, bed allocation, inventory, and billing. Fragmented information systems can make these workflows difficult to coordinate and can limit timely operational decisions. This paper proposes a modular AI-based hospital management framework that combines a conventional hospital information system with carefully scoped machine-learning services. The proposed architecture includes a data integration layer, role-based workflow modules, AI services for appointment and resource forecasting, operational anomaly detection, and decision-support interfaces. The design emphasizes that AI outputs should assist—not replace—qualified staff, particularly where patient care is involved. A reproducible evaluation plan is specified using workflow completion time, scheduling utilization, forecast error, alert precision/recall, system latency, and user acceptance measures. Since no prototype deployment or controlled experiment has been performed for this manuscript, all outcome metrics are marked “To be measured”; no performance improvements are claimed. The paper discusses interoperability, data quality, privacy, bias, explainability, cybersecurity, and implementation constraints. The contribution is a system design and evaluation blueprint that can guide subsequent implementation and empirical study.
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
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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 — Planned
- Google Scholar — Planned
- DOAJ Application — Planned