From Packet Traces to Contradiction Scores: A Neutrosophic Signature Calculus for Real-Time IoT Intrusion Attribution

Rozina

doi:https://doi.org/10.54216/NIF.050202

International Journal of Artificial Intelligence and Education Technology | 2026

A Systematic Literature Review on AI-Based Quiz and Assessment Systems for Adaptive Learning

AI-based quiz and assessment tools are widely studied for supporting adaptive learning, yet existing work is distributed across different tasks (e.g., question generation, automatic evaluation, feedback, and conversational assessment) and often uses inconsistent datasets and metrics, making comparisons difficult. This paper reports a Systematic Literature Review (SLR) conducted under PRISMA 2020 to summarize approaches and evaluation practices for AI-based quiz and assessment systems. Searches were performed in IEEE Xplore, ACM Digital Library, and Google Scholar using keyword combinations related to automated question generation, assessment, evaluation, and large language models. The search returned Nidentified=57 records; after duplicate removal, Ndedup=55 records remained for screening. Following title/abstract screening and full-text eligibility assessment, Nincluded=9 studies were included for qualitative synthesis and structured data extraction. The reviewed studies show strong attention to transformer/LLM-based question generation, automatic scoring and evaluation frameworks, and formative feedback generation for learning. However, recurring limitations include reliability of automated judging, lack of standardized benchmarks, domain transferissues, and risks impacting fairness and academic integrity. We conclude with practical recommendations for stronger evalua-tion design (e.g., shared benchmarks, transparent rubrics, and human-in-the-loop validation) to improve trust and real-world adoption.

groups

Islombek Abdurakhmanov mail

link https://doi.org/10.54216/IJAIET.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2026

Sustainable Development and Green Technology: A Critical Review of Advances, Challenges, and Strategic Pathways for the Post-Carbon Era (2020–2025)

The intersection of sustainable development and green technology has emerged as one of the most intensively studied and consequential domains in contemporary science and engineering, and between 2020 and early 2026, accelerating climate commitments, post-pandemic economic recovery packages, and unprecedented cost reductions across clean energy pathways fundamentally altered the terms of the decarbonisation debate. This paper presents a systematic review of more than 50 peer-reviewed studies and authoritative reports published during this period, synthesising evidence across six thematic clusters—solar photovoltaics and concentrated solar power, wind energy, green hydrogen, electrochemical energy storage, carbon dioxide removal, and the circular economy—and map-ping publication trends, performance benchmarks, and knowledge gaps across disciplines. Beyond the bibliometric synthesis, the paper introduces a novel integrated assessment instrument: the Green Technology Sustainability Convergence (GTSC) Framework, which scores technologies simultaneously on five weighted dimensions (technology readiness, economic viability, environmental performance, social equity and justice, and policy and governance readiness) to yield a composite index enabling cross-sector comparison and research prioritisation. Applied to six technology clusters, the GTSC reveals a persistent hierarchy in which solar PV and onshore wind achieve the highest convergence scores (≥7.8 out of 10), while direct air capture and bioenergy with carbon capture and storage remain below 5.0, constrained by cost barriers, nascent infrastructure, and unresolved governance frameworks. Three over-arching research challenges emerge from the synthesis: the critical mineral bottleneck that threatens supply chains underpinning virtually every green technology; the widening digital–physical sustainability divide, whereby AI-assisted optimisation tools are advancing faster than the physical infrastructure and institutional capacity required to act on their outputs; and the persistent gap between nationally determined contributions and the technology deployment rates needed to remain within 1.5 °C of warming. The paper concludes with a structured research agenda and decision-support guidance for researchers, funding bodies, and policymakers working in this field.

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Irina V. Austokhina mail -

Aenis A. Austokhin mail

link https://doi.org/10.54216/JSDGT.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2026

Early Identification of At-Risk Students in Virtual Learning Environments Using Ensemble Machine Learning and Behavioural Analytics

The academic success of students who are nearing academic failure should be Identifying students who are at risk of academic failure or course withdrawal at an early stage of their enrolment remains one of the most pressing challenges in higher and distance education. The research assesses the performance of seven machine learning classifiers which include Logistic Regression Decision Tree Random Forest Gradient Boosting Decision Tree (GBDT) AdaBoost Naive Bayes and Multilayer Perceptron for predicting student risk at an early stage based on a behavioural and demographic dataset derived from the Open University Learning Analytics Dataset (OULAD). The dataset contains 7895 student records which represent a single module and show eight demographic factors together with eight Virtual Learning Environment (VLE) usage patterns. All classifiers were evaluated through five-fold stratified cross-validation. The GBDT model achieved the best results with an AUC-ROC value of 0.782 (} 0.003) and an accuracy rate of 0.708 (} 0.005) which produced an F1 score of 0.729 (} 0.006) and a recall rate of 0.769 (} 0.006). The analysis of feature importance showed that late sub-mission count (I = 0.304) and total VLE clicks (I = 0.150) together with first assessment score (I = 0.135) serve as the three most valuable predictive indicators because they help identify student engagement patterns which become evident through VLE traces that educational institutions collect from students during their first module. Educational institutions can utilize learning management system data to implement effective combi-nation methods which enable them to execute necessary teaching methods even though they do not need to gather additional expense data. The article presents design elements which both create early warning systems and manage the ethical use of predictive analytics within educational systems.

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Ahmed Abd El-Badie Abd Allah Kamel mail

link https://doi.org/10.54216/IJAIET.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2025

A Single-Valued Neutrosophic Weighted Aggregation Framework for Multi-Attribute Heart Disease Risk Assessment: An Information Fusion Perspective

Reliable early detection of cardiovascular disease requires integrating multiple clinical indicators under conditions of uncertainty, partial measurement, and inconsistent expert knowledge. This paper introduces a Single-Valued Neutrosophic Weighted Aggregation (SVNS-WA) framework that systematically models three independent dimensions of clinical information—truth-membership (T ), indeterminacy-membership (I), and falsity membership (F)—to produce an interpretable composite risk score for binary heart disease classification. Feature weights are derived from an entropy measure defined over neutrosophic components, ensuring that more discriminative attributes receive proportionally greater influence during aggregation. A score function S(x) = (2 + Tagg − Iagg −Fagg)/3 maps each aggregated neutro-sophic value to the unit interval, and an optimal decision threshold is identified via Youden’s J statistic. Experiments on the publicly available UCI Cleveland Heart Disease Dataset (n = 303) yield an area under the ROC curve (AUC) of 0.765 and a sensitivity of 83.45%, demonstrating the framework’s ability to capture indeterminate, disease-relevant information without supervised parameter optimisation. A detailed mathematical analysis establishes the convergence and monotonicity properties of the proposed aggregation operator, and a comparative study against Logistic Regres-sion, Decision Tree, Random Forest, and SVM classifiers contextualises the trade-off between predictive accuracy and interpretable uncertainty quantification. The discussion section examines implications for clinical decision support and identifies directions for extending the framework with interval neutrosophic operators and deep-feature integration.

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Jeong Chan Park mail -

Sajid Khan mail

link https://doi.org/10.54216/NIF.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2025

Dynamic Reliability Kernels for Single-Valued Neutrosophic Evidence Fusion: A Mathematical Model for Multi-Source Market-State Classification

Multi-source decision systems require a representation in which supportive evidence, contradictory evidence, and weak evidence are not collapsed into the same numerical channel. This paper develops a dynamic reliability-kernel model for single-valued neutrosophic evidence fusion. Given a matrix of source signals, each source is transformed into a single-valued neutrosophic triplet whose truth, indeterminacy, and falsity memberships are governed by signed evidence strength. A time-varying reliability kernel then assigns larger mass to sources with lower recent instability, and a dispersion-augmented fusion operator produces a global neutrosophic state. The final decision rule is formulated as a penalized neutrosophic score and as a regularized probabilistic classifier over the fused triplet. The model is evaluated on a public weekly stock dataset containing six technology-market sources. The results show that the proposed representation achieves competitive chronological classification performance while providing explicit mathematical control over indeterminacy, disagreement, and reliability. Ablation and penalty-sensitivity analyses demonstrate that indeterminacy is a functional component of the decision model rather than a cosmetic label. The paper offers a reproducible mathematical framework for neutrosophic information fusion in uncertain intelligent decision-support systems.

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Samandarboy Sulaymanov mail -

Maha Ibrahim mail

link https://doi.org/10.54216/NIF.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2025

Neutrosophic Cosine Similarity Fusion with CRITIC-Weighted Ideal Profile Matching for Multi-Attribute Diabetes Risk Stratification: Evidence from the CDC BRFSS 2021 Dataset

Accurate stratification of diabetes risk requires integrating clinically heterogeneous indicators under conditions of measurement ambiguity, borderline readings, and inconsistent self-reported data. This paper introduces a Neutrosophic Cosinesimilarity with CRITIC-weighted ideal-profile matching (NCRS-CRITIC) framework that maps each patient record to an ideal disease profile and an ideal healthy profile simultaneously, using neutrosophic truth, indeterminacy, and falsity membership functions. The degree of closeness to each profile is measured through a weighted neutrosophic cosine similarity, where feature weights are derived via the CRITIC (CRIteria Importance Through Intercriteria Correlation) method— capturing both the discriminative variability and the inter-feature correlation structure objectively. A relative closeness coefficient (RC) aggregates dual-profile similarity into a scalar risk score that respects both the evidence for and against disease simultaneously. Experiments on a balanced 2000-instance subset of the CDC Behavioral Risk Factor Surveillance System (BRFSS) 2021 Diabetes Health Indicators Dataset achieve an area under the ROC curve (AUC) of 0.869 and accuracy of 79.5% under ten-fold cross-validation, competitive with fully supervised classifiers including Gradient Boosting Trees, Logistic Regression, and Gaussian Naive Bayes. The framework’s mathematical properties—symmetry of the cosine measure, triangle inequality satisfaction, and weight convergence under vanishing intra-feature variance—are formally proved. A comprehensive discussion examines the clinical implications of the dual-profile architecture, the role of CRITIC weighting in capturing correlated health indicators, and directions for extending the framework to interval neutrosophic representations and ensemble neutrosophic fusion.

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Dae Yu Kim mail -

Jeong Chan Park mail

link https://doi.org/10.54216/NIF.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2025

Rough-Neutrosophic Evidence Lattices for Healthcare-Utilization Stratification: Fusing Sleep and Wellness Indicators in the 2023 NPHA Dataset

Healthcare-utilization prediction from survey data is mathematically difficult because the observable variables are categorical, self-reported, and partially discordant. A respondent may report poor physical health but no sleep disruption, or regular sleep-medication use with favorable mental-health ratings. Such cases are not well represented by classifiers that collapse all evidence into a single likelihood vector. This paper proposes a rough neutrosophic evidence-lattice model for stratifying older adults according to the number of doctors visited in a year. The model maps categorical sleep and wellness indicators into single-valued neutrosophic triples, estimates entropy-based evidence weights, introduces a rough boundary term from local equivalence classes, and ranks each respondent using an indeterminacy-penalized decision functional. The method is evaluated using the 2023 UCI National Poll on Healthy Aging schema and a reproducible computational implementation. The results show that the proposed lattice-based formulation improves macro-F1 over conventional categorical baselines while preserving interpretable truth, falsity, and indeterminacy degrees for each utilization class.

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Sajid Khan mail -

Arash Salehpour mail

link https://doi.org/10.54216/NIF.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2025

Neutrosophic Cubic Correlation Fusion with Jensen–Shannon Divergence Weighting for Multi-Pollutant Urban Air Quality Index Estimation

Estimating whether ambient air quality exceeds regulatory thresholds requires combining evidence from multiple co-measured pollutants whose concentrations are simultaneously uncertain, interdependent, and subject to instrument noise. This paper introduces a Neutrosophic Cubic Correlation Fusion (NC-CF) model that represents each pollutant observation as a neutrosophic cubic value—a structure that simultaneously encodes an interval-valued membership [𝑇𝐿 , 𝑇𝑈] capturing measurement uncertainty and a crisp neutrosophic triple (𝑡, 𝑖, 𝑓 ) capturing the nominal risk assessment—and then quantifies closeness to ideal pollution profiles through a novel neutrosophic cubic correlation coefficient (NCC). Feature weights are derived from Jensen–Shannon (JS) divergence between class-conditional NCC distributions, providing an information-theoretically justified allocation of influence across pollutants without requiring labelled calibration. Experiments on a balanced 1500-instance subset of the Global Air Quality Dataset (Kaggle, 2023), comprising PM2.5, CO, Ozone, and NO2 measurements from world cities, demonstrate classification accuracy of 99.0% and AUC of 0.9996 under ten-fold cross-validation, matching or exceeding Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Trees. A systematic sensitivity analysis over the interval-to-crisp interpolation parameter 𝜆 ∈ [0, 1] reveals stable performance across the full range, confirming that the NCC’s interval component does not introduce instability. The mathematical properties of the neutrosophic cubic correlation coefficient—its reduction to standard cosine similarity for crisp inputs, its behaviour under ideal profile extremes, and the convergence of JS weights under increasing class separability—are formally established.

groups

Anvar Suleymanov mail -

Murod Khidoyatov mail

link https://doi.org/10.54216/NIF.050105

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Vol. Volume 5 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2025

Recurrence Shadow Mapping under Neutrosophic Clinical Evidence: An Uncertainty-Oriented Model for Post-Treatment Healthcare Decision Support

Post-treatment follow-up in differentiated thyroid cancer requires a decision model that is not limited to binary recurrence prediction. Patients may present with partially reassuring anatomical findings, incomplete biochemical response, hetero-geneous pathological subtype, or contradictory clinical history. These situations are better described as a triadic state composed of support for recurrence, support against recurrence, and unresolved indeterminacy. This paper proposes a recurrence shadow mapping model based on single-valued neutrosophic clinical evidence. The model transforms clinico-pathologic descriptors into truth, indeterminacy, and falsity memberships; aggregates evidence through entropy-contrast weighting; and produces a recurrence-shadow index that separates stable, observation, alert, and high-alert follow-up states. The proposed method is designed for healthcare decision support rather than automatic replacement of clinical judgment. Its mathematical contribution is a bounded neutrosophic score that penalizes inconsistent evidence without suppressing clinically meaningful warning signals. Experimental evaluation demonstrates that recurrence-oriented evidence sources can be expressed in a transparent mathematical form, and that indeterminacy itself becomes an interpretable clinical quantity. The findings support the use of neutrosophic information fusion for medical cases where uncertainty is structural rather than merely statistical.

groups

Murodbek Ahrorov mail -

Ahmed Aziz mail

link https://doi.org/10.54216/NIF.050201

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2025

From Packet Traces to Contradiction Scores: A Neutrosophic Signature Calculus for Real-Time IoT Intrusion Attribution

Real-time Internet of Things intrusion attribution is often formulated as direct multi-class classification, although packet traces contain incomplete, conflicting, and imbalanced evidence. This paper develops a mathematical neutrosophic signature calculus in which each flow is represented by truth, indeterminacy, and falsity memberships over class-specific attack signatures. The proposed model constructs entropy-contrast behavioral channels, maps each flow to class prototypes through a contradiction-aware single-valued neutrosophic transformation, and derives a closed-form attribution rule by coupling prototype truth, opposite-region falsity pressure, and explicit indeterminacy penalization. The study uses RT-IoT2022, a public UCI benchmark donated in 2024 with 123,117 flows, 83 features, and 12 normal/attack labels. The results show that the proposed calculus provides interpretable class attribution and stable macro-level behavior under severe class imbalance. The work supports neutrosophic signature modeling as a transparent route for IoT security decision support under inconsistent network evidence.

groups

Rozina Ali mail

link https://doi.org/10.54216/NIF.050202

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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