Public Education Investment, Instructional Resources, and Student Achievement: Cross-National Evidence from PISA 2022 in the Context of Sustainable Development Goal 4

Reshma; Hanadi Osman

doi:https://doi.org/10.54216/IJAIET.040203

Neutrosophic and Information Fusion | 2025

A Neutrosophic Dempster-Shafer Evidence Fusion Framework with Conflict-Redistribution and Pignistic Decision for Multi-Source Water Potability Classification

Assessing drinking water safety requires integrating evidence from nine independent physicochemical measurements—pH, hardness, total dissolved solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity—each of which independently provides only weak discriminative power, so that conflicting evidence and high indetermi-nacy are structural features of the problem rather than anomalies. This paper develops a Neutrosophic Dempster-Shafer Evidence Theory (N-DSET) framework in which each measurement is treated as an independent evidence source mod-elled by a Neutrosophic Basic Probability Assignment (NBPA) constructed from class-conditional kernel densities. Evidence is fused through a modified Dempster combination rule that redirects inter-source conflict mass into the neutrosophic indeterminacy component rather than discarding it via normalisation—preserving epistemic information about measurement disagreement throughout the reasoning chain. Source reliability weights are derived from Deng entropy, and the final binary decision uses the pignistic probability transformation. Experiments on the Kaggle Water Quality Dataset (𝑛 = 3,276, Kaggle 2021) yield an AUC of 0.618 under ten-fold cross-validation, exceeding all five supervised baselines including Logistic Regression, Gradient Boosting Trees, and AdaBoost, whose AUC values lie in [0.521, 0.552] on this inherently ambiguous dataset. A sequential waterfall analysis demonstrates monotonically increasing AUC as each evidence source is successively fused, confirming the incremental value of each measure-ment. The belief-plausibility interval [𝐵𝑒𝑙(𝑃), 𝑃𝑙(𝑃)] provides a rigorous geometric characterisation of the three-way decision regions (Positive, Negative, Boundary), and its width—approximately 0.83—quantifies the structural indeter-minacy inherent in the potability classification task. Mathematical properties of the N-DSET operator—commutativity, associativity, convergence of conflict mass under growing evidence sets, and the equivalence of the combined pignistic probability to Bayesian posterior when no conflict is present—are formally established.

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Abd-Alrida Basheer mail

link https://doi.org/10.54216/NIF.050203

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2025

Indeterminacy Lattices for Diagnosing Mathematical Misconception Boundaries in Higher-Education Assessment Logs

Assessment records in digital mathematics platforms contain a form of uncertainty that is not sufficiently expressed by binary correctness labels. A wrong answer may indicate a stable misconception, a temporary slip, or an unobserved knowledge boundary; similarly, a correct answer may reflect mastery or procedural guessing. This paper proposes a neutrosophic-oriented diagnostic model for higher-education mathematics assessment logs. Each topic and subtopic is represented as a single-valued neutrosophic object whose truth component denotes observed mastery, falsity denotes misconception pressure, and indeterminacy denotes the conflict between local evidence and global answer tendency. A lattice ordering is then defined over these objects to identify misconception boundaries rather than only low-performing concepts. The model is evaluated on the 2024 MathE assessment dataset, which contains 9,546 student-question responses from 372 students answering 833 questions across eight countries. Results show that the proposed indeterminacy-aware calculus separates difficult mathematical regions more clearly than accuracy-only and association-rule baselines. Partial Differentiation, Derivatives, Complex Numbers, and algebraic expressions form the highest falsityindeterminacy region, while level alone has very weak association with answer polarity. The findings support neutrosophic diagnosis as a principled alternative to crisp pass/fail analytics in educational decision-support systems.

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Agnes Osagie mail

link https://doi.org/10.54216/NIF.050204

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2026

Truth–Indeterminacy–Falsity Fusion in Neutrosophic Intelligent Systems: A Mathematical Review, Algorithmic Taxonomy, and Research Agenda

Neutrosophic information fusion has become a rigorous computational approach for modeling evidence that is simultaneously supportive, opposing, and unresolved. This review synthesizes recent studies published from 2020 to 2025 and organizes the field around the operational semantics of truth, indeterminacy, and falsity. Rather than presenting neutrosophic sets only as an extension of fuzzy sets, the paper analyzes neutrosophic fusion as a mathematical problem of evidence representation, operator design, source weighting, contradiction control, and decision reduction. The review covers single-valued neutrosophic similarity measures, EDAS and TOPSIS extensions, neutrosophic Z-number aggregation, Einstein and Aczel–Alsina operators, trigonometric credibility operators, dynamic aggregation, divergence measures, uncertainty-aware multi-source information fusion, and evidence theoretic comparisons. A relatedwork section of more than twenty verified 2020–2025 studies is added, followed by a selection protocol, formal definitions, propositions, algorithms, operator-property analysis, and research directions. The paper concludes with a research agenda for benchmark construction, data-driven membership learn-ing, explainable indeterminacy, scalable dynamic fusion, and trustworthy integration of neutrosophic logic with intelligent decision-support systems.

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Murat Ozcek mail -

Arash Salehpour mail

link https://doi.org/10.54216/NIF.060101

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2026

Indeterminacy-Balanced Evidence Granulation for Ecotoxicological Prioritization under Single-Valued Neutrosophic Assessments

Decision environments that combine laboratory indicators, expert warnings, chemical descriptors, and regulatory traces rarely produce a single consistent description of risk. Classical aggregation rules usually collapse incomplete, contradictory, and partially reliable evidence into one scalar before the contradiction itself has been modelled. This paper develops an indeterminacy-balanced neutrosophic granulation method for prioritization problems in which truth, falsity, and hesitation must remain simultaneously visible during fusion. Each alternative is represented by a single-valued neutrosophic profile, criterion weights are obtained from a contrast-sensitive entropy functional, and the final ranking is produced by an indeterminacy-penalized evidence score. The mathematical contribution is a bounded fusion operator that separates positive support, negative pressure, and contradiction-induced hesitation. A numerical study reports detailed intermediate matrices, criterion weights, fused memberships, ranking stability, sensitivity to the indeterminacy penalty, ablation results, and computational complexity. The findings show that retaining indeterminacy during fusion changes the ordering of borderline alternatives and makes the decision trace easier to audit than scalar aggregation alone.

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Arwa Hajjari mail

link https://doi.org/10.54216/NIF.060102

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Vol. Volume 6 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2026

Neutrosophic Information Fusion: Foundations, Frameworks, Algorithms, and Research Frontiers

Neutrosophic set theory, which explicitly models truth (T ), indeterminacy (I), and falsity (F) as independent membership components, has emerged as one of the most active mathematical frameworks for uncertain information fusion over the 2020–2025 period. This comprehensive survey reviews, synthesises, and critically analyses more than 200 research contributions spanning single-valued neutrosophic sets (SVNS), interval neutrosophic sets (INS), neutrosophic cubic sets (NCS), neutrosophic Z-numbers, linguistic neutrosophic sets, and their integration with Dempster-Shafer evidence theory. We organise the literature across four interlocking axes— mathematical foundations, aggregation operators, information measures, and decision-support methods—and map these onto seven application domains including medical diagnosis, supply chain management, environmental assessment, and engineering fault diagnosis. Three representative algorithms are formally presented with pseudocode, complexity analysis, and mathematical justifications: (i) the SVNWA entropy weighted aggregation framework, (ii) the Neutrosophic Dempster-Shafer Evidence Theory (N-DSET) fusion pipeline with conflict r edistribution, a nd (iii) the Neutrosophic TOPSIS multi-criteria d ecision-making a lgorithm. A comparative performance analysis shows that neutrosophic methods achieve mean AUC improvements of +4.2% to +7.1% over intuitionistic fuzzy set baselines across reported experimental studies. Six precisely formulated open problems are identified, and a five-horizon research roadmap from 2025 to 2030 is proposed, covering mathematical completeness, computational scalability, hybrid deep-learning architectures, domain expansion to quantum and large language model settings, and the long-term vision of a unified neutrosophic information quality standard.

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Agnes Osagie mail -

Mohammad Abobala mail

link https://doi.org/10.54216/NIF.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Uncertainty-Aware Radar-LiDAR Fusion for PoE-Constrained Smart Infrastructure Perception with Asynchronous Sensing

Infrastructure-based autonomous perception operates under fundamentally different constraints than vehicle mounted systems: elevated-mounting geometries producing depression-angle-dependent sparse point clouds, a 12.95 W IEEE 802.3af Power-over- Ethernet (PoE) power ceiling, and distributed asynchronous sensing governed by IEEE 1588v2 precision time protocol (PTP) synchronization uncertainty. Existing automotive radar–LiDAR fusion frameworks assume abundant power, dense sensing, and synchronous measurements — assumptions that all fail in fixed infrastructure deployments. This paper presents XADAR, an uncertainty-aware multi-modal fusion framework designed for these infrastructure-specific constraints. XADAR makes three princi-pal contributions: (1) a covariance inflation mechanism that propagates PTP synchronization uncertainty continuously through the fusion pipeline, replacing hard synchronization thresholds with a smooth degradation curve proportional to temporal offset; (2) adap-tive sensor-specific fusion weights derived from modality covariance matrices that account for IWR6843 77 GHz FMCW radar Doppler ambiguity and ground-reflection multipath, and TFS20-L ToF LiDAR atmospheric scattering and range-zone limitations; and (3) a complete reproducible architecture including an IEEE 802.3af-compliant power budget (5.78 W maximum concurrent load; 41.6% PoE safety margin), quantitative 77 GHz propagation analysis based on ITU-R P.676-12 and P.838-3 (10.7 dB fade margin at 100 m under 50 mm/hr rain), and an MIL-STD-1629 FMEA covering twelve failure modes with severity classifications. A structured five-stage validation pathway from synthetic temporal-offset experiments to sixmonth field trials is defined for future empirical work.

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Mostafa Borhani mail

link https://doi.org/10.54216/FPA.210229

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Thermal Vehicle Detection and Tracking for Intelligent Transportation Systems: A Modular IoT Architecture and Staged Deployment Roadmap

Automated vehicle monitoring in intelligent transportation systems must operate reliably around the clock, including under conditions that routinely cripple conventional visible-light cameras: night, glare, shadows, and adverse weather. This paper proposes a modular Internet of Things (IoT) architecture for thermal-based vehicle detection, classification, and trajectory analysis, together with a four-phase deployment roadmap that connects public-dataset evaluation to live-traffic field validation. The system integrates longwave infrared (LWIR) imaging (8–14 𝜇m) with YOLO-family deep learning detectors (YOLOv8/v11/v12) and multi-object tracking algorithms (ByteTrack, BoTSORT, StrongSORT), deployed across NVIDIA Jetson edge devices and cloud infrastructure through JSON/MQTT formalized data contracts. The primary novel contribution is a system-level integration framework that bridges the gap between component-level algorithmic research and operational deployment. Concretely, this work: (i) defines five functionally independent modules with explicit interface specifications and latency budgets not previously formalized in the thermal-ITS literature; (ii) introduces quantified decision gates linking progression criteria directly to published benchmark values; (iii) provides region-specific operational availability estimates derived from empirical weather-degradation data; and (iv) integrates domain adaptation, GDPR compliance, edge hardware budgets, and regulatory WIM frameworks within a single coherent system blueprint. Domain adaptation strategies reported in peer-reviewed literature recover 20–50% of cross-dataset mAP degradation (typically 10–30%) caused by sensor and scene variability; these figures are literature benchmarks, not results obtained in this work. An optional weight-estimation module (Module 4) based on recent vision-based and bridge WIM validation studies is treated as an exploratory extension requiring site-specific validation.

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Mostafa Borhani mail

link https://doi.org/10.54216/JISIoT.180231

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2025

Predicting Academic Outcomes in Secondary Education: Ensemble Classification with Grade Trajectories, Attendance Behaviour, and Socioeconomic Context

Early identification of students at risk of academic failure is a persistent challenge in educational technology, with direct implications for student retention, institutional equity, and the allocation of support resources. Although supervised machine learning has been widely applied to student outcome prediction, the relative merit of competing algorithm classes and the degree to which demographic and behavioural features contribute predictive power beyond prior academic assessments remain incompletely resolved in the secondary school context. This paper presents a structured comparative evaluation of five supervised classifiers trained on a rich combination of periodic grades, attendance records, sociodemographic characteristics, and lifestyle indicators drawn from secondary school students. A dual importance analysis— combining impurity-based measures with held-out permutation importance—disentangles the distinct predictive roles of grade trajectories, absenteeism, parental background, and lifestyle variables. Ensemble methods demonstrate consistent superiority across all evaluation criteria, with prior periodic assessments and attendance emerging as the dominant predictors. Parental education level introduces a socioeconomic gradient that operates independently of student controlled factors, generating structural inequities that standard grade-monitoring systems are unlikely to address. These findings provide both a methodological benchmark for secondary school prediction tasks and practical guidance for institutions designing equitable and evidence-based early warning interventions.

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Jehad Mousa mail -

Abdallah Salama mail

link https://doi.org/10.54216/IJAIET.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2025

Governed Early-Warning Analytics for Student Success in Digital Higher Education: A Business-Oriented Evidence Model

Student-success analytics has moved from experimental prediction toward an institutional capability for reducing attrition, allocating support resources, and improving digital learning governance. This paper develops a business oriented early-warning model for education technology environments in which predictive performance, interpretability, intervention priority, and governance are treated as joint design requirements. The study uses a public student-success dataset from a higher education institution and evaluates decisive outcome prediction for dropout and graduation, while preserving a wider discussion of the enrolled group as an unresolved operational state. The proposed model combines a transparent predictive layer, a risk-to-action prioritization layer, and a governance layer that restricts how predictions are translated into student support decisions. The results show that a parsimonious logistic specification can provide competitive performance compared with more complex tree based models, while producing clearer accountability for academic advising and digital student-success units. The discussion argues that student-success technology should not be judged by accuracy alone, but by whether the analytics pipeline produces timely, explainable, privacy-aware, and operationally usable support signals.

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Nahla Moussa mail -

Low Hon Loon Alfred mail

link https://doi.org/10.54216/IJAIET.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2025

Public Education Investment, Instructional Resources, and Student Achievement: Cross-National Evidence from PISA 2022 in the Context of Sustainable Development Goal 4

All people must have access to educational opportunities which meet their needs through Sustainable Development Goal 4. The goal requires education systems to obtain sufficient funds and use their resources properly. The relationship between public education spending and student academic performance remains disputed because different countries achieve different results from their spending levels. The study employs PISA 2022 country-level scores which represent the first international assessment data published after COVID-19 to analyze public education expenditure as a GDP share together with pupil–teacher ratio and per-capita GDP in relation to student academic performance across three subjects. The study found that public education funding as percentage of GDP does not connect with PISA score results across 35 countries, showing no statistical link to tests (r = −0.095, p = 0.586). The pupil–teacher ratio serves as an effective predictor because it shows a strong negative relationship to student performance (β&circ; = −4.097, R2 = 0.312, p < 0.001). A three-variable regression model which combines expenditure share with pupil–teacher ratio and GDP per capita explains 59% of cross-country score variance (R2 = 0.592). High-income economies dominate the upper achievement tier, but several upper-middle-income systems— notably Estonia and Poland—substantially outperform their GDP-predicted  scores. The results show that organizations should focus their resources on developing teaching skills.

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Reshma Shaik mail -

Hanadi Osman Diab mail

link https://doi.org/10.54216/IJAIET.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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