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

Reshma Shaik; Hanadi Osman Diab

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

Neutrosophic and Information Fusion | 2026

A Neutrosophic Control Chart for Monitoring Processes with Indeterminate Measurements: An Information-Fusion Approach to Statistical Quality Control

Classical Shewhart control charts assume each measurement is a single determinate number. In many real processes— automated gauges reporting tolerance bands, human inspectors giving ranges, or sensors whose readings are only trustworthy within an interval—each observation is better described by a lower and an upper value together with a degree of indeterminacy. We formulate process monitoring in the neutrosophic statistics framework, where a measurement is a neutrosophic number xN = xL+xUIN with indeterminacy interval IN ∈ [IL, IU], and we treat the reconciliation of the determinate and indeterminate parts as an information-fusion step. We derive neutrosophic control limits for the process mean, propose a three-state signalling rule (in-control / watch / out-of-control), and study the average run length (ARL) by simulation. The neutrosophic chart reduces to the Shewhart chart when indeterminacy vanishes, raises the in-control ARL modestly, and under moderate measurement indeterminacy detects a one-sigma mean shift with a smaller out-of-control ARL than a Shewhart chart applied to interval midpoints.

groups

Maha Ibrahim mail -

Sajid Khan mail

link https://doi.org/10.54216/NIF.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2026

Trust-Aware Cluster-Head Selection in IoT Wireless Sensor Networks via Interval-Neutrosophic Information Fusion

Cluster-based routing extends the lifetime of an Internet-of-Things wireless sensor network (WSN), but the quality of a cluster head (CH) depends on several mutually uncertain factors—residual energy, link quality, centrality and behavioural trust—each measured imperfectly and reported as a range rather than a point. We model each candidate node with an interval-neutrosophic set (INS), so every factor carries an interval of truth, indeterminacy and falsity, and we fuse the factors with an interval-neutrosophic weighted aggregation operator into a single suitability score. Cluster heads are then elected by a possibility-degree ranking of the fused interval scores, subject to a minimum-trust guard. Simulation of a 200-node network shows that interval-neutrosophic CH selection extends first-node-death time by 18–24% over a standard energy-and-distance heuristic and cuts the share of misbehaving nodes elected as heads from 14.7% to 1.3%. The method degrades gracefully as measurement indeterminacy grows and adds only modest per-round overhead.

groups

Durdona Uktamova mail -

Adnan Manzoor mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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Neutrosophic and Information Fusion | 2026

A Neutrosophic Information-Fusion Framework for Renewable-Energy Site Selection under Conflicting Sustainability Criteria

Siting a solar or wind farm forces planners to reconcile criteria that disagree by nature: irradiation or wind resource, land cost, grid proximity, ecological sensitivity and social acceptance. The evidence for each criterion is heterogeneous and uncertain—remote-sensing estimates, cadastral records, and public-consultation sentiment—and experts often cannot commit to a definite rating. This paper proposes a neutrosophic information-fusion framework that encodes each expert rating as a single-valued neutrosophic number, derives objective criterion weights by neutrosophic entropy, fuses the ratings with a single-valued neutrosophic weighted geometric operator (which, unlike the arithmetic operator, penalises a poor score on any single criterion), and ranks candidate sites by a deneutrosophied score. Applied to six candidate sites and seven criteria, the framework selects a site that balances a strong resource against low ecological conflict, and a full sensitivity analysis over the weight scheme and the risk attitude shows the choice is robust. A comparison against fuzzy-AHP and SVN-TOPSIS indicates the neutrosophic geometric model better exposes sites whose ranking rests on contested, high-indeterminacy criteria.

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Abudulkadir Shermatov mail -

Safina Tashabayeva mail

link https://doi.org/10.54216/NIF.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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Galoitica: Journal of Mathematical Structures and Applications | 2026

On Edge-MetaGraphs

This paper studies graph-based higher-order structures related to metagraphs and edge-labeled hierarchical networks. After reviewing MetaGraphs and Iterated MetaGraphs, we introduce the notion of an Edge-MetaGraph, in which each edge is labeled by a two-ported internal graph, allowing edge-substitution expansion through port gluing. We then define Iterated Edge-MetaGraphs recursively, so that edges may carry nested Edge-MetaGraph structures. Concrete examples from biomedical systems, software pipelines, and logistics are presented to illustrate the expressive power of the proposed framework

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Takaaki Fujita mail -

Ajoy Kanti Das mail -

Suman Das mail -

Sankar Prasad Mondal mail -

Volkan Duran mail

link https://doi.org/10.54216/GJMSA.130203

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Galoitica: Journal of Mathematical Structures and Applications | 2026

A Note on q-Fractional Neutrosophic Sets and Graphs

A neutrosophic set assigns to each element three (generally independent) degrees: truth, indeterminacy, and falsity. A q-fractional fuzzy set assigns membership and nonmembership degrees in [0,1] to each element, constrained by (μ +ν)/q ≤ 1 for q ≥ 2. A q-fractional fuzzy graph is a graph whose vertices and edges carry q-fractional fuzzy degrees, with edge membership bounded by endpoints and edge nonmembership dominating them. In this paper, we introduce q-fractional neutrosophic sets and graphs as an extension of q-fractional fuzzy sets and q-fractional fuzzy graphs, and we investigate their fundamental properties.

groups

Takaaki Fujita mail -

Ajoy Kanti Das mail -

Suman Das mail -

Sankar Prasad Mondal mail

link https://doi.org/10.54216/GJMSA.130204

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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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.

groups

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.

groups

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.

groups

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.

groups

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 (βˆ = −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.

groups

Reshma Shaik mail -

Hanadi Osman Diab mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new