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

Dae Yu Kim; Jeong Chan Park

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

International Journal of Wireless and Ad Hoc Communication | 2026

Trust-Aware Early Detection of Grey-Hole Behaviour in Flying Ad Hoc Wireless Networks: A Data-Driven Study Using Recent FANET Traces

Flying ad hoc networks (FANETs) enable dynamic multi-hop communication in un-manned aerial nodes, but their routing plane is vulnerable to selective forwarding attacks that decrease packet delivery rates while avoiding the sudden effects of denial. This paper proposes a trust-aware routing and detection approach for early detection of grey-holes in ad hoc flying networks. The paper employs an analysis-ready data set based on the public FAN-GHETS24 data set, a new data set for early time-series classification of attacks in FANETs. The Trust-Aware Routing Grey-Hole Detection (TAR-GHD) model uses a com-bination of link quality evidence, route stability, packet consistency and trust dynamics in a lightweight detection layer that can be executed alongside traditional ad hoc routing. A mathematical formulation is given for evidence aggregation, temporal trust evolution, risk assessment and route warning. The empirical study measures the detection of normal, mild, moderate and heavy grey-hole attacks in various node-density, mobility, observation window, and classification settings. The findings demonstrate that trust and packet-loss dynamics offer reliable early indicators of grey-hole attacks, while mobility and route changes make it harder to distinguish normal loss from malicious loss. The best-performed configuration resulted in an F1-score over 0.93 (held-out evaluation), with the most influential features related to packet delivery, forwarding ratio, trust score and drop-rate dynamics. The results highlight lightweight and explainable trust evidence as a viable technique for enhancing the security of wireless ad hoc routing in UAV-assisted applications.

groups

Meinhaj Hussain mail -

Andino Maseleno mail

link https://doi.org/10.54216/IJWAC.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2026

ADML-IDS: An Adaptive Ensemble Machine Learning Framework for Intrusion Detection in Wireless Ad Hoc and Sensor Networks

As wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs) are (DoS) attacks has become a critical security concern in mission-critical wireless (DoS) attacks has become a critical security issue. This paper proposes ADML-IDS, an Adaptive Machine Learning Intrusion Detection System that integrates ensemble of Random Forest, XGBoost and Gradient Boosting classifiers using a Flooding, and Scheduling—as well as normal traffic. Flooding, Scheduling and normal traffic. Experiments are conducted on the open-source WSN-DS dataset, which contains 166,000 network observations using the LEACH hierarchical routing protocol with 23 features obtained from NS-2 simulation. The data preprocessing steps include Min- Max normalisation and Synthetic Minority Over-Sampling Technique (SMOTE) to balance classes, and importance-based feature selection to retain 19 features. A rigorous ten-fold crossvalidation strategy is followed. ADML-IDS achieves an overall accuracy of 99.57%, weighted F1-score of 0.9956 and AUC-ROC of 0.9985. AUC-ROC of 0.9985, outperforming each of the sub-classifiers and five state-of-the-art methods. Scalability experiments demonstrate that the accuracy of detection remains above network size reaches 200 nodes, and with a reasonable computational cost. A formal presentation of the energy-aware network model and ensemble decision rule is tables are also included along with a full description of the algorithm tables.

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Ahmed Aziz mail -

Mahmoud Abdel-Salam mail

link https://doi.org/10.54216/IJWAC.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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International Journal of Wireless and Ad Hoc Communication | 2026

Key-Aware Link Selection for Quantum Wireless Networks: A Data-Driven Study of Satellite-to-Ground QKD Access Links

The application of quantum key distribution, satellite communication and programmable wireless access in future secure wireless networks is anticipated to enhance secure communication infrastructure. But their realisation demands more than just the physical realisation of quantum links. The network controller needs to determine when a quantum-secured wireless link can be used to serve a request, how orbit type and weather conditions impact the volume of usable keys, and whether the secure key rate is high enough to admit a route. In this paper, we introduce Q-SARA, a quantum-secure access and routing admission model for satellite-assisted quantum wireless networks. It assesses candidate QKD access links with secure key rate (SKR), quantum bit error rate (QBER), link loss, contact duration, visibility probability and propagation delay. A smaller, pre-processed dataset is derived from the public Satellite-to-Ground QKD SKR dataset, which contains the calculated key performance indicators for Low Earth Orbit, Medium Earth Orbit and Geostationary satellite-to-ground QKD links using the prepare-and-measure and entanglement-based protocols. The empirical analysis examines 7,200 link-level data and assesses Q-SARA across orbit, protocol, optical ground station, elevation, atmospheric, and service classes. The findings demonstrate that link selection based only on key volume can be misleading when assessing service quality, while the multi-criteria score provides better balance between security, visibility and latency. LEO links have better instantaneous key rates, GEO links have better visibility, and MEO links lie in between and can be exploited when link quality and service are taken into account together. The results suggest that quantum wireless access should be considered as a service admission problem rather than a physical-layer key generation problem.

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Khaled Sh. Gaber mail -

S. K. Towfek mail

link https://doi.org/10.54216/IJWAC.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2026

Sustainable Decarbonization Under Renewable Energy Penetration: A Hybrid Fixed Effects and Machine Learning Framework for Multi-Country Panel Evidence

Realizing the carbon reduction capabilities of deploying renewable energy. is core to the constructive plan of effective climate policy in heterogenous national. contexts. Even though there is an accumulating corpus of panel econometric and machine learning. literature dealing with this relationship, methodological inconsistencies and limited geographic scope leave important empirical questions unanswered. This paper put forward a mixed analytical model combining a within-group Fixed Effects. country-clustered standard errors estimator and a Random Forest ensemble. model to measure the combined effect of renewable energy penetration, economic growth, energy consumption and reliance on fossil fuels per capita carbon. emissions. Findings affirm that the growth of renewable energy has a statistically significant impact. strong and economically significant negative impact on carbon intensity, which remains. following the elimination of country-specific unobserved heterogeneity. Economic structure and energy efficiency are shown to be co-dominant determinants, highlighting. that the energy transition is not decoupled of larger structural. transformation. Articulated income-group and regional heterogeneity issues. single-coefficient policy prescriptions, which propose decarbonization. plans have to be aligned to the national development levels. The machine learning complement validates econometric variable rankings and proves. good cross-country generalizability with country-stratified. cross-validation.

groups

Citra Dewi mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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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.

groups

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.

groups

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.

groups

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.

groups

Dae Yu Kim mail -

Jeong Chan Park mail

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

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new