Risk-Aware Cyberattack Analytics for Unmanned Aerial Vehicle Communications: A Publication-Ready Gradient- Boosting Framework

Andino; Aa

doi:https://doi.org/10.54216/JCIM.180105

Journal of Cognitive Human-Computer Interaction | 2026

An Interaction-Centric Wireless Multimodal Fusion Model for Cognitive State Recognition in Computer Interfaces

Wireless human-computer interaction increasingly depends on distributed sensing, yet adaptive computer interfaces are still commonly modelled from isolated evidence streams. This paper presents an interaction-centric wireless multimodal fusion model for recognizing cognitive state during computer-based task execution. The model integrates wearable physiology, ocular behaviour, compact neurophysiological summaries, and direct interaction evidence obtained from the task interface, then adjusts each sensing channel through a reliability term that reflects wireless degradation. The experimental workflow follows a public stress-resilience human-computer interaction protocol involving synchronized task phases and computer interaction logs. The analysis shows that interaction variables such as task error, response latency, and click activity are among the strongest indicators of cognitive state and complement physiological information in a meaningful way. The results support the design of adaptive computer interfaces that respond not only to what the user is doing on the screen, but also to how reliably the supporting wireless sensing infrastructure is functioning.

groups

Khaled Sh. Gaber mail -

Mahmoud Elshabrawy Mohamed mail

link https://doi.org/10.54216/JCHCI.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2026

Cross-Modal Memory Support in Visually Demanding Environments: A Controlled Study of Haptic Pulses and Spatial Audio Cues for Reducing Prospective Memory Failures During Multitasking

When people are immersed in a visually demanding task, the attentional resources required to monitor the environment for cues that should trigger a remembered intention are frequently captured by the primary task, causing prospective memory failures that range from the inconvenient to the safety-critical. This problem is pervasive in modern work environments in which digital interfaces compete continuously for visual attention, yet the overwhelming majority of reminder and notification systems rely on the same visual channel that is already congested. This paper reports a controlled user study examining whether carefully designed haptic and spatial audio cues can compensate for this visual saturation and restore prospective memory performance without substantially increasing cognitive burden. Thirty-two participants completed a counterbalanced within-subjects protocol in which they performed primary cognitive tasks—document editing on a virtual desktop and navigating in a driving simulation—while managing a set of time-critical intentions delivered through four reminder conditions: visual-only, haptic-only, spatial audio only, and the combined haptic-plus-audio channel. The study measures prospective memory hit rate, task-switching errors, cue response latency, and multidimensional subjective workload across both scenarios and all four conditions. Results consistently favour the combined modality, which produces substantially fewer memory failures and lower reported workload than any single channel, while individual differences in baseline workload predict the magnitude of benefit from non-visual cueing. These findings carry direct implications for the design of ambient notification systems in high-demand professional and safety-critical environments.

groups

A. Nithya mail -

B. Chitra mail -

V. Sathya Preiya mail

link https://doi.org/10.54216/JCHCI.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2026

Measuring Visibility and Usability Features in Mobile Application Interface Design

Mobile application usability is often discussed after deployment through user reviews or task testing, but many visible design problems can be measured earlier from the interface itself. This paper presents a feature-based framework for quantifying mobile interface visibility, usability, and accessibility risk from screen-level design properties. The study defines a Mobile Interface Visibility–Usability Quality score using observable measures such as primary-action salience, visual density, tap-target adequacy, label completeness, contrast proxy, navigation depth, whitespace, and clutter. The analysis uses a structured extract following public Rico and UICrit-style mobile UI data, where screenshots, hierarchy information, and designer critique concepts support data-driven assessment. The results show that usability quality is not determined by a single visual property. Screens with strong contrast may still be difficult to use if feature discoverability is weak, and screens with many functions may remain usable when hierarchy and labels are clear. The paper contributes a measurement protocol, design risk taxonomy, empirical score analysis, and practical remediation loop for mobile app teams seeking objective evidence before user-facing release.

groups

Wadhah Abdullah mail -

Aygul Z. Ibatova mail

link https://doi.org/10.54216/JCHCI.110105

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

From Signals to Action: Explainable AI for Engagement-Responsive Instructional Support in Digital Higher Education

Artificial intelligence is increasingly used to monitor learning processes in higher education; however, many analytics pipelines still terminate at prediction and provide limited support for instructional action. The research establishes an explainable artificial intelligence framework which utilizes digital learning environment behavioral data and contextual information to create customized instructional support solutions. The analysis uses xAPIEdu-Data dataset which contains 480 records to build engagement index and create support profiles and predict multiclass performance through rule based action allocation. The study tests three classification models using stratified cross validation. The study selects Random Forest as the most effective system because it delivers superior results across all tests. The selected model demonstrates 0.8021 accuracy and 0.8204 macro precision and 0.8010 macro recall and 0.8084 macro F1 score and 0.9140 macro area under the curve on the hold-out sample. The analysis shows that student absence and composite engagement index andgender and student guardian relationship and support profile and digital resource access arethe most important factors that determine student performance. The final decision layer manages student assignment to instructional support plans which contain attendance-first intervention and adaptive engagement support and family-engagement reinforcement and structured progression coaching and challenge-and-extend pathways. The study develops an analytical framework which connects explainable artificial intelligence to digital higher education instructional decision support systems.

groups

Andino Maseleno mail -

Meinhaj Hussain mail -

Aygul Z. Ibatova mail

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

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Early Detection of Student Dropout Risk in Higher Education through Optimized Machine Learning

Student retention in higher education institutions is a critical problem that causes academic and financial challenges to individual students and to schools and entire countries. The field of study should be in the area of student retention as it enables educational facilities to provide appropriate intervention. The present study implements a comparative analysis of five machine learning classifiers; Linear Discriminant Analysis, K-Nearest Neighbours, Support Vector Machine, Random Forest and Gradient Boosting classifiers on dataof 4424 students who were selected from the Realinho et al. (2022) data set which contains demographic and socioeconomic, and macroeconomic and academic performance data from a Portuguese higher education institution over a decade. The mutual information feature selection step reduces the 22-dimensional feature space prior to model trainingby selecting 12 features that have, statistically, the highest discriminative power. Five-fold stratified cross-validation shows that the best overall performance is achieved by a SVM with a radial basis function kernel with accuracy of 97.1% and F1 score of 0.954 and all five models achieve AUC greater than 0.981. The importance analysis reveals that the combination of four measures of academic success from the first two semesters constructs 87.6% of the signal that Random Forest model uses for prediction which is driven by the most important predictor - number of curricular units that the student passes during the secondsemester (importance= 0.335). The impact of all socioeconomic and demographic and macroeconomic factors is less than 13%. The findings of the study have three implications about risk factors in student retention via empirical measurement.

groups

Aa Hubur mail -

Aygul Z. Ibatova mail

link https://doi.org/10.54216/IJAIET.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Analytic Trait Scoring of English Language Learner Essays Using Fine-Tuned DeBERTa-v3 on the ELLIPSE Corpus

Automated Essay Scoring (AES) technologies have been extensively researched for holistic, topic-specific scoring, but their use to predict multiple analytic writing quality traits of English Language Learner (ELL) student essays has received less attention. This research contributes to this knowledge gap by systematically investigating multi-trait AES on the ELLIPSE corpus (Learning Agency Lab, 2022), a publicly accessible dataset of 6,482 argumentative essays written by grades 8-12 ELLs and rated by human raters on six analytic traits: cohesion, syntax, vocabulary, phraseology, grammar, and conventions. We experiment with five models: Ridge regression, Support Vector Regression (SVR) with a radial basis function (RBF) kernel, Random For-est, fine-tuned BERT-base-uncased and fine-tuned DeBERTa-v3-base. The mean Quadratic Weighted Kappa (QWK) across six traits is highest for DeBERTa-v3 (0.726) - a 26.5-point improvementover the Ridge base-line (0.461) and a 6-point improvement over BERT (0.666). Phraseology is the most difficult trait to score automatically (DeBERTa QWK = 0.701) and cohesion the easiest (DeBERTa QWK = 0.742). Analysis of inter-trait correlations reveals high co-variation between vocabulary and phraseology (r = 0.79), which may reflect common linguistic skills that can be leveraged by multi-task learning. Thisresearch sets a replicable baseline for multi-trait AES on the ELLIPSE corpus, and suggests that phraseology scoring is the most urgent area for future architectural innovation.

groups

Andino Maseleno mail -

Meinhaj Hussain mail

link https://doi.org/10.54216/IJAIET.040103

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Educational Value Formation Around Intelligent Learning Tools: Student Readiness, Usage Archetypes, and Support Pathways in Higher Education

Modern higher education campuses now use intelligent learning tools as standard educational resources yet students learning results depend on their understanding of these tools and their implementation in academic work. The study analyzes how students pre-pare to use educational tools while investigating the connection between their preparedness and their judgment of educational benefits. The study uses an open student-perception dataset to conduct empirical research which includes developing constructs and profiling readiness and creating predictive models and establishing pathways. The study introduces two measurement methods which include source breadth to measure how students acquire knowledge about intelligent tools through different information channels and an advantage score to present perceived benefits for educational activities. The three-profile segmentation method shows that different groups in the sample display distinct levels of preparedness and value assessment. The Random Forest model demonstrates superior performance because it achieves the highest accuracy among all tested models in the predictive stage. The selected model exhibits an accuracy rate of 0.789 and a precision rate of 0.714 and a recall rate of 1.000 and an F1 score of 0.833 and an area under the receiver operating characteristic curve of 0.806 in hold-out evaluation. The analysis of variable importance indicates that AI knowledge and grade-point average and information breadth and profile membership serve as the main factors that explain the results. The final stage of the process transforms analytical results into distinct educational pathways which focus on developing essential literacy skills and implementing structured curriculum materials and providing support for governance matters and enabling advanced collaborative learning. The results demonstrate that the educational benefits of intelligent tools depend more on students’ preparedness to use them than on their initial exposure to the tools.

groups

Marina Sagatovna Abdurashidova mail -

Muhammad Eid Balbaa mail

link https: // doi. org/ 10. 54216/ IJAIET. 040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2026

Green Finance, Renewable Energy Adoption, and Carbon Emissions: Evidence from a Ten-Country Panel Dataset (2010–2022)

The capital mobilisation needed to achieve the energy transition has put green finance on the agenda of climate policy discussions, but it is empirically unclear whether financial flows branded green translate into emissions abatement. Drawing on publicly available World Bank World Development Indicators and Climate Policy Initiative data for ten major economies over 2010–2022, this paper quantifies the relationships among green finance flows, renewable energy share, and per-capita CO2 emissions using pooled ordinary least squares regression, panel correlation analysis, and a cross-sectional multiple regression model. Renewable energy share is a strong negative predictor of per-capita emissions (r = −0.661; p < 0.001; βˆ = −0.1104), with each percentage-point increase in renewable penetration associated with a reduction of approximately 0.11 metric tons of CO2 per capita. A three-variable cross-sectional model combining renewable energy share, green finance, and GDP per capita accounts for 61% of cross-country variance in emissions in 2022 (R2 = 0.613). There is a positive and heterogeneous relationship between green finance flows and renewable adoption, which is most notable when the financial commitment is combined with strong regulatory frameworks. The country level trajectory analysis shows that all the ten economies have been on the rise in their renewable share between 2010 and 2022, but at a rate varying between 0.37 and 2.88 percentage points annually, and that the institutional environment is a moderating factor. The results have immediate implications on the design of green bond markets, the lending policy of multilateral development banks, and the structures of additionality that support net-zero transition schemes.

groups

Ihtisham ul Haq mail -

Andino Maseleno mail

link https://doi.org/10.54216/JSDGT.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2025

Machine Learning for At-Risk Student Identification in Virtual Learning Environments: A Multi-Classifier Analysis Using the Open University Learning Analytics Dataset

The detection of students who will face academic difficulties or leave their studies during their initial course period provides universities with a brief time frame to develop effective solutions. This research paper conducts a systematic analysis which tests multiple machine learning classifiers on the Open University Learning Analytics Dataset (OULAD) which serves as one of the most widely used public educational datasets that presents data from 32593 students who studied 22 different courses through distance learning. The four classification methods include logistic regression decision tree random forest and gradient boosting which use a feature set that combines student demographic information and virtual learning environment (VLE) clickstream-based engagement data. The primary discovery shows that VLE behavioral characteristics constitute the most important elements for Random Forest which identifies total click volume and active VLE days and typical daily click volume as its top four elements which make up 92.8% of total importance while demographic information has less impact. Random Forest achieves the strongest held-out test performance (AUC = 0.998, F1 = 0.978, accuracy = 98.2%) while Decision Tree shows lower results with AUC = 0.959 which demonstrates how performance losses occur when systems need to be understandable. At-risk students in the two groups present a 75.8% decrease in total VLEclicks which results in an average of 49.0 clicks compared to 203.0 clicks with a t value of 104.0 and a p value less than 0.001. The research describes its complete end-to-end prediction pipeline which includes details about its model evaluation framework and its dataset to enable future researchers to reproduce the study. The results have direct implications for the design of early-alert systems and the ethical deployment of predictive analytics in higher education.

groups

Emad Bashkail mail -

Nesrin Merhi mail

link https://doi.org/10.54216/IJAIET.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Risk-Aware Cyberattack Analytics for Unmanned Aerial Vehicle Communications: A Publication-Ready Gradient- Boosting Framework

Cyberattack detection in unmanned aerial vehicle environments has become an essential requirement for dependable digital operations. Security analytics for these environments should not only separate benign and malicious traffic, but should also provide interpretable evidence that can support timely triage and intervention. This paper presents a risk-aware classification framework for UAV communication security based on a leakage-screened feature design and a gradient-boosting ensemble model. The framework combines multiclass discrimination, probability-based decision logic, and feature-level interpretation within one coherent workflow. The study demonstrates that a carefully designed ensemble approach can provide balanced and operationally meaningful cyberattack recognition while remaining transparent enough for practical cybersecurity management. The results also show that communication-structure variables provide strong discriminatory power and that replay-type activity remains more difficult to separate than benign or denial-of-service behavior. The proposed framework therefore contributes a reproducible analytical design and a managerial reading of cyberattack classification for UAV operations.

groups

Andino Maseleno mail -

Aa Hubur mail

link https://doi.org/10.54216/JCIM.180105

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new