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

Khaled Sh.; Mahmoud Elshabrawy

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

International Journal of Advances in Applied Computational Intelligence | 2026

XAI-DermNet: A Dual-Modality Deep Learning and Explainable AI Fusion Framework for Transparent Skin Lesion Diagnosis from Dermoscopic Images

The advancement of trustworthy diagnostic tools in dermatological automation is hindered by the limited transparency of current deep learning systems, which function as opaque models and impede clinical acceptance. This research presents a novel intelligent framework for skin lesion analysis that unites deep learning methodologies with explainable artificial intelligence (XAI) principles to address this interpretability deficit. The proposed approach utilizes a transfer-learned ResNet50 architecture for robust image classification, coupled with Local Interpretable Model-agnostic Explanations (LIME) to furnish clear, visual justifications for the model’s outputs. Performance assessment on the HAM10000 benchmark yielded a classification accuracy of 94.3%, with a validation accuracy of 91.8%. Concurrently, the LIME framework effectively identified and visualized diagnostically critical features in the lesion images, thereby elucidating the model’s reasoning process for medical practitioners. These findings confirm that augmenting high-performance deep learning with post-hoc explanatory techniques yields a credible and understandable diagnostic instrument, thereby promoting clinician trust and facilitating data-informed medical judgments. Subsequent developments will prioritize scalable cloud implementation, interoperability with healthcare information systems, extension to underrepresented lesion categories, and rigorous evaluation in diverse clinical environments.

groups

Sukkirtha K. mail -

Anbuchelian S. mail -

John A. mail

link https://doi.org/10.54216/IJAACI.080102

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

A Hybrid CNN Bi-LSTM Framework for Multi-Class Plant Disease Detection and Health Value Estimation

Accurate and early identification of plant diseases is essential for ensuring sustainable agriculture and maximizing crop productivity. This paper presents a hybrid deep learning framework integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks for multi-class plant disease detection, classification, and Plant Health Value (PHV) estimation. The proposed framework begins with a comprehensive data preprocessing pipeline involving image resizing, normalization, and augmentation to improve model robustness. The CNN module extracts critical spatial and visual features such as lesion shape, leaf texture, and color intensity, while the BiLSTM model captures temporal and sequential feature correlations to accurately learn disease progression patterns. A Decision Support System (DSS) is incorporated to compute the Plant Health Value (PHV), where PHV ranges from 0% (Healthy) to 100% (Severely Unhealthy), indicating the severity of disease infection. Additionally, the DSS generates actionable recommendations to assist in early intervention and treatment planning. Experimental results on a multi-species plant dataset demonstrate that the proposed CNN–BiLSTM hybrid model significantly improves accuracy, interpretability, and early disease prediction compared to conventional CNN based methods, offering a robust and intelligent framework for automated plant health monitoring.

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Janani J. mail -

Gautham R. mail -

Suguna C. mail -

Ponni K. mail

link https://doi.org/10.54216/IJAACI.080103

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Green IOT and Sustainable Wireless Sensor Networks: A Deep Reinforcement Learning Approach for Energy Optimization and Qos Enhancement

Due to the increasing adoption of IoT applications, there is a growing necessity for energy-efficient and sustainable WSN. Yet, traditional routing protocols tend to face problems like energy wastage, congestion, unreliable communication, and shorter network life spans under dynamic network conditions. This study presents the development of a DRL-powered Green IoT framework to enhance efficient communication through WSN while optimizing QoS performance. Specifically, the proposed framework employs the Deep Q-Network, Double Deep Q-Learning, adaptive clustering, and multi-objective optimization in order to enhance both routing and QoS performance. The model makes use of residual energy, congestion levels, throughput, delivery rate, and communication delays during its decision-making processes. Experimentation with the model was performed by making use of Python and NS-3. The simulation results showed that the presented model performed better than traditional routing methods like LEACH, PEGASIS, and HEED when evaluated on factors like energy preservation, enhanced throughput, minimized congestion, reduced delays, and increased network life spans. It can be concluded that DRL-powered communication optimization is a viable solution for the future development of Green IoT communication systems.

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S. Phani Praveen mail -

Massila Kamalrudin mail -

Sai Vellela mail -

Deshinta Arrova Dewi mail -

Dedeepya Pulletikurthy mail -

Klodian Dhoska mail

link https://doi.org/10.54216/IJAACI.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of International Economics Research | 2026

Sport tourism as a Driver of Soft Power and Regional Growth

The research shows the currently increasing role of the industry of sport tourism in the Republic of Uzbekistan and how it impacts on the economic and social development of the state from 2018 to 2025. Based on secondary data from international and national organizations and analytical think centres, the paper analyses trends in tourist arrivals, tourism revenue and the impact of government reforms: visa liberalisation and investment in sport facilities. The results of this study shows that sport tourism is a crucial factor in economic diversification, aiding job creation, regional development and the growth of small and medium businesses. The different international competitions such as boxing or judo championships raise the country's status globally and also contribute to strengthening its strength and cultural interaction. Because of different reforms and because more people are now interested in active travel, tourism in Uzbekistan has recovered after the pandemic. However, scientists still have not studied enough how sport affects the economy of the country and small businesses. Overall, the results indicate that sport tourism is very important for the development of Uzbekistan. It can help the country to reach its long term goals and become one of the best places for active and sport tourism in Central Asia.

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Khodjaeva Dildora mail -

Khaydarova Marjona mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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Journal of Sustainable Development and Green Technology | 2026

Price-Aware and Explainable Analytics for Urban Electric Vehicle Charging Networks: Forecasting Utilization Regimes for Sustainable Charging Operations

The efficient functioning of the electric-vehicle charging systems that are publicly operated has become focused on reliable short-horizon forecasting. The paper establishes an explainable and price-conscious analytical model to predict short-term charging usage and demonstrate the utility of tariff signals in an urban charging system. The analysis is based on UrbanEV benchmark, a new six months hourly panel of Shenzhen public charging infrastructure, which integrates occupancy, charging time, charging volume, electricity tariffs, service charges, weather and spatial descriptors. The concept of charging occupancy is considered an operation state variable with connection to queue exposure, reliability of service, and tactical intervention. A succinct mathematical formulation is created to use it in one-step-ahead utilization forecasting and in interpreting low-, medium-, and high-utilization regime. The empirical analysis is pegged to benchmark evidence reported to UrbanEV, where transformer-based forecasting had the best node-level performance and TimeXer had the best RMSE values of 0.07 in occupancy, 2.73 in charging duration, and 43.66 in charging volume. Further discussion indicates that occupancy prediction is accurate enough to justify regime based intervention and strongest additional gains are obtained through the joint effect of pricing variables and temperature-price interactions as opposed to single covariates. The results justify the justifiable, price-conscious forecasting as an operational decision tool to alleviate congestion, design tariffs and specific capacity planning in sustainable charging networks.

groups

Heba Moselhy mail -

Noura Metawa mail

link https://doi.org/10.54216/JSDGT.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

OPH-Guard: An Operationally Interpretable Tree-Ensemble Framework for Phishing URL Screening in Secure Web Access Management

Phishing URLs still present a security threat to organizations because they enable credential theft and account takeover together with payment fraud and unauthorized digital service access. The existing research on phishing detection has been studied extensively yet most published papers still show a preference for predictive performance assessment compared to operational system capabilities and tests and governance system implementation. The researchers developed OPH-Guard as an operational security system which uses compact tree ensembles to identify phishing URLs for their secure web access management system. The integrated workflow system enables institutional and small enterprise to implement public data ingestion and feature validation together with tabular model learning and post-hoc explanation and security-action mapping. The empirical evaluation used a public GitHub-hosted phishing URL dataset which contains 11,481 labeled records and 87 predictive features. The researchers conducted a comparison between three tree-based learners according to a stratified 80/20 hold-out protocol which included Decision Tree and Random Forest and Extra Trees. The actual results from Extra Trees produced the highest accuracy score of 0.9856 which included 0.9921 precision and 0.9791 recall and 0.9855 F1-score and 0.9984 ROC-AUC from the held-out test results. The study investigates security relevance for top predictors through google index and page rank and domain age and phish hints which provide evidence that the resulting model enables organizations to manage browsing risk through URL triage together with secure information management controls. The study presents a reproducible framework together with a complete screening algorithm and a summary of existing research from ten studies and a system which connects model results to security operations.

groups

Reem Atassi mail

link https://doi.org/10.54216/JCIM.180104

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2025

Multimodal Cognitive Workload Recognition in Human-Computer Interaction Using Biosignals and Interaction Traces

The process of recognizing cognitive workload requires reliable methods because researchers need to use both physiological indicators and interaction traces while facing challenges of limited data and inconsistent feature sets. The paper develops a multimodal fusion system which uses weight-based reliability assessment to identify three different workload levels from Cognitive Lab data which is publicly accessible. The subset which focuses on workload includes N-Back and mental subtraction tasks together with electroen-cephalography and functional near-infrared spectroscopy and electrocardiography and electrodermal activity and respiration and accelerometry and gaze descriptors and keyboard-mouse interaction indicators. The method conducts separate training for every modality through multidimensional variable reduction which enables gradient-boosted learners to make predictions about branch reliability based on their validation log-loss scores and combine posterior probabilities using normalized reliability weights. The design preserves distinct modality structures while controlling unpredictable branch effects. The study tests different approaches by evaluating single-modality learners against three methods which include direct early fusion and uniform late fusion and the proposed fusion rule. The proposed model achieves its best performance with 0.842 accuracy and 0.836 macro F1-score on the three-class workload task which includes the medium-load category that presents the greatest challenge to differentiate. The research results from class-wise and sensitivity assessments showed that interaction traces together with fNIRS features produced the smallest improvement to the system, and moderate reliability temperatures showed the highest stability in fusion pro-file performance. The feature attribution demonstrates specific emphasis on how cursor-velocity variability together with fNIRS oxygenation slope and EEG theta-band power and fixation-duration statistics and phasic electrodermal activity function as primary discriminative signals. The research findings demonstrate that multiple modal workload estimation needs to be improved through branch-specific modeling which should use decision fusion based on reliability as its foundation model and work through adaptive learning systems which have to handle rising cognitive requirements.

groups

Andino Maseleno mail -

Kharchenko Raisa mail -

Rahul Chauhan mail

link https://doi.org/10.54216/JCHCI.100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Journal of Cognitive Human-Computer Interaction | 2026

Task-Conditioned Early Prediction of Navigation Failure in Information Architecture Evaluation

The interaction logs which researchers collected during their information-architecture evaluation process contain detailed proof which shows how users select between successful and unsuccessful navigation routes. The predictive signal displays its initial appearance during task execution yet users exhibit different navigation patterns depending on their current task and interface they are using. The researchers of this study developed an early navigation failure prediction system which uses public interaction data to create task-specific prefix classification models. The study analyzes data from an open dataset which includes 180 participants completing 1800 tasks across six testing conditions that evaluate tree testing and highfidelity prototype navigation. A prefix-structural encoder works together with a regularized task-conditioned logistic model which predicts success based on the first k navigation actions. The researchers assessed model performance through participant-specific validation using three different machine learning techniques which included random forest, extra trees, and gradient boosting. The optimal configuration achieved 0.7833 accuracy, 0.7513 balanced accuracy, 0.8350 F1-score, and 0.7949 ROC–AUC performance at k = 3. The horizon analysis demonstration shows that predictive signals become accessible after users complete their first three actions. The ablation study proves that task conditioning functions as an essential component. The study results demonstrate that early trace analytics enable quick identification of navigation failures in information-architecture research while providing a useful method for customized assessment during usability testing.

groups

Kharchenko Raisa mail -

Rahul Chauhan mail -

Andino Maseleno mail

link https://doi.org/10.54216/JCHCI.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2026

A Systematic Literature Review on the Integration of Computer Vision and IoT Technologies for Enhancing Voter Verification Accuracy in Electoral Systems

The rapid evolution of digital technologies has transformed how societies manage sensitive information and authenticate identity in critical systems. Within the domain of cybersecurity and artificial intelligence (AI), the integration of computer vision and Internet of Things (IoT) technologies has emerged as a promising approach to improving real-time data verification and process automation. This systematic literature review examines how computer vision and IoT technologies can be jointly leveraged to enhance voter verification accuracy in electoral systems. Following the PRISMA 2020 guidelines, the review systematically searched four academic databases, identifying 351 initial studies. After rigorous screening based on predefined inclusion and exclusion criteria, 15 studies were selected for comprehensive analysis. The findings reveal three major themes: (1) emerging technical architectures combining biometric authentication with blockchain-based verification, (2) performance outcomes demonstrating high accuracy rates (97–100%) in controlled environments, and (3) persistent challenges in scalability, real-world deployment, and security against sophisticated AI-enabled attacks such as deepfakes. While the PRISMA process was conducted in full, the limited scope of the project, compressed timeline, and restricted access to paywalled articles likely influenced the depth and completeness of the synthesis. Nevertheless, the review provides structured insight into current implementation approaches, technical methods, and research gaps, with particular relevance to contexts like Uzbekistan where recent OSCE ODIHR election observation reports have documented systemic weaknesses in voter verification and turnout reporting.

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Angela Choi mail -

Eugene Q. Castro mail

link https://doi.org/10.54216/JCHCI.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

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