Deema Mohammed Alsekait

Artificial intelligence (AI) and digitally mediated learning have intensified debate about flexibility, lifelong learning, and quality assurance in higher education. Education 4.0 is commonly associated with technology-enabled learning and the capabilities linked to Industry 4.0, whereas Education 5.0 places greater emphasis on human-centred, ethical, and socially responsible innovation. Against this background, this study examines stakeholder perceptions of the non-recognition of fully online doctoral education in India, with attention to career barriers, psychological burden, acceptance of digitally enabled education, and support for policy reform. The study draws on a cross-sectional questionnaire dataset of 832 respondents and uses descriptive statistics, exploratory factor analysis (EFA), group comparisons, and ordinary least squares (OLS) regression. The reported results show comparatively high mean scores for career barriers and Education 4.0 acceptance. Group comparisons did not detect statistically significant differences across the categories examined. The OLS model was statistically significant overall (F(7, 824) = 64.28, p < .001; R² = .353; adjusted R² = .348), with institutional resistance positively associated with policy-reform support and career barriers negatively associated with it. These results describe associations in the sample and should not be interpreted as causal effects or as evidence that any particular qualification is formally recognised. The study concludes by discussing a cautious, quality-assured policy framework that distinguishes delivery mode from supervision, assessment, research integrity, and institutional accountability.

A crucial factor for smart cities, which are more vulnerable to cyber threats, is Cyber Resilience (CR). Nevertheless, the conventional frameworks didn't concentrate on assuring the Backup Data (BD) integrity before restoration, showing less resilience. Therefore, this article implements an AI-powered BD integrity verification approach for CR in smart infrastructure using Murmur Polytopes Hash (MPH). Initially, the nodes are initialized in the smart city, followed by node clustering, data security, and storage (cloud server and Interplanetary File System (IPFS) (backup)). Now, the hash code is generated and updated in the Merkle tree. Besides, to perform data collection, pre-processing, clustering, correlation heatmap generation, feature extraction, and attack classification, the proposed ransomware attack detection module is designed. If the data is attacked, then the BD verification is done using MPH. Then, the BD is restored. If the data is normal, then the data is downloaded from the cloud server. Thus, the proposed work had a high security level and accuracy of 98.45% and 98.65%, respectively, showing better resilience.

This research develops a decision-making system for aircraft wing-spar tooling design that employs ontology-based Knowledge Retrieval Practices (KRP) to reduce search effort, improve traceability, and deliver decision-ready guidance. This research formalized a domain ontology, encode rule-based constraints, and structure case libraries, then introduce a query-information model that maps natural-language questions to machine-interpretable intents. The system orchestrates three retrieval modes, including ontology based semantic (OBS), rule-based inference (RBI), and case-based reasoning instances (CBRI), within a five-layer browser and server platform integrated with Product Development Management (PDM)/ Computer Aided Design (CAD). Evaluation spans twenty decision-like tasks, graded tooling corpora (100-420 documents), and a domain-agnostic stress test. The system achieved mean task-level accuracy of 93.1%, with the hybrid OBS+RBI+CBRI configuration reaching 96.9% confidence. Document-level accuracy was 98-99% on the tooling corpora, and the stress test showed small but consistent gains over traditional retrieval. OBS excelled for conceptual and attribute queries; RBI for computable dimensioning and sequencing; and CBRI for structural analogies and fine adjustments. Precision declined for legacy materials lacking ontology tags or using obsolete terminology, motivating retro-tagging and synonym expansion. Practically, it delivers a deployable KRP platform with role-based governance and enterprise interfaces, offering explainable, decision-ready guidance for complex assembly tasks. The study provides actionable guidance on when to use each retrieval strategy and establishes a reproducible evaluation protocol, laying the groundwork for longitudinal impact studies and public benchmarks in aerospace knowledge management. Limitations include the domain focus and reliance on curated corpora; these inform the roadmap for broader validation.

High-dimensional genomic datasets often contain redundant, noisy, and sparse features that make accurate classification challenging for conventional deep learning (DL) models. Existing approaches generally fail to maintain interpretability and stability when confronted with heterogeneous genomic structures. To address these limitations, this study proposes a hybrid TabNet–CNN framework that combines attention-driven feature selection with adaptive convolutional refinement. The attention mechanism in TabNet highlights the most relevant genomic attributes, while the convolutional layers enhance localized feature interactions for accurate decision boundaries. Experimental results on multiple genomic datasets demonstrate superior performance in accuracy, AUC, and interpretability compared to state-of-the-art models. The proposed framework holds promise for real-world applications such as biomarker identification, disease subtyping, and clinical decision-support systems in precision medicine.

The emerging growth of Quantum computing has significant challenges to change the classical cryptographic protocols, the security of AI–Blockchain systems to provide long-term security and provide federated learning (FL) for current cryptographic systems. This paper introduces a new framework as Quantum-Resistant Federated AI on Blockchain (QFAIB) that integrates Post-Quantum Cryptography (PQC) algorithms with real-time Federated AI Learning (FAIL) on distributed ledgers creation. The proposed QFAIB ensures end-to-end data security and confidentiality, decentralized trust, and adaptive intelligence, resistant to quantum decryption attacks. Through the integration of hybridization of CRYSTALS-Kyber encryption to create challenging task, that makes the Dilithium digital signatures, and Zero-Knowledge Proofs (ZKP) for privacy-preserving model validation and decentralized federated AI models, the proposed QFAIB compared with baseline and evaluated as the performance metrics as data integrity verification accuracy (DIVA), auditing efficiency (AE), quantum resistance efficiency (QRE), privacy leakage reduction (PLR) and throughput (TT) in multi-cloud and IoT environments and this work proved that real time distributed ledger creations.

Financial institutions are finding it more challenging to accurately assess credit risk, particularly when dealing with a variety of data sources that include both unstructured textual data and structured financial records. In order to analyse financial risk utilising both structured and unstructured data, this research suggests an improved predictive modelling framework that combines conventional Machine Learning (ML) and Deep Learning (DL) techniques. The structured dataset comes from the Home Credit Default Risk dataset (307,511 applicant records, 122 characteristics). To give additional predictive capabilities, unstructured data from relevant news and social media sources is interpreted using Natural Language Processing (NLP) methods including sentiment analysis. A standard preprocessing pipeline, including SMOTEENN, label encoding, feature scaling, and missing value imputation, is utilised to resolve class imbalance in the fused dateset. While the ML pipeline employs XGBoost, LightGBM, and Random Forest in a Stacking ensemble, the DL pipeline utilises a CNN-LSTM architecture to capture both local patterns and temporal correlations. Model evaluation using Accuracy, Precision, Recall, F1-score, ROC-AUC, and confusion matrix analysis shows that the addition of unstructured variables significantly improves predictive performance over just structured baselines. While the Stacking Classifier has the best ROC-AUC of 0.9816 and the highest F1-score of 0.95, the CNN-LSTM has the highest ROC-AUC of 0.9592 and an F1-score of 0.91. The proposed hybrid approach emphasizes the advantages of combining structured and unstructured data for accurate and scalable credit risk assessment in modern financial systems.

When the number of white blood cells (WBCs) in the human body becomes imbalanced, leukemia (blood cancer) can develop, affecting individuals of all ages. Early and accurate detection is crucial for improving patient outcomes, yet manual diagnosis is time-consuming and prone to subjectivity. This study proposes an efficient hybrid convolutional neural network (CNN)-transformer model for automated blood cancer detection, integrating convolutional layers for localized feature extraction with transformer-based attention mechanisms for capturing global dependencies. The architecture employs depthwise separable convolutions, efficient multi-head self-attention (EMHSA), and an efficient multilayer perceptron (EMLP) block, optimized via Bayesian hyperparameter tuning. The proposed model was evaluated on two publicly available datasets: the Blood Cancer dataset (binary classification) and the Blood Cells Cancer (ALL) dataset (four-class classification). Using a 50:50 training-testing split, the model achieved 100% accuracy, 100% precision, 100% recall, and 100% F1-score on the Blood Cancer dataset, and 99% accuracy, 99% precision, 100% recall, and 99% F1-score on the ALL dataset. Additional experiments with 80:20, 70:30, and 60:40 splits confirmed consistent performance above 98% across all metrics, demonstrating strong robustness. The model contains only 2.04 million trainable parameters, significantly fewer than standard CNN or transformer-based architectures, making it computationally lightweight and suitable for deployment in resource-constrained clinical environments. These results highlight the potential of the proposed hybrid framework to provide accurate and efficient blood cancer classification, advancing the applicability of deep learning in hematological diagnostics.

The growing demand for high-priority and delay-sensitive data transmission in Optical Wireless Sensor Networks (OWSNs) exposes considerable deficiencies in the IEEE 802.15.7 MAC superframe, particularly its inadequacy in accommodating various traffic types. This study proposes an enhanced, priority-aware MAC superframe that integrates dual-phase contention and contention-free access with dynamic slot partitioning, a priority beacon system, and opportunistic sleep scheduling. Architectural enhancements comprise the incorporation of a Time-To-Report Contingency (TTRC) interval for urgent data reporting, adaptive GTS allocation prioritizing emergency traffic while maintaining periodic flows, and mini-slot partitioning that expands GTS availability from 7 to 28 allocations per superframe. The proposed strategy accommodates both CSMA/CA and GTS-based transmissions, optimizing channel utilization in mixed traffic scenarios. Comprehensive simulation investigations demonstrate that the IEEE 802.15.7 MAC baseline exhibits up to 68% reduction in end-to-end delay, 55% enhancement in link utilization, and a significant decrease in collision probability under conditions of high-priority traffic. The suggested design utilizes around 22% less power on average compared to the most efficient MAC protocols, demonstrating its energy efficiency and suitability for resource-constrained and time-sensitive OWSN deployments, such as healthcare monitoring and emergency alert systems.

Traditional diagnostic methods of leukemia, a blood cancer disease, are based on visual assessment of white cells in microscopic peripheral blood smears, and as a result, they are arbitrary, laborious, and susceptible to errors. This study proposes a new automated deep learning-based framework for accurately classifying leukemia cancer. A novel lightweight algorithm based on the hyperbolic sin function has been designed for contrast enhancement. In the next step, we proposed a customized convolutional neural network (CNN) model based on a parallel inverted dual self-attention network (PIDSAN4), and a tiny16 Vision Transformer (ViT) has been employed. The hyperparameters were tuned using the grey wolf optimization and then used to train the models. The experiment is carried out on a publicly available leukemia microscopic images dataset, and the proposed model achieved 0.913 accuracy, 0.892 sensitivity, 0.925 specificity, 0.883 precision, 0.894 F-measure, and 0.901 G-mean. The results were compared with state-of-the-art pre-trained models, showing that the proposed model improved accuracy.