In today’s world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System’s (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user’s behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user’s trust value, A .net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

Effective student management is crucial for fostering productive learning environments. This study presents a hybrid framework integrating machine learning (ML) techniques with rough set theory to enhance student management by identifying at-risk students and enabling personalized interventions. The model combines classification algorithms with rough set-based decision rules to analyze complex student data, including academic performance, behavior patterns, and levels of engagement. The ML layered approach detects patterns and outliers, supporting data-driven decisions to improve student well-being and educational outcomes. Evaluation on the Open University Learning Analytics Dataset (OULAD) demonstrated high accuracy (97.85%) in predicting student outcomes and precision (94.62%) in identifying students needing support. The hybrid approach outperformed conventional methods by approximately 15%, showcasing its transformative potential. This framework effectively monitors student performance and enables customized interventions to meet individual learning needs, fostering a more supportive educational environment.

Background: Large Language Models often struggle with multi-step reasoning due to cascading errors, rigid prompt structures, and underutilized intermediate reasoning steps. While prompting strategies such as Chain-of-Thought , CoT with Self-Consistency, and Least-to-Most offer partial improvements, they typically lack mechanisms for feedback-driven learning or structured reuse of prior thought sequences. Objectives: This work introduces Recursive Decomposition of Logical Thoughts (RDoLT), a cognitively inspired prompting framework that enhances LLM reasoning through hierarchical decomposition, multi-feature scoring, and knowledge propagation. The framework aims to overcome linear reasoning limitations by enabling structured, memory-aware exploration of thought spaces. Methods: RDoLT executes a three-stage iterative reasoning process across Easy, Intermediate, and Final tiers. At each level, multiple candidate thoughts are generated and scored on Logical Validity, Coherence, Simplicity, and Adaptiveness. The Knowledge Propagation Module (KPM) persistently tracks both selected and rejected thoughts, allowing future reasoning stages to reuse contextually relevant but previously discarded knowledge. The framework supports adaptive thresholding, controlled reasoning depth, and edge-case regeneration through structured feedback loops. Results: Extensive evaluation across five reasoning benchmarks demonstrates that RDoLT outperforms the most competitive prompting strategies in both accuracy and stability. On GSM8K, RDoLT achieves 90.98% accuracy with ChatGPT-4o, surpassing CoT-SC (89.4%) and ReAct (90.5%). It improves Gemma 2 (27B) performance on SVAMP from 69.86% (Vanilla) to 75.27%, and on MultiArith from 67.96% (Vanilla) to 72.49%. Across all benchmarks, RDoLT outperforms or matches the strongest baseline in over 60% of settings, highlighting its robustness across diverse reasoning tasks and model scales. Ablation studies reveal that generating three thoughts per stage yields the best trade-off between performance and efficiency, while the Knowledge Propagation Module (KPM) consistently reduces reasoning variance by leveraging both accepted and discarded thoughts across stages. Conclusions: RDoLT presents a scalable reasoning paradigm grounded in cognitive principles. Its integration of hierarchical decomposition, structured scoring, and selective memory propagation enables more reliable and adaptive reasoning in LLMs. These results establish RDoLT as a robust prompt engineering framework with broad applicability, and future work will focus on optimizing token efficiency and extending to domain-specific use cases.

This paper investigates the use of deep learning ensemble strategies in outcome-based education (OBE) and examines how ideology and politics influence vocational training outcomes. The study evaluates three ensemble strategies, namely bagging, boosting, and stacking, for predicting vocational outcomes and explores how political and institutional contexts relate to training effectiveness. The dataset comprises vocational training results together with political-context information linked to vocational programs. Political ideology was quantified using policy and governance indicators and encoded as contextual features aligned with vocational training records. A deep learning ensemble framework was developed to predict outcomes such as skill acquisition, employment rates, and overall learner success. The results show that ensemble learning improves predictive performance, with the best-performing model achieving an accuracy of 98.45% in predicting student performance from training-related parameters. Model robustness was assessed using 10-fold cross-validation, and performance is reported as the cross-validated mean with confidence intervals. The findings further indicate that political and ideological factors are associated with variations in vocational outcomes, highlighting the importance of policy context in program design and alignment with labor market demands. Overall, the integration of ensemble learning with vocational education systems offers a promising approach for improving outcome prediction and supporting more effective and inclusive vocational training initiatives.

The increasing digitalization of the healthcare system, driven by electronic health records, medical IoT devices, and AI-aided diagnostics, has significantly expanded the attack surface for cyber threats. Although current cryptographic protocols provide sufficient security against classical adversaries, new developments in quantum computing threaten the confidentiality, integrity, and availability of medical information as never before. Shor’s algorithm could break widely used asymmetric encryption systems, and Grover’s algorithm weakens symmetric ones, which could render modern healthcare security systems obsolete. This review provides an in-depth and organized analysis of the evolving quantum threat environment in healthcare ecosystems and of the need to move toward quantum-resilient security architectures. It synthesizes the existing literature on post-quantum cryptography (PQC) and identifies lattice-based, hash-based, multivariate, and code-based algorithms as potentially viable long-term replacements. Moreover, it examines the cybersecurity issues that quantum-enabled attacks pose to medical IoT systems, clinical decision-support systems, wearable sensors, telemedicine systems, and cloud-based health information exchanges. The review also covers hybrid quantum-classical defenses, quantum key distribution (QKD), quantum random number generation (QRNG), and blockchain-based trust systems that aim to make systems resilient to advanced persistent quantum attacks. This paper identifies key gaps and outlines future research directions for developing quantum-secure medical systems. The results highlight the importance of early adoption of PQC standards and a layered cyber defense approach to protect patient privacy, ensure regulatory compliance, and retain trust in next-generation digital medical data communication.