The integration of technology into educational institutions has led to the generation of vast data, creating opportunities for Educational Data Mining (EDM) to improve learning outcomes. This study introduces a novel feature selection model, “Dynamic Feature Ensemble Evolution for Enhanced Feature Selection” (DE-FS), which combines traditional methods such as correlation matrix analysis, information gain, and Chi-square with heat maps to select the most relevant features for predicting student performance. The core innovation of DE-FS lies in its dynamic and adaptive thresholding mechanism, which adjusts thresholds based on evolving data patterns, addressing the limitations of static methods and mitigating issues like overfitting and underfitting. This research makes three key contributions: it introduces an advanced ensemble-based feature selection methodology, incorporates dynamic and adaptive thresholding to improve accuracy and flexibility, and demonstrates DE-FS’s superior predictive performance across diverse educational datasets. The results highlight DE-FS’s ability to adapt to fluctuating data patterns, enabling precise and reliable student performance predictions, supporting targeted interventions, and improving resource allocation to enhance personalized learning experiences.

The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically improved the functionalities and services of Healthcare 5.0, giving rise to a new domain termed Smart Healthcare. A proactive healthcare system may prevent long-term harm by recognizing issues early. This would improve patients’ quality of life while alleviating their worry and healthcare expenses. The IoMT facilitates several capabilities in information technology, including intelligent and interactive healthcare. Consolidating medical information into a singular repository to train a robust ML model engenders apprehensions around privacy, ownership, and adherence to regulatory standards due to increased concentration. Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. The local participant controls patient data, ensuring data confidentiality and security. Hence, this study proposes the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS) for a secure health monitoring system. Additionally, this paper presents the Intrusion Detection System (IDS) as a tool for healthcare network intrusion detection, allowing doctors to track patients’ vitals using medical sensors and anticipate when they could become sick so they can take preventative steps. The suggested system proves that the method is well-suited for medical monitoring. In contrast, the high prediction accuracy for intrusion detection and the high efficiency in disease detection achieved by the proposed FBI-SHS healthcare 5.0 system. The proposed method achieves data privacy and security by 98.73%, intrusion detection efficiency by 97.16%, disease detection accuracy by 96.425, proactive healthcare management by 98.37%, and interoperability by 96.74%.

Population-based metaheuristic optimization algorithms have gained prominence for tackling complex optimization problems. They balance exploration and exploitation, essential for finding optimal solutions. While algorithms like Genetic Algorithms, Particle Swarm Optimization, and Gravitational Search Algorithm have shown success, they have limitations, such as premature convergence and sensitivity to parameters. To address these issues, we have introduced Quantum-Inspired Gravitationally Guided Particle Swarm Optimization (QIGPSO) for addressing complex optimization challenges, particularly in the context of medical data analysis for diagnosing Non-Communicable Diseases (NCDs). The Quantum Particle Swarm Optimization (QPSO) and Gravitational Search Algorithm (GSA) are both used in QIGPSO. It takes advantage of each algorithm’s strengths in both global and local search processes. We used an absolute Gaussian random variable to improve the search, changed the position update equations and used a wrapper-based method with Support Vector Machine (SVM) for feature selection and classification. The findings suggest that QIGPSO is effective at identifying key features, achieving high accuracy rates, and lowering the number of incorrect classifications across several NCD datasets. Doctors can use QIGPSO data to make better treatment decisions for their patients. QIGPSO overcomes the limitations of conventional optimization methods by faster convergence while improving exploitation balance.

Heart disease is one of the major causes of death worldwide, and the traditional diagnostic procedures typically cause delays in treatment, particularly in low-resource regions. In this article, we propose a novel IoT-based Quantum Kernel-Enhanced Sardine Diffusion Attention Network (Qua-KSar-DCK-ArNet) for real-time prediction of heart disease. The system is capable of continuously monitoring heart-related data such as ECG and heart rate via IoT sensors. Quantum Clustering with k-Means is applied to cluster the data, and Z-score Min–Max Normalization is applied for preprocessing. Fast Point Transformer is utilized to identify salient features. The Qua-KSar-DCK-ArNet model, a combination of quantum and classical deep learning methods, classifies the data for predicting the risk of heart disease. The system is fast and accurate, with an accuracy of 99%, significantly improving patient outcomes, especially in resource-scarce regions.

The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions such as inconsistent lighting, leaf deformities, and subtle distinctions between disease symptoms. To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). EfficientNet-B3 offers a strong foundation for feature extraction due to its compound scaling and efficient computation, while the spatial attention module improves classification accuracy by directing the model to focus on critical regions of diseased leaves. Additionally, the integration of ancillary convolutional layer to this architecture enhances the ability of the model to detect subtle disease variations. To further improve the adaptability, the proposed method incorporates a preprocessing and data augmentation techniques. Together, these enhancements create a more effective process for identifying disease pattern in wide range of plant species. The model was evaluated using an extensive crop disease dataset and against state-of-the-art methods such as EffiNet-TS, PlantXViT, and MobileNet V2 to assess its effectiveness. The proposed approach achieved an accuracy of 99.89% and a recall rate of 99.87%, demonstrating its suitability for crop classification with minimal computational overhead. Ablation studies further validate the significant contributions of the spatial attention module and the ancillary convolutional layer to the overall performance of the proposed model.

The traditional curing processes necessitate a large amount of water. This is especially difficult in locations where there is a lack of water and for construction of high-rise structures. In this research article, we provide a remedy by inventing concrete that will not require additional water for curing. In the present work, polyethylene glycol was utilized as an internal curing agent in varying percentages. The internally cured concrete with polyethylene glycol was cured at ambient conditions, whereas the conventional concrete without polyethylene glycol was kept in water for curing. Fresh and hardened characteristics of concrete with and without polyethylene glycol are compared. The results revealed that 1.5% polyethylene glycol is an optimum percentage where maximum strength and durable properties are achieved. The microstructure of internal curing concrete indicates that the pore sizes are small compared to conventional concrete. The crystallite size is smaller in internal curing compared to conventional concrete mixtures, resulting in an acceleration of the hydration process in concrete with smaller crystallites.Further experimental results are compared with ANN. Predicted results are very close to experimental values.