Background: Alzheimer’s disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention. Methods: We present an enhanced hybrid deep learning framework that amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using an adaptive weight selection process informed by the Cuckoo Search optimization algorithm. The procedure commences with the pre-processing of neuroimaging data to guarantee quality and uniformity. Features are subsequently retrieved from the neuroimaging data by utilizing the EfficientNetV2B3 and Inception-ResNetV2 models. The Cuckoo Search algorithm allocates weights to various models dynamically, contingent upon their efficacy in particular diagnostic tasks. The framework achieves balanced usage of the distinct characteristics of both models through the iterative optimization of the weight configuration. This method improves classification accuracy, especially for early-stage Alzheimer’s disease. A thorough assessment was conducted on extensive neuroimaging datasets to verify the framework’s efficacy. Results: The framework attained a Scott’s Pi agreement score of 0.9907, indicating exceptional diagnostic accuracy and dependability, especially in identifying the early stages of Alzheimer’s disease. The results show its superiority over current state-of-the-art techniques.Conclusions: The results indicate the substantial potential of the proposed framework as a reliable and scalable instrument for the identification of Alzheimer’s disease. This method effectively mitigates the shortcomings of conventional diagnostic techniques and current deep learning algorithms by utilizing the complementing capabilities of EfficientNetV2B3 and Inception-ResNetV2 by using an optimized weight selection mechanism. The adaptive characteristics of the Cuckoo Search optimization facilitate its application across many diagnostic circumstances, hence extending its utility to a wider array of neuroimaging datasets. The capacity to accurately identify early-stage Alzheimer’s disease is essential for facilitating prompt therapies, which are crucial for decelerating disease development and enhancing patient outcomes.

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time monitoring, data collection, and intelligent decision-making. However, the massive volume of data generated by these devices presents significant challenges for data processing and analysis. Intrusion Detection Systems (IDS) for IoT require efficient and accurate identification of malicious activities amidst vast amounts of data. Feature selection is a critical step in this process, aiming to identify the most relevant features that contribute to accurate intrusion detection, thus reducing computational complexity and improving model performance. Traditional Mutual Information-based Feature Selection (MIFS) methods face challenges when applied to IoT data due to their inherent noise, uncertainty, and imprecision. This study introduces a novel Fuzzy Mutual Information-based Feature Selection (Fuzzy-MIFS) method that integrates fuzzy logic with Gaussian membership functions to address these challenges. The proposed method enhances the robustness and effectiveness of the feature selection process, resulting in improved accuracy and efficiency of IDSs in IoT environments. Experimental results demonstrate that the Fuzzy-MIFS method consistently outperformed existing feature selection techniques across various neural network models, such as CNN, LSTM, and DBN, showcasing its superior performance in handling the complexities of IoT data. The results show that Fuzzy-MIFS increased the accuracy from 0.962 to 0.986 for CNN, from 0.96 to 0.968 for LSTM, and from 0.96 to 0.97 for DBN.

Technology has recently gained relevance within collaborative learning environments to provide robustness, agility and flexibility. Several recent studies have investigated the role of technology, as well as researchers have defined different metrics to assess learning outcomes and experience along the collaborative knowledge development process. More recently, technology has played a key role to face the new challenges related to COVID-19, which forced to move on remote or hybrid learning. This research focuses on the quality of learning experience in terms of academic performance and perceived satisfaction. From a methodological point of view, a conceptual framework has been proposed and a quantitative study has been conducted among undergraduate and postgraduate students that are undertaking programs related to System Design in Saudi Arabia universities. 152 responses have been collected through an online survey and analysed using SPSS and SmartPLS. Results show a positive impact of technology along the collaborative knowledge development process and a strong correlation among the different quality of learning experience parameters considered. Indeed, despite some challenges, an integrated use of technology seems to properly support the most pressing needs in terms of quality experience, while the well-known social/educational issues related to the COVID-19 pandemic are not object of this study. Those findings are expected to contribute to the Saudi Arabia’s vision 2030 and, more holistically, to the assessment of collaborative learning environments that extensively rely on technology.

Predictive models for early identification of heart disease must be precise and efficient because it is a major worldwide health concern. To improve classification performance while protecting data privacy, this study investigated a combined method that uses ensemble learning, feature selection, and federated learning (FL). The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. In addition, federated learning was implemented to allow decentralized training while preserving sensitive medical data. The results highlight the effectiveness of combining these techniques in the detection of heart disease, providing a scalable and privacy-preserving solution for real-world healthcare applications. Two benchmark datasets were used to validate the proposed approach, ensuring the reliability and generalizability of the findings. Furthermore, we used four performance metrics, namely accuracy, precision, recall, and F1score, to evaluate the selected models. Finally, federated learning was included to handle privacy issues and guarantee safe access to private medical data. This distributed method allows model training without centralizing patient data, so it is compatible with strict data privacy rules. With up to 95% precision, our method shows a notable increase in prediction accuracy according to the testing results. This work offers a strong, scalable, and safe solution for the early identification of cardiovascular diseases by combining ensemble learning, feature selection, and federated learning, opening the way for more general uses in medical diagnostics.

Accurate classification of cancer subtypes is crucial for personalised therapies and targeted interventions. In this study, we propose BioGAT-LGG, a deep learning framework that integrates multi-omics data, including mRNA, miRNA, and DNA methylation, using a correlation-based Graph Attention Network version 2 (GATv2) for biomarker discovery and Lower-Grade Glioma (LGG) subtype classification. Unlike existing methodologies that rely on external biological priors, such as protein-protein interaction networks or reference graphs, BioGAT-LGG constructs gene-driven correlation graphs, enabling the model to learn biologically meaningful molecular interactions. To improve feature interpretability and reduce dimensionality, LASSO regression is performed during model training. The model achieved 98.03% accuracy, with precision (98.12%), recall (97.74%), and F1-score (97.87%) in a stratified 10-fold cross-validation. Extensive analysis and enrichment of known cancer-related pathways, including PI3K-Akt signalling, Small Cell Lung Cancer, and Transcriptional Misregulation in Cancer, identified the biomarkers hsa-mir-3936, MTCO1P40, and CCND2, which were subsequently validated. These results indicate that BioGAT-LGG effectively captures biologically validated mechanisms and can enable clinically significant subtype classification and biomarker-guided decision-making. This framework thus lays a scalable foundation for multi-omics integration in oncology, which can be further adopted in other tumour types.

System design is an essential subject taught in information systems and has become a core course in its curriculum. Unified modelling language (UML) has been broadly adopted, and it is common to support the system design process using different diagrams. Each diagram serves a purpose by focusing on a specific part of a particular system. Design consistency ensures a seamless process, as the diagrams are generally interrelated. However, creating a well-designed system takes a lot of work, especially for university students with work experience. To overcome this challenge, aligning the concepts across diagrams is essential, which can help achieve better consistency and management of the design system, especially in an educational setting. This article is an extension of our previous work, as we have discussed a simple scenario of Automated teller machines to demonstrate the alignment concepts between UML diagrams. From a more technical perspective, the current contribution provides a Java program that aligns concepts by converting text-based use cases to text-based sequence diagrams. Then, the text is transformed in PlantUML to generate its graphical representation. The developed alignment tool is expected to contribute to helping students and instructors during the system design phases to be more consistent and practical. Limitations and future work are presented.