Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients’ health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models’ performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient’s profile, allowing for personalized decision-making in resource-constrained settings.

Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area.

Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction method proposed here utilizes temporal feature extraction to delineate six distinct time frames before the onset of sepsis. These time frames adhere to the sepsis-3 standard requirement, which incorporates 12-h observation windows preceding sepsis onset. All models were trained using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which sourced 61,522 patients with 40 clinical variables obtained from the IoT medical environment. The confusion matrix, the area under the receiver operating characteristic curve (AUCROC) curve, the accuracy, the precision, the F1-score, and the recall were deployed to evaluate the models. Result: The CNNBDLSTM model demonstrated superior performance compared to the benchmark and other models, achieving an AUCROC of 99.74% and an accuracy of 99.15% one hour before sepsis onset. These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset, particularly within a close proximity of one hour. Implication: The results could assist practitioners in increasing the potential survival of the patient one hour before sepsis onset.

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization.

The vegetable industry is a fundamental part of Malaysia's agricultural and economic landscape, providing essential food supplies, creating employment opportunities, and contributing to economic growth. However, recent challenges, such as labor shortages in the agricultural sector, have limited the capacity of Malaysian vegetable businesses to meet increasing market demand. This study proposes the Smart Vertical Farming System (SVFS), utilizing the Internet of Things (IoT) as part of a broader 'green revolution' focused on applying advanced Information and Communication Technologies (ICT) in agriculture. Using a waterfall development framework and a qualitative research methodology, the SVFS was designed to monitor critical agricultural parameters such as soil moisture, light exposure, and ambient temperature. Initial implementation involved cultivating two different vegetable varieties alongside traditional farming methods. Remarkably, after 19 days, the results showed that vegetables grown using the SVFS experienced faster growth rates and increased leaf production compared to those cultivated conventionally. These findings suggest that integrating innovative farming technologies can significantly improve vegetable quality and yield, while also stabilizing market supply to meet consumer needs at competitive prices. This research demonstrates the potential for technological advancements to address pressing issues within the agricultural sector and enhance overall food security in Malaysia.