The rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities to penetrate IoT networks. Although IoT devices are utilized across a wide range of domains, the Internet of Medical Things (IoMT) holds particular significance due to the sensitive and critical nature of medical information. Consequently, the security of these devices must be treated as a paramount concern within the IoT landscape. In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. The results demonstrate that the proposed novel approach achieved an accuracy of 98% for 19 classes, which is remarkably high for classifications and presents a significant and promising outcome for IoMT environments.

Rolling bearings are key components in most mechanical facilities; hence, the diagnosis of their faults is very important in predictive maintenance. Up to date, vibration analysis has been widely used for fault diagnosis in practice. However, acoustic analysis is still a novel approach. In this study, acoustic analysis with classification is used for fault diagnosis of rolling bearings. First, Hilbert transform (HT) and power spectral density (PSD) are used to extract features from the original sound signal. Then, decision tree algorithm C5.0, support vector machines (SVMs) and the ensemble method boosting are used to build models to classify the instances for three different classification tasks. Performances of the classifiers are compared w.r.t. accuracy and receiver operating characteristic (ROC) curves. Although C5.0 and SVM show comparable performances, C5.0 with boosting classifier indicates the highest performance and perfectly discriminates normal instances from the faulty ones in each task. The defect sizes to create faults used in this study are notably small compared to previous studies. Moreover, fault diagnosis is done for rolling bearings operating at different loading conditions and speeds. Furthermore, one of the classification tasks incorporates diagnosis of five states including four different faults. Thus, these models, due to their high performance in classifying multiple defect scenarios having different loading conditions and speeds, can be readily implemented and applied to real-life situations to detect and classify even incipient faults of rolling bearings of any rotating machinery.

Integrating new generation and storage resources within power systems is challenging because of the stochastic nature of renewable generation, voltage regulation, and the use of microgrids. Classical optimization methods struggle with these nonlinear, multifaceted issues. This paper presents a novel optimization framework for integrating, sizing, and siting distributed renewable generation and energy storage systems in power distribution networks. To accurately reflect load variability, the framework considers four distinct load models—constant impedance, current, power, and ZIP (constant impedance, constant current, constant power). Our approach utilized three metaheuristic approaches to enhance the efficiency of power system management. The validation results on the IEEE 33 Bus System conclude that the Elephant Herding Optimization (EHO) emerged as the best performer regarding voltage stability and real power loss reduction with a voltage stability index of 0.0031346. Modified Ant Lion Optimization (ALO) achieved a best voltage stability index of 0.0024115 and power losses of 7.5092 MVA. The Red Colobus Monkey Optimization (RMO) algorithm realized a voltage stability index of 0.0052053 and real power losses of 20.7564 MVA. Overall, the results conclude that ALO is the most effective approach for optimizing distributed renewable energy systems under different climatic conditions. According to the analysis, the algorithm works best in ideal circumstances when the percentages of wind and irradiance are 60% or greater.

The identification and classification of broken conductor faults in power distribution systems (PDSs) is challenging because of the nonlinear and complex nature of the unbalanced three-phase currents. Previous approaches to identifying faults fail to resolve the issue of precise fault identification because of limited data availability and inefficient data modeling approaches. In this study, to identify the broken conductor faults and classify them into several scenarios in three-phase open-circuit distribution networks, a new hybrid method of deep learning based on Complex Value Neural Networks (CVNNs) and Long Short Term Memory (LSTM) is introduced. Our method refines input using the Proportional Topology Optimization (PPTO) algorithm that enhances the signal quality of unbalanced three-phased current signals for fault identification. Moreover, we use an appropriate DenseNet pre-trained model to extract features and estimate the topology state with reliable data representation. The developed CVNN framework was built with TensorFlow’s Sequential API, which provided efficient computing of complex-valued inputs and weights and, thus, the proper handling of the intricate signals often encountered in power systems. The proposed approach was validated on three publically accessible datasets using a five-fold cross-validation technique. Results were realized in the form of Classification Accuracy (98.22% / 99.83% / 98.22%), F-measure (0.8881 / 0.9983 / 0.88), Precision (0.9207 / 0.9983 /0.92), Recall (0.8578 / 0.9983 / 0.85), and Area Under the Curve (AUC) (0.9433 / 0.9917/ 0.9958). It is shown that the proposed method has higher fault detection and classification accuracy compared to three traditional classification approaches, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and three state-of-the-art methods: (1) Stockwell transform +SVM, (2) Fast Fourier Transform + SVM, and (3) Hilbert-Huang transform of vibration data and power spectral density + Artificial Neural Network. Overall, the performance of the proposed methodology was satisfactory and can be used in real-time fault identification from PDSs installed in complex load environments and industries.

A gel card incubator is an important clinical interface device that maintains gel cards at body temperature, completing the initial step in determining blood grouping. Achieving a much faster closed-loop response to shorten the warm-up period and maintaining a uniform temperature inside the incubator are crucial requirements for medical incubation devices. To accomplish incubator control requirements and deal with deadtime, this study proposes a novel nonlinear plus integral control (NPI) scheme coupled with a time-delay compensator using an incubator dynamic model. The NPI successfully shaped the control structure by utilizing a few nonlinear parameters. The control strategy, which promises a significant performance combining linear and nonlinear control actions, is easy to realize, simple to tune, and allows a smooth transition between control actions. Experiments were performed in a microprocessor-controlled gel card incubator using a computer interface to demonstrate the superiority of the proposed control technique. The comparative results prove that the proposed control scheme exhibits significantly faster and superior responses for transient and steady states. It also achieves successful disturbance rejection and less energy consumption than the Smith predictor-based PI control.