Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.

Chronic kidney disease (CKD) is a prevalent and debilitating condition worldwide, characterized by progressive loss of kidney function over time. Early detection plays a crucial role in mitigating its impact on patient health and healthcare systems. In recent years, there has been a burgeoning interest in leveraging machine learning (ML) and deep learning (DL) techniques to enhance the early diagnosis of CKD. This comprehensive review explores the advancements in ML and DL models applied to CKD diagnosis, focusing on their ability to integrate diverse data sources including clinical biomarkers, imaging modalities, and patient demographics. Key ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and neural network architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are examined in the context of their performance in predicting CKD progression, classifying disease stages, and identifying at-risk populations. Furthermore, the review discusses challenges such as data quality, model interpretability, and integration into clinical practice, alongside emerging trends in explainable AI, transfer learning, federated learning, and integration with electronic health records (EHRs). By synthesizing findings from recent literature, this paper aims to provide insights into current methodologies, identify gaps for future research, and underscore the transformative potential of ML and DL in revolutionizing early CKD diagnosis and management..

Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier.

It is widely known that an appropriately built unpowered bipedal robot can walk down an inclined surface with a passive steady gait. The features of such gait are determined by the robot's geometry and inertial properties, as well as the slope angle. The energy needed to keep the biped moving steadily comes from the gravitational potential energy as it descends the inclined surface. The study of such passive natural motions could lead to ideas for managing active walking devices and a better understanding of the human locomotion. The major goal of this study is to further investigate order, chaos and bifurcations and then to demonstrate the complexity of the passive bipedal walk of the compass-gait biped robot by examining different bifurcation diagrams and also by studying the variation of the eigenvalues of the Poincaré map's Jacobian matrix and the variation of the Lyapunov exponents. We reveal also the exhibition of some additional results by changing the inertial and geometrical parameters of the bipedal robot model.