Occurring faults in microgrids (MGs) and their installed sensors is dispensable and should be detected as fast as possible to prevent damages to the power system. This paper investigates the problem of fault detection and isolation in direct current (DC) MGs with nonlinear loads. A novel fusing Unscented Kalman filter (UKF) for fault detection and isolation (FDI) is proposed to estimate the states of the system in the presence of faults. The proposed approach groups the multi-sensors of the system into several local measurements and local UKFs estimate the system states. Then, the local estimations are analyzed in the information mixture block to evaluate the faults and compute the fault-free state estimations. Compared to state-of-the-art methods, the proposed approach offers a low computational burden, is applicable for nonlinear systems and with multi-sensors and multi-faults, and offers a systematic approach to isolate the faulty sensors. The results show that the proposed algorithm has a good performance in fault detection and isolating it and outperforms the recent methods.

Breast cancer’s high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks—EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50—integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system’s detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system’s superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.

There has been an increase in customer churn over the past few years—customers decide not to continue purchasing products or services from an organization. Customers’ data lie in two categories: soft and hard. The term “hard data” refers to the records generated by various devices and programs, including but not limited to smartphones, computers, sensors, smart meters, fleet management systems, call detail records (CDRs), and consumer bank transaction data. On the other hand, information that is subject to interpretation and viewpoint is known as “soft data.” Fusing these two types of data leads to better customer behaviour analysis. This paper uses a supervised machine learning algorithm, namely a decision tree (DT), and the change mining method to model hard data. K-means clustering, an unsupervised machine learning algorithm, is also used along with the data preprocessing techniques. This paper also considers the Dempster-Shafer theory and other steps for soft data modelling. By fusing soft and hard data, the churn rate of customers compared with each other can be calculated. Besides, the customers’ banking data are leveraged for data modelling. The results show that the banking industry will gain a more dynamic and efficient customer relationship management system by using this model.