Summary: The rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike.

Purpose: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. Methods: In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. Results: The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. Conclusion: Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.

The advent of renewable energy sources (RESs) in the power industry has revolutionized the management of these systems due to the necessity of controlling their stochastic nature. Deploying RESs in the microgrid (MG) as a subset of the utility grid is a beneficial way to achieve their countless merits in addition to controlling their random nature. Since a MG contains elements with different characteristics, its management requires multiple applications, such as demand response (DR), outage management, energy management, etc. The MG management can be optimized using machine learning (ML) techniques applied to the applications. This objective first calls for the microgrid management system (MGMS)’s required application recognition and then the optimization of interactions among the applications. Hence, this paper highlights significant research on applying ML techniques in the MGMS according to optimization function requirements. The relevant studies have been classified based on their objectives, methods, and implementation tools to find the best optimization and accurate methodologies. We mainly focus on the deep reinforcement learning (DRL) methods of ML since they satisfy the high-dimensional characteristics of MGs. Therefore, we investigated challenges and new trends in the utilization of DRL in a MGMS, especially as part of the active power distribution network (ADN).