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Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.

An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care.

Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datasets. This is the first study to compare several AutoML tools and shows that AutoML tools can be utilized to create well-performing solutions for the diagnosis of epilepsy via processing hard-to-analyze EEG timeseries data.

AbstractCeramic materials are an indispensable part of our lives. Today, ceramic materials are mainly used in construction and kitchenware production. The fact that some deformations cannot be seen with the naked eye in the ceramic industry leads to a loss of time in the detection of deformations in the products. Delays that may occur in the elimination of deformations and in the planning of the production process cause the products with deformation to be excessive, which adversely affects the quality. In this study, a deep learning model based on acoustic noise data and transfer learning techniques was designed to detect cracks in ceramic plates. In order to create a data set, noise curves were obtained by applying the same magnitude impact to the ceramic experiment plates by impact pendulum. For experimental application, ceramic plates with three invisible cracks and one undamaged ceramic plate were used. The deep learning model was trained and tested for crack detection in ceramic plates by the data set obtained from the noise graphs. As a result, 99.50% accuracy was achieved with the deep learning model based on acoustic noise.

Purpose: Detection of deformation of devices in high voltage electricity transmission line systems is an important issue in terms of economy and reuse. This study is aimed to detect devices that are deformed or thought to have suffered due to environmental and electrical reasons. Design/methodology/approach: In this experimental study, it was ensured that the sound and deformed insulators used in energy transmission lines were determined by the analysis of the sounds obtained by using the impact method. Equal intensity impact was applied to the isolator using the pendulum and the resulting sound noise signal analyses were made using power spectral density (PSD), magnitude scalogram (MS), multitape power spectrum density (MPSD) and continuous wavelet transform (CWT) methods in the study. In the analysis results, the isolators that are not visible to the eye and have certain damage were successfully separated from the intact insulators. Especially, MPSD and CWT analysis results are quite satisfactory. Findings: Damage analysis of insulators used in electricity transmission lines has been made. A total of 40 insulators were examined in two categories in their group, both damaged and not damaged. Data collection system was established. The data obtained from the data collection system were analysed and compared using four analysis methods. PSD, MS, MPSD and CWT analyses were made in the study. All the analyses carried out generally contain features that distinguish damaged and undamaged insulators from each other, the most successful results are MS and CWT results. CWT results are very successful in terms of time and amplitude, and it has been proposed as a method that can be used to separate damaged and undamaged insulators. Originality/value: It can be suggested as a result of experimental tests that the results of CWT analysis can be used in the pulse noise method in isolators to be tested for reuse in electrical power transmission lines.

In this study, some measurements like the current, voltage and hydrogen flow based on the fuel cell are investigated in spectral-domain as well as their time-domain representations and then, their spectral properties are extracted. Besides this, taking the simplified transfer function approach into account, which is defined between the hydrogen flow and current of the cell as an input-output pair, more detailed results are obtained. Therefore, the spectral parts of the fuel cell are put into categories under the impacts coming from the process, measurement circuits and digitizers. The process noise to be defined at very small frequencies (

Security systems play a crucial role in protecting individuals and assets within diverse infrastructures, including seaports. The uninterrupted operation of these systems heavily relies on a continuous power supply, as any disruptions can lead to severe consequences. Therefore, security systems are classified as critical loads requiring uninterrupted power availability. This study focuses on the investigation of an optimal hybrid energy system (HES) to ensure a reliable power supply for security systems in two seaports located in Turkiye. Through the utilization of the HOMER software, optimization analyses were conducted, considering both conventional sources such as grid-generator or grid-generator-battery configurations, as well as off-grid and on-grid HES solutions integrating photovoltaic (PV) and wind turbine technologies. The findings reveal that on-grid HES solutions incorporating PV and wind technologies offer a more cost-effective and dependable energy supply for security systems in seaports, surpassing traditional alternatives. This study represents a significant contribution to the existing literature, as it presents the first comprehensive optimization study on the design of HES for security systems. The outcomes serve as a valuable reference for future research endeavors in this field. [ABSTRACT FROM AUTHOR]

Light emitting diodes (LEDs) play an essential role in lighting, and green earth activities because of their high efficiency, longevity, and reduction of carbon dioxide emissions during illumination. However, the brightness level of LED light sources must be adjusted appropriately for the backlight source or illumination; therefore, pulse amplitude modulation (PWM) is a commonly used method of LED control. This article experimentally investigated human interaction with the visual comfort effect of the light obtained using different PWM frequencies on an object in a sensor-based intelligent lighting system. Critical light frequencies are vital for the eye to distinguish light stimuli according to time. Histograms of the object were created according to the light frequency, and the results are discussed. The eye's response to light frequencies changing over time is important for visual comfort, and examining light frequencies in the range of 25–250 Hz was sufficient to conclude the study. It has been experimentally shown that light frequencies around 160 Hz, and above this value provide visual comfort. [ABSTRACT FROM AUTHOR]