Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis.

Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the performance of Machine Learning (ML) models resulting in biased predictions. Numerous techniques have been proposed to address CIP, including, but not limited to, Oversampling, Undersampling, and cost-sensitive approaches. Due to its ability to generate synthetic data, oversampling techniques such as the Synthetic Minority Oversampling Technique (SMOTE) are the most widely used methodology by researchers. However, one of SMOTE's potential disadvantages is that newly created minor samples overlap with major samples. Therefore, the probability of ML models' biased performance toward major classes increases. Generative adversarial network (GAN) has recently garnered much attention due to their ability to create real samples. However, GAN is hard to train even though it has much potential. Considering these opportunities, this work proposes two novel techniques: GAN-based Oversampling (GBO) and Support Vector Machine-SMOTE-GAN (SSG) to overcome the limitations of the existing approaches. The preliminary results show that SSG and GBO performed better on the nine imbalanced benchmark datasets than several existing SMOTE-based approaches. Additionally, it can be observed that the proposed SSG and GBO methods can accurately classify the minor class with more than 90% accuracy when tested with 20%, 30%, and 40% of the test data. The study also revealed that the minor sample generated by SSG demonstrates Gaussian distributions, which is often difficult to achieve using original SMOTE and SVM-SMOTE.

An Overview of Challenges for the Future of Hydrogen.

Hydrogen's wide availability and versatile production methods establish it as a primary green energy source, driving substantial interest among the public, industry, and governments due to its future fuel potential. Notable investment is directed toward hydrogen research and material innovation for transmission, storage, fuel cells, and sensors. Ensuring safe and dependable hydrogen facilities is paramount, given the challenges in accident control. Addressing material compatibility issues within hydrogen systems remains a critical focus. Challenges, roadmaps, and scenarios steer long-term planning and technology outlooks. Strategic visions align actions and policies, encompassing societal and ecological dimensions. The confluence of hydrogen's promise with material progress holds the prospect of reshaping our energy landscape sustainably. Forming collective future perspectives to foresee this emerging technology's potential benefits is valuable. Our review article comprehensively explores the forthcoming challenges in hydrogen technology. We extensively examine the challenges and opportunities associated with hydrogen production, incorporating CO2 capture technology. Furthermore, the interaction of materials and composites with hydrogen, particularly in the context of hydrogen transmission, pipeline, and infrastructure, are discussed to understand the interplay between materials and hydrogen dynamics. Additionally, the exploration extends to the embrittlement phenomena during storage and transmission, coupled with a comprehensive examination of the advancements and hurdles intrinsic to hydrogen fuel cells. Finally, our exploration encompasses addressing hydrogen safety from an industrial perspective. By illuminating these dimensions, our article provides a panoramic view of the evolving hydrogen landscape.

Hydrogen-Assisted Aging Applied to Storage and Sealing Materials: A Comprehensive Review.

Hydrogen is a possible alternative to fossil fuels in achieving a sustainable energy future. Unlike other, older energy sources, the suitability of materials for storing, distributing, and sealing systems in a hydrogen environment has not been comprehensively studied. Aging, the extended exposure of a material to an environmental condition, with hydrogen causes degradation and damage to materials that differ from other technologies. Improved understanding of the physical and chemical mechanisms of degradation due to a gaseous hydrogen atmosphere allows us to better select and develop materials that are best suited to carrier and sealing applications. Damage to materials from aging is inevitable with exposure to high-pressure hydrogen. This review discusses the specific mechanisms of different categories of aging of storage and sealing materials in a hydrogen environment. Additionally, this article discusses different laboratory test methods to simulate each type of aging. It covers the limitations of current research in determining material integrity through existing techniques for aging experiments and explores the latest developments in the field. Important improvements are also suggested in terms of material development and testing procedures.

How stereotype threat affects the brain dynamics of creative thinking in female students.

When people are placed in a situation where they are at risk of substantiating a negative stereotype about their social group (a scenario termed stereotype threat), the extra pressure to avoid this outcome can undermine their performance. Substantial and consistent gender disparities in STEM fields leave women vulnerable to stereotype threat, including the stereotype that women are not as good at generating creative and innovative ideas as men. We tested whether female students' creative thinking is affected by a stereotype threat by measuring power in the alpha frequency band (8-12Hz oscillations) that has been associated with better creative thinking outcomes. Counter to expectations that a stereotype threat would reduce alpha power associated with creative thinking, analyses showed increased alpha power following the introduction of the stereotype threat. This outcome suggests that women may have attempted to increase their internal attention during the task in order to disprove the stereotype. Behaviorally, this effort did not lead to changes in creative performance, suggesting that the stereotype threat decoupled alpha power from creative thinking outcomes. These results support a growing school of thought in the neuroscience of creativity literature that the alpha power often seen in conjunction with creative behavior is not necessarily related to the creativity processes themselves, but rather might be part of a larger network modulating the distribution of attentional resources more broadly.

Machine learning-based heart disease diagnosis: A systematic literature review.

Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patients' data, detecting heart disease during the early stage is feasible. However, both ECG and patients' data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the existing literature, this study takes a systematic literature review (SLR) approach to uncover the challenges associated with imbalanced data in heart diseases predictions. Before that, we conducted a meta-analysis using 451 reference literature acquired from the reputed journals between 2012 and November 15, 2021. For in-depth analysis, 49 referenced literature has been considered and studied, taking into account the following factors: heart disease type, algorithms, applications, and solutions. Our SLR study revealed that the current approaches encounter various open problems/issues when dealing with imbalanced data, eventually hindering their practical applicability and functionality. In the diagnosis of heart disease, machine learning approaches help to improve data-driven decision-making. A metadata analysis of 451 articles and content analysis of 49 selected articles of heart disease diagnosis. Researchers primarily concentrated on enhancing the performance of the models while disregarding other issues such as the interpretability and explainability of Machine learning algorithms.

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME.

The COVID-19 global pandemic caused by the widespread transmission of the novel coronavirus (SARS-CoV-2) has become one of modern history's most challenging issues from a healthcare perspective. At its dawn, still without a vaccine, contagion containment strategies remained most effective in preventing the disease's spread. Patient isolation has been primarily driven by the results of polymerase chain reaction (PCR) testing, but its initial reach was challenged by low availability and high cost, especially in developing countries. As a means of taking advantage of a preexisting infrastructure for respiratory disease diagnosis, researchers have proposed COVID-19 patient screening based on the results of Chest Computerized Tomography (CT) and Chest Radiographs (X-ray). When paired with artificial-intelligence- and deep-learning-based approaches for analysis, early studies have achieved a comparatively high accuracy in diagnosing the disease. Considering the opportunity to further explore these methods, we implement six different Deep Convolutional Neural Networks (Deep CNN) models-VGG16, MobileNetV2, InceptionResNetV2, ResNet50, ResNet101, and VGG19-and use a mixed dataset of CT and X-ray images to classify COVID-19 patients. Preliminary results showed that a modified MobileNetV2 model performs best with an accuracy of 95 ± 1.12% (AUC = 0.816). Notably, a high performance was also observed for the VGG16 model, outperforming several previously proposed models with an accuracy of 98.5 ± 1.19% on the X-ray dataset. Our findings are supported by recent works in the academic literature, which also uphold the higher performance of MobileNetV2 when X-ray, CT, and their mixed datasets are considered. Lastly, we further explain the process of feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19.

Volume Resistivity of Viton Polymer under Thermal Aging.

This study examines the influence of various electrical parameters on the volume resistivity of the Viton fluoroelastomer. The transient current, the temperature dependence of volume resistivity, the voltage dependence of resistivity, and the surface morphology of Viton insulators are investigated for new and aged specimens. An accelerated aging process has been employed in order to simulate the natural aging of insulators in service. A detailed comparison between the new and aged samples is presented. The transient effect, which is a challenge to the resistivity measurement of insulators, has been investigated. The first 60 s of the resistivity measurement test showed a significant influence from the transient effect and should be excluded from the data. The volume resistivity of both new and aged samples decreased when the temperature increased. However, the resistivity of the aged sample was lower than the new one at all tested temperatures. When the temperature increased from 35 to 190 °C, resistivity decreased from 4.77 × 1010 to 6.99 × 108 Ω-cm for the new sample and from 2.6 × 1010 to 6.68 × 108 Ω-cm for the aged sample under 500 V. Additionally, the results from this study showed that the volume resistivity is inversely proportional to the applied voltage. Finally, scanning electron microscope (SEM) micrographs/images allowed us to closely examine the surface morphology of new and aged Viton samples. The surface of aged samples has been recognized with higher surface roughness and more significant surface cracks leading to poor performance under high voltage applications.

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence.

Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.

Engineering creativity: Prior experience modulates electrophysiological responses to novel metaphors.

Novel metaphorical language use exemplifies human creativity through production and comprehension of meaningful linguistic expressions that may have never been heard before. Available electrophysiological research demonstrates, however, that novel metaphor comprehension is cognitively costly, as it requires integrating information from distantly related concepts. Herein, we investigate if such cognitive cost may be reduced as a factor of prior domain knowledge. To this end, we asked engineering and nonengineering students to read for comprehension literal, novel metaphorical, and anomalous sentences related to engineering or general knowledge, while undergoing EEG recording. Upon reading each sentence, participants were asked to judge whether or not the sentence was original in meaning (novelty judgment) and whether or not it made sense (sensicality judgment). When collapsed across groups, our findings demonstrate a gradual N400 modulation with N400 being maximal in response to anomalous, followed by metaphorical, and literal sentences. Between-group comparisons revealed a mirror effect on the N400 to novel metaphorical sentences, with attenuated N400 in engineers and enhanced N400 in non-engineers. Critically, planned comparisons demonstrated reduced N400 amplitudes to engineering novel metaphors in engineers relative to non-engineers, pointing to an effect of prior knowledge on metaphor processing. This reduction, however, was observed in the absence of a sentence type × knowledge × group interaction. Altogether, our study provides novel evidence suggesting that prior domain knowledge may have a direct impact on creative language comprehension.