The Greek Day-Ahead Electricity Market facilitates a diverse array of order types and offers participants a plethora of choices. While Block Orders are theoretically supported, their accessibility is confined to a sub...
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the net of things (IoT) promises to revolutionize the way humans interact with their surroundings. Notwithstanding this promise, the deployment of IoT networks is hindered by using several challenges, particularly rel...
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The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine...
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The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.
Cybersecurity is the top priority for most businesses and government agencies. Teaching students practical cybersecurity is one of the challenges for most academic institutions. Some institutions focus on theory and n...
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ISBN:
(纸本)9798350351507
Cybersecurity is the top priority for most businesses and government agencies. Teaching students practical cybersecurity is one of the challenges for most academic institutions. Some institutions focus on theory and neglect the practical, which is more appealing to students. Besides, it equips students with the skills the industry needs and reduces the gap between academic institutions and the industry. National Cyber League NCL provides a platform where all students nationwide can join and practice. In the fall of 2023, West Virginia University cybersecurity students joined the NCL competition as part of the course requirements. Participating in team and individual competitions was considered a substitute for the project for the course, which consists of 30% of the total grade. In this paper, we evaluated their background and skills before entering the competition (Who they are?), their experience in the competition, their achievements and skills after completing the competition (what they learned?), and how they evaluate their experience (what they think?). In this paper, we measure their involvement with the competition and how the competition assists them in developing their skills. We rely on their opinion and compare that with their performance according to the report card produced by NCL for every team and each individual. We also found that most students were involved between 10 to 30 hours during the semester with the games. Few students were more than 30 hours during the semester involved with NCL. We found that students were reasonably engaged in the competitions in different areas. Students spent more time in the gymnasium and practice games than in actual competitions. Students achieved an average completion of 35% with 73% average accuracy in the gymnasium, average completion was 19%, and the average accuracy was 76% in the practice game, average completion was 29%, and the average accuracy was 60% in the individual games, and 38% average completion an
Determining electromagnetic (EM) coupling to printed circuit boards (PCBs) is essential to finding potential EM susceptibilities early in the design process. For realistic PCB structures, analysis usually relies heavi...
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The electrolysis of renewable energy to produce hydrogen has become a strategy for supporting a decarbonized economy. However, it is typically not cost-effective compared to conventional carbon-emitting methods. Due t...
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We present a miniature dual-wavelength fd-NIRS device for wearable real-time physiological monitoring. Based on a custom CMOS circuit, wearable device demonstrates accurate phase and amplitude measurements for modulat...
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Recent years have witnessed a paradigm shift in deep learning from task-centric model design to task-agnostic representation learning and task-specific fine-tuning. Pretrained model representations are commonly evalua...
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Recent years have witnessed a paradigm shift in deep learning from task-centric model design to task-agnostic representation learning and task-specific fine-tuning. Pretrained model representations are commonly evaluated extensively across various real-world tasks and used as a foundation for different downstream tasks. This paper proposes a solution for assessing the quality of representations in a task-agnostic way. To circumvent the need for real-world data in evaluation, we explore the use of synthetic binary classification tasks with Gaussian mixtures to probe pretrained models and compare the robustness-accuracy performance on pretrained representations with an idealized reference. Our approach offers a holistic evaluation, revealing intrinsic model capabilities and reducing the dependency on real-life data for model evaluation. Evaluated with various pretrained image models, the experimental results confirm that our task-agnostic evaluation correlates with actual linear probing performance on downstream tasks and can also guide parameter choice in robust linear probing to achieve a better robustness-accuracy trade-off. Copyright 2024 by the author(s)
In this work, reduction of fourth-order bio-ethanol dehydration system (BEDS) is proposed based on truncation approach (TA) and Routh approximation (RA) for sustainable bio-fuel generation. The fourth-order BEDS is ap...
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