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3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Mohanty, Sarthak Parida, Jyotiranjan Parida, Shubhranshu Mohan Rout, Pravat Kumar Samanta, Indu Sekhar Department of Electrical Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350304732

This paper introduces a novel three-phase Reduced Switch Reduced Component (RSRC) Multilevel Inverter (MLI) topology to reduce switching losses, cost-effectiveness, and overall performance within power electronic systems. The suggested topology addresses the challenges of complexity and high cost associated with traditional MLIs by significantly minimizing the number of necessary components. Our design process, in-depth analysis, and comprehensive evaluation center around two key performance indicators: voltage waveform quality and total harmonic distortion (THD). To illustrate the superiority of the new design, we draw comparative analyses between our proposed topology and prevailing MLI topologies, underlining the novel advantages our design offers. Particularly designed for compact applications, our topology is relevant to e-mobility loads. Simulation results show that applying an assortment of Pulse Width Modulation (PWM) techniques makes a notable reduction in THD achievable, thus solidifying the proposed topology as a feasible and economically-sound solution for future prototyping. Future work will explore applying optimization methods to regulate duty cycle signals and introducing advanced modulation techniques for achieving further THD reduction. © 2023 IEEE.

关键词： Multilevel inverters (MLIs) Pulse Width Modulation (PWM) Reduced Switch Reduced Component (RSRC) Three-phase RSRC MLI topology Total Harmonic Distortion (THD)

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Movie Sense—Hybrid Movie Recommendation System with Sentimental Analysis 3rd

Movie Sense—Hybrid Movie Recommendation System with Sentime...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sudhane, G. Sai Rao, G. Venkateswara Vani, V. Karthik, N. Department of Electrical and Electronics Engineering Puducherry India Department of Computer Science and Engineering National Institute of Technology Puducherry Puducherry Karaikal609609 India

ISBN: (纸本)9789819783540

Post-pandemic, with the advent of many OTT platforms, there are a number of different movies and web series available which makes it difficult for the users to find a suitable movie to watch. So the movie recommendation system helps the users by recommending some movies based on their past search history, their interests and also considering the movie genre, actors, director and movie ratings. Our system uses a hybrid filtering technique which makes the system suitable for new users as well by considering the interests of various other users using SVD. Sentimental Analysis is done using a Sentiment Intensity Analyzer (SIA). Since it uses the most accurate methods, it makes the system a robust one. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recommender systems

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Green Computing Adoption: Understanding the Role of Individual, Social, and Organizational Factors 19

Green Computing Adoption: Understanding the Role of Individu...

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19th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2024

作者： Anni, Fahima Akter Islam, Muhammad Rezaul Sadia, Farzana Hasan, Mahady Rokonuzzaman, M. Department of Software Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9789897586965

This study investigates the factors influencing the attitudes of software developers and IT professionals towards Green Information Technology (GIT) in Bangladeshi IT/software firms and examines their impact on engagement in green computing practices. Data was collected from 130 participants. A thorough literature review was conducted. The findings highlight key individual factors that influence employees’ attitudes, including awareness, knowledge, and perception of environmental issues. The study demonstrates the connection with GIT attitudes and behavior modification, especially through stated usage of green computing methods. Data analysis confirms 5 out of 8 hypotheses and reveals the complexity of the relationships between the constructs. The report promotes the adoption of Green IT technology by software companies in Bangladesh and highlights the significance of an organized office system integrating Green IT. Limitations include a relatively small sample size and the multidimensional nature of the relationships between the constructs. The findings can assist software companies in addressing customer concerns about the performance and functionality of green computing practices, ultimately promoting sustainable computing practices in the IT sector of Bangladesh. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Green computing

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Illuminating the Interconnected Complexity of Cyberbullying Intentions within the Holistic Social Media Ecosystem 15

Illuminating the Interconnected Complexity of Cyberbullying ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shraya, Bejgum Dopathi, Hari Charan Reddy Pattanaik, Balachandra Anitha, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Wallaga University Department of Electrical and Computer Engineering Africa Nekemte Ethiopia

ISBN: (纸本)9798350370249

The research being presented here looks into the problems related to cyberbullying using data from Twitter and Wikipedia. To try and understand why people behave in this way online, the research included several challenging techniques, including a Support Vector Machine, Random Forest, Gaussian Naive Bayes, and a Voting Classifier that combines Logistic Regression, Random Forest, and Gaussian Naive Bayes. The accuracy of the Voting Classifier was 92.86% after Twitter data analysis. This demonstrates how well it detects and comprehends abusive tendencies. Not to mention, the Wikipedia dataset showed strong performance from the Voting Classifier, which had a success rate of 97.12%. Compared to other methods of research, it performed better. The research shows how machine learning algorithms can recognize and determine the identity of individuals attempting to cause harm to others via the internet. © 2024 IEEE.

关键词： Cyber Harassment Gaussian Naive Bayes Machine Learning Random Forest Support Vector Machine Voting Classifier

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Integrating 4QS Factors to Enhance Learning Quality Using Machine Learning 2

Integrating 4QS Factors to Enhance Learning Quality Using Ma...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Kale, Ruchita Tasare, Mohan Dhumale, Yogita Rawat, Manoj Kumar Sawale, Gaurav Prmit&r Department of Computer Science & Engineering Badnera Amravati India Prof. Ram Meghe College of Engineering & Management Department of Electrical Engineering Badnera Amravati India Medicaps University Department of Computer Science & Engineering Indore India

ISBN: (纸本)9798331528713

The ultimate goal of any educational institution is to offer the best educational experience and impart knowledge effectively to students. Identifying students who need extra support and taking appropriate actions to enhance their performance is crucial in achieving this goal. This study explores the application of machine learning techniques to predict students' final grades based on their historical data. The proposed strategy integrates the 4QS Model, which focuses on intelligence, adversity, spirituality, emotional, and stress factors to address essential aspects of the academic environment. By targeting these key factors, the aim is to improve the teaching and learning environment, leading to enhanced student performance and future success. This approach enables educators to identify areas where students may struggle and tailor their teaching methods accordingly, resulting in a more effective and supportive educational experience for both faculty and students. A classification machine learning algorithm was employed to predict student performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Agent Imitation Learning Based Energy Management of a Microgrid With Hybrid Energy Storage and Real-Time Pricing

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IEEE Internet of Things Journal 2025年第12期12卷 19801-19817页

作者： Gao, Shuhua Xu, Yizhuo Zhang, Zhaoqian Wang, Zhengfang Zhou, Xiaoyu Wang, Jing Shandong University School of Control Science and Engineering Jinan250100 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore Second Construction Limited Company of China Construction Eighth Engineering Division Jinan Shandong China

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver;then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method's proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy's multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids. © 2014 IEEE.

关键词： Costs

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A Novel Vision Transformer Approach for Driver Fatigue Detection from EEG Signal with Correlation-Based Channel Selection

A Novel Vision Transformer Approach for Driver Fatigue Detec...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khan, Robiul Roy, Dipon Ali, Md. Wajed Refat, Kawsar Ahmed Naziullah, Shekh Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh Daffodil International University Department of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Driver fatigue poses a critical threat to road safety, necessitating the development of robust detection methods to minimize traffic accidents and societal burdens. Deep neural networks have recently been effectively applied to Electroencephalography (EEG)-based driving fatigue detection. Nevertheless, most of the existing models, particularly those relying on extensive pooling, often struggle to capture long-range dependencies within images. To address this issue, we propose a Correlation-based Channel Selection (CCS) with a Vision Transformer (ViT) approach for driver fatigue detection using an EEG. Our methodology integrates a pioneering Channel Selection (CS) block to extract discriminative channels via CCS. This mechanism systematically identifies the most informative EEG channels crucial for fatigue detection. Subsequently, we leverage Continuous Wavelet Transform (CWT) to convert the selected EEG channels into a time-frequency spectral image. Finally, the resulting time-frequency spectral images, encompassing both temporal and spectral information, are concatenated and fed into the Vision Transformer (ViT) model to classify them as either normal or fatigued. The proposed model is evaluated on a publicly available EEG dataset containing recordings from twelve subjects. The model achieved superior accuracy: 95.83% for the effective selected combined subject and 99.925% for the average accuracy of each subject, demonstrating its potential for robust driver fatigue detection. © 2024 IEEE.

关键词： Deep neural networks

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Sizing electrolyzer capacity in conjunction with an off-grid photovoltaic system for the highest hydrogen production

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Energy Harvesting and Systems 2023年第2期10卷 331-348页

作者： Hassan, Qusay Abbas, Majid K Tabar, Vahid Sohrabi Tohidi, Sajjad Abdulrahman, Imad Saeed Salman, Hayder M. Department of Mechanical Engineering University of Diyala Baqubah Iraq Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq

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 to the predicted intermediate of low-and zero-marginal-cost renewable energy sources, the ability of electrolysis to connect with electricity pricing offers a novel way to cost reduction. Moreover, renewables, particularly photovoltaics, have a deflationary effect on the value of the grid when they are deployed. This study investigates solar electrolysis configurations employing photovoltaic cells to feed a proton exchange membrane water electrolyzer for hydrogen production. Using experimental meteorological data at 1-min precision, the system has been evaluated in Baghdad, the capital of Iraq. Positioned at the yearly optimum tilt angle for the selected site, the solar array is rated at 12 kWp. Temperature effects on solar module energy loss are taken into account. Several electrolyzers with capacities ranging from 2 to 14 kW in terms of hydrogen production were examined to determine the efficacy and efficiency of renewable sources. MATLAB was utilized for the simulation procedure, with a 2021-2035 project lifespan in mind. The results suggest that a variety of potentially cost-competitive options exist for systems with market configurations that closely approximate wholesale renewable hydrogen. At 4313 h of operation per year, the planned photovoltaic array generated 18,892 kWh of energy. The achieved hydrogen production cost ranges between & amp;dollar;5.39/kg and & amp;dollar;3.23/kg, with an ideal electrolyzer capacity of 8 kW matching a 12 kWp photovoltaic array capable of producing 450 kg/year of hydrogen at a cost of & amp;dollar;3.23/kg. & amp;copy;2022 Walter de Gruyter GmbH, Berlin/Boston. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Electrolysis

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Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach 4

Forecasting EV Charging Demand: A Graph Convolutional Neural...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Fahim, Shahriar Rahman Atat, Rachad Kececi, Cihat Takiddin, Abdulrahman Ismail, Muhammad Davis, Katherine R. Serpedin, Erchin Texas A&M University Electrical & Computer Engineering Department College StationTX77843 United States Texas A&M University at Qatar Electrical & Computer Engineering Department Doha23874 Qatar Florida State University Electrical & Computer Engineering Department TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States

ISBN: (纸本)9798350306262

Electric vehicles (EVs) are expected to revolutionize the global transportation sector by promoting sustainability and eco-friendliness. The continuous proliferation of EVs requires an expansion of the existing charging infrastructure to meet the corresponding increase in electricity demand. Such an expansion requires an accurate forecasting of charging demand in both space and time domains for a well-planned allocation of charging stations (CSs). This paper proposes a Graph Convolutional Neural Networks (GCNN) based approach combined with a Long Short-Term Memory (LSTM) to predict the future charging demands of EVs. The proposed architecture fuses the benefits of both GCNN and LSTM to extract the underlying spatio-temporal features from the collected dataset. The training dataset reflects the coupling between the power and transportation systems, and thereby it helps the proposed deep learning architecture to capture the spatio-temporal patterns of the inter-connected environment. A comparative analysis is conducted with other state-of-the-art EV charging load prediction models to assess the prediction performance of the proposed load forecasting strategy. © 2024 IEEE.

关键词： Forecasting

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Early-Stage Segmentation and Characterization of Brain Tumor

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computers, Materials & Continua 2022年第10期73卷 1001-1017页

作者： Syed Nauyan Rashid Muhammad Hanif Usman Habib Akhtar Khalil Omair Inam Hafeez Ur Rehman Department of Computer Science National University of Computer and Emerging Sciences IslamabadPeshawar CampusPakistan Faculty of Computer Science and Engineering Ghulam Ishaq Khan(GIK)Institute of Engineering Sciences and TechnologyTopiPakistan IFahja Pvt Limited PeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadPakistan

Gliomas are the most aggressive brain tumors caused by the abnormal growth of brain *** life expectancy of patients diagnosed with gliomas decreases *** gliomas are diagnosed in later stages,resulting in imminent *** average,patients do not survive 14 months after *** only way to minimize the impact of this inevitable disease is through early *** Magnetic Resonance Imaging(MRI)scans,because of their better tissue contrast,are most frequently used to assess the brain *** manual classification of MRI scans takes a reasonable amount of time to classify brain *** this,dealing with MRI scans manually is also cumbersome,thus affects the classification *** eradicate this problem,researchers have come up with automatic and semiautomatic methods that help in the automation of brain tumor classification ***,many techniques have been devised to address this issue,the existing methods still struggle to characterize the enhancing *** is because of low variance in enhancing region which give poor contrast in MRI *** this study,we propose a novel deep learning based method consisting of a series of steps,namely:data pre-processing,patch extraction,patch pre-processing,and a deep learning model with tuned hyper-parameters to classify all types of gliomas with a focus on enhancing *** trained model achieved better results for all glioma classes including the enhancing *** improved performance of our technique can be attributed to several ***,the non-local mean filter in the pre-processing step,improved the image detail while removing irrelevant ***,the architecture we employ can capture the non-linearity of all classes including the enhancing ***,the segmentation scores achieved on the Dice Similarity Coefficient(DSC)metric for normal,necrosis,edema,enhancing and non-enhancing tumor classes are 0.95,0.97,0.91,0.93,0.95;respectively.

关键词： Segmentation CNN characterization brain tumor MRI

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