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Optimization of Intelligent Classroom based on CAD Multi-Valued Color Image Segmentation Algorithm

computer-Aided Design and Applications 2023年第S3期20卷 16-28页

作者： Wang, Nanlan Li, Jianqi Balaji, Prasanalakshmi Vaiyapuri, Thavavel Verma, Amit School of Computer and Electrical Engineering Hunan university of Arts and Science Hunan Changde415000 China International Education Center Philippine Christian University Manila1772 Philippines Department of Computer Science King Khalid University Saudi Arabia Department of Computer Sciences College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Saudi Arabia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India

In order to solve the problem that the efficiency of smart classroom is not high enough, a multi valued color image segmentation algorithm based on CAD is proposed to optimize the smart classroom. This paper explores the notion of smart education as well as a conceptual framework to enable children to receive excellent education and to spark their interest through creative learning. Firstly, CAD software is used to improve software design efficiency and optimize product technology;Secondly, the core technology of the smart classroom is improved by using the national value color image segmentation algorithm, and compared with the smart classroom without the multi value color image segmentation algorithm;Finally, the utilization efficiency of the improved smart classroom is counted. It is proved that the smart classroom based on CAD multi valued color image segmentation algorithm can give greater play to the role of smart classroom, reduce the teaching pressure of teachers by 20% and improve the learning efficiency of students by 20%. The intelligent classroom has a higher degree of informatization and more powerful functions, which is of great help to promote classroom communication and improve communication efficiency. The smart classroom is easier to run and adapt thanks to the optimization of the CAD multi threshold color picture segmentation algorithm, which considerably improves the system's work efficiency and capability. However, the smart classroom also has its own shortcomings, which are mainly reflected in the high investment cost, the complex design of the system and the high maintenance cost in the later stage. With the optimization of CAD multi valued color image segmentation algorithm, the smart classroom is easier to operate and adjust, which greatly enhances the work efficiency and ability of the system. © 2023 CAD Solutions, LLC.

关键词： computer aided design

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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Microgrid digital twins: concepts and their controlling through multi-agent systems

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International Journal of High Performance Systems Architecture 2023年第4期11卷 225-236页

作者： Negi, Gaurav Singh Mishra, Anupama Gupta, Mukul Kumar Saxena, Nitin Kumar Saini, DilipKumar Jang Bahadur Joshi, Kapil Electrical and Electronics Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Uttarakhand Dehradun248140 India School of Engineering University of Petroleum and Energy Studies Dehradun248006 India Department of Electronics & Instrumentation Engineering MJP Rohilkhand University UP Bareilly243006 India Electrical and Electronics Engineering KIET Group of Institutions Ghaziabad201206 India Electrical and Computer Engineering University of Denver Denver80210 United States Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Pune411044 India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India

Real-time grid analysis is made possible by the electric digital twin grid, which combines history and present data to convey system status and project future circumstances. Cooperative smart agents that can solve problems bigger than one agent's scope make up a multi-agent system (MAS). A micro-grid digital twin (MGDT) uses real-time data interchange and high-fidelity models to simulate a microgrid’s operation. Hybrid energy storage systems are managed by agents who maximise renewable resources and minimise expenses. Microgrids combine sensing, control, and intelligence technology in utility networks. Plant management, communication protocols, and power system operations are all made easier with MAS-based control. For dependable microgrid operation, dispersed energy resources are coordinated by MAS. In order to handle complexity and improve resilience and control of the energy system, this study investigates the application of MAS in microgrid control. Energy management and agent communication in microgrids are the main topics of MAS research. Integrating microgrid digital twins with MAS holds promise for advancing energy systems. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Multi agent systems

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A Novel Deep Learning Framework for the Detection of Brain Tumor 3

A Novel Deep Learning Framework for the Detection of Brain T...

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3rd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2023

作者： Abbas, Muhammad Sohail Jamil, Sonain School of Electrical Engineering and Computer Science Islamabad Pakistan Sejong University Department of Electronics Engineering Seoul Korea Republic of

ISBN: (纸本)9798350330816

Brain Tumor segmentation has been in focus for the past couple of years. Several researchers have developed state-of-the-art techniques to solve the problem. In recent years, people started working with deep learning for this problem because machine learning did not significantly improve after a stage. Most researchers used Magnetic Resonance Image (MRI) for brain tumor segmentation. The proposed methodology improves this segmentation and classification compared to the state-of-the-art techniques. The methodology works in three phases: In the first phase, segmentation uses two machine learning techniques, K-Means Clustering and Fuzzy C-Means (FCM) Clustering. In the second phase, we worked on feature extraction from our images using deep learning models AlexNet and ResNet-50 with small alterations. In the third phase, we used a Support Vector Machine (SVM) to classify the tumor as benign and malignant. We used two separate algorithms from Machine Learning for segmentation and two techniques from Deep Learning for classification to compare which works better in both cases. K-Means and FCM showed significant improvements with results in segmentation, and for classification, we got the accuracy of 96.6% and 98.3% for ResNet-50 and AlexNet, respectively. © 2023 IEEE.

关键词： Support vector machines

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Modeling and Experimental Investigation of Energy Management for Hybrid Electric Vehicle based on Variable Structure Control Strategy

Modeling and Experimental Investigation of Energy Management...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Hamza, Ali Uneeb, Muhammad Saleem, Komal Ali, Zunaib School of Engineering LondonSE1 0AA United Kingdom School of Electrical Engineering and Computer Science Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9781665471640

The current study presents real-time modeling and non-linear controllers-based energy management system (EMS) for multi-energy hybrid Electric Vehicle (EV), where a detailed physics-based dynamic vehicle model has been considered. The main objective of the paper is to regulate the power flow, stabilize DC voltage for an EV driven by a brushless DC motor, and ensure effective power sharing in a hybrid electric system under complex driving circumstances. The approach is based on tracking the reference battery current by backstepping sliding mode control for optimal power distribution. Subsequently, Integral Sliding Mode Control based on barrier function (NBS-ISMC), and Fractional Order Terminal Sliding Mode Control (FOTSMC) are implemented to control the switching operation of converters for Photovoltaic (PV) and Ultra-capacitor (UC), respectively. User-defined and practical standard drive cycles are selected to test the effectiveness of proposed reference current controllers. Real-time experimental validation of the proposed framework using the Speedgoat Performance Real-Time Target Machine will be included in the final paper. © 2023 IEEE.

关键词： Drive cycles Energy management system Hybrid Electric vehicle Nonlinear Control algorithms Vehicle dynamics

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Integration Of Convolutional Neural Network and Support Vector Regression For Electricity Price Forecasting

Integration Of Convolutional Neural Network and Support Vect...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Iqbal, Rashed Mokhlis, Hazlie Khairuddin, Anis Salwa Mohd Mansor, Nurulafiqah Nadzirah Zahari, Nur Elida Mohamad Sankaranarayanan, Suresh School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW Australia Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science King Faisal University Saudi Arabia

ISBN: (纸本)9798350325188

Accurate electricity price forecasting (EPF) plays a crucial role in bid strategies and risk reduction for market participants in the competitive electricity market. However, forecasting of electricity prices accurately is a highly demanding task due to the intricate nonlinearity present in the price dynamics. This paper introduces a combination of deep learning (DL) technique with machine learning (ML) model to forecast electricity prices in the Australian energy market operator (AEMO) for hourly price prediction utilizing load and renewable energy supply data from five major economical states New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS) and Victoria (VIC). The proposed work integrates the convolutional neural network (CNN) and support vector regression (SVR) to identify short-term local dependency patterns among variables and uncover long-term correlations for time series patterns and forecast the hourly ahead price. The suggested model (CNN-SVR) is contrasted to various models such as CNN-LSTM, BiLSTM, SVR, and their performance is evaluated using various metrics, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). © 2023 IEEE.

关键词： convolutional neural network deep learning electricity price forecasting machine learning

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An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction 4th

An Improved Grey Wolf Optimization Algorithm for Heart Disea...

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4th International Conference on Advanced Intelligent Technologies, ICAIT 2023

作者： Niu, Sihan Zhou, Yifan Li, Zhikai Huang, Shuyao Zhou, Yujun Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States School of Computer Engineering and Science Shanghai University Shanghai China School of Information Sciences University of Illinois Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9789819732098

This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show potential in medical imaging but suffer from issues like overfitting and lack of interpretability due to imbalanced and scarce data. Traditional grey wolf optimization (GWO) also has its drawbacks, such as a lack of population diversity and premature convergence. This paper addresses these problems by introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid function. This algorithm was extensively compared to four leading algorithms using six well-known test functions, outperforming them effectively. Moreover, by utilizing the ACGWO, we increase the robustness and generalization of the neural network, resulting in more interpretable predictions. Applied to the publicly accessible Cleveland Heart Disease dataset, our technique surpasses ten other methods, achieving 86.8% accuracy, indicating its potential for efficient heart disease prediction in the clinical setting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical image processing

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AI-Driven Personalization of E-Therapy Interventions for Anxiety, Stress, and Depression 18

AI-Driven Personalization of E-Therapy Interventions for Anx...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Ramzan, Hafiz Arslan Abdulah, Fatima Ahmad, Muddaber Ramzan, Sadia Ashraf, Munazza School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad Pakistan National University of Computer and Emerging Sciences Department of Computer Science Chiniot Faisalabad Pakistan GIFT University Department of Humanities and Social Sciences Gujranwala Pakistan

ISBN: (纸本)9798331508692

This study explores the use of artificial intelligence (AI) to personalize e-therapy interventions for anxiety, stress, and depression. Leveraging machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees, we analyze a dataset of depression, anxiety, and stress scales responses to develop predictive models that tailor therapeutic interventions. The accuracy and efficacy of proposed models vary, indicating that AI has the potential to improve the level of personalization of mental health therapies. The results indicate that AI-driven techniques can significantly improve the effectiveness and efficiency of e-therapy platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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Editorial: Special Issue on Transfer Learning

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Neural Processing Letters 2023年第3期55卷 1997-2000页

作者： Chao, Guoqing Zhu, Xingquan Ding, Weiping Bi, Jinbo Sun, Shiliang School of Computer Science and Technology Harbin Institute of Technology Weihai264209 China Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431-0991 United States School of Information Science and Technology Nantong University Nantong226019 China Department of Computer Science & Engineering University of Connecticut StorrsCT06269-4155 United States School of Computer Science and Technology East China Normal University Shanghai200062 China

The paper entitled “Improving the Polarity of Text through Word2Vec Embedding for Primary Classical Arabic Sentiment Analysis” by Aoumeur Nour Elhouda, Zhiyong Li and Eissa M. Alshari created a new classical Arabic data set CASAD from many art books and then use word embedding to extract features to feed for several classification methods for sentiment analysis. The paper entitled “An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19” by Bo-Lun Chen, Yi-Yun Shen, Guo-Chang Zhu, Yong-Tao Yu and Min Ji provided an empirical mode decomposition model to obtain the change trends of epidemic data at different time scales and then trained with extreme learning machine to get the corresponding prediction values and finally predict by fitting through adaptive network-based fuzzy inference system. The paper entitled “COVID-19 Detection from Chest X-rays using Trained Output based Transfer Learning Approach” by Sanjay Kumar and Abhishek Mallik proposed a trained output-based transfer learning method for COVID-19 detection from chest X-rays images. Besides the above works, a survey named “A survey on adversarial domain adaptation” by Mahta HassanPour Zonoozi and Vahid Seydi categorized adversarial domain adaption methods according to their structures and compared on common datasets to obtain some conclusions.

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Progress Analytics in Support of engineering Advising and Program Reform

Progress Analytics in Support of Engineering Advising and Pr...

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2023 ASEE Annual Conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Al Yusuf, Husain Heileman, Gregory L. Islam, Raian Sharifi, Melika Sharifi, Roxana Zhang, Yiming Agrawal, Tanmay Sharma, Bhavya Manasil, Kristi Free, Hayden W. Department of Electrical & Computer Engineering University of Arizona United States Department of Computer Science University of Arizona United States School of Information University of Arizona United States School of Computer Science Georgia Institute of Technology United States

Students in engineering programs are typically among those having the highest time-to-degree for any of the programs offered on a university campus. Keeping a cohort of students on track towards on-time graduation is extremely difficult given the tightly prescribed nature of engineering programs. Any deviation from the standard degree plan, for any reason, including not passing a class, taking courses out of sequence, etc., often precludes the ability to graduate in four years. In this paper, we describe a cohort tracking analytics platform that can be used by advisors as an aid in keeping students on track, and by program administrators as a tool to better understand the curricular impediments associated with delays in graduation. This cohort analytics platform provides analyses over a population of students, rather than individual students, yielding valuable (often hidden) information regarding the impediments that students face. For instance, this platform makes it easier to determine what courses are most significant in blocking the progress of a cohort, the efficiency of credit hour production within a cohort, where students are losing credit hours (i.e., generating credit hours that do not count towards the satisfaction of any degree requirements), etc. Advisors and administrators often suggest programmatic improvements based on anecdotal evidence and experiences related to individual students, not because they are lazy, but because it is inherently difficult to compute cumulative student progress over a cohort. The reason for this is that accurate student progress information is typically difficult to obtain and out of reach for many decision makers, as degree audit capabilities have not been designed with analytics in mind. In an attempt to make this data accessible and actionable, we have developed a platform that can organize student cohorts according to any criteria, and compute progress analytics relative to these cohorts, while also providing useful ana

关键词： Students

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