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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

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Undergraduate Talents Training System of Chinese international Education Major Based on Artificial Intelligence 3rd

Undergraduate Talents Training System of Chinese Internation...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Chen, Dongping Jing, Zhang Yunnan Technology and Business University Kunming65000 China

ISBN: (纸本)9783031631351

With the prosperity of the global economy, the languages of all countries are in urgent need of breaking national boundaries to show the world their unique language charm, and at the same time expect that languages can serve the people of the world. The size of the scope of language use determines the influence of a country's national image accordingly. As the representative of China's image, Chinese participates in the competition of world languages. More and more people begin to understand Chinese. Chinese is warming up around the world. Colleges of Chinese international Education have been established in major universities across the country. This paper focuses on the research on the talent training system for Chinese international education undergraduates based on AI, and mainly develops the talent training system based on AI for Chinese international education undergraduates. The research team has developed an intelligent learning management system (ILMS) to support the development process of ILM courseware. ILMS integrates with MOOC platform, student information system and other systems to provide online courses for students majoring in international education or those who want to study. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Students

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Optimal Design of Nano Scale Voltage Amplifier Using Evolutionary Techniques for DG-MOSFET 3rd

Optimal Design of Nano Scale Voltage Amplifier Using Evoluti...

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3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Chowdhury, Dibyendu Maiti, Madhusudan DasMahapatra, Suddhendu De, Bishnu Prasad Kar, Rajib Mandal, Durbadal Department of ECE HIT Haldia India VLSI CoE Lab Department of ECE C. V. Raman Global University Odisha India Department of ECE Manipal University Jaipur Jaipur India School of Electronics Engineering KIIT University Bhubaneswar India Department of ECE National Institute of Technology Durgapur India

ISBN: (纸本)9789819784288

In this paper, an optimize the electrical characteristics of the Double Gate-Metal Oxide Semiconductor Field Effect Transistor (DG-MOSFET) is endeavored by Cat Swarm Optimization (CSO) algorithm for its integration into CMOS logic. The Cat Swarm Optimization (CSO) algorithm is employed for optimization. It addresses the multi-objective optimization challenge, formulating a cost function that encompasses critical electrical parameters, including subthreshold swing, OFF-state current, output conductance, threshold voltage roll-off, and transconductance. The main object is to concurrently improve subthreshold and saturation parameters, elevating MOSFET performance. The proposed design demonstrates an enhancement in the performance of voltage gain and cut-off frequency compared to existing works, marking a notable improvement in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cost functions

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Enhancing Network Lifetime of WSNs through the Implementation of a Modified Ant Colony Optimization Algorithm 3

Enhancing Network Lifetime of WSNs through the Implementatio...

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3rd international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Murugesan, Malathi Kanimozhi, R. Dharani, K.G. Devi, D. Dept. of Electronics and Communication Engineering E. G. S. Pillay Engineering College Nagapattinam611002 India Dept. of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Deviyakurichi India Dept. of Electronics and Communication Engineering Akshaya College of Engineering & Technology Coimbatore India Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology India

Energy efficient routing has been the mainstay of research in wireless sensor networks. With rising utility of WSNs in a broad range of applications, a significant volume of research contributions are targeted towards obtaining an energy efficient routing with a good quality of service (QoS) metric. In recent times, the utility of evolutionary algorithm for such multi-objective problems has been on the rising trend as evolutionary models closely mimic the real time scenario of any problem optimization. In this research work, a modified ant colony optimization (M-ACO) has been proposed and implemented for optimization in terms of energy conservation and thereby to extend the network lifetime of the network. © 2023 Elsevier B.V.. All rights reserved.

关键词： Ant colony optimization

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A Technique Based on Ensemble Machine Learning for The Analysis of Electronic Nose Signals 3

A Technique Based on Ensemble Machine Learning for The Analy...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Taneja, Amit Rajamani, Santhosh Kumar Shekhawat, Deepika Chanti, Yerrolla Joshi, Kapil Garg, Ashish Chandigarh University AIT-CSE India MAEER MIT Pune's MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Department of E.N.T Maharashtra Pune India Manipal University Department of Computer Science & Engineering Rajasthan Jaipur India SR University School of Computer Science and Artificial Intelligent Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs) - reliefF, chi-square, and Gini index - for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction. © 2023 IEEE.

关键词： Support vector machines

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Deep Belief Network Algorithm-Based Intrusion Detection System in Internet of Things Environments 1

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3rd international conference on Information technology, InCITe 2023

作者： Geetha, C. Gilda, A. Jasmine Neelakandan, S. Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu Kavaraipettai601206 India

ISBN: (数字)9789819959945

ISBN: (纸本)9789819959938

Intelligent environments can improve the quality of human existence by reducing inconveniences and maximizing productivity. An Internet of Things (IoT) concept has recently established into a method for making smart atmospheres. Protecting users’ personal information and preventing unauthorized access are paramount in any IoT-based smart ecosystem used in the real world. Smart environment applications face security risks from Internet of Things-based technologies. Accordingly, intrusion detection systems (IDSs) that are adapted to IoT settings are crucial for reducing the risk of attacks that exploit vulnerabilities in the IoT. Due to the inadequate processing and storage abilities of IoT technology and the specific protocols in use, traditional IDSs may not be applicable. In this article, we will discuss the most up-to-date intrusion detection systems (IDSs) for the IoT, with an emphasis on the appropriate approaches, features, and procedures. This research presents a Deep Belief Network (DBN) based Intrusion Detection System (IDS) employing the K-Nearest Neighbor technique for monitoring IoT networks for intrusions (KNN). Step one of this process involved using an Absolute Maximum Scaling (AMS) notion of normalization to the UNSW-NB15 dataset in order to cut down on data loss. This dataset, which consists of both recent attacks and normal network activity, covers nine distinct attack types. The next step was to use Principal Component Analysis (PCA) to minimize the dimensionality (PCA). Additionally, we have compared our method to other state-of-the-art studies. Experiments showed that DBN-KNN was superior to other intrusion detection systems in IoT technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Internet of things

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Utilizing Blockchain and Deep Learning for Decentralized Discovery of Deceptive Practices in Healthcare Insurance 3

Utilizing Blockchain and Deep Learning for Decentralized Dis...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shaik, Nazeer Kar, Nihar Ranjan Thankachan, Blessy Pathak, Amit Kumar Singh, Jagendra Gupta, Shelly SRIT-Autonomous Department of Computer Science Engineering Anantapur India School of Pharmacy University of Technology and Management Gopalpur India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Ghaziabad India

ISBN: (纸本)9798350342338

To address the complex issues of detecting fraudulent practices in healthcare insurance, this research employs sophisticated machine learning, especially the Long Short-Term Memory (LSTM) model, to provide a complete framework for reliable fraud detection. The study meticulously examines performance metrics such as accuracy, precision, recall, and the F1 score by employing and evaluating the LSTM model across two distinct datasets-Dataset A (198810 samples) and Dataset B (434319 samples)-illuminating the model's capacity to detect fraudulent activities while minimizing misclassifications. The evaluation process, as shown by confusion matrices displayed as percentages, reveals the model's strengths and points up areas for improvement. This study makes an important contribution to the field of fraud detection by providing practical insights to strengthen healthcare insurance against misleading practices. This research proclaimers a paradigm shift by combining innovative methodologies, extensive dataset curation, and stringent evaluation, ushering in increased security, transparency, and efficacy in healthcare insurance fraud detection, ultimately fostering a future of resilient and precise fraud detection mechanisms. © 2023 IEEE.

关键词： IoT machine learning performance metrics security attacks

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Research on lightweight of UAV based on topology optimization theory 3

Research on lightweight of UAV based on topology optimizatio...

引用

3rd international conference on Mechanical Design and Simulation, MDS 2023

作者： Zhuo, Liyun Zhang, Huiju Zeng, Xihang Zhang, Zhihua Ou, Qingyuan Fujian Xiamen361000 China School of Mechanical Science and Electrical Engineering Xiamen Institute of Technology Fujian Xiamen361000 China

ISBN: (纸本)9781510664890

The weight reduction of UAVs can reduce power consumption, improve the carrying capacity and range, and is of great significance to the green development of the civil aviation industry. Based on the topology optimization theory, the finite element analysis and lightweight design of the UAV fuselage are carried out. Through the optimization and comparison of five schemes, i.e., symmetry, bidirectional draft, unidirectional draft, bidirectional draft+symmetry and squeeze, the comparison results show that the symmetrical optimization is the best scheme. After the optimization of the UAV fuselage model, the mass reduction ratio is 50.24%, the maximum Mises equivalent stress of the model under the load condition is 10.82Mp, the maximum displacement is 0.7512mm, and the minimum safety factor is 4.2, which meets the strength and lightweight design requirements of the parts, and provides a new idea and feasible scheme for the lightweight design of the UAV. © 2023 SPIE.

关键词： Fuselages

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Design and Implementation of College Students’ Psychological Prediction System Based on BP Algorithm 3rd

Design and Implementation of College Students’ Psychologica...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Yuan, Hui Yan, Xu Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9783031631351

Because the mental health problems of college students have been paid more and more attention, it is necessary to design the psychological prediction system of college students through BP algorithm, and actually use the system to help carry out psychological intervention of college students. In order to achieve the purpose, this paper will carry out related research, first introduced the basic concept of BP algorithm, then based on the algorithm proposed the design ideas and implementation methods of college students psychological prediction system, and the system has been tested. Through the research, the prediction results of the BP algorithm of college students psychological prediction system are accurate and comprehensive, can help do a good job of college students psychological intervention, so that college students maintain healthy psychological state. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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Reconstruction and Practice of Higher Vocational English Classroom Ecosystem Based on AHP Algorithm 3rd

Reconstruction and Practice of Higher Vocational English Cla...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Song, Yanyan Shandong Vocational College of Science and Technology Shandong Weifang261053 China

ISBN: (纸本)9783031631290

The construction of English classroom ecosystem in higher vocational colleges is very important for students’ learning and development. Therefore, in the face of various problems caused by the current imperfect classroom ecosystem, it is necessary to reconstruct the ecosystem. Therefore, this paper focuses on the AHP algorithm. The study mainly introduces the basic concept of AHP algorithm and the characteristics of the idealized ecosystem of English classroom, and then analyzes the classroom ecosystem by using the algorithm, and finds out the problems in the classroom ecosystem combined with the idealized ecosystem, and then carries on the reconstruction. Through the study, the AHP algorithm can help people understand the current situation of the English classroom ecosystem in higher vocational colleges. Combined with the idealized system standards, it can accurately analyze the problems and set strategies, and the goals of ecosystem reconstruction can be achieved by using strategies. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Ecosystems

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Low Resource-Cost Depthwise Separable Convolutional Co-Processor Architecture : Embedded Co-Processor Design 3

Low Resource-Cost Depthwise Separable Convolutional Co-Proce...

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3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Chen, Huangxu Zhao, Mingjian South China University of Technology Institute of Microelectronics Guangzhou China

ISBN: (纸本)9798350316674

Depthwise separable convolution (DSC) is an efficient convolutional neural network (CNN) structure that can reduce computation and parameter amounts while maintaining high accuracy. However, due to the processing capability, memory, and bandwidth limitations of embedded processors, the performance of DSC is still affected. To solve this problem, this paper proposes a DSC coprocessor designed for embedded processors, which can effectively accelerate the execution of DSC and reduce power consumption and memory overhead. The DSC coprocessor adopts the following optimization strategies. Firstly, the Z-shaped traversal method is used to access data by utilizing the channel grouping feature of DSC, which reduces the number of memory access and data transfer, avoids the use of large caches, and thus reduces the overall design area and power consumption. And then, a low precision quantization method is used to compress the data bit width of convolution kernels and feature maps to 8 bits, thereby reducing the computational complexity and power consumption. Lastly, a set of reusable adder tree structure is designed to reduce the area and power consumption of the adder tree structure in view of the difference in addition content between deep convolution (DWC) and point convolution (PWC). This paper discusses the pipeline architecture of the coprocessor and implements the DSC coprocessor based on a Xilinx XC7A200T field-programmable gate array (FPGA). Experimental results show that the DSC coprocessor has much lower resource usage than other coprocessors, and it does not use large amounts of on-chip memory. With the use of 800MHz 16bit DDR3 and clock frequency of 50MHz, it can achieve a peak computing performance of 6.3 GOP/S. This DSC coprocessor provides an efficient and low-cost solution for edge computing. © 2023 IEEE.

关键词： coprocessor depthwise separable convolution edge computing hardware accelerator VSLI design

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