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

共 11233 条记录，以下是101-110 订阅

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Automatic Identification Mechanism for Access Control System 3rd

Automatic Identification Mechanism for Access Control System

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sandhya, G. Suresha, S. Shivalingappa, P. Yogish Rao, Vishal Kodabagi, Mallikarjuna Sumitha, B.S. Balasundaram, Tarun School of Computing & Information Technology REVA University Bengaluru India Information Science & Engineering Nagarjuna College of Engineering and Technology Bengaluru India

ISBN: (纸本)9789819743582

The majority of educational institutions still track student attendance using the old-fashioned method (paper-based). Paper-based attendance registers have a variety of problems, including the inability to evaluate the data because it is not uploaded to a centralized system. The embedded cameras on campus will take a picture of it. The faces of all the students are in the database. Students’ photos for future usage. Face recognition was used to capture the student's image. Either students are arriving, or everyone is seated in the space. The student's presence is recognized right away. The final step in the recognition process is to compare the detected face photos to the database of student images. if the image and database are identical. The pupil is noted as being present. Parents will send the student attendance report by mail. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Bera, Ruchira Sen, Soumya Khosla, Mamta Raman, Ashish Kumar, Naveen Department of Basic Science and Humanities Seacom Engineering College West Bengal Howrah India Department of Electronics and Communication Engineering Dr B. R. Ambedkar National Institute of Technology Jalandhar India Department of Electronics and Nanoscale Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819719426

Higher education institutions must modify existing English curricula to fit the needs of today’s learners who grew up with the Internet. This entails raising consciousness and making use of the advantages offered by technology and resources from the Internet+ era. Positive changes have resulted from the present-day college English teaching approach, renewing guidance, modernizing it, and improving effectiveness. In higher education, assessing English instructors is essential since both instructors and students value passing required English exams. computer-based remote instruction has increased online learning, advancing digitalization and lifetime learning. This article discusses problems with college English instruction, including low efficacy, uneven competency, and a mismatch between grading and instruction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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The On-Load Simulation Model of Electric Drive System of Electric Vehicle Under Complex Operating Conditions 3rd

The On-Load Simulation Model of Electric Drive System of Ele...

引用

3rd international conference on Information Control, electrical engineering, and Rail Transit, ICEERT 2023

作者： Wang, Huihui Xie, Jun Liu, Bin Jia, Jin Beijing Jiaotong University Electronic and Information Engineering College Beijing100044 China China Faw Group CO. LTD Intelligent Internet Connection Development Institute Changchun130000 China Chongqing University of Technology Key Laboratory of Advanced Manufacturing Technology for Automobile Parts Ministry of Education Chongqing404100 China

ISBN: (纸本)9789819788903

The on–off of high voltage inverter of the motor drive system is the main source of electromagnetic interference in new energy vehicles. According to GB/T 18,655-2018, GB/T 36,282-2018 and related enterprise standards, the electromagnetic compatibility performance of the electric drive system must be strictly controlled within the specified range. To solve this problem, a simulation and development technology of a new energy vehicle electric drive system under load conditions is proposed. This paper mainly introduces the simulation modeling method of IGBT and establishes the on-load simulation platform based on an integrated electric drive system. The accuracy of the model is verified by simulation and test. In this paper, parameters analysis and electromagnetic interference performance optimization are carried out to form a closed-loop electromagnetic compatibility performance control system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric drive system EMI High power IGBT module

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TranslateKar: An Efficient Transformer Based Konkani Translator 3rd

TranslateKar: An Efficient Transformer Based Konkani Transla...

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3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Pai, Shreya Mandrekar, Sajal Phadte, Saylee Naik, Atit Prasad, Shitala Computer Science Goa College of Engineering Goa Ponda India School of Mathematics and Computer Science IIT Goa Goa Ponda India

ISBN: (纸本)9789819730896

In this research, we introduce a bidirectional Neural Machine Translation (NMT) model that employs the Transformer architecture to facilitate translations between Konkani and English languages. Inspired by "Attention is All You Need", we delve into variations in hyperparameters, including dropout rates and vocabulary size, to assess their impact on translation performance. The Transformer model, renowned for its capacity to handle long-range dependencies efficiently and in parallel, proves highly suitable for sequence-to-sequence tasks such as machine translation. We meticulously conduct a series of experiments, training the model with diverse hyperparameters configuration, and subsequently compare the outcomes to discern the optimal setup. Our findings underscore the superiority of the dash model, as it attains the highest BLEU score on the 3k test dataset, demonstrating its enhanced translation accuracy for both Konkani to English and English to Konkani translations. These results shed light on the paramount importance of hyperparameters selection in bidirectional NMT systems, underscoring the Transformer architecture’s potential in multilingual translation tasks. The study’s outcomes represent a noteworthy contribution to the advancement of machine translation techniques and set the stage for more effective communication tools in multilingual contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural machine translation

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Ultra-Short-Term Wind Speed Prediction for Wind Farms Based on GA-LSTM Hybrid Model 3rd

Ultra-Short-Term Wind Speed Prediction for Wind Farms Based ...

引用

3rd international conference on Information Control, electrical engineering, and Rail Transit, ICEERT 2023

作者： Huang, Meng Shanghai Jiao Tong University Shanghai Minghang200240 China Shanghai Electric Digital Technology Co. Ltd. Shanghai Minghang201100 China

ISBN: (纸本)9789819788903

Ultra-short-term wind speed prediction is of great significance to the dispatch and control of wind farms and the safe operation of smart grids. Existing prediction methods based on neural network algorithms suffer from the shortcoming of lack of guidance in hyperparameter optimization. This paper proposes an ultra-short-term wind speed prediction method based on a genetic algorithm-long short-term memory (GA-LSTM) network hybrid model. The number of neurons in the hidden layer of LSTM and the dropout rate of the dropout layer are optimized by GA to improve prediction accuracy. A preservation strategy is proposed to record the excellent LSTM structures that may appear during the training process and reduce the computational effort by half. The proposed method is validated using actual data from a wind farm. The results show that GA can effectively optimize the hyperparameters of LSTM, and the GA-LSTM hybrid model has good accuracy in wind speed prediction tasks from one to four steps ahead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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Impact Analysis of Electrode Placement for Cognitive Measurement Using Intelligent Algorithms 3rd

Impact Analysis of Electrode Placement for Cognitive Measu...

引用

3rd international conference on Emerging Electronics and Automation, E2A 2023

作者： Keerthika, N. Kiruthika, V. Sathish, E. Neeharika, M. Sai Vellore Institute of Technology Tamil Nadu Chennai India Danlaw lnc Karnataka Bengaluru India

ISBN: (纸本)9789819768011

The Brain-computer Interface (BCI) has significant potential for use in cognitive computation within the field of human-machine interaction. The study was performed to identify the best electrode leads to measure the cognitive activity. Intelligent algorithms were deployed to identify the best electrode leads. EEG signal dataset was obtained from a standard dataset where the signal was acquired from various lead positions. The features from several leads were extracted and contributing features were fed into the Machine Learning classifiers (ML) such as Support Vector Machine (SVM), K-nearest neighbour (KNN), Artificial Neural Network (ANN), and Random Forest (RF). Performance metrics were computed for all the classifiers. SVM achieved 93.71% accuracy for the F3 electrode and 100% accuracy for the F4, P3, and P4 electrodes, while the ANN model achieved 100% accuracy for the F3 electrode and 93.71% for the F4, P3, and P4 electrodes. F3, F4, P3, and P4 are the best suitable leads for cognitive analysis using EEG. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nearest neighbor search

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Improving Emotion Recognition in Audio Signals: Leveraging Novel Features and Deep Learning for Improved Classification 3rd

Improving Emotion Recognition in Audio Signals: Leveraging N...

引用

3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Chaudhary, Poonam Choudhary, Neeraj Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science and Engineering University College of Engineering and Technology Bikaner India

ISBN: (纸本)9789819726707

Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is insufficient for identifying the emotions, necessitating the adoption of novel feature extraction approaches. This paper aims to provide an improved framework which uses a detailed study on audio sound features like mel-frequency cepstral coefficients (MFCCs), zero crossing rate (ZCR), spectral bandwidth and many other features for audio analysis and uses the same for predicting emotions. The paper also emphasises on various audio augmentation techniques to improve the generalising power of the model. With the above-mentioned improvements in the feature vector and a carefully selected deep neural network, state-of-the-art classification accuracy is achieved on the two state-of-the-art datasets Toronto emotional speech set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The paper is concluded with the improved approach for emotion recognition using audio signals and leads to future scope of efficient speech recognition applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Investigating the Efficacy of ML and DL Models in Detecting Tomato Leaf Diseases under Varying Environmental Factors 3rd

Investigating the Efficacy of ML and DL Models in Detecting ...

引用

3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Vinta, Surendra Reddy Raju, Yeligeti Nagaraj, P. Kumar, K. Ashok Dhanalaxmi, B. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Telangana Hyderabad India Department of Computer Science and Engineering Anurag University Telangana Hyderabad India Department of Computer Science and Engineering Nalla Narasimha Reddy Educational Society’s Group of Institutions Telangana Hyderabad India Telangana Hyderabad India

ISBN: (纸本)9789819743582

For sustaining food security and guaranteeing good crop production, tomato leaf disease must be found. This study examines the efficacy of several ML and DL models for identification. Under various climatic conditions, tomato leaf disease occurs in Bangladesh. TMV, Early Blight, Spider Mites, Late Blight, and Early Blight The dataset utilized in this study includes information on Septoria leaf spots, healthy tomato leaves, and two-spotted spider mite. The AlexNet, ResNet50V2, DenseNet121, InceptionV3, EfficientNet-B4, Support Vector Machine Classification, and ResNet150 models’ recall, accuracy, and precision are compared. The model’s performance is also evaluated in terms of environmental conditions such as occlusion and illumination fluctuations. Our findings provide useful insight into the benefits and drawbacks of various ML and DL models for detecting tomato leaf disease. They also offer recommendations for the creation of disease detection systems that are reliable, accurate, and effective in a variety of environmental conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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A Multi-objective Smart Grid Optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency 3rd

A Multi-objective Smart Grid Optimization: Integrating Swarm...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Ammasai, Viswanathan Katbar, Nek Muhammad Kothalanka, Amarendra Vandadi, Venkata Ramana Udhaya Shankar Latha, Sindhu Sharma, Dilip Kumar Sengan, Sudhakar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering KSRM College of Engineering Andhra Pradesh Kadapa516003 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli62745 India

ISBN: (纸本)9789819783540

The realm of Smart Grid Systems (SGS) has witnessed unprecedented progress, driven by increasing energy demands and the integration of Renewable Energy Sources (RES). While these grids promise enhanced efficiency and sustainability, their involved and dynamic nature poses significant optimization challenges. Existing solutions, although innovative, often overlook the intricate interplay between diverse factors, leaving room for potential improvements in optimization approaches. Recognizing this gap, the current study delves into developing a novel method that integrates Ant Colony Optimization (ACO) with domain-specific heuristics. By focusing on analytical sections like Load Forecasting, Renewable Energy Integration, Peak Shaving, and Grid Resilience, this method attempts to bridge the divide that existing approaches miss. The proposed method not only ensures optimal energy distribution but also tailors the solution to address the unique features and requirements of modern SGS. A more reliable, effective, and Renewable Energy (RE) future is within reach according to the outcomes of this investigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Diagnosing the Early Stages of Alzheimer’s Disease by Applying the Modified Ant Colony Optimization Technique 3rd

Diagnosing the Early Stages of Alzheimer’s Disease by Apply...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Kumari, Rashmi Das, Subhranil Singh, Raghwendra Kishore School of Computer Science Engineering and Technology Bennett University Greater Noida India Dehradun India School of CS and AI SR University Warangal India

ISBN: (纸本)9789819726707

Treating Alzheimer’s Disease (AD) and preventing further degeneration are becoming increasingly important. Doctors who could view many morphological aspects for improved clinical practices would evaluate patients more extensively. Previous studies have shown the value of applying deep learning to T1-weighted MRI images to differentiate AD from Normal Control. This paper proposes the classification of three binary classifications by applying 3D CNN Network. A Modified Ant Colony Optimization (MACO) technique is proposed for optimizing the different weights of the network. The effectiveness of the proposed model is assessed by using a sample of 259 ADNI individuals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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