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4th international conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Nie, Zihao Wang, Raofen School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9798350384062

Steady-state visual evoked potentials (SSVEP) systems are considered ideal for practical brain-computer interfaces, with a focus on precise decoding of multi-class datasets in short time. Convolutional Correlation Analysis (Conv-CA) combines convolutional neural networks with canonical correlation analysis, extracting features from electroencephalography signals and template signals in two branches. These features are then classified using a correlation layer. This paper introduces residual connections to the network, proposing the improved Residual-based Convolutional Correlation Analysis (ResConv-CA) model. All experiments were validated on a 40-class public dataset, and considering the 140ms visual delay of SSVEP, we selected an effective data length of [0.64s, 5.64s]. In experiments, the ResConv-CA model showed increased average classification accuracy and improved accuracy for challenging subjects. This confirms the improved stability and accuracy of the enhanced model with the addition of residual connections. © 2024 IEEE.

关键词： Brain computer interface

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Human Activity Recognition Using Deep Learning Techniques for Healthcare Applications 15

Human Activity Recognition Using Deep Learning Techniques fo...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shekhar, Raj Tomar, Deepak Singh Sharan, Bhagwati Pateriya, R.K. Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350370249

The idea of smart healthcare is gradually gaining traction with the rapid advancement in information technologies. Smart healthcare is the intelligent transformation of the current medical system to make it more reliable, efficient, and individualized through the intelligent use of next-generation technologies like artificial intelligence, and the Internet of Things (IoT). This study offered the recent advancement of Deep learning (DL) techniques for healthcare systems, and the use of DL techniques to identify human physical activity with wearable sensors. In this paper, convolutional neural networks (CNN), CNN-LSTM, multi Headed CNN-LSTM, and multi Headed CNNBiLSTM are used. The obtained outcomes of these Deep Learning models are compared in terms of accuracy, precision, recall and F1 score. The multi-Headed CNN-BiLSTM model exhibits better performance than the rest of the models when applied to the UCI HAR dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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A Lightweight multi-Vehicle Detection Method Based on multi-Scale Feature Fusion and multi-Head Self-Attention 7

A Lightweight Multi-Vehicle Detection Method Based on Multi-...

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7th IEEE international conference on computer and Communication engineering Technology, CCET 2024

作者： Zhang, Yong-Mei Ao-Guo Du, Zhi-Rong Oi, Hao-Yu School of Information Science and Technology North China University of Technology Beijing100144 China School of Electrical and Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9798350355673

Aiming at the problems of high computational complexity, low recognition accuracy, and poor real-time performance of vehicle detection methods based on deep learning, this paper proposes a lightweight multi-vehicle detection method based on multi-scale feature fusion and multi-head self-attention. By adopting the FasterNet to replace the original CSPDarkNet53 backbone network of YOLOv8, and combining the idea of partial convolution, the C2f module in the neck is reconstructed to achieve efficient fusion of multi-scale features. After the last layer of the backbone network, a multi-head self-attention mechanism is introduced to enhance the ability of the model to understand complex spatial relationships and contextual information in images. Furthermore, the Focal-EIoU loss function is used to replace the CIoU loss function, accurately defining the aspect ratio of the predicted bounding box and alleviating the issue of imbalance between positive and negative samples. Experiment results show that the proposed method achieves 0.1% higher accuracy, 3.08% higher mean average precision, and 1.95 higher frames per second compared with the original model on the UA-DETRAC dataset. © 2024 IEEE.

关键词： Deep learning

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Extreme Learning Machine Soft-sensing Model of SMB Chromatographic Separation Process Based on multi-strategy Fusion Coati Optimization Algorithm

IAENG International Journal of Computer Science

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IAENG international Journal of computer science 2025年第2期52卷 447-463页

作者： Xing, Cheng Yang, Qing-Da Wang, Jie-Sheng ShuangGuan, Yi-Peng Sun, Yong-Cheng School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114044 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

Simulated Moving Bed (SMB) chromatographic separation technology is an innovative method that combines traditional fixed-bed adsorption operation with real moving bed (TMB) chromatographic separation technology. In order to accurately evaluate the purity and yield of extracted and residual in the SMB chromatography separation process, a extreme learning machine soft-sensing model based on multi-strategy fusion coati optimization algorithm (COA) was proposed. This model selects auxiliary variables based on the analysis of the SMB chromatographic separation process. The proposed multi-strategy fusion COA includes logistic mapping, prey position vector and meme grouping to enhance the randomness and diversity of COA. Simulation results demonstrate that the optimized ELM can proficiently predict the key pecuniary and technical index of the SMB chromatography separation process. © (2025), (international Association of Engineers). All rights reserved.

关键词： Machine learning

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Accurate Photovoltaic Forecasting through a multi-connected Structural Network 4

Accurate Photovoltaic Forecasting through a Multi-connected ...

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4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Xie, Jiangshan Hu, Yanming Rong, Fangying International Business School Henan University Zhengzhou China School of Information Communications Engineering Beijing Information Science & Technology University Beijing China School of Information Science and Engineering Shandong Agricultural University Tai'an China

ISBN: (纸本)9798350359589

With the growing demand for renewable energy sources, the importance of accurate photovoltaic (PV) forecasting has become crucial for effective energy management. Predicting PV output remains a challenge due to the need for high accuracy and low computational complexity. This paper introduces a novel method for general PV forecasting through a multi-connected structural network to pursue accurate PV generation. We explore the impact of different neural network interactions on actual power data within the network layer and test its relevance by constantly changing parameters. Stacked gate recurrent unit (GRU) and bidirectional long short-term memory (BiLSTM) networks are used to learn universal meteorological data feature as input for network layers, and after incorporating an attention mechanism, they are trained in a hierarchical manner of multi model fusion, interaction, and infiltration. Experimental results indicate that the proposed method can achieve competitive performance when compared to traditional single approaches. © 2024 IEEE.

关键词： Attention CNN Feature fusion PV forecasting

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multi-Scale Quantum Harmonic Oscillator Behaved Algorithm with Three-Stage Perturbation for High-Dimensional Expensive Problems

Multi-Scale Quantum Harmonic Oscillator Behaved Algorithm wi...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ye, Xinggui Li, Jianping Wang, Peng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Southwest Minzu University Chengdu610225 China

ISBN: (纸本)9781665410205

Quantum perturbation plays an important role in quantum movement. This paper proposes a three-stage perturbation (TSP) framework to enhance the performance of multi-scale quantum harmonic oscillator algorithm (MQHOA). Three perturbations are adopted in the population initialization process with opposition-based learning (OBL), in quantum harmonic oscillator (QHO) process with ensemble of three differential evolution (DE) strategies and in multi-scale (M) process with a rollback mechanism to enhance the diversity of the particles and prevent from falling into local optima. The proposed approach has been evaluated on several high-dimensional expensive problems from 50-D to 500-D. The empirical data are compared with recent MQHOA variants and some state-of-the-art similar metaheuristic algorithms (MAs). The experimental results reveal the superiority or competitiveness of the the proposed approach. © 2024 IEEE.

关键词： Harmonic analysis

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Comparative Study of Machine Learning and Deep Learning Models for Food Waste Detection Using Image Processing

Comparative Study of Machine Learning and Deep Learning Mode...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rajagopal, R. Chaithanya, K. Sonth, Ankita Kallappagoudar, Prasanna Basanagouda Mohammadishaq Nadaf, Mohammadzaid Ananda Theertha, H. Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Alliance College of Engineering and Design Dept of Computer Science Bangalore India Alliance College of Engineering and Design Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used. © 2025 IEEE.

关键词： k-nearest neighbors

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RIS-assisted multi-user visible light communication in IIoT scenarios: prototype design and feasibility study 6

RIS-assisted multi-user visible light communication in IIoT ...

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6th international conference on Optoelectronic science and Materials, ICOSM 2024

作者： Ye, Ziwei Li, Dun Zhang, Erfeng Zhu, Yijun Information Engineering University Henan Province Zhengzhou450000 China

ISBN: (数字)9781510686823

ISBN: (纸本)9781510686816

As the multi-user wireless communications in Industrial Internet of Things (IIoT) scenarios are increasingly demanded, conventional visible light communication (VLC) solutions suffer from the problems such as the inter-cell crosstalk and the latency when switching the cells. The introduction of reconfigurable intelligent surface (RIS) supplies new approach of reconfigurable optical cells based on digital holography. This paper proposes the prototype model of the holography-based multi-user VLC system. The computer-generated holograms (CGH) generation quality is analyzed, and the lens system to adjust the positions and the sizes of the generated cells is designed. The feasibility of the system is verified by simulation, which shows that the method can support 5 users data transmission. © 2024 SPIE.

关键词： computer generated holography

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Collaborative Learning for multi-Source Domain Adaptative Object Detection 4

Collaborative Learning for Multi-Source Domain Adaptative Ob...

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4th international conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Cheng, Yansong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350394375

multi-Source domain adaptation is a more general scenario than single-source domain adaptation, however, it encounters challenges arising from the problem of model effect degradation caused by distribution differences between multiple source domains. To address this issue, we propose a novel approach, namely collaborative learning(CL). CL can retain public intra-domain knowledge and improve the generalization ability of the model. Extensive experiments on different adaptation scenarios demonstrate the effectiveness of the proposed model. © 2024 IEEE.

关键词： Object detection

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Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

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international Journal of Advanced computer science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings international conference on Software engineering and ACM international conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (science and Information Organization). All Rights Res

关键词： Software design

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