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EEG-Based Emotion Recognition Using Morlet Dual-Level Wavelet Contextual Neural Network with Snow Geese Algorithm 4

EEG-Based Emotion Recognition Using Morlet Dual-Level Wavele...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sivaramkrishnan, M. Siva Ramkumar, M. Kannaiyan, Mathi Kanan, Mohammad Baig, Rahmath Ulla Giri, Jayant Karpagam College of Engnieering Dept of Eee India SNS College of Technology Dept of Ece India University College of Kancheepuram Anna University Department of Mechanical Engineer Kanchipuram631552 India University of Business and Technology College of Engineering Department of Industrial Engineering Jeddah21448 Saudi Arabia Zarqa University College of Engineering Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

EEG-based Emotion Recognition (ER) works by analyzing brainwave patterns from EEG signals to understand and identify a person's emotional state. the problem of emotion identification from EEG signals remains challenging because brain activity presents both non-linear dynamic properties and individual-specific EEG patterns along with biological noise. Traditional approaches to emotion detection become inefficient due to EEG signal complexity thereby complicating accuracy and reliability measures. To tackle the challenges in EEG-based emotion recognition, this study presents a new approach called the Morlet Dual-level Wavelet Contextual Neural Network with Snow Geese Algorithm (MorDWCNNet+SGA). the method uses the SEED dataset, starting with preprocessing through the Observability-Constrained Resampling-Free Cubature Kalman Filter (OCRCKF) to enhance important EEG patterns. For feature extraction, the Multi-Discrete Wavelet Transform (MDWT) is applied to capture key EEG characteristics. Emotion classification is then performed using the Morlet Dual-level Wavelet Contextual Neural Network (MorDWCNNet), which is further optimized by the Snow Geese Algorithm (SGA) to improve accuracy. Implemented in Python and tested on the SEED dataset, the MorDWCNNet+SGA framework achieves remarkable results, with 99.9% accuracy and 99.3% sensitivity, significantly outperforming existing methods. the outcomes of the proposed method achieved high accuracy in effectively distinguishing emotional states through brainwave patterns. this method demonstrates how advanced techniques can combine synergistically to enhance ER performance resulting in improved real-world application accuracy and speed. © 2025 IEEE.

关键词： Kalman filters

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Pluggable Compensation Module for Twofold Propagation-based Hologram Generation 4

Pluggable Compensation Module for Twofold Propagation-based ...

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4th international Computational Imaging conference, CITA 2024

作者： Yuan, Ganzhangqin Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (纸本)9781510688834

Recent advancements in learning-based computer-generated holography (CGH) have enabled sophisticated methods for generating phase-only holograms (POHs). However, a key challenge remains unaddressed: ringing artifacts, which arise due to the Gibbs phenomenon during the diffraction propagation process, significantly degrade the quality of reconstructed holographic images. To address this, we introduce a novel pluggable compensation module designed to predict and mitigate these artifacts by adjusting for residual values during propagation. this pluggable module, easily integrated with existing CGH networks, alleviates the network's burden by aligning the diffraction process more closely with ideal conditions. Our results demonstrate that embedding this module in state-of-the-art CGH frameworks, such as HoloNet and CCNN, elevates the PSNR by up to 3.9 dB and 0.6 dB, respectively. Notably, our method achieves considerable artifact reduction in real-world scenarios, thereby enhancing the overall fidelity of holographic displays. this pluggable module represents a substantial advancement, offering versatility and improved performance across diverse CGH algorithms. © 2025 SPIE.

关键词： Holograms

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Fault diagnosis method of soft-switching inverter based on deep learning 4

Fault diagnosis method of soft-switching inverter based on d...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Jia, Xiaodong College of Information Science and Engineering Northeastern University Liaoning province Shenyang110819 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Aiming at the complexity of open-circuit fault diagnosis of soft-switching inverters, a dual-branch fault diagnosis method based on one-dimensional convolutional neural network (1-D CNN) combined with improved deep residual shrinkage network (IDRSN) and two-dimensional convolutional neural network (2-D CNN) with attention mechanism (IDRSCNN-ACNN) is proposed. this method combines the advantages of 1-D CNN that can extract the primitive features from the original time series with the advantages of 2-D CNN that can extract high-dimensional features from images. It can mine more effective spatial features for inverter fault diagnosis. In addition, the method integrates an improved deep shrinkage residual network (IDRSN) and coordinate attention (CA) mechanism to improve performance. Firstly, the input current data on the power side of the soft-switching inverter is expanded by sliding window overlapping sampling, and the extended data is converted into time-dependent Markov images by using Markov transition field. Secondly, a parallel dual-branch IDRSCNN-ACNN fault diagnosis model is designed. then, the original time series data and Markov image are used as the input of 1-D branch and 2-D branch respectively to train the model. Finally, the Softmax classifier is employed for precise fault classification. Experimental results show the method’s efficacy in classifying mixed-noise data across 79 fault types. IDRSCNN-ACNN has better fault diagnosis performance through ablation experiment and comparison with some traditional fault diagnosis models. © 2025 SPIE.

关键词： Markov processes

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Point cloud classification method based on local dynamic edge graph convolution and global information fusion 4

Point cloud classification method based on local dynamic edg...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Du, Jie Sheng, Zhiwei Jin, Xingyu Huang, Yuanyuan Tian, Junbiao Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the development of automatic driving, unmanned aerial vehicle detection and virtual reality cannot be separated from the research of 3D point cloud. Existing studies either lack of local shape capture and local spatial feature description of point cloud, resulting in poor accuracy of point cloud classification network, or pursue fine and complex local feature extractors, sacrificing extraction time and memory overhead to extract classification progress, but the results show that the effect is not significantly improved. therefore, this paper proposes a Point cloud classification method based on Local Point Dynamic Edge Graph Convolution and Global Information Fusion. Among them, the local point dynamic edge graph convolution module improves the edge graph convolution, which can improve the local extraction feature effect without deepening the local feature extractor, and reduce the time and memory overhead. the global information fusion block concatenates multi-layer local features to extract and fuse feature information to improve the classification accuracy of the network. Our network is tested on the public datasets ModelNet40 dataset and ScanObjectNN dataset. the results show that compared with the current mainstream point cloud classification algorithms, the point cloud classification accuracy of the proposed classification method is improved, and it has good robustness. © 2025 SPIE.

关键词： Point clouds Feature extraction Convolution Data modeling Information fusion Data conversion Education and training Transformers Deep learning Statistical modeling

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

5th International Conference on Innovative Product Design an...

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5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

ISBN: (纸本)9789819767311

the proceedings contain 57 papers. the special focus in this conference is on Innovative Product Design and Intelligent Manufacturing Systems. the topics include: Development of an Intuitive Autonomous Ground Robot for Indoor Delivery applications;development of a computer-Assisted Vision-Based Inspection Monitoring System for Quality Assurance;analysis of Challenges of IoT Adoption for Smart City application;finite Element Modelling and Comparative Assessment of Single-Level Cervical Disc Replacement with Fusion Surgery;a Statistical Method-Based Auto-Fitted Genetic Algorithm for Damage Diagnosis;emovere Agnitio by Textual Tweets Using machine learning;government Bidding Security Management System Using Blockchain;resource Forecasting of Geographical Area Using Big Data Analytics;design Optimization and Aerothermodynamic Analysis Over a Supersonic Vehicle;Evaluation of Weldability and Optimization of TIG Welding Process Parameters for Maximized Yield Strength;terahertz-Frequency Propagation for Autonomous Transport Infrastructure;energy-Efficient Coffee Pulper;community Perception on Groundwater Quality Versus Analytical Quality in the Coastal Areas of Visakhapatnam, India—Opinion Versus Actuality;simulation Modeling and Analysis for Supply Chain Disruptions: A Case Study from a Process Industry in Kerala;integrating Hand Gesture Tracking with Physical and Digital Twins of Robotic Arm;performance Enhancement of Pump Parameters through Innovative Spring Design of Mechanical Seal for a Heat Exchanger;enhancing Biocompatibility and Performance of Multi-directionally Expanding Materials for Dental applications: Design and Analysis;synthesis of Hybrid Nanofluids, Properties, and Its applications—A Comprehensive Review;application of Hybrid Decision-Making Method in Ranking Polymer Biomaterials for Interim Dental Crowns;Analysis on Mechanical Properties of Aluminium MMC with Graphite and Boron Nitride Powder;textile Crafts of Bharat.

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Applying machine learning to Improve Weather Forecasting in a Mediterranean Area: Preliminary Experiments 4th

Applying Machine Learning to Improve Weather Forecasting in...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Yousaf, Umair De Rango, Alessio Furnari, Luca D’Ambrosio, Donato Senatore, Alfonso Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

We investigate the effectiveness of machine learning (ML) as a complementary tool in enhancing the accuracy of precipitation forecasting in the Calabria region (southern Italy), a Mediterranean area frequently affected by extreme rainfall events, with significant material damage and potential loss of human lives. the operational weather forecasting system developed at the University of Calabria, based on the Weather Research and Forecasting (WRF) model, is considered to this end. We test two ML models, namely an artificial neural network and a random forest, to improve the accuracy of WRF forecasting. We provide several WRF output variables to the ML models. After a proper calibration (learning and test phases) over a two-year-long extended dataset, we assess the results to the original WRF outcomes (validation phase). Preliminary experiments resulted in a significant improvement by the WRF-ML coupled systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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30th international Joint conference on Industrial Engineering and Operations Management, IJCIEOM 2024

30th International Joint Conference on Industrial Engineerin...

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30th international Joint conference on Industrial Engineering and Operations Management, IJCIEOM 2024

ISBN: (纸本)9783031807848

the proceedings contain 37 papers. the special focus in this conference is on Industrial Engineering and Operations Management. the topics include: Proposal for an Optimized Model to Analyze Customer Satisfaction—B2B;mechatronic Framework for Smart Beehives: Prototyping and applications Perspective;digital Fashion: the Impact of Service Quality on Customer Satisfaction;computer Vision as a Resource in Smart Warehouses: A Systematic Review;port Logistics Emissions Control Using machine learning to Coordinate the Truck Flow;Evolution of Logistics Service Providers: A Study of 5 PL;reinforcement learning and Discrete Event Simulation Applied to Production Scheduling: A Scoping Review;a Simulation Model to Determine Production Shifts for Pallet Packaging;the Enhanced Factory for Extra-Terrestrial Space Technology Operations: Conceptualization and Scenarios’ Definition;digital Twins in Asset Prognosis and Health Management: Definitions, applications, State of the Art, and Future Trends;Benefits of ERP Information Systems in ESG Management Within Organizations;an Assignment Model for High-Cognitive-Workload Maintenance Activities in Industry 5.0;Forecast of Brazilian GDP Growth through Consumption of Diesel Oil and Electrical Energy;impact of Distribution Configurations on Supply Chain Performance: A System Dynamics Approach;lean Healthcare Management through Kaizen in a Public Brazilian Hospital;multicriteria Decision Analysis for Natural Risk Management Context: Knowledge Mapping and Analysis (2013–2023);assessment of a Project Management Maturity Diagnostic Tool;Utilizing GPT-4 in the Selection of Health-Tech Startups for a Brazilian Acceleration Program: A Validation Study with 168 Startups;Applicability Analysis of the Power BI with the OKR Methodology in the Strategic Planning Enhancement;customer Satisfaction in Tourism Services Center.

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A machine learning Approach to Speed up the Solution of the Distributor’s Pallet Loading Problem 4th

A Machine Learning Approach to Speed up the Solution of ...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Magnani, Matteo Franchini, Giorgia Dell’Amico, Mauro Zanni, Luca Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Via Amendola 2 Reggio Emilia42122 Italy Department of Physics Informatics and Mathematics University of Modena and Reggio Emilia Via Campi 213/B Modena41125 Italy Institute of Informatics and Telematics National Research Council Pisa56124 Italy

ISBN: (纸本)9783031812408

We consider the distributor’s pallet loading problem, whose aim is to pack on the lowest number of pallets a set of different boxes, satisfying a given set of constraints. We focus on a real-world scenario where a stability restriction and a compression limit must be respected. Each pallet is loaded with a set of layers of boxes that must not overhang beyond the maximum dimensions of the pallet. the problem has been addressed in [12] with a two phases method that starts by defining a huge set of possible layers made of boxes, and uses an integer linear program (ILP) to select the layers which pack all boxes while minimizing the number of pallet used and satisfy all constraints. Unfortunately, the computing time needed to solve the ILP can be extremely long. In this paper we present a proposal for solving the problem which, first, builds layers heuristically, secondly, through machine learning algorithms, classifies by "importance" and then, uses the classification to resolve a ILP. With "importance" we are meaning how much high is the "probability" that each layer will be used to resolve the instance. We validate our proposed method, comparing results obtained with and without the machine learning approach. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Combinatorial optimization

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Research on the Method of Integrating Teaching Resources of Traditional Chinese Medicine Pharmacology Based on Deep learning Algorithm 4th

Research on the Method of Integrating Teaching Resources of ...

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4th international conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Fu, Yao Li, Yumei Changchun University of Humanities Changchun130117 China Changchun University of Traditional Chinese Medicine Changchun China

ISBN: (纸本)9783031900839

In order to improve the application value of the teaching resources of traditional Chinese medicine pharmacology in practical teaching, a method of integration of teaching resources of traditional Chinese medicine pharmacology based on deep learning algorithm was proposed. the teaching resources of TCM pharmacology were collected by association rule mining. From three aspects of text/data, image and video, complete the preprocessing of course teaching resources. the convolutional neural network in the deep learning algorithm is used to extract the characteristics of teaching resources of TCM pharmacology, and finally, the integration of teaching resources is realized according to the measurement results of similarity between resources. through the performance test experiment, it is concluded that compared with the traditional integration methods, the resource loss rate of the integration results obtained by the optimization design method is reduced by about 3%, and the redundancy of the integration results is significantly reduced. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Teaching

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the Research on the application Modes of Intelligent Instructional Design Generation in Primary and Secondary Schools Supported by Large Models 24

The Research on the Application Modes of Intelligent Instruc...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Lin, Jian Wang, Xiaoyi Li, Bingjun He, Musheng Luo, Yufei School of Computer and Intelligent Education Lingnan Normal University Guangdong Zhanjiang China Guangdong Provincial Key Laboratory of Development and Education for Special Needs Children Lingnan Normal University Guangdong Zhanjiang China School of Electronic and Electrical Engineering Lingnan Normal University Guangdong Zhanjiang China

ISBN: (纸本)9798400717468

As the development of large language models and generative artificial intelligence technologies continues to advance, the application of digital teaching resources is exhibiting a new trend of human-machine collaboration and co-creation. As an important digital teaching resource for the implementation of smart teaching, the application of instructional design documents has been significantly influenced by the development of intelligent technologies, especially in the field of primary and secondary education, which holds significant research value. However, there is a clear gap between the value claims and actual effects of traditional instructional design, which limits the professional development of teachers and hinders the comprehensive development of students. this research aims to combine ChatGPT-4 models and generative artificial intelligence technology to construct an intelligent instructional design system platform, achieving the intelligent transformation and upgrading of instructional design. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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