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共 3334 条记录，以下是621-630 订阅

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Distinctive Approach for Speech Emotion recognition Using machine learning 4th

Distinctive Approach for Speech Emotion Recognition Using Ma...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Singh, Yogyata Neetu Rani, Shikha Rajkiya Engn Coll Dept Comp Sci Bijnor Uttar Pradesh India

ISBN: (纸本)9783031243516;9783031243523

Emotions have an important role in human-computer interaction. It improves the efficiency of Human-Computer Interaction (HCI) applications. Speech Emotions recognition (SER) modules are critical in helping robots in interpreting data through the use of emotions. With precision, vigorousness, and dormancy taken into account, this is a difficult assignment. the proposed methodology in this experiment is to implement two approaches on the same dataset and compare the efficiency. the classification strategy employed is to classify data by implementing four different machine learning algorithms. the Support Vector Method (SVM) model has a 99.57% accuracy on the Tess dataset and a 63.73% accuracy on the Ravdess dataset. According to the overall analysis, the model performs better without augmentation, and the model that produced the best possible performance is the Support Vector machine Algorithm (SVM). Two more potential additions to the model's capabilities are mood swings and depression. Psychologist may implement these techniques to monitor their patients' anxiety attacks. these additions can be made for analyzing emotions of depressed person. It can be advantageous when dealing with a person's mental state.

关键词： Feature extraction Visualization Support Vector machine Decision Tree Random Forest

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Exploring the thematic Clusters of Artificial Intelligence Applications in Supply Chains Using Topic Modelling and Text mining: A machine learning Insight 4

Exploring the Thematic Clusters of Artificial Intelligence A...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Olasiuk, Hanna Kumar, Sanjeev Singh, Sudhanshu Nagpal, Gaurav Ganushchak, Tetiana Op Jindal Global University Jindal Global Business School Haryana Sonipat India Galgotias University School Of Business Uttar Pradesh Gautam Buddh Nagar India Birla Institute Of Technology And Science Pilani Department Of Management Pilani Campus Rajasthan India State University Of Trade And Economics Economic And Finance Enterprise Department 19 Kyoto st. Kyiv Ukraine

ISBN: (纸本)9798350384628

Artificial Intelligence (AI) applications are vital for optimising supply chain operations, enhancing efficiency in logistics, inventory management, and demand forecasting. AI's real-time data analysis capabilities empower decision-making and risk management. Despite significant growth in AI supply chain research over the past two decades, a notable gap exists in comprehensive reviews that synthesise key themes. To address this, this study combines bibliometric analysis and structural topic modelling, examining a dataset of 773 articles from Elsevier's Scopus database spanning 2002 to 2022. this study focuses on AI applications in supply chains, emphasising theme identification, publication trends, and prominent journals. Notably, the "international Journal of Production Research"emerges as the top source, with 46 articles, constituting 6.0% of the total articles. the research uncovers four thematic clusters: AI-driven cost optimisation, AI-powered decision-making, mitigating supply chain risks, and AI integration in technological advancements. Future studies in artificial intelligence applied to supply chain management should focus on employing sophisticated algorithms. At the same time, it is essential to consider issues related to privacy and security. Additionally, researchers should tackle environmental obstacles and ensure compliance with regulatory standards. these findings enhance the understanding of AI applications in supply chains, providing insight into key themes and essential keywords. these findings are highly pertinent to various audiences, including researchers, financial institutions, and industry professionals. © 2023 IEEE.

关键词： machine learning

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819911448

the proceedings contain 51 papers. the special focus in this conference is on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning. the topics include: Research on the Application of 3D Modeling Technology in Mechanical Structure Teaching;2D Numerical Model Used to Investigate the Influence of Vegetation on Geomorphological Evolution of Mudflats;design and Experimental Verification of High Functional Density Cubesat System;space Design of Exhibition Hall Based on Virtual Reality;Identification of Expressway Traffic States Based on the Enhanced FCM Algorithm;algorithms Applied in Soft Tissue Deformation Simulation: A Survey;soft Tissue Cutting Based on Position Dynamics;research on Application Technology of Multi-source Fusion of 3D Digital Resources in Power Grid;rapid Identification of Herbaceous Biomass Based on Raman Spectrum Analysis;a Knowledge Representation Method for Hierarchical Diagnosis Decision-Making Under Uncertain Conditions;research on Spam Detection with a Hybrid machine learning Model;research on Weld Defect Object Detection Based on Multi-channel Fusion Convolutional Neural Network;Design and Research of Highway Tunnel Construction Positioning System Based on UWB;research on Cross Domain data Analysis and data mining Technology of Power Grid Digital Benefits;graph Convolutional Neural Networks for Drug Target Affinity Prediction in U-Shaped and Skip-Connection Architectures;SOC Estimation of Lithium Titanate Battery Based on Variable Temperature Equivalent Model;research and Implementation of Hybrid Storage Method for Multi-source Heterogeneous data of Electric Distribution Network;Research on Measuring Method of Ball Indentation Based on MATLAB Image Edge Detection;multi-feature Extraction of Mineral Zone of Tabling through Deep Semantic Segmentation.

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Research data Reusability with Content-Based Recommender System 1

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4th international conference on Deep learning theory and Applications (DeLTA)

作者： Yazdi, M. Amin Politze, Marius Heinrichs, Benedikt Rhein Westfal TH Aachen IT Ctr Seffenter Weg 23 Aachen Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

the use of content-based recommender systems to enable the reusability of research data artifacts has gained significant attention in recent years. this study aims to evaluate the effectiveness of such systems in improving the accessibility and reusability of research data artifacts. the study employs an empirical study to identify content-based recommender systems' strengths and limitations for recommending research data-collections (repositories). the empirical study involves developing and evaluating a prototype content-based recommender system for research data artifacts. the literature review findings reveal that content-based recommender systems have several strengths, including providing personalized recommendations, reducing information overload, and enhancing retrieved artifacts' quality, especially when dealing with cold start problems. the results of the empirical study indicate that the developed prototype content-based recommender system effectively provides relevant recommendations for research data repositories. the evaluation of the system using standard evaluation metrics shows that the system achieves an accuracy of 79% in recommending relevant items. Additionally, the user evaluation of the system confirms the relevancy of recommendations and enhances the accessibility and reusability of research data artifacts. In conclusion, the study provides evidence that content-based recommender systems can effectively enable the reusability of research data artifacts.

关键词： Recommender systems Content-based RDF graph data models Research data management

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Proceedings of the international conference on Computer and Applications, ICCA 2022 - Proceedings

Proceedings of the International Conference on Computer and ...

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4th international conference on Computer and Applications, ICCA 2022

ISBN: (纸本)9781665452946

the proceedings contain 37 papers. the topics discussed include: improve data mining techniques with a high-performance cluster;optimum operation and cost scenarios of a hybrid wind/PV/battery in a radial network using firefly algorithm and surrogate optimization;tibia player profiling and modeling using machine learning;an advanced deep learning medical image recognition and diagnosis of respiratory system viruses;measuring google classroom's usability level from an expert perspective;a novel non-inverting quadratic double switch buck boost converter for charging lithium-ion batteries in electrical vehicles;the influential factors for ICT adoption: survey of teachers in the higher educational institutions in Bauchi state;ranking of SADC countries e-government information services based on citizen satisfaction;and machine learning methods for dysgraphia screening with online handwriting features

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machine learning Assisted Intelligent Reflecting Surface MIMO Communication-Gateway for 6G—A Review 4th

Machine Learning Assisted Intelligent Reflecting Surface MIM...

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Srivastava, Praveen Khera, Shelej School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara144411 India Department of Electronics and Communication Engineering Pranveer Singh Institute of Technology Kanpur208001 India

ISBN: (纸本)9789819914784

Intelligent reflecting surfaces (IRSs) are gaining interest due of their potential coverage and spectral efficiency advantages. However, there are a number of issues that must be resolved in order to materialise these surfaces in practise. A collaborative design for the BS and IRS beamformers is necessary, and the IRS components must be easily modified accordingly. Channel information is crucial to the accuracy of beamformer design deep learning (DL) is a data-driven technique that is essential in overcoming these difficulties. Deep learning is resistant to data flaws and environmental changes because of its faster computation speeds and model-free structure. DL has been demonstrated to be effective at the physical layer for active/passive beamforming, channel estimation, and IRS signal recognition utilising supervised, unsupervised, and reinforcement learning architectures. the methods for creating deep learning-based IRS-assisted wireless systems are summarised in this article. It gives insight to the different deep learning techniques like reinforcement learning, supervised learning, and federated learning which could be used along with IRS. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Beamforming

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Real Time Quality Detection and Automatic recognition of Concrete Structures: Image Processing Algorithm Based on Deep learning 4

Real Time Quality Detection and Automatic Recognition of Con...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Liu, Bing College of Intelligent Manufacturing and Automobil Chongqing Vocational College of Transportation Chongqing402247 China

the safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. this algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set;then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. the maximum detection time is 366ms and the shortest is 213 ms. the successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value i

关键词： Decision making

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819912292

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

4th International Research Conference on Machine Learning, D...

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

ISBN: (纸本)9783031882364

the proceedings contain 9 papers. the special focus in this conference is on machine learning, Deep learning, and Blockchain. the topics include: Development of an Intrusion Detection Model Using a Support Vector machine to Identify Cyber Attacks;vector Quest: A Faster and Better Open-Domain Question-Answering System;deep learning Approach to Indian Sign Language recognition;false data Injection Attack Prediction Using Federated Deep learning Approach;block Chain Ballot: A Secure and Transparent E-Voting System;combination of IoT and Block Chain for Safe data Management;A Prototype for Secure and Efficient Real Estate Transactions Using IPFS and Block Chain Technology.

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Big data Analytics and Knowledge Discovery - 25th international conference, DaWaK 2023, Proceedings

Big Data Analytics and Knowledge Discovery - 25th Internatio...

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Big data Analytics and Knowledge Discovery - 25th international conference, DaWaK 2023, Proceedings

ISBN: (纸本)9783031398308

the proceedings contain 36 papers. the special focus in this conference is on Big data Analytics and Knowledge Discovery. the topics include: Discovery of Contrast Itemset with Statistical Background Between Two Continuous Variables;DBGAN: A data Balancing Generative Adversarial Network for Mobility pattern recognition;bitwise Vertical mining of Minimal Rare patterns;inter-item Time Intervals in Sequential patterns;Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record;DAT@Z21: A Comprehensive Multimodal dataset for Rumor Classification in Microblogs;dealing with data Bias in Classification: Can Generated data Ensure Representation and Fairness?;random Hypergraph Model Preserving Two-Mode Clustering Coefficient;a Non-overlapping Community Detection Approach Based on α -Structural Similarity;preventing Technical Errors in data Lake Analyses with Type theory;improving Stochastic Gradient Descent Initializing with data Summarization;feature Analysis of Regional Behavioral Facilitation Information Based on Source Location and Target People in Disaster;exploring Dialog Act recognition in Open Domain Conversational Agents;uniCausal: Unified Benchmark and Repository for Causal Text mining;accounting for Imputation Uncertainty During Neural Network Training;supervised Hybrid Model for Rumor Classification: A Comparative Study of machine and Deep learning Approaches;attention-Based Counterfactual Explanation for Multivariate Time Series;DRUM: A Real Time Detector for Regime Shifts in data Streams via an Unsupervised, Multivariate Framework;hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching;unified Views for Querying Heterogeneous Multi-model Polystores;EXOS: Explaining Outliers in data Streams;RODD: Robust Outlier Detection in data Cubes;data-driven and On-Demand Conceptual Modeling;FLOWER: Viewing data Flow in ER Diagrams;Supporting Big Healthcare data Management and Analytics: the Cloud-Based QFLS Framework;be

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