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共 1778 条记录，以下是381-390 订阅

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Increasing Accuracy of Hand Gesture recognition using Convolutional Neural Network 4

Increasing Accuracy of Hand Gesture Recognition using Convol...

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4th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2022

作者： Park, Gyutae Chandrasegar, V.K. Park, Joonggun Koh, Jinhwan Gyeongsang National University Electronic Engineering Jinju Korea Republic of JD Co. Ltd Jinju Korea Republic of Gyeongsang National University Dept. of Electronics Jinju Korea Republic of

ISBN: (纸本)9781665458184

Human gestures play important roles in the interaction between humans and machines. these human gestures are becoming more important, yet complex gesture input and noise induced by external elements are important problems to solve in order to improve the accuracy of hand gesture recognition methods. Convolutional Neural Networks (CNN) are offered as a technology that can solve this problem in this research. CNN has the advantage of being able to learn image data, and this technology will greatly improve human-machine interaction accuracy. data was extracted using Vivaldi antennas with a frequency bandwidth of 7.4-9.0 GHz and gain characteristics of 8 dB in five sign language operations, and data that went through the preprocessing process was learned through CNN. the classification results of the proposed CNN showed about 90% accuracy. © 2022 IEEE.

关键词： machine learning

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HR Analytics in Predicting Attrition pattern Among Women in Private Education Colleges: Comprehensive Evaluation 4

HR Analytics in Predicting Attrition Pattern Among Women in ...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Nazir, Hena Vij, Renu Tiwari, Yukti Chandigarh University Department of AIT-Management Mohali India

ISBN: (纸本)9798350393248

Businesses may now effectively understand and lower employee turnover with the use of analytics for human resources (HR). With an emphasis on women's attrition trends, this research investigates the topic of private education institutions. Specifically, gender-specific dynamics in the workplace are emphasised in the first portion of the research, which examines the factors that cause women to leave these institutions. this is followed by the development of a comprehensive evaluation system that combines statistical analysis, machine learning algorithms, and historical data to estimate and understand the attrition patterns. Based on the investigation's findings, female employee turnover at private education institutes may be significantly predicted by a variety of criteria, including work-life balance, opportunities for professional progress, and workplace culture. Sophisticated statistical models, including logistic regression and decision tree analysis, are used in the study to estimate attrition trends and show these variables' relative relevance. through the implementation of HR analytics, organisations may enhance their ability to predict these concerns, formulate policies that consider gender parity, and foster a fairer and more supportive workplace culture. Apart from emphasising the importance of data-driven decision-making in retaining talent, this study provides HR professionals and organisational leaders with a valuable framework to enhance employee contentment and reduce attrition rates among female students enrolled in private Universities. © 2023 IEEE.

关键词： Decision trees

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Application of Image recognition for On-board Sailor Behavior Based on Broad learning System 4

Application of Image Recognition for On-board Sailor Behavio...

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4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Liu, Wenting Zuo, Yi Li, Tieshan Philip Chen, C.L. Dalian Maritime University Collaborative Innovation Center of Maritime Big Data & Artificial General Intelligence Navigation College Dalian China University of Electronic Science and Technology of China School of Automatic Engineering Chengdu China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Based on broad learning system (BLS), this paper monitors and identifies the behavior of crew members on ships. the main recognition scenarios include both of on deck and in the cabin, and the main recognition tasks include crew tracking and identification. In this paper, the video data is divided into frames and images are segmented. In the preprocessing of images, the filter is used to enhance the information and reduce the noise of the images. Finally, the recognition model is established by the BLS. In the experiment, we use BP neural network (BPNN) and support vector machine (SVM) as comparisons. the proposed method in this paper has achieved the best results in terms of recognition accuracy and training time. © 2022 IEEE.

关键词： learning systems

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

4th International Conference on Intelligent Systems and Patt...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

ISBN: (纸本)9783031821493

the proceedings contain 77 papers. the special focus in this conference is on Intelligent Systems and pattern recognition. the topics include: Tunisian Sign Language recognition and Translation Using Deep learning;Beyond Spatial: A Wavelet Fusion-Based Deep learning CAD for Skin Cancer Diagnosis;integrating machine learning and Deep learning Techniques for Enhanced Historical Script Classification;efficiency Meets Resilience: Accelerating Object Detection in Embedded Environments through Compressive Sensing;PMoET: Going Wider than Deeper Using the Parallel Mixture of Experts Transformer for 3D Hand Gesture recognition;Enhancing Forest Fire Classification with Feature Selection and machine learning Based on PRISMA Hyperspectral data;semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using SegFormer;handcrafted and Deep Features for Synthetic data Generation in Offline Handwritten Signature Verification;refining U-Net Architecture through Genetic Algorithms for Improved Skin Lesion Image Segmentation;An RNN-LSTM Approach for Algerian Accent Identification;ensemble Methods for Predicting Cyanobacteria’s Potential Toxicity in Water Dams;cross-Organ Investigation of Tumor Histological Features Similarities Using Transfer learning: A Case Study on Breast and Colorectal Tumors;behavior-Based Insider threat Detection Using a Deep Neural Network;holistic Ontology Alignment Using Ontologies Embeddings;learning Different Separations in Branch and Cut: A Survey;Cancer Classification in Breast Imaging via Enhanced CNN Deep learning Architecture;X-Ray Insights: A Siamese with CNN and Spatial Attention Network for Innovative Person Identification;hybrid Wiener Filter and Sharpening Filter for Image De-blurring;feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection;artificial Intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review;automated Breast Cancer Detection: A

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Plant Stress Classification with Deep learning Models Using STFT-Based 2D Representation of 6-Channel 500 kHz Plant Sound 4th

Plant Stress Classification with Deep Learning Models Using ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Baek, Eu-Tteum Yoon, Hye-Seong AI&Bigdata Honam University Gwangju Korea Republic of

ISBN: (纸本)9789819787043

Plant stress is a physiological response triggered by various environmental factors, such as extreme weather conditions, diseases, or mechanical disturbances. Monitoring and classifying plant stress are crucial for assessing the overall health of vegetation and implementing timely interventions to ensure sustainable agricultural practices. However, estimating the magnitude of stress proves challenging due to a lack of comprehensive datasets and limited technological capabilities. therefore, in this study, we present a plant classification method leveraging a 2D representation based on Short-Time Fourier Transform of 6-channel 500 kHz sounds, recorded in scenarios such as tree cutting, drought, and other common environmental conditions. the experimental results demonstrate that our proposed method effectively predicts stress factors;however, due to a lack of a comprehensive dataset and limitations in data quality, the accuracy is slightly lower. these endeavors are expected to offer significant assistance in the analysis and judgment of plant stress. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning plant stress STFT

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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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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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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 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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