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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Facilitating Enterprise Model Classification via Embedding Symbolic Knowledge into Neural Network Models 1

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

作者： Smirnov, Alexander Shilov, Nikolay Ponomarev, Andrew SPC RAS 14 Line St Petersburg 199178 Russia

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

In many real life applications, the volume of available data is insufficient for training deep neural networks. One of the approaches to overcome this obstacle is to introduce symbolic knowledge to assist machine-learning models based on neural networks. In this paper, the problem of enterprise model classification by neural networks is considered to study the potential of the approach mentioned above. A number of experiments are conducted to analyze what level of accuracy can be achieved, how much training data is required and how long the training process takes, when the neural network-based model is trained without symbolic knowledge vs. when different architectures of embedding symbolic knowledge into neural networks are used.

关键词： Neuro-symbolic artificial intelligence Deep neural networks machine learning Enterprise model classification

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machine learning and Deep learning Models for Early Detection of Heart Disease 4

Machine Learning and Deep Learning Models for Early Detectio...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Singh, Gurpreet Guleria, Kalpna Sharma, Shagun Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350306118

the prevalence of cardiovascular disease is a significant worldwide health concern. health concern and is responsible for an increased fatality rate. Effective treatment and prevention of heart disease depend on early detection and precise prediction. this work proposes various machine learning (ML) and deep learning (DL) models for the prediction of heart disease based on a large dataset of patient traits and medical indicators defining the presence of heart disease. this work uses various classifiers namely Naive Bayes, Multilayer Perceptron (MLP), decision tree, and Logistic Regression for developing an effective heart disease prediction model. these models have been fed with a dataset containing 1025 tuples and 11 attributes such as sex, age, thalach, restecg etc. the proposed model has been developed with a train and test ratio of 80:20, the aforementioned phenomenon has led to the emergence of certain measures, including precision, recall, accuracy, and Fl-measure, as indicators of the achieved results. the results have depicted that out of all the ML and DL models, the Decision Tree exhibited the highest accuracy with a rate of 98.04%. However, MLP has been also identified as the second outperforming model by showing 95.51 % for binary heart disease classification. Furthermore, the performance of Naive Bayes and logistic regression have shown an accuracy of 83.12%, and 84.48%, respectively. © 2023 IEEE.

关键词： Decision trees

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Web-based Safety Eyewear Detection System in Workplace Using machine learning 9

Web-based Safety Eyewear Detection System in Workplace Using...

引用

9th international conference on computer and Communication Engineering, ICCCE 2023

作者： Zulkurnain, Nurul Fariza Ahmad, Yasser Asrul Nazri, Najla Athirah Mohd International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Artificial Intelligence (AI) and computer vision have provided various ways to solve problems in our daily life. In this paper, a web-based safety eyewear detection system to detect the presence of safety eyewear in input images or video streams is developed using OpenCV, TensorFlow/Keras, and deep learning. this detection system is necessary to be deployed at risky workplaces such as construction sites to help reduce the risks of accidents and facilitate supervisors to detect workers who do not adhere to the regulations of wearing safety eyewear before entering a construction site. this paper uses a combination of transfer learning techniques using a pre-trained MobileNet architecture and Single Shot Detection framework to build a fast and efficient deep learning-based method for safety eyewear detection. With the help of Streamlit, the model is deployed into a web application to provide a user-friendly interface for the users. this web application can detect faces instantly by applying the safety eyewear classifier efficiently and quickly. Experimental results on the dataset collected demonstrated the superior performance of the proposed model with 98% accuracy. © 2023 IEEE.

关键词： computer vision

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Biomedical Named Entity Recognition Based on Reinforcement learning Label Correction 4

Biomedical Named Entity Recognition Based on Reinforcement L...

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4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Hao, Ziqiang Tian, Fang Tang, Ying Zhang, Yan Zhan, Weida Tang, Yanfeng College of Electronics and Information Engineering Changchun University of Science and Technology Changchun130000 China

ISBN: (纸本)9781510674448

Biomedical named entity recognition is of great significance in the field of natural language *** learning approaches are mainly used at present, and the BERT-BiLSTM-CRF model is one of them. Although the BiLSTM structure in this model can capture bi-directional long-distance dependencies in sentences, there is the problem that the model performance can easily reach the bottleneck by simply adjusting the hyperparameters or changing the network structure, and the quality of its recognition results can hardly be further improved. To address the above problems, this paper adds the label correction process based on deep reinforcement learning to the BERT-BiLSTM-CRF model to form the BERT-BiLSTM-CRF-DRL *** intelligent body Agent based on deep reinforcement learning is used as a label corrector, and the label correction threshold is set, so that the uncertainty in the annotation results of the BERT-BiLSTM-CRF model is greater than the label correction threshold. the labels with uncertainty greater than this threshold in the results are processed for correction. the experimental results show that the F1 values on the datasets BC4CHEMD, BC5CR-CHEM, and NCBI-DISEASE reach 91.07%, 94.02%, and 89.56%, which are all improved compared with the existing advanced biomedical named entity recognition models. © 2024 SPIE.

关键词： Reinforcement learning

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Revolutionising ADHD diagnosis: Deep learning in 3D medical imaging 4

Revolutionising ADHD diagnosis: Deep learning in 3D medical ...

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4th international conference on Signal Processing and machine learning, CONF-SPML 2024

作者： Luo, Siting Meng, Xianghui Niu, Xinran Kong, Hanyue Department of Computer Science and Technology Xidian University Xian710126 China Department of Grainger Engineering University of Illinois Urbana-Champaign Urbana61802 United States Department of Internet of Things Hohai University Changzhou213200 China Department of Artificial Intelligence Nanjing Agricultural University Nanjing210095 China

ISBN: (纸本)9781510674721

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder, necessitating accurate diagnostic methods. Our research introduces a deep learning approach using Convolutional Neural Network (CNN) integrated with Bidirectional Long Short-Term Memory (BiLSTM) networks to analyse resting-state functional Magnetic Resonance Imaging (fMRI). this novel method captures intricate spatiotemporal patterns in brain activity, offering insights into ADHD characteristics that surpass traditional diagnostic techniques. Employing the ADHD-200 Sample, our study presents a comparative analysis demonstrating the enhanced efficacy of deep learning in ADHD diagnosis. the integration of CNN with BiLSTM allows for comprehensive analysis of fMRI data, revealing complex neural dynamics associated with ADHD. this approach marks a significant advancement in neuroimaging-based clinical neuroscience, potentially transforming ADHD diagnosis by providing a more objective, accurate, and efficient diagnostic tool. Our findings highlight the potential of deep learning technologies in medical imaging and diagnosis, opening new avenues for research and application in clinical neuroscience. the study underscores the importance of integrating advanced computational methods with clinical expertise to improve diagnostic accuracy and patient care in ADHD and potentially other neurodevelopmental disorders. © 2024 SPIE.

关键词： Magnetic resonance imaging

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10th international Workshop on machine learning and Data Mining for Sports Analytics, MLSA 2023

10th International Workshop on Machine Learning and Data Min...

引用

10th international Workshop on machine learning and Data Mining for Sports Analytics, MLSA 2023

ISBN: (纸本)9783031538322

the proceedings contain 16 papers. the special focus in this conference is on machine learning and Data Mining for Sports Analytics. the topics include: Field Depth Matters: Comparing the Valuation of Passes in Football;ETSY: A Rule-Based Approach to Event and Tracking Data SYnchronization;exploring Table Tennis Analytics: Domination, Expected Score and Shot Diversity;Boat Speed Prediction in SailGP;identifying Player Roles in Ice Hockey;pass Receiver and Outcome Prediction in Soccer Using Temporal Graph Networks;Momentum Matters: Investigating High-Pressure Situations in the NBA through Scoring Probability;preface;Are Sports Awards About Sports? Using AI to Find the Answer;sports Data Analytics: An Art and a Science;the Big three: A Practical Framework for Designing Decision Support Systems in Sports and an application for Basketball;what Data Should Be Collected for a Good Handball Expected Goal model?;elite Rugby League Players’ Signature Movement Patterns and Position Prediction;performance Measurement 2.0: Towards a Data-Driven Cyclist Specialization Evaluation.

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28th international conference on computer-Aided Architectural Design Research in Asia, CAADRIA 2023

28th International Conference on Computer-Aided Architectura...

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28th international conference on computer-Aided Architectural Design Research in Asia, CAADRIA 2023

ISBN: (纸本)9789887891802

the proceedings contain 150 papers. the special focus in this conference is on computer-Aided Architectural Design Research in Asia. the topics include: COMMUNICATION WIth DETROIT: machine learning in Open Source Community Housing Design;PREDICTION AND OPTIMISATION OF thE TYPICAL AIRPORT TERMINAL CORRIDOR FAÇADE SHADING USING INTEGRATED machine learning AND EVOLUTIONARY ALGORIthMS;RESEARCH ON ARCHITECTURAL SKETCH TO SCHEME IMAGE BASED ON CONTEXT ENCODER;‘;TEXT-TO-GARDEN: Generating Traditional Chinese Garden Design From Text-Descriptions at Scale With Multimodal machine learning;INTEGRATION OF EEG AND DEEP learning ON DESIGN DECISIONMAKING: A Data-Driven Study of Perception in Immersive Virtual Architectural Environments;INCORPORATING PHYSICAL EXPERIMENTATION INTO CREATIVE DL-DRIVEN DESIGN SPACE EXPLORATION;AN IMAGE-BASED machine learning MEthOD FOR URBAN FEATURES PREDICTION WIth thREE-DIMENSIONAL BUILDING INFORMATION;AN INTEGRATED application OF BUILDING INFORMATION MODELING, computer-AIDED MANUFACTURING, machine learning, AND thE INTERNET OF thINGS: A Hybrid Stadium as a Case Study;CROSS-DISCIPLINARY SEMANTIC BUILDING FINGERPRINTS: Knowledge Graphs To Store Topological Building Information Derived From Semantic Building Models (Bim) To Apply Methods Of Artificial Intelligence (Ai) throughout the Life Cycle Of Buildings;SYNthESIZING STYLE-SIMILAR RESIDENTIAL FACADE FROM SEMANTIC LABELING ACCORDING TO thE USER-PROVIDED EXAMPLE;VARIABILITY IN machine learning FOR MULTI-CRITERIA PERFORMANCE ANALYSIS;SKYWAYS VERSUS SIDEWALKS: Evaluating the Perceptual Qualities and Environmental Features of Elevated Pedestrian Systems in Hong Kong;BESPOKE 3D PRINTED CHAIR: Research On the Digital Design And Fabrication Method Of Multi-body Pose Fusion;TRADITIONAL CHINESE VILLAGE MORPHOLOGICAL FEATURE EXTRACTION AND CLUSTER ANALYSIS BASED ON MULTI-SOURCE DATA AND machine learning;GESTURE MODELING: In Between Nature and Control;PAINTERLY EXPANSION.

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28th international conference on computer-Aided Architectural Design Research in Asia, CAADRIA 2023

28th International Conference on Computer-Aided Architectura...

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28th international conference on computer-Aided Architectural Design Research in Asia, CAADRIA 2023

ISBN: (纸本)9789887891796

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Sentiment Analysis application in E-Commerce: Current Models and Future Directions 12th

Sentiment Analysis Application in E-Commerce: Current Models...

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12th international conference on Electronic Government and the Information Systems Perspective (EGOVIS)

作者： Huang, Huang Asemi, Adeleh Mustafa, Mumtaz Begum Malaya Univ Kuala Lumpur Malaysia

ISBN: (纸本)9783031398407;9783031398414

Sentiment analysis (SA), which is also known as opinion mining, is an increasingly popular practical application of Natural Language Processing (NLP). SA is especially useful in e-commerce fields, where comments and reviews often contain a wealth of valuable business information that has great research value. this study aims to investigate three related aspects of SA in e-commerce: the methods used to address the SA problem in this domain, the most commonly used e-commerce platforms where researchers get data from, and the future direction of research in this area. To achieve this goal, we reviewed 15 papers that covered many machine learning models and deep learning models. In the results and discussion section, we suggest several future directions that can improve the current SA models in this review. By addressing the limitations of existing SA models and exploring new approaches, we believe that future research in this area will lead to more accurate and effective sentiment analysis tools that can benefit both businesses and consumers.

关键词： Sentiment analysis (SA) e-commerce Natural language processing machine learning Deep learning Opinion mining

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Predictive Wi-Fi Network Selection: Enhancing User Experience through a machine learning-based Approach 4

Predictive Wi-Fi Network Selection: Enhancing User Experienc...

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Akash, V. Manoj Howale, S. Gupta, Ajit J. Kayarvizhy, N. Saranappa, Raj Kumar Department of Machine Learning B.M.S. College of Engineering Bangalore India Department of Computer Science B.M.S. College of Engineering Bangalore India Department of Electrical and Electronics B.M.S. College of Engineering Bangalore India SRIB/SAMSUNG Research Institute India Bangalore India

ISBN: (纸本)9798350305258

With the increasing use of Wi-Fi in our devices at home and in offices, etc. it's imperative to connect to an AP which provides seamless and smooth performance. this research paper addresses the need for an efficient solution to predict and select the most suitable Access Point, thus enhancing the user's quality of application usage. We have worked on a machine learning model which takes care of different parameters such as signal strength, bandwidth, distance, frequency, traffic parameters and previous connections with Access Point and predicts the best Access Point user should connect to. the methodology described in this paper has been developed based on the Google scoring algorithm, which we referred as a foundation for our approach. the integration of the Google scoring algorithm empowers our model to further optimize Access Point selection, ensuring an enhanced user experience while seamlessly switching between networks, thus solving the Sticky band or Sticky Client problem in Wi-Fi. the research which has been proposed by this paper could potentially revolutionize the way transitions are made between networks to make the process smooth and latency free. © 2023 IEEE.

关键词： Best Access Point machine learning WiFi

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