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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"

共 4173 条记录，以下是161-170 订阅

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Opening the Black Box: Interpretable machine learning Reveals the Relationship between Lexical Diversity and Writing Quality 4

Opening the Black Box: Interpretable Machine Learning Reveal...

引用

4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Li, Minzi Lin, Zhiqing Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies Guangzhou China School of English Studies Shanghai International Studies University Shanghai China

ISBN: (纸本)9798350343113

machine learning serves as the core component of artificial intelligence (AI), however, there is scant interdisciplinary research between AI and applied linguistics. Very few studies touched upon the aspect of how to leverage interpretable machine learning to solve the problem in language learning, teaching, and assessment. this study aims to lift the veil and fill this gap by employing interpretable machine-learning approaches to explore the relationship between lexical diversity and writing quality. the findings contributed to providing a finer-grained understanding of the CET-4 writing construct and its rating scale. Furthermore, it may lend empirical evidence for the Dynamic Systematic theory (DST) in language acquisition, verify the validity of automatic essay scoring, and advance teachers' language assessment literacy in the long run. © 2023 IEEE.

关键词： machine learning

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Incorporating Large Language Models into Named Entity Recognition: Opportunities and Challenges 4

Incorporating Large Language Models into Named Entity Recogn...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Luo, Zhizhao Yang, Yuting Qi, Rui Fang, Zhi Guo, Yikai Wang, Youchen Beijing Institute of Computer Technology and Application Beijing China China Life Property & Casualty Insurance Company Limited Changsha China

ISBN: (纸本)9798350373233

Named entity recognition (NER) is a critical task in natural language processing, involving the identification and classification of entities in text. Traditional approaches to NER, such as rule-based methods and supervised machine learning, faced limitations in adapting to diverse domains and languages due to the need for large labeled datasets. Large language models (LLMs) such as GPT3, which undergo vast pretraining, demonstrate the promising ability to substantially improve NER beyond supervised baselines as evidenced by over a 100% increase in F1 score on domain-specific evaluation *** survey comprehensively examines the pivotal roles and methodologies of LLMs in NER. It provides a thorough analysis of two key techniques: in-context learning and fine-tuning. In-context learning enables quick NER task resolution without major model changes, while fine-tuning offers significant improvements, especially in domain-specific applications. the survey also covers essential resources, including NER corpora, LLM types, and methods of fine-tuning. It addresses challenges related to computational resources and model size. the survey concludes by outlining potential future research directions, emphasizing LLMs' contributions to advancing NER technologies. © 2023 IEEE.

关键词： Supervised learning

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Smart Energy Optimization Harnessing IoT and Advanced learning Techniques 4

Smart Energy Optimization Harnessing IoT and Advanced Learni...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Liyon Raj, S. Malleeshwaran, T. Prasanna, T. Alfred Daniel, J. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

the paper gives an extensive investigation of the research findings on the optimization of energy management in smart microgrids. the study specifically focuses on integrating renewable energy sources with the Internet of things. Smart microgrids have become an essential option due to the increasing global energy demand and environmental problems arising from the use of fossil fuels. these grids assure better efficiency in renewable energy usage energy storage, and improved user interaction using IoT-based technologies. this research investigates the application of machine learning algorithms to predict energy consumption patterns, electricity prices, and optimal energy storage management strategies. the main idea of the proposed mathematical model in this paper is to minimize the cost of energy procurement under the condition of a balanced energy supply/demand based on the optimization problem. this model is simulated with several scenarios using the linear programming tool of MATLAB software and confirms high cost savings and system efficiency. this study also investigates how EVs and BESS in the grid exert better optimization of smart energy systems. © 2024 IEEE.

关键词： Procurement Renewable energy sources Costs machine learning algorithms Mathematical models Smart grids MATLAB Energy management Optimization Energy storage

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Saliency detection method for Panoramic Images based on GCN-ELM 4

Saliency detection method for Panoramic Images based on GCN-...

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

作者： An, Nan Yu, Haiyang Zhang, Ripei Hu, Xiaojuan Li, Yanfeng School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In the field of saliency detection for panoramic images, traditional equirectangular and cube projection methods in panoramic image saliency detection often face issues like distortion and discontinuities, impacting detection accuracy. this study introduces an innovative image resampling technique and a GCN-ELM joint model. By evenly distributing spherical pixels onto a 2D plane, the method reduces pixel redundancy from equirectangular projection. Experimental results show that this approach significantly enhances saliency detection performance compared to existing methods. © 2025 SPIE.

关键词： Adversarial machine learning

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Assessment of the Applicability of machine learning Methods for the Detection of Pathology of Internal Human Organs based on the Results of Bioelectrography 4

Assessment of the Applicability of Machine Learning Methods ...

引用

4th international conference on Neural Networks and Neurotechnologies, NeuroNT 2023

作者： Shichkina, Yulia A. Fatkieva, Roza R. Sychev, Aleksandr Y. Prasad, M.S. Verma, Noida S. Saint Petersburg Electrotechnical University 'Leti' Department of Computer Science and Engineering St.Petersburg Russia Amity Institute of Space Science and Technology Amity University Noida India

ISBN: (纸本)9798350301526

the possibility of using the bioelectrography method for detecting pathology of internal organs is considered. Sets of data sets for machine learning are formed on the basis of ultrasonic diagnostics readings. machine learning methods were analyzed with an assessment of the feasibility of their use. the results of detection of pathology of internal organs with the use of classifiers are presented: Decision tree;KNN;Logistic regression;Random forest;SVC;XGBoost. © 2023 IEEE.

关键词： Pathology

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A Grading Model for Jasmine Flowers Using Deep learning and Quantum machine learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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

作者： Pratiksha Cauvery, K.P. thrisha, R. Harshitha, T. Panigrahi, Suman thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

the flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. the already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. this paper handles these limitations by introducing automation in the grading process using Deep learning and Quantum machine learning. the conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. the deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. this DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. the findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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Surface Anomaly Detection Using machine learning Technique 4

Surface Anomaly Detection Using Machine Learning Technique

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Weiqi, Xiao Chee Hooi, Steve Teoh Sindiramutty, Siva Raja A L Asirvatham, David A L Kumar, Devender Verma, Sahil School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Punjab Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Department of Computer Science and Engineering PunjabJhanjeri Mohali140307 India

ISBN: (纸本)9798350353266

the world is developing rapidly, and the industrial field is gradually reducing labor work and moving to a state of smart work. machine intelligence operations can often help humans reduce stress in many aspects. Here, we will use related technologies of machine learning. At present, convolutional neural network algorithms are mainly used to apply image-related application scenarios. On this basis, using YOLO technology can achieve faster and more accurate target detection and improve accuracy of detection. through this improvement, production efficiency can be greatly improved and the stability of the production rhythm can be enhanced. the stability of production rhythm is an extremely important part. It can minimize unnecessary losses and expenditures of enterprises and at the same time obtain greater profits. the improvement of detection accuracy can better protect the health, safety and rights of consumers from a social perspective. © 2024 IEEE.

关键词： Adversarial machine learning

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An AI-Powered application to Enhance Cognitive Health and Provide Caregiver Support for Dementia Patients 4

An AI-Powered Application to Enhance Cognitive Health and Pr...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Lulle, Kirti Agrawal, Pooja Amrutwar, Mansi Khiani, Simran G. H. Raisoni College of Engineering and Management Department of Computer Engineering Pune India

ISBN: (纸本)9798331529635

Dementia, a slowly progressing nerve disorder, has millions of sufferers around the world. this paper proposes an AI-powered app to promote cognitive health and help to dementia patients' caregivers. the application contains a machine learning model for early dementia detection, preventive advice, location tracking, memory workout, digital brain games, and cognitive rehabilitation tools. Created with Flutter for universal device support and Firebase for backend help, this tool will help enhance the quality of life for patients and lessen the burden on caregivers. A new evaluation methodology and comparative analysis prove its effectiveness. © 2024 IEEE.

关键词： Diagnosis

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Mastering Beaglebone AI & AI-64 on its Multifaceted applications 4

Mastering Beaglebone AI & AI-64 on its Multifaceted Applicat...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Sivakumar, P. Premkumar, T. Ani Sowntharya, A. Navin, S. Pavithra, T. PSG College of Technology Department of EEE Coimbatore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

the rapid growth of embedded systems has led to the integration of artificial intelligence capabilities at the edge of computer devices. the Beagle-Bone AI and AI64 platform is a potent development board intended to make AI and machine learning activities easier in embedded environments. this study investigates the device's diverse possibilities. the paper provides a comprehensive guide about configuring the Beagle-Bone AI and AI64, offering insights into hardware specifications, software setup, and communication interfaces. Beyond technical configuration, this paper delves into a wide spectrum of applications and beyond. through case studies and practical implementations, this paper demonstrates the platform's utility as a catalyst for innovation and research at the intersection of edge computing and artificial intelligence. © 2024 IEEE.

关键词： application programs

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Insights into Diabetes Prediction: A Multi-Algorithm machine learning Analysis 4

Insights into Diabetes Prediction: A Multi-Algorithm Machine...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Usha, V. Rajalakshmi, N.R. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350300888

Diabetes mellitus is a long-term metabolic disorder marked by sustained high levels of glucose in the blood. It is brought on by a confluence of genetic and environmental variables that affect insulin action, production, or both. If Diabetes is identified at its earliest stages, it can be easier to implement preventative action and slow the progression of the illness. the higher probability for Diabetus is exacerbated due to the excessive sweetness, fibre, and fatty substances of diet in modern culture. Human computerised monitoring aids with initial illness diagnosis by clinicians. For the present work, accuracy, precision, and sensitivity metrics have been employed for assessing the success rates of several machine learning methods. these methods encompass the application of Support vector machines, Artificial Neural Networks, Neural networks with Deep learning, and Convolutional neural networks. the suggested technique might be applied to preliminary diabetes diagnosis and worsening prediction. Although there are various classification and regression methods obtainable for predicting the result of diabetes using clinical information, it is unknown whether or not projected outcomes will have a greater accuracy. Python was used for executing the functionality via the Navigator edition of the Anaconda Integrated Development Environment pycharm editor. According to the implementation findings, the RandomForestClassifier maintained a 91% accuracy rate. © 2023 IEEE.

关键词： Support vector machines

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