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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261-270 订阅

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Disease prediction using machine learning by analysing the symptoms 4th

Disease prediction using machine learning by analysing the s...

引用

4th Annual international conference on Data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Vishwanath, K.R. Vinutha, D.C. Amrutha, A. Shreya, B.S. Rohith, V. Vijay, C.P. VTU India VVCE India

ISBN: (纸本)9781032426853

the incorporation of novel technologies in the biomedical industry is becoming increasingly popular. Key players in this field include Artificial Intelligence (AI), machine learning (ML), and Data Science. AI is a term used to describe computer programs that mimic human intelligence. ML is a subset of AI that can automatically extract raw data. With the vast amount of data available today, it is now possible to analyze medical data without human intervention. However, for this to be effective, the available data needs to be of high quality, meaning it should be clean data. ML has the ability to learn from sample data, thereby improving the reliability and efficiency of computational processes while reducing costs. © 2024 the Author(s).

关键词： Computational efficiency

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Correlation based Feature Selection and Hybrid machine learning Approach for Forecasting Disease Outbreaks 4

Correlation based Feature Selection and Hybrid Machine Learn...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Bhunia, Swayon Abirami, S. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350300093

According to WHO, Dengue is a viral infection transmitted to humans through the bite of infected mosquitoes i.e., Aedes aegypti mosquitoes. there is currently no known cure for dengue or severe dengue. Artificial Intelligence (AI) in the form of machine learning (ML) allows software programs to predict outcomes more correctly without explicit instructions. machine learning algorithms use historical data as input to forecast new output values. the aim of this study is to identify, evaluate and interpret suitable hybrid algorithms/approaches relevant to the application of machine learning in limiting the spread of deadly disease outbreaks. It focuses on finding a way of predicting the next dengue fever local epidemic by comparing the bench mark approaches available until now. For this the study proposes the use of XGBoost coupled with Moving Average Rolling Features in order to learn the long-term temporal relations in the features to get accurate predictions. the dataset used for evaluating the proposed approach contains number of cases in the two locations: San Juan and Iquitos and it includes information on temperature, precipitation, humidity, vegetation, and what time of the year the data was obtained. A correlation analysis-based feature selection along with Moving Average Rolling Features has been used for getting more precise data implemented with ML approach resulting in MS E 11.37 in San Juan and MSE 6.37 in Iquitos. © 2023 IEEE.

关键词： Forecasting

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Exploring Heart Disease Prediction through machine learning Techniques 7

Exploring Heart Disease Prediction through Machine Learning ...

引用

7th international conference on Electronic Information Technology and computer Engineering (EITCE)

作者： Lin, Zhicong Chen, Shujing Chen, Jichang Guangxi Univ Sch Comp Elect & Informat Nanning Guangxi Peoples R China

ISBN: (纸本)9798400708305

Currently, heart disease stands as the most formidable threat to human life. the application of machine learning in scrutinizing data holds the promise of augmenting early detection and prevention strategies for this ailment. Within this study, a suite of six distinctive and classical machine learning models-Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Classifier, and Neural Network-are introduced and meticulously evaluated. these models leverage data gathered from heart patients across four distinct regions, contributing to a comprehensive assessment. the investigative process encompasses five pivotal stages: data collection, preprocessing, K-Means clustering, application of classification algorithms models for heart disease prediction, and rigorous evaluation. At the end of the study, a comprehensive summary of heart disease prediction was presented.

关键词： Heart Disease machine learning Classification Algorithms

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RTA-CSIT 2021 - proceedings of the 4th international conference on Recent Trends and applications in computer Science and Information Technology

RTA-CSIT 2021 - Proceedings of the 4th International Confere...

引用

4th international conference on Recent Trends and applications in computer Science and Information Technology, RTA-CSIT 2021

the proceedings contain 29 papers. the topics discussed include: a state-of-the-art survey of advanced optimization methods in machine learning;a comparison of exploration strategies used in reinforcement learning for building an intelligent tutoring system;adaptive learning and testing in Moodle;deep learning for law enforcement: a survey about three application domains;e-learning and COVID-19 - the Nigerian experience: challenges of teaching technical courses in tertiary institutions;in-memory database testing performance measurements in azure;and inference speed comparison using convolutions in neural networks on various SoC hardware platforms using MicroPython.

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the analysis and interpretation of higher education teachers based on student and teachers feedback 4th

The analysis and interpretation of higher education teachers...

引用

4th Annual international conference on Data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Singh, Vinita Singh, Dharm Raj Sharma, Vijay Kant Singh, Ranjana Mansarovar Global University Sehore Vill Gadia Bilkisganj MP Sehore India Department of Computer Application Jagatpur P G College UP Varanasi India Ghamahapur Gangapur Uttar Pradesh Varanasi India

ISBN: (纸本)9781032426853

the National Assessment and Accreditation Council (NAAC), which rates and accredits higher education institutions (HEIs), as well as the perspectives and perceptions of university/college instructors, are all subjects of our investigation in this paper. We create a mixed method to examine several components of quality indicators in higher education, if any, for estimation and acceptance. the mixed method is used to combine quantitative and qualitative data. the fundamental assumption is that integrating quantitative and qualitative approaches produces a better understanding of the research problem and subject than either method used independently. the design and implementation of the programme assessment frame of reference are used to evaluate quality assurance in higher education institutions. © 2024 the Author(s).

关键词： Quality assurance

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Review of Research Progress on Small Sample learning 4

Review of Research Progress on Small Sample Learning

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4th international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Zhao Zhihao Hong Kong Baptist Univ Comp Sci Hong Kong 999077 Peoples R China

ISBN: (纸本)9781728146898

the neural network model has achieved very good results in the field of computer vision, but the requirements for the amount of data are directly proportional to performance. Data hungry is not only the cause of the high performance of the neural network model, but also a hindrance to its further development. there is not a lot of available data in real-world application scenarios, so how to obtain better performance and more robust models in a small sample environment becomes the goal of less sample learning research. this paper reviews the past small sample learning methods, sorts out its performance and theoretical principles, and points out the existing deficiencies and proposes possible future research directions.

关键词： Few shot learning Meta-learning machine learning computer Vision Data Science

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Research on unsupervised abnormal sound detection based on self-coding model 4

Research on unsupervised abnormal sound detection based on s...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Wang, Heng Zhang, Shuai Liu, Jie Yu, Lei Wang, Chenglong School of Mathematics and Computer Wuhan Polytechnic University Wuhan430048 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

In view of the difficulty of machine abnormal sound detection under the condition that abnormal sound samples are difficult to collect, this paper proposes an unsupervised abnormal sound detection model based on a self-coding model, which effectively improves the accuracy of abnormal sound detection under this condition. In this paper, location coding in Transformer is replaced with relational awareness self-attention to improve the representation capability of location coding. Secondly, the relevance scores in multi-head attention are mixed to enhance the understanding of context in the attention matrix. At the same time, Layer Normalization was replaced with Batch Normalization to speed up model training, and improved Transformer was introduced into the encoders and decoders of self-coding models. Finally, the improved self-coding model is used for unsupervised learning of the machine's normal sound to obtain the potential feature distribution of its normal sound. ToyADMOS and MIMII open data sets are used for experiments. Compared with traditional autoencoders and two improved self-coding models, the AUC score of toycar, Toycar, fan, slider and valve machines increased by 2.1%, 1.97%, 3.06%, 0.34% and 2.99%, respectively. © 2024 SPIE.

关键词： Unsupervised learning

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Comparison of text sentiment analysis based on traditional machine learning and deep learning methods 4

Comparison of text sentiment analysis based on traditional m...

引用

4th international conference on computer Engineering and application, ICCEA 2023

作者： Xu, Lisha Song, Yuan Silc Business School Shanghai University Shanghai China

ISBN: (纸本)9798350347548

Due to the rapid development and widespread use of Internet technology, the Internet has provided a convenient platform for individuals to express their emotions and view comments on microblogs. the collection of user feedback has resulted in a vast amount of data resources, including information on public sentiment. Such data is crucial for the platform's operation and direction, making sentiment analysis of these data sources important for social development. In this paper, we utilize various methods, such as support vector machine (SVM) and recurrent neural network (RNN), including Bayesian, CNN, LSTM, BERT, among others, to analyze the sentiment of online commentary text represented quantitatively as vectors. We evaluate the performance of each model based on accuracy, recall, and F1 score. Our experimental results demonstrate that Bert+CNN outperforms other models in terms of accuracy, recall, and F1 verification. Additionally, we find that LSTM achieves the highest recall, while GRU exhibits the highest F1 score. © 2023 IEEE.

关键词： Sentiment analysis

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application of machine learning in automatic fault detection for passive optical network 4

Application of machine learning in automatic fault detection...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Song, Miao Zhang, Peng College of Computer Science IoT of Chongqing Institute of Engineering Chongqing400056 China China Broadnet Chongqing Corporation Chongqing401147 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

the transformation of all-optical networks is an important task for communication operators to meet the needs of the new diversified comprehensive information services. the service experience of users was affected by quality of the network directly, network operation and maintenance is an important task of operation. this paper focus on the application of machine learning technology in network operation and maintenance quality analysis;automatically determine fault points by training historical network performance indicators and status data. the method proposed can predict the location of faults, improve the quality and efficiency of PON maintenance and reduce daily maintenance workload. through practical, the solution proposed has demonstrated feasibility and can provide reference for the construction of intelligent operation and maintenance platforms for network operators. © 2024 SPIE.

关键词： Passive optical networks

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An Efficient Diabetes Prediction Model using machine learning 4

An Efficient Diabetes Prediction Model using Machine Learnin...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Daniel, Esther Johnson, Jobin Victor, Ujjal Amitya Aditya, G.V. Sibby, Steve Abraham Karunya Institute of Technology & Sciences Department of Computer Science & Engineering Coimbatore India

ISBN: (纸本)9798350300093

Diseases are the ones that affect the continual life of human beings. One such illness which affects millions of people worldwide is diabetes. By examining huge datasets of patients, machine learning models offer a viable answer to the problems associated with diabetes detection. the proper management of diabetes depends on early identification this article aims to assess the effectiveness of diverse machine learning methodologies and strategies utilized for forecasting diabetes, utilizing the PIMA Indian Diabetes dataset as a reference. the dataset contains information about crucial factors such as the patient's age, BMI, blood pressure, and blood sugar levels, which are utilized for the analysis. the models which were taken into account in this research were compared to one another and then LightGBM was chosen as the primary model on the basis of its high accuracy. the Hyper parameters were then modified to produce the best performance possible. the results show that machine learning models can accurately detect diabetes and provide insights into the factors that contribute to the disease. this work provides a foundation for future research on diabetes prediction using machine learning models. © 2023 IEEE.

关键词： Forecasting

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