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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是61-70 订阅

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Visualizing Extracted Feature by Deep learning in P300 Discrimination Task 7

Visualizing Extracted Feature by Deep Learning in P300 Discr...

引用

7th international conference of soft computing and Pattern Recognition (SoCPaR)

作者： Kawasaki, Koki Yoshikawa, Tomohiro Furuhashi, Takeshi Nagoya Univ Grad Sch Engn Nagoya Aichi 4648601 Japan

ISBN: (纸本)9781467393607

P300 speller is a system that allows users to input words using electroencephalogram (EEG). A component called P300 is used to interpret the EEG in P300 speller. In order to make a high performance P300 speller, it is essential to discriminate P300 from nonP300 precisely and automatically. In this study, deep learning (DL) is used to discriminate P300. the experimental result shows that DL was possible to discriminate P300 in EEG data, especially in the higher level layer. Furthermore, this study refers to the extracted feature by DL. We can see that DL learns feature from the waveforms correctly to discriminate P300 from others.

关键词： Electroencephalography

来源：评论

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GUI based Heart using Disease Classification using machine learning 7

GUI based Heart using Disease Classification using Machine L...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Revathy, G. Priya, P. Muruga Senthilnathan, K. Mythili, P. Haridharani, S.V. School of Computing SASTRA Deemed University Thanjavur India Department of CSE Surya Engineering College Erode India Department of EEE Surya Engineering College Erode India

ISBN: (纸本)9781665464086

Heart disease is a serious health problem that has afflicted a lot of people all over the world. In our work, we have proposed a GUI-based machine learning-basedapproach that is efficient and accurate for identifying heart illness. An correct diagnosis and the right treatment can save several lives. this testing method not only costs a lot of money, but the results also fail to correctly identify HD patients. © 2023 IEEE.

关键词： Intelligent systems

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SBL-PM-M: A system for partial memory learning

引用

7th international conference on Artificial Intelligence and soft computing

作者： Grudzinski, K Acad Bydgoszcz Dept Phys PL-85072 Gydgoszcz Poland

ISBN: (纸本)3540221239

Partial Memory learning (PML) is a machine learning paradigm in which only a subset of cases generated from an original training set is used for classification. this paper concerns a new method for partial memory learning. the SBL-PM-M method is a completely new model. We evaluate the performance of the new algorithm on several real-world datasets and compare it to a few other PML systems and to the base classifier.

关键词： learning systems

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Predictive Analysis for Retail Shops using machine learning for Maximizing Revenue 7

Predictive Analysis for Retail Shops using Machine Learning ...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Malik, Aman Dargar, Gauri Sharma, Arti Pandey, Priyambada KIET Group of Institutions Computer Science Ghaziabad India

ISBN: (纸本)9798350397253

the issue of difficulties in controlling the inventory of small retailers with limited space and limited capital to buy just a few merchandises has just been led due to the continuous increase in the variety of products. In order to successfully manage the inventory of small stores, this article intends to investigate several machine learning algorithms for forecasting future product demand. the quality of the forecasts that are offered has a significant impact on the effectiveness of procedures not only in retail locations but also throughout the supply chain they are a part of Nowadays, there is a large amount of information that can be cleansed and used for forecasting by utilizing the right machine learning algorithms. this prediction system will help small retail shop owners in maximizing their profit margins. Along with sales forecasting, this study intends to perform customer segmentation to make effective decisions in order to generate a good amount of revenue. © 2023 IEEE.

关键词： machine learning

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Efficient Fetal Health Monitoring and Classification with machine learning 7

Efficient Fetal Health Monitoring and Classification with Ma...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Singh, Kabir Shyry, Prayla Franklin, Ramya G Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

the goal of the research on fetal health categorization using machine learning is to create a model that can precisely predict the condition of a fetus during pregnancy. this is crucial because prompt action following early identification of fetal health issues might enhance the pregnancy's outcome. this research study suggests classifying fetal health status using machine learning approaches based on ultrasound images and other clinical parameters. To train and test our model, we will gather a sizable dataset of ultrasound images and clinical characteristics from pregnant women. To categorize the state of fetal health, the proposed model will employ machine learning algorithms and image processing techniques. Our approach should categorize fetal health status with a high degree of accuracy, and it will help obstetricians and gynecologists provide better treatment for pregnant patients. © 2023 IEEE.

关键词： Genetic programming

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Bird Call Recognition using Acoustic based Feature Selection approach in machine learning 7

Bird Call Recognition using Acoustic based Feature Selection...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mishra, Shivam Ghatak, Rumpa Singh, Vishal Kumar Singh, Shivam Kumar Mahato, Anisha Information Technology Techno International New Town Kolkata India

ISBN: (纸本)9798350304268

the recognition of bird calls has been a challenging task in the field of bioacoustics. With the advancement of machine learning algorithms, automatic bird call recognition has become an active research area. In this paper, acoustic feature selection is used which involves the extraction of relevant features from audio recordings of bird calls. then different classification algorithms are explored for the recognition of the bird calls. Evaluation of the approach is done on a dataset consisting of recordings from multiple bird species and compared it with other state-of-the-art machine learning algorithms. this research has the potential to contribute to the conservation of bird species and their habitats by enabling the efficient monitoring of bird populations. the data is classified into four different classes (namely Astfly, Bulori, Warvir and Woothr). All of which is found in the depths of Amazon Rainforest. © 2023 IEEE.

关键词： acoustic features bird call recognition machine learning

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machine learning based Ideal Job Role Fit and Career Recommendation System 7

Machine Learning based Ideal Job Role Fit and Career Recomme...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sunil Kumar Aithal, S. Santhosh, S. Ashwin Shenoy, M. Sandeep Kumar, S. Nitte India Nitte India Nitte India

ISBN: (纸本)9781665464086

With the advancement of technologies and in order to pace with the digital era, robust automated job recommendation systems need to be implemented which overcome the limitations of traditional systems and manual approaches. Identifying field specific jobs with personalized systems by incorporating awareness and information rich which can address many domains. For fresh graduates with a lack of experience and lack of awareness who need to look for a huge number of companies, the proposed system helps them to easily search for their respective job efficiently. In order to save time while searching and applying for jobs and also to avoid misleading job recommendations, it is highly valuable to recommend the most suitable education qualification specific to all the job finders. In the proposed methodology the requirements of the fresh graduates will be considered such as demographic information of the company, organization type, and job preferences using machine learning techniques. the candidate's background information, interests based skills and abilities and profile information will be considered by the automated recommendation system. © 2023 IEEE.

关键词： Recommender systems

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Management of Tourism Resources and Demand based on Neural Networks 7

Management of Tourism Resources and Demand based on Neural N...

引用

7th international conference Intelligent Computation Technology Automation (ICICTA)

作者： Xue-Bo Lin Shi-ting Qinhuangdao Inst Technol Qinhuangdao 066100 Hebei Peoples R China

ISBN: (纸本)9781479966363

Accurate prediction for inbound tourism demand is important for development and implement of Chinese inbound tourism strategy. It has positive significance. BP neural network as a common traditional machine learning methods is widely used in travel demand forecasting model. However, BP neural network suffers from several drawbacks, such as overfitting, difficulties in setting parameters and local minima problem. Hence the performance of BP neural network is very unstable in practical applications. this paper combines an advanced machine learning paradigm named ensemble learning with BP neural network to build neural network ensemble for tourism demand prediction. the state-of-art methods for predictive modeling used in tourism research include traditional statistical methods, soft computing methods, and artificial intelligence methods. Note that artificial intelligence methods, which were introduced to tourism research in 1990s, have greatly improved the predictive accuracy of modeling methods. this study conducts tourism demand modeling of three important tourist source countries of US, Britain and Australia. the results show that, neural network ensemble significantly improves the predictive accuracy over traditional statistical methods and traditional machine learning methods including single BP neural network. Such method provides a better choice for more accurate predictive modeling for tourism demand of China.

关键词： Inbound Tourism Development Strategy Neural Network Ensemble machine learning Tourism Demand Prediction

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Strategies for Determining Effective Step Size of the Backpropagation Algorithm for On-Line learning 7

Strategies for Determining Effective Step Size of the Backpr...

引用

7th international conference of soft computing and Pattern Recognition (SoCPaR)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Pei, Yan Univ Aizu Dept Comp & Informat Syst Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781467393607

In this paper, we investigate proper strategies for determining the step size of the backpropagation (BP) algorithm for on-line learning. It is known that for off-line learning, the step size can be determined adaptively during learning. For on-line learning, since the same data may never appear again, we cannot use the same strategy proposed for off-line learning. If we do not update the neural network with a proper step size for on-line learning, the performance of the network may not be improved steadily. Here, we investigate four strategies for updating the step size. they are 1) constant, 2) random, 3) linearly decreasing, and 4) inversely proportional, respectively. the first strategy uses a constant step size during learning, the second strategy uses a random step size, the third strategy decreases the step size linearly, and the fourth strategy updates the step size inversely proportional to time. Experimental results show that, the third and the fourth strategies are more effective. In addition, compared with the third strategy, the fourth one is more stable, and usually can improve the performance steadily.

关键词： Decision Boundary Making Multilayer Perceptron Backpropagation Algorithm On-Line learning

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Enhancing Cardiovascular Disease Prediction Using Ensemble learning 7

Enhancing Cardiovascular Disease Prediction Using Ensemble L...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ambade, Sharwari Chikmurge, Diptee School of Computer Engineering Mit Academy of Engineering Alandi India

ISBN: (纸本)9798350304268

the proposed ensemble learning framework integrates diverse machine learning algorithms. Each base model is trained on a diverse set of features derived from comprehensive patient data. To evaluate the ensemble model's performance, a large-scale dataset comprising anonymized electronic health records from a diverse patient population is employed. the dataset includes longitudinal data, allowing for the incorporation of temporal information. Preliminary results demonstrate that the ensemble learning approach outperforms individual models in terms of predictive accuracy and stability. Moreover, the model exhibits robustness across different subpopulations, indicating its potential for generalizability and applicability in diverse healthcare settings. the accuracy metrics is being utilized as a foundation for assessing the effectiveness of all classifications. With an accuracy of 93.01% the stacking classifier has proven to be the finest model. © 2023 IEEE.

关键词： Cardiovascular disease Ensemble learning machine learning algorithms

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