Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470896
Shunru Zhang
With the rapid development of science and technology, artificial intelligence (AI) has penetrated into every aspect of our lives. Especially in the field of intelligent control system, the application of AI algorithm is increasingly becoming the key force to promote technological innovation and industrial upgrading. Intelligent control system relies on advanced AI algorithm, which can realize highly automated and intelligent decision-making and operation, and is widely used in industrial manufacturing, smart city, medical and health and other fields.
{"title":"Design and Research of Intelligent Control System Based on New Artificial Intelligence Algorithm","authors":"Shunru Zhang","doi":"10.1109/ICOCWC60930.2024.10470896","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470896","url":null,"abstract":"With the rapid development of science and technology, artificial intelligence (AI) has penetrated into every aspect of our lives. Especially in the field of intelligent control system, the application of AI algorithm is increasingly becoming the key force to promote technological innovation and industrial upgrading. Intelligent control system relies on advanced AI algorithm, which can realize highly automated and intelligent decision-making and operation, and is widely used in industrial manufacturing, smart city, medical and health and other fields.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"29 4","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470755
A. Rengarajan, Zahid Ahmed, Rajendra P. Pandey
deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance Imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis.
{"title":"Accurate Diagnosis by Magnetic Resonance Imaging Using Deep Learning","authors":"A. Rengarajan, Zahid Ahmed, Rajendra P. Pandey","doi":"10.1109/ICOCWC60930.2024.10470755","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470755","url":null,"abstract":"deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance Imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"181 3-4","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The end member hybrid profile (EMHP) representing end individuals extracted from multispectral photos (MSI) and spectral libraries has been delivered for unsupervised land cowl mapping. Compared to traditional unsupervised land cover mapping techniques, EMHP correctly reduces the records loss compared to digital numbers (DNs) by maintaining the spectral library, and MSI ceases members independently. This advancement can improve mapping accuracy substantially. Furthermore, EMHP can represent more details than traditional mapping gear because of the potential to assemble cease individuals from hyperspectral images (HSI). The cease contributors from the HSI include more spectral facts than MSI and feature the ability to represent land covers in each vicinity accurately. These blessings make EMHP a promising approach for unsupervised land cover mapping. However, computational value and a wide variety of quit members produced from the HSI want to be addressed for this method to be extra powerful in applications.
{"title":"Building End Member Hybrid Profiles from Hyper Spectral Images for Unsupervised Land Cover Mapping","authors":"Rakesh Kumar Yadav, Vijay Kumar Pandey, Feon Jaison","doi":"10.1109/ICOCWC60930.2024.10470713","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470713","url":null,"abstract":"The end member hybrid profile (EMHP) representing end individuals extracted from multispectral photos (MSI) and spectral libraries has been delivered for unsupervised land cowl mapping. Compared to traditional unsupervised land cover mapping techniques, EMHP correctly reduces the records loss compared to digital numbers (DNs) by maintaining the spectral library, and MSI ceases members independently. This advancement can improve mapping accuracy substantially. Furthermore, EMHP can represent more details than traditional mapping gear because of the potential to assemble cease individuals from hyperspectral images (HSI). The cease contributors from the HSI include more spectral facts than MSI and feature the ability to represent land covers in each vicinity accurately. These blessings make EMHP a promising approach for unsupervised land cover mapping. However, computational value and a wide variety of quit members produced from the HSI want to be addressed for this method to be extra powerful in applications.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"221 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470624
M.S. Nidhya, Preeti Naval, Ravindra Kumar
This research paper examines the capability of fuzzy time collection for hyperspectral photograph classification. Fuzzy time series (FTS) is a time series in which fuzzy standards are used to model the styles within the facts. FTS can be used to explain complex temporal styles in the records, and as a consequence making it possible to categorize photographs more extraordinarily accurately., this look proposes an optimization method primarily based on genetic seek techniques. The optimization algorithm is designed to discover the high-quality FTS parameters that yield first-rate type accuracy. The efficacy of the proposed technique is evaluated on hyperspectral facts set with extraordinary experimental scenarios. The results of the test display that the proposed method can enhance the accuracy of photo classification and the use of FTS considerably. Hence, the proposed method gives a promising technique that can be used to classify hyperspectral snapshots efficiently. The paper affords an optimized fuzzy machine of fuzzy time collection for the hyperspectral photograph category. The proposed device consists of 3 levels: pre-processing, version creation, and optimization. Throughout the pre-processing level, statistical and spectral analyses are executed to acquire the applicable attributes for developing the fuzzy time collection. The model construction degree then uses the bushy time series to extract between-class separability for the photo type. It is followed utilizing the optimization stage, related to the software of differential evolution, to minimize the complexity of the proposed machine while still enhancing the type accuracy. The proposed machine has been correctly carried out to a real-international hyperspectral dataset and demonstrates widespread upgrades in class accuracy over existing methods.
{"title":"Optimizing Fuzzy System of Fuzzy Time Series for Hyper Spectral Image Classification","authors":"M.S. Nidhya, Preeti Naval, Ravindra Kumar","doi":"10.1109/ICOCWC60930.2024.10470624","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470624","url":null,"abstract":"This research paper examines the capability of fuzzy time collection for hyperspectral photograph classification. Fuzzy time series (FTS) is a time series in which fuzzy standards are used to model the styles within the facts. FTS can be used to explain complex temporal styles in the records, and as a consequence making it possible to categorize photographs more extraordinarily accurately., this look proposes an optimization method primarily based on genetic seek techniques. The optimization algorithm is designed to discover the high-quality FTS parameters that yield first-rate type accuracy. The efficacy of the proposed technique is evaluated on hyperspectral facts set with extraordinary experimental scenarios. The results of the test display that the proposed method can enhance the accuracy of photo classification and the use of FTS considerably. Hence, the proposed method gives a promising technique that can be used to classify hyperspectral snapshots efficiently. The paper affords an optimized fuzzy machine of fuzzy time collection for the hyperspectral photograph category. The proposed device consists of 3 levels: pre-processing, version creation, and optimization. Throughout the pre-processing level, statistical and spectral analyses are executed to acquire the applicable attributes for developing the fuzzy time collection. The model construction degree then uses the bushy time series to extract between-class separability for the photo type. It is followed utilizing the optimization stage, related to the software of differential evolution, to minimize the complexity of the proposed machine while still enhancing the type accuracy. The proposed machine has been correctly carried out to a real-international hyperspectral dataset and demonstrates widespread upgrades in class accuracy over existing methods.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"21 3","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470893
Parag Agarwal, M.S. Nidhya, Trapty Agarwal
This paper investigates the effect of switch studying on clinical photo segmentation performance. Switch learning entails the usage of a pre-trained model as the basis for a new technique for a comparable project. By leveraging pre-educated models, the manner of schooling a version to perform a project can be made greener. This paper evaluates the effect of transfer getting to know on medical photograph segmentation performance in terms of accuracy and speed of schooling. Moreover, the paper compares the overall performance of transfer getting to know and non-switch gaining knowledge of tactics for segmenting the tumors in MRI and CT scans. Effects from the experiments display that transfer learning outperforms non-transfer mastering approaches in the challenge of scientific image segmentation. Further, the paper offers insights into the VGG16 and U-internet architectures and indicates feasible guidelines for in addition research.
{"title":"Investigating the Effect of Transfer Learning on Medical Image Segmentation Performance","authors":"Parag Agarwal, M.S. Nidhya, Trapty Agarwal","doi":"10.1109/ICOCWC60930.2024.10470893","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470893","url":null,"abstract":"This paper investigates the effect of switch studying on clinical photo segmentation performance. Switch learning entails the usage of a pre-trained model as the basis for a new technique for a comparable project. By leveraging pre-educated models, the manner of schooling a version to perform a project can be made greener. This paper evaluates the effect of transfer getting to know on medical photograph segmentation performance in terms of accuracy and speed of schooling. Moreover, the paper compares the overall performance of transfer getting to know and non-switch gaining knowledge of tactics for segmenting the tumors in MRI and CT scans. Effects from the experiments display that transfer learning outperforms non-transfer mastering approaches in the challenge of scientific image segmentation. Further, the paper offers insights into the VGG16 and U-internet architectures and indicates feasible guidelines for in addition research.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"43 26","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470810
Yanwu Wang
Enhancement algorithms and comparative research play an important role in sales forecasting models, but there are problems of inaccurate forecasting models. Traditional deep learning cannot solve the enhancement and forecasting problems in the sales forecast model, and the prediction effect is not satisfactory. Therefore, this paper proposes an enhanced algorithm and comparative research on sales forecasting model based on data mining technology and analyzes the enhancement algorithm and comparison of sales forecasting model. Firstly, the decision tree theory is used to locate the influencing factors, and the indicators is divided according to the requirements of the enhanced algorithm and comparative research, to reduce the interference factors in the reinforcement algorithm and comparative research. Then, the decision tree theory is used to form a data mining technology enhancement algorithm and a comparative research scheme, and the enhanced algorithm and comparative research results is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the data mining technology is superior to the traditional deep learning in terms of enhanced algorithm and comparative research accuracy, enhanced algorithm and comparative research influencing factor time.
{"title":"Enhanced Algorithm and Comparative Study of Sales Forecasting Model Based on Data Mining Technology","authors":"Yanwu Wang","doi":"10.1109/ICOCWC60930.2024.10470810","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470810","url":null,"abstract":"Enhancement algorithms and comparative research play an important role in sales forecasting models, but there are problems of inaccurate forecasting models. Traditional deep learning cannot solve the enhancement and forecasting problems in the sales forecast model, and the prediction effect is not satisfactory. Therefore, this paper proposes an enhanced algorithm and comparative research on sales forecasting model based on data mining technology and analyzes the enhancement algorithm and comparison of sales forecasting model. Firstly, the decision tree theory is used to locate the influencing factors, and the indicators is divided according to the requirements of the enhanced algorithm and comparative research, to reduce the interference factors in the reinforcement algorithm and comparative research. Then, the decision tree theory is used to form a data mining technology enhancement algorithm and a comparative research scheme, and the enhanced algorithm and comparative research results is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the data mining technology is superior to the traditional deep learning in terms of enhanced algorithm and comparative research accuracy, enhanced algorithm and comparative research influencing factor time.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"27 6","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470683
Laxmi Goswami, Arun Gupta, Preethi D
Photo in-portray for video processing is an crucial region within the field cutting-edge image and video processing that focuses on restoring photograph and video frames suffering from obstructions or defects. Usually, diffusion trendy strategies inclusive of orders aggregation, nearby growth, and deep trendy architectures had been used to carry out photograph in painting responsibilities, and these strategies can also be carried out to video frames. In this paper, we overview the strategies for picture in-painting video frames, which include the latest deep latest procedures. We then offer a comprehensive analysis brand new the performance assessment modern-day the exclusive techniques that have been proposed for this challenge. We conclude the paper via summarizing the blessings and drawbacks modern approach in addition to outlining capability future studies instructions.
{"title":"Image In-Painting for Video Processing: Techniques and Performance Evaluation","authors":"Laxmi Goswami, Arun Gupta, Preethi D","doi":"10.1109/ICOCWC60930.2024.10470683","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470683","url":null,"abstract":"Photo in-portray for video processing is an crucial region within the field cutting-edge image and video processing that focuses on restoring photograph and video frames suffering from obstructions or defects. Usually, diffusion trendy strategies inclusive of orders aggregation, nearby growth, and deep trendy architectures had been used to carry out photograph in painting responsibilities, and these strategies can also be carried out to video frames. In this paper, we overview the strategies for picture in-painting video frames, which include the latest deep latest procedures. We then offer a comprehensive analysis brand new the performance assessment modern-day the exclusive techniques that have been proposed for this challenge. We conclude the paper via summarizing the blessings and drawbacks modern approach in addition to outlining capability future studies instructions.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"52 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470647
Meie Shi
The role of group communication in the study of cultural group behavior is very important, but there is a problem of large research error. Information statistics cannot solve the communication problem in the study of cultural group behavior, and the behavior recognition rate is low. Therefore, this paper proposes a deep belief network algorithm for the analysis of cultural group behavior communication. Firstly, the belief network theory is used to study the communication behavior, and in-depth mining is carried out according to group communication requirements to reduce the irrelevant factors in communication. Then, the deep belief network algorithm is used to continuously divide the behavior of cultural groups and form the final behavior recognition set. MATLAB simulation shows that the deep belief network algorithm's behavior recognition accuracy and behavior recognition time are better than the information statistics method when the communication requirements are known.
{"title":"Analysis of Cultural Group Communication Behavior based on Deep Belief Network Algorithm","authors":"Meie Shi","doi":"10.1109/ICOCWC60930.2024.10470647","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470647","url":null,"abstract":"The role of group communication in the study of cultural group behavior is very important, but there is a problem of large research error. Information statistics cannot solve the communication problem in the study of cultural group behavior, and the behavior recognition rate is low. Therefore, this paper proposes a deep belief network algorithm for the analysis of cultural group behavior communication. Firstly, the belief network theory is used to study the communication behavior, and in-depth mining is carried out according to group communication requirements to reduce the irrelevant factors in communication. Then, the deep belief network algorithm is used to continuously divide the behavior of cultural groups and form the final behavior recognition set. MATLAB simulation shows that the deep belief network algorithm's behavior recognition accuracy and behavior recognition time are better than the information statistics method when the communication requirements are known.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"48 12","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470668
D. Yadav, A. Dadhich, Ananya Saha
Wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. Wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously.
{"title":"Wavelet-Based Data Compression for Remote Sensing and Image Processing","authors":"D. Yadav, A. Dadhich, Ananya Saha","doi":"10.1109/ICOCWC60930.2024.10470668","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470668","url":null,"abstract":"Wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. Wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"109 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470506
Sunil Kumar Gaur, Preethi D, Monika Abrol
This paper seeks to evaluate the performance of generative adverse networks (GANs) in opposition to conventional strategies for predicting cardiac arrests. Via the usage of GANs, the paper examines the capability to assess hazard factor accuracy and generate new synthetic facts regarding the threat of cardiac arrest. The paper explores methods that GANs can be applied to generate new representations of respective cardiac arrest danger factors. Moreover, it evaluates the superiority of the GANs-based model in evaluation to traditional gadget learning techniques constructed on existing data. ultimately, the look tries to assess the accuracy of GANs in cardiac arrest prediction and its capability to assess hazard elements. This paper investigates the capability of using Generative antagonistic Networks (GANs) to assess chance factors for the early detection of cardiac arrest. First, a deep generative community consisting of two convolutional vehicle Encoder (CAE) sub-networks is employed to examine discriminative representations from clinical databases. Then, a supervised discriminative network is used to analyze the encodings and classify hazard factors that hint at the opportunity of cardiac arrest. The paper also demonstrates strategies for optimizing the GAN's training technique to further improve the device's accuracy. Subsequently, experimental consequences at the MIMIC scientific database display the effectiveness of the proposed GAN architecture in ascertaining cardiac arrest hazard elements..
本文旨在评估生成式逆向网络（GAN）与传统的心脏骤停预测策略相比的性能。通过使用 GANs，本文研究了评估危险因素准确性和生成有关心脏骤停威胁的新合成事实的能力。论文探讨了应用 GANs 生成各自心脏骤停危险因素新表征的方法。最后，该研究试图评估 GANs 在心脏骤停预测中的准确性及其评估危险因素的能力。本文研究了使用生成式对抗网络（GANs）评估心脏骤停早期检测中偶然因素的能力。首先，采用由两个卷积车辆编码器（CAE）子网络组成的深度生成社区来检查临床数据库中的判别表征。然后，利用监督判别网络分析编码，并对提示心脏骤停机会的危险因素进行分类。论文还展示了优化 GAN 训练技术的策略，以进一步提高设备的准确性。随后，在 MIMIC 科学数据库中的实验结果表明，所提出的 GAN 架构在确定心脏骤停危险因素方面非常有效。
{"title":"Assessing Risk Factors with Generative Adversarial Networks for Cardiac Arrest Detection","authors":"Sunil Kumar Gaur, Preethi D, Monika Abrol","doi":"10.1109/ICOCWC60930.2024.10470506","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470506","url":null,"abstract":"This paper seeks to evaluate the performance of generative adverse networks (GANs) in opposition to conventional strategies for predicting cardiac arrests. Via the usage of GANs, the paper examines the capability to assess hazard factor accuracy and generate new synthetic facts regarding the threat of cardiac arrest. The paper explores methods that GANs can be applied to generate new representations of respective cardiac arrest danger factors. Moreover, it evaluates the superiority of the GANs-based model in evaluation to traditional gadget learning techniques constructed on existing data. ultimately, the look tries to assess the accuracy of GANs in cardiac arrest prediction and its capability to assess hazard elements. This paper investigates the capability of using Generative antagonistic Networks (GANs) to assess chance factors for the early detection of cardiac arrest. First, a deep generative community consisting of two convolutional vehicle Encoder (CAE) sub-networks is employed to examine discriminative representations from clinical databases. Then, a supervised discriminative network is used to analyze the encodings and classify hazard factors that hint at the opportunity of cardiac arrest. The paper also demonstrates strategies for optimizing the GAN's training technique to further improve the device's accuracy. Subsequently, experimental consequences at the MIMIC scientific database display the effectiveness of the proposed GAN architecture in ascertaining cardiac arrest hazard elements..","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"40 3","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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