2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)最新文献_第6页

Design and Research of Intelligent Control System Based on New Artificial Intelligence Algorithm 基于新型人工智能算法的智能控制系统设计与研究

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470896

Shunru Zhang

引用次数: 0

Accurate Diagnosis by Magnetic Resonance Imaging Using Deep Learning 利用深度学习通过磁共振成像进行精确诊断

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470755

A. Rengarajan, Zahid Ahmed, Rajendra P. Pandey

{"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}

引用次数: 0

Optimizing Fuzzy System of Fuzzy Time Series for Hyper Spectral Image Classification 用于超光谱图像分类的模糊时间序列优化模糊系统

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470624

M.S. Nidhya, Preeti Naval, Ravindra Kumar

{"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}

引用次数: 0

Enhanced Algorithm and Comparative Study of Sales Forecasting Model Based on Data Mining Technology 基于数据挖掘技术的销售预测模型的增强算法与比较研究

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470810

Yanwu Wang

{"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}

引用次数: 0

Wavelet-Based Data Compression for Remote Sensing and Image Processing 基于小波的遥感和图像处理数据压缩技术

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470668

D. Yadav, A. Dadhich, Ananya Saha

引用次数: 0

Assessing Risk Factors with Generative Adversarial Networks for Cardiac Arrest Detection 利用生成式对抗网络评估心脏骤停检测中的风险因素

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470506

Sunil Kumar Gaur, Preethi D, Monika Abrol

{"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}

引用次数: 0

Investigating the Effect of Transfer Learning on Medical Image Segmentation Performance 研究迁移学习对医学图像分割性能的影响

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470893

Parag Agarwal, M.S. Nidhya, Trapty Agarwal

引用次数: 0

Analysis of Cultural Group Communication Behavior based on Deep Belief Network Algorithm 基于深度信念网络算法的文化群体交流行为分析

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470647

Meie Shi

引用次数: 0

Image In-Painting for Video Processing: Techniques and Performance Evaluation 用于视频处理的图像内绘：技术与性能评估

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470683

Laxmi Goswami, Arun Gupta, Preethi D

引用次数: 0

Building End Member Hybrid Profiles from Hyper Spectral Images for Unsupervised Land Cover Mapping 利用超光谱图像建立末端成员混合剖面图，实现无监督土地覆被测绘

2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) Pub Date : 2024-01-29 DOI: 10.1109/ICOCWC60930.2024.10470713

Rakesh Kumar Yadav, Vijay Kumar Pandey, Feon Jaison

引用次数: 0