找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Cybernetics, Cognition and Machine Learning Applications; Proceedings of ICCCM Vinit Kumar Gunjan,P. N. Suganthan,Amit Kumar Conference pro

[復制鏈接]
查看: 50073|回復: 63
樓主
發(fā)表于 2025-3-21 18:15:34 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cybernetics, Cognition and Machine Learning Applications
副標題Proceedings of ICCCM
編輯Vinit Kumar Gunjan,P. N. Suganthan,Amit Kumar
視頻videohttp://file.papertrans.cn/242/241888/241888.mp4
概述Presents recent innovative research in the field of machine learning applications.Discusses the outcomes of ICCCMLA 2020, held in Goa, India.Serves as a reference resource for researchers and practiti
叢書名稱Algorithms for Intelligent Systems
圖書封面Titlebook: Cybernetics, Cognition and Machine Learning Applications; Proceedings of ICCCM Vinit Kumar Gunjan,P. N. Suganthan,Amit Kumar Conference pro
描述This book includes the original, peer reviewed research articles from the 2nd?International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2020), held in August, 2020 at Goa, India. It covers the latest research trends or developments in areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber physical systems and cybernetics.
出版日期Conference proceedings 2021
關鍵詞ICCCMLA 2020; Ubiquitous Intelligence and Computing; Mobile Computing; Human-Computer Interaction; Patte
版次1
doihttps://doi.org/10.1007/978-981-33-6691-6
isbn_softcover978-981-33-6693-0
isbn_ebook978-981-33-6691-6Series ISSN 2524-7565 Series E-ISSN 2524-7573
issn_series 2524-7565
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

書目名稱Cybernetics, Cognition and Machine Learning Applications影響因子(影響力)




書目名稱Cybernetics, Cognition and Machine Learning Applications影響因子(影響力)學科排名




書目名稱Cybernetics, Cognition and Machine Learning Applications網(wǎng)絡公開度




書目名稱Cybernetics, Cognition and Machine Learning Applications網(wǎng)絡公開度學科排名




書目名稱Cybernetics, Cognition and Machine Learning Applications被引頻次




書目名稱Cybernetics, Cognition and Machine Learning Applications被引頻次學科排名




書目名稱Cybernetics, Cognition and Machine Learning Applications年度引用




書目名稱Cybernetics, Cognition and Machine Learning Applications年度引用學科排名




書目名稱Cybernetics, Cognition and Machine Learning Applications讀者反饋




書目名稱Cybernetics, Cognition and Machine Learning Applications讀者反饋學科排名




單選投票, 共有 1 人參與投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用戶組沒有投票權限
沙發(fā)
發(fā)表于 2025-3-21 21:25:20 | 只看該作者
Simulating User Journeys with?Active Objectstabase along with the patient’s ID. The key objective of this automatic update of pulse or ECG measurements of patients is to prevent any errors caused due to their manual entry. The data of any patient can be readily accessed anytime through the EMR system.
板凳
發(fā)表于 2025-3-22 01:37:19 | 只看該作者
Fundamentals of Corrosion Kineticson v3 model gave the best training accuracy of 99.22%, while custom made CNN3 gave a promising training accuracy of 96.61%. Both models gave a similar validation accuracy of 97.89%. Sensitivity and specificity for COVID-19 were (100% and 98.5%) and (100% and 100%) for Inception v3 and CNN3, respectively.
地板
發(fā)表于 2025-3-22 08:10:25 | 只看該作者
5#
發(fā)表于 2025-3-22 10:31:18 | 只看該作者
6#
發(fā)表于 2025-3-22 13:13:28 | 只看該作者
Detecting COVID-19 Using Convolution Neural Networks,on v3 model gave the best training accuracy of 99.22%, while custom made CNN3 gave a promising training accuracy of 96.61%. Both models gave a similar validation accuracy of 97.89%. Sensitivity and specificity for COVID-19 were (100% and 98.5%) and (100% and 100%) for Inception v3 and CNN3, respectively.
7#
發(fā)表于 2025-3-22 20:33:19 | 只看該作者
IoT-Enabled Logistics for E-waste Management and Sustainability, create a win–win situation for both, the developed and developing countries economically and also in an environment-friendly manner. This review paper contributes to the knowledge of e-waste management and is expected to benefit the industry and society.
8#
發(fā)表于 2025-3-22 23:36:06 | 只看該作者
Navigation Through Proxy Measurement of Location by Surface Detection,considered surface types are captured by moving the system containing embedded IMU sensor with raspberry pi on different surfaces. Hence, a robust and portable system is developed which is capable of recognizing the type of surface on which it is navigating.
9#
發(fā)表于 2025-3-23 03:16:47 | 只看該作者
10#
發(fā)表于 2025-3-23 08:24:00 | 只看該作者
Feature Construction Through Inductive Transfer Learning in Computer Vision,nd will publish the experiment results for Inception V3, VGG16, and Resnet50 models on the ImageNet dataset by varying the image sizes for source and target datasets, for Inductive transfer Learning and various techniques or methods specially by doing the model transfer.
 關于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-5 03:23
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
阿坝县| 博爱县| 金川县| 南华县| 巢湖市| 泸州市| 正宁县| 大洼县| 奉贤区| 静海县| 深泽县| 堆龙德庆县| 万载县| 洞口县| 青冈县| 巴林右旗| 沾化县| 黑龙江省| 白沙| 黄龙县| 涪陵区| 大洼县| 儋州市| 曲沃县| 蒙阴县| 景洪市| 张北县| 利川市| 威信县| 公安县| 沂源县| 彰化县| 通山县| 潮安县| 砚山县| 揭西县| 于都县| 峨眉山市| 临泽县| 板桥市| 甘洛县|