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: Intelligent Human Computer Interaction; 8th International Co Anupam Basu,Sukhendu Das,Samit Bhattacharya Conference proceedings 2017 Spring

[復(fù)制鏈接]
樓主: 夸大
21#
發(fā)表于 2025-3-25 06:02:16 | 只看該作者
A Voting-Based Sensor Fusion Approach for Human Presence Detectionor. A voting based approach has been used to classify signals obtained from human beings and non-human objects, thereby facilitating human presence detection. Results obtained from indoor experiments performed using this approach substantiate the viability of its use in real environments.
22#
發(fā)表于 2025-3-25 08:23:42 | 只看該作者
23#
發(fā)表于 2025-3-25 13:33:40 | 只看該作者
Hands Up! To Assess Your Sustained Fitnessin many healthy persons too. To detect the amount of weakness in arm, we perform a simple test of lifting hand using a smart phone. With this approach, we can basically quantify the fitness of arm and routinely track whether the condition of the subject sustains or not.
24#
發(fā)表于 2025-3-25 16:43:31 | 只看該作者
25#
發(fā)表于 2025-3-25 21:36:11 | 只看該作者
26#
發(fā)表于 2025-3-26 01:04:00 | 只看該作者
Classification of Indian Classical Dance Formsges must be separated. The resultant images are then converted to binary. Since it is a multiclass classification problem, SVM using one vs one approach as well as one vs all approach has been implemented and the results are contrasted with linear and RBF kernels for both the approaches.
27#
發(fā)表于 2025-3-26 04:59:01 | 只看該作者
Study of Engineered Features and Learning Features in Machine Learning - A Case Study in Document Cleep autoencoder for learning features while engineering features are extracted by exploiting semantic association within the terms of the documents. Experimentally it has been observed that learning feature based classification always perform better than the proposed engineering feature based classifiers.
28#
發(fā)表于 2025-3-26 09:11:22 | 只看該作者
Towards Learning to Handle Deviations Using User Preferences in a Human Robot Collaboration Scenariondle deviations in an assembly process, while taking different user preferences into consideration. In this way, the robotic system could both benefit from interaction with users by learning to handle deviations and operate in a fashion that is preferred by the user.
29#
發(fā)表于 2025-3-26 16:32:30 | 只看該作者
30#
發(fā)表于 2025-3-26 17:49:23 | 只看該作者
 關(guān)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(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-5 00:35
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
大方县| 山丹县| 杨浦区| 惠东县| 宜春市| 繁峙县| 伊春市| 新河县| 长顺县| 淅川县| 双峰县| 邳州市| 类乌齐县| 墨竹工卡县| 成武县| 南涧| 石台县| 沙雅县| 亳州市| 阳山县| 朔州市| 高安市| 水城县| 白沙| 屯留县| 九江县| 綦江县| 新晃| 遵化市| 乌海市| 定南县| 濮阳县| 阿坝县| 聂拉木县| 固安县| 德令哈市| 保德县| 安新县| 军事| 和政县| 五峰|