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: Advances in Data-driven Computing and Intelligent Systems; Selected Papers from Swagatam Das,Snehanshu Saha,Jagdish Chand Bansal Conference

[復(fù)制鏈接]
樓主: 法官所用
51#
發(fā)表于 2025-3-30 09:36:33 | 只看該作者
https://doi.org/10.1007/978-94-6091-719-6llowers may see each other’s ideas and sentiments, which may spread to more users in the future. Therefore, this research proposes a concept named “sentiment community”. The purpose is to explore the feelings and interactions of users on social networking sites. We have used graphs for the modelling
52#
發(fā)表于 2025-3-30 15:39:32 | 只看該作者
Anita Hussenius,Kathryn Scantlebury. Electroencephalogram (EEG) trials from eight channels collected during an oddball experiment were used in this analysis. All the trials were divided into non-target (non-P300) and target (P300) trial cohorts. Data-driven correlated component analysis (CorrCA) was applied to both cohorts separately
53#
發(fā)表于 2025-3-30 19:46:52 | 只看該作者
Sherry A. Southerland,Sibel Uysal Bahbaht there should exist a system that can handle the queries raised by a farmer in regional language and respond to queries asked with minimal human involvement. The proposed framework accepts the farmers‘ queries spoken in Kannada language and translates the Kannada query into English query. The trans
54#
發(fā)表于 2025-3-30 21:18:57 | 只看該作者
M.-H. Chiu,P. J. Gilmer,D. F. Treagusts based on their speech features extracted from the speech utterances. After the recent developments of deep learning (DL) models, deep convolutional neural networks (DCNNs) have been widely used for solving the SI tasks. A CNN model consists of mainly two parts, deep convolutional feature extractio
55#
發(fā)表于 2025-3-31 01:05:14 | 只看該作者
https://doi.org/10.1007/978-3-030-50797-8 speech processing. It plays a vital role in various real-life applications such as Internet of things (IoT) devices and assistive technology to name a few. The deep learning models like convolutional neural networks (CNNs) have shown the potential ability to solve SCR tasks. However, these models’
56#
發(fā)表于 2025-3-31 08:03:59 | 只看該作者
https://doi.org/10.1007/978-3-030-50797-8n and prevention measures help in improving the farm productivity. Internet of Things (IoT)-based monitoring techniques are required to reduce the manual efforts and improve the precision in decision making. Sensor-based Internet of Things (SBIoT) is capable of providing a framework for remote monit
57#
發(fā)表于 2025-3-31 10:00:25 | 只看該作者
58#
發(fā)表于 2025-3-31 16:33:20 | 只看該作者
59#
發(fā)表于 2025-3-31 21:28:06 | 只看該作者
60#
發(fā)表于 2025-4-1 01:37:51 | 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 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) 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, 2025-10-7 22:17
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
万年县| 南乐县| 沈阳市| 武鸣县| 闽清县| 邵武市| 吉水县| 澄江县| 涡阳县| 灌云县| 石首市| 米林县| 巨鹿县| 宁远县| 马公市| 乌兰察布市| 石狮市| 象山县| 台东市| 吉水县| 全州县| 寻乌县| 天峻县| 姜堰市| 深圳市| 秦皇岛市| 当阳市| 泸西县| 枣强县| 老河口市| 东乌| 勐海县| 南溪县| 葵青区| 望江县| 江油市| 博野县| 嘉善县| 城口县| 阿瓦提县| 庆安县|