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 and Cloud Computing; Proceedings of ICICC Debahuti Mishra,Rajkumar Buyya,Srikanta Patnaik Conference proceedings 2021 Springer

[復(fù)制鏈接]
樓主: Affordable
31#
發(fā)表于 2025-3-26 23:10:18 | 只看該作者
A Power Optimization Technique for WSN with the Help of Hybrid Meta-heuristic Algorithm Targeting Fosensor area networks (WSNs) is presented in this research, which further results in energy efficiency in the concern network. An ant colony optimization (ACO)-based technique at random deployment has been considered for our proposed research in simulation. Results obtained in simulation and related
32#
發(fā)表于 2025-3-27 03:16:33 | 只看該作者
A Model for Probabilistic Prediction of Paddy Crop Disease Using Convolutional Neural Networkole in the life of humans, especially in the Indian Subcontinent. So, it is necessary for humans to protect the importance and productivity of agriculture. The IT industry is very significant for the agriculture and especially for the healthcare of agricultural industry. Machine Learning and Artific
33#
發(fā)表于 2025-3-27 06:14:55 | 只看該作者
A Hybridized ELM-Elitism-Based Self-Adaptive Multi-Population Jaya Model for Currency Forecastingnce and less efficiency of the popular forecasting methods, an Extreme Learning Machine (ELM)—elitism-based self-adaptive multi-population Jaya model—is designed with the possibilities of getting maximum prediction accuracy. The model has evaluated by using the exchange rate data of USD to INR and U
34#
發(fā)表于 2025-3-27 11:14:21 | 只看該作者
35#
發(fā)表于 2025-3-27 15:30:17 | 只看該作者
36#
發(fā)表于 2025-3-27 19:05:46 | 只看該作者
37#
發(fā)表于 2025-3-27 22:25:33 | 只看該作者
Performance Analysis of ERWCA-Based FLANN Model for Exchange Rate Forecastingate data are nonlinear and dynamic in nature, variants of artificial neural network (ANN) models are the common choice for developing forecasting models. To overcome the drawbacks of neural network models, different nature-inspired optimization algorithms have been proposed. In this paper, the FLANN
38#
發(fā)表于 2025-3-28 04:16:47 | 只看該作者
Multi-document Summarization Using Deep Learningg exponentially, there is a high chance of duplication of data; it is difficult and tedious for the manual reading of all the documents as well as the rejection of the duplicates and extraction of useful information. One of the solutions to this issue is “Text Summarization,” through which a huge vo
39#
發(fā)表于 2025-3-28 09:35:26 | 只看該作者
sen und Information im Interesse auch der Informationswirtschaft ist. Je freizügiger der Umgang mit Wissen jeder Art ist, desto gr??er die Chancen für einen hohen Innovationsgrad der Wirtschaft, für einen hohen Inventionsgrad der Wissenschaft und einen hohen Demokratisierungs-/Transparenzgrad des po
40#
發(fā)表于 2025-3-28 11:04:19 | 只看該作者
sen und Information im Interesse auch der Informationswirtschaft ist. Je freizügiger der Umgang mit Wissen jeder Art ist, desto gr??er die Chancen für einen hohen Innovationsgrad der Wirtschaft, für einen hohen Inventionsgrad der Wissenschaft und einen hohen Demokratisierungs-/Transparenzgrad des po
 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, 2026-1-19 21:53
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
蓬安县| 兴义市| 扶绥县| 兰西县| 怀化市| 巴楚县| 稻城县| 喜德县| 潮州市| 多伦县| 龙山县| 浦东新区| 郁南县| 明光市| 河北省| 托克逊县| 普陀区| 天祝| 图木舒克市| 丰顺县| 宁波市| 班玛县| 龙南县| 金平| 静安区| 夏津县| 乌兰县| 广昌县| 正安县| 米林县| 廉江市| 南漳县| 宝鸡市| 神池县| 惠来县| 孟州市| 英德市| 宣城市| 虞城县| 丘北县| 南京市|