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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification 基于黏菌优化的统计数据挖掘智能降雨分类
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.034213
Ramyasri Nemani, G. J. Moses, Fayadh S. Alenezi, K. Kumar, Seifedine Kadry, Jungeun Kim, Keejun Han
Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning (SDMIRPSMODL) model. In the presented SDMIRP-SMODL model, the feature subset selection process is performed by the SMO algorithm, which in turn minimizes the computation complexity. For rainfall prediction. Convolution neural network with long short-term memory (CNN-LSTM) technique is exploited. At last, this study involves the pelican optimization algorithm (POA) as a hyperparameter optimizer. The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class. The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.
由于来自金融、医学、科学、工程等多个领域的大量数据的可访问性,统计数据比以往任何时候都更加重要。统计数据挖掘(SDM)是一个跨学科领域,它检查庞大的现有数据库,从数据中发现模式和联系。在经典统计学中,数据集的大小和数据不能主要基于某些实验策略收集的细节有所不同,但在其他解决方案中则相反。因此,本文介绍了一种利用深度学习黏菌优化(SDMIRPSMODL)模型进行智能降雨预测的有效统计数据挖掘方法。在SDMIRP-SMODL模型中,特征子集的选择过程由SMO算法完成,从而使计算复杂度最小化。用于降雨预测。利用卷积神经网络长短期记忆(CNN-LSTM)技术。最后,本研究将鹈鹕优化算法(POA)作为超参数优化器。利用一个降雨数据集对SDMIRP-SMODL方法的实验评估进行了测试,该数据集包括23682个阴性类样本和1865个阳性类样本。比较结果报告了与现有技术相比,SDMIRP-SMODL模型的优势。
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引用次数: 0
A Novel Approximate Message Passing Detection for Massive MIMO 5G System 一种新的大规模MIMO 5G系统近似消息传递检测方法
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.033341
Nidhi Gour, Rajneesh Pareek, K. Rajagopal, Himanshu Sharma, Mrim M. Alnfiai, M. Alzain, Mehedi Masud, Arun Kumar
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引用次数: 1
Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model 基于生成对抗网络的股票市场预测:混合智能模型
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.037903
Fares Abdulhafidh Dael, Ömer Yavuz, Ugur Yavuz
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引用次数: 1
On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach 基于元启发式方法的无线传感器网络布局优化研究
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032024
Abeeda Akram, K. Zafar, A. Mian, Abdul Rauf Baig, R. Almakki, Lulwah Alsuwaidan, Shakir Khan
{"title":"On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach","authors":"Abeeda Akram, K. Zafar, A. Mian, Abdul Rauf Baig, R. Almakki, Lulwah Alsuwaidan, Shakir Khan","doi":"10.32604/csse.2023.032024","DOIUrl":"https://doi.org/10.32604/csse.2023.032024","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"75 1","pages":"3685-3701"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74418825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA 基于Q学习控制的GWO-AOA的无人机三维路径优化
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032737
K. Sreelakshmy, Himanshu Gupta, Om Prakash Verma, K. Kumar, Abdelhamied A. Ateya, N. Soliman
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引用次数: 1
ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression 基于elm的水下图像形状自适应DCT压缩技术
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.028713
M. Jamunarani, C. Vasanthanayaki
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引用次数: 0
An Unsupervised Writer Identification Based on Generating Clusterable燛mbeddings 基于可聚类燛嵌入的无监督写器识别
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032977
M. Mridha, Zabir Mohammad, Muhammad Mohsin Kabir, Aklima Akter Lima, S. Das, Md. Rashedul Islam, Y. Watanobe
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引用次数: 0
Deep Neural Network for Detecting Fake Profiles in Social Networks 基于深度神经网络的社交网络虚假资料检测
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.039503
Daniyal Amankeldin, L. Kurmangaziyeva, A. Mailybayeva, Natalya Glazyrina, A. Zhumadillayeva, Nurzhamal Karasheva
,
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引用次数: 2
Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network 无线传感器网络能量最小化的遗传鸡群算法
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.025503
A. Jameer Basha, S. Aswini, S. Aarthini, Yun-Seung Nam, M. Abouhawwash
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引用次数: 0
Development of Pandemic Monitoring System Based on Constellation of Nanosatellites 基于纳米卫星星座的流行病监测系统研制
IF 2.2 4区 计算机科学 Q2 Computer Science Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032677
Omar Ben Bahri, Abdullah Alhumaidi Alotaibi
Covid-19 is a global crisis and the greatest challenge we have faced. It affects people in different ways. Most infected people develop a mild to moderate form of the disease and recover without hospitalization. This presents a problem in spreading the pandemic with unintentionally manner. Thus, this paper provides a new technique for COVID-19 monitoring remotely and in wide range. The system is based on satellite technology that provides a pivotal solution for wireless monitoring. This mission requires a data collection technique which can be based on drones' technology. Therefore, the main objective of our proposal is to develop a mission architecture around satellite technology in order to collect information in wide range, mostly, in areas suffer network coverage. A communication method was developed around a constellation of nanosatellites to cover Saudi Arabia region which is the area of interest in this paper. The new proposed architecture provided an efficient monitoring application discussing the gaps related to thermal imaging data. It reached 15.8 min as mean duration of visibility for the desired area. In total, the system can reach a coverage of 5.8 h/day, allowing to send about 21870 thermal images. © 2023 CRL Publishing. All rights reserved.
Covid-19是一场全球性危机,也是我们面临的最大挑战。它以不同的方式影响着人们。大多数感染者会发展成轻度至中度的疾病,无需住院即可康复。这就造成了以无意的方式传播大流行病的问题。为新型冠状病毒远程大范围监测提供了一种新技术。该系统基于卫星技术,为无线监控提供了关键的解决方案。这项任务需要一种基于无人机技术的数据收集技术。因此,我们建议的主要目标是围绕卫星技术发展一种任务架构,以便在大范围内收集信息,主要是在受网络覆盖的地区。围绕纳米卫星星座开发了一种覆盖沙特阿拉伯地区的通信方法,这是本文感兴趣的领域。新提出的架构提供了一个有效的监测应用程序,讨论与热成像数据相关的差距。期望区域的平均能见度达到15.8分钟。总的来说,该系统可以达到5.8小时/天的覆盖范围,允许发送大约21870张热图像。©2023 CRL Publishing。版权所有。
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引用次数: 1
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