Computer Systems Science and Engineering最新文献_第2页

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

Computer Systems Science and Engineering 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

{"title":"Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification","authors":"Ramyasri Nemani, G. J. Moses, Fayadh S. Alenezi, K. Kumar, Seifedine Kadry, Jungeun Kim, Keejun Han","doi":"10.32604/csse.2023.034213","DOIUrl":"https://doi.org/10.32604/csse.2023.034213","url":null,"abstract":"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.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"68 1","pages":"919-935"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90661817","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

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

Computer Systems Science and Engineering 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

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

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.037903

Fares Abdulhafidh Dael, Ömer Yavuz, Ugur Yavuz

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

Computer Systems Science and Engineering 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

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

Computer Systems Science and Engineering 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

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

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.028713

M. Jamunarani, C. Vasanthanayaki

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

Computer Systems Science and Engineering 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

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

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.039503

Daniyal Amankeldin, L. Kurmangaziyeva, A. Mailybayeva, Natalya Glazyrina, A. Zhumadillayeva, Nurzhamal Karasheva

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

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.025503

A. Jameer Basha, S. Aswini, S. Aarthini, Yun-Seung Nam, M. Abouhawwash

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

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032677

Omar Ben Bahri, Abdullah Alhumaidi Alotaibi

{"title":"Development of Pandemic Monitoring System Based on Constellation of Nanosatellites","authors":"Omar Ben Bahri, Abdullah Alhumaidi Alotaibi","doi":"10.32604/csse.2023.032677","DOIUrl":"https://doi.org/10.32604/csse.2023.032677","url":null,"abstract":"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.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"46 1","pages":"1249-1263"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69724920","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: 1