Computer Systems Science and Engineering最新文献_第6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":null,"pages":null},"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

CD-FL: Cataract Images Based Disease Detection Using Federated Learning CD-FL:基于联合学习的白内障图像疾病检测

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.039296

A. Khan, Shtwai Alsubai, Chitapong Wechtaisong, Ahmad S. Almadhor, N. Kryvinska, Abdullah Al Hejaili, Uzma Ghulam Mohammad

引用次数: 1

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns 利用Gabor块局部二值模式对胃病变进行分类

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032359

M. Tahir, F. Riaz, I. Usman, M. Habib

引用次数: 0

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking 多域软件定义网络拓扑性能评价

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.031531

Jiangyuan Yao, Weiping Yang, Shuhua Weng, Minrui Wang, Zheng Jiang, Deshun Li, Yahui Li, Xingcan Cao

引用次数: 0

A Multi-Objective Genetic Algorithm Based Load Balancing Strategy for Health Monitoring Systems in Fog-Cloud 基于多目标遗传算法的雾云健康监测系统负载均衡策略

4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.038545

Hayder Makki Shakir, Jaber Karimpour, Jafar Razmara

引用次数: 0

Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm 基于GCAN指纹和集成机器学习算法的药物发现中分子化合物配体虚拟筛选

4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.033807

R. Ani, O. S. Deepa, B. R. Manju

{"title":"Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm","authors":"R. Ani, O. S. Deepa, B. R. Manju","doi":"10.32604/csse.2023.033807","DOIUrl":"https://doi.org/10.32604/csse.2023.033807","url":null,"abstract":"The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein. The use of virtual screening in pharmaceutical research is growing in popularity. During the early phases of medication research and development, it is crucial. Chemical compound searches are now more narrowly targeted. Because the databases contain more and more ligands, this method needs to be quick and exact. Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint (ECFP). Only the largest sub-graph is taken into consideration to learn the representation, despite the fact that the conventional graph network generates a better-encoded fingerprint. When using the average or maximum pooling layer, it also contains unrelated data. This article suggested the Graph Convolutional Attention Network (GCAN), a graph neural network with an attention mechanism, to address these problems. Additionally, it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant. The generated fingerprint is used to classify drugs using ensemble learning. As base classifiers, ensemble stacking is applied to Support Vector Machines (SVM), Random Forest, Nave Bayes, Decision Trees, AdaBoost, and Gradient Boosting. When compared to existing models, the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy, sensitivity, specificity, and area under the curve. Additionally, it is revealed that our ensemble learning with generated molecular fingerprint yields 91% accuracy, outperforming earlier approaches.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135563771","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

Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN Hadoop YARN中基于资源可用性控制的自适应节点和容器感知调度器的性能改进

4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036320

J. S. Manjaly, T. Subbulakshmi

{"title":"Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN","authors":"J. S. Manjaly, T. Subbulakshmi","doi":"10.32604/csse.2023.036320","DOIUrl":"https://doi.org/10.32604/csse.2023.036320","url":null,"abstract":"The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs. This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator (YARN) scheduler, called the Adaptive Node and Container Aware Scheduler (ANACRAC), that aligns cluster resources to the demands of the applications in the real world. The approach performs to leverage the user-provided configurations as a unique design to apportion nodes, or containers within the nodes, to application thresholds. Additionally, it provides the flexibility to the applications for selecting and choosing which node’s resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as needed. Node or container awareness can be utilized individually or in combination to increase efficiency. On top of this, the resource availability within the node and containers can also be investigated. This paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job type. The results proved that 15%–20% performance improvement was achieved compared with the node and container awareness feature of the ANACRAC. It has been validated that this ANACRAC scheduler demonstrates a 70%–90% performance improvement compared with the default Fair scheduler. Experimental results also demonstrated the success of the enhancement and a performance improvement in the range of 60% to 200% when applications were connected with external interfaces and high workloads.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135563782","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

Fast and Accurate Detection of Masked Faces Using CNNs and LBPs 基于cnn和lbp的被遮挡人脸快速准确检测

4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.041011

Sarah M. Alhammad, Doaa Sami Khafaga, Aya Y. Hamed, Osama El-Koumy, Ehab R. Mohamed, Khalid M. Hosny

{"title":"Fast and Accurate Detection of Masked Faces Using CNNs and LBPs","authors":"Sarah M. Alhammad, Doaa Sami Khafaga, Aya Y. Hamed, Osama El-Koumy, Ehab R. Mohamed, Khalid M. Hosny","doi":"10.32604/csse.2023.041011","DOIUrl":"https://doi.org/10.32604/csse.2023.041011","url":null,"abstract":"Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the real-world masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performance metrics for the proposed technique were measured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135563956","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

An Intelligent Approach for Accurate Prediction of Chronic Diseases 一种用于慢性病准确预测的智能方法

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.031761

S. Priya, N. Saranya

引用次数: 0