Azerbaijan Journal of High Performance Computing最新文献_第4页

Connected Vehicles Versus Conventional Traffic Congestion Mitigation Measures: An Operational Economic Analysis 互联汽车与传统交通拥堵缓解措施:运营经济分析

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.155.169

Mutasem Alzoubaidi, A. Al-Balbissi, Abdel-Rahman Alzoubaidi, Amr Alzoubaidi, Baha Azzeh, Ahmed Al-Mansour, Ahmed Farid

{"title":"Connected Vehicles Versus Conventional Traffic Congestion Mitigation Measures: An Operational Economic Analysis","authors":"Mutasem Alzoubaidi, A. Al-Balbissi, Abdel-Rahman Alzoubaidi, Amr Alzoubaidi, Baha Azzeh, Ahmed Al-Mansour, Ahmed Farid","doi":"10.32010/26166127.2021.4.2.155.169","DOIUrl":"https://doi.org/10.32010/26166127.2021.4.2.155.169","url":null,"abstract":"This paper conducted an operational, economic analysis to assess alternative solutions to traffic congestion. They involved integrating adaptive traffic signal control (ATSC) with connected vehicle technology (ATSC-CV) and the application of various conventional and unconventional solutions. The studied conventional scenarios include signal timing optimization, signal actuation, and upgrading existing intersections to interchanges. There were unconventional scenarios involving converting two intersections to interchanges and the third to a continuous green-T intersection (CGTI). Different unconventional alternatives involved deploying ATSC-CV-based systems assuming varying market penetration rates (MPRs). The operational performance of each alternative was analyzed using VISSIM microsimulation software. To model the driving behavior of CVs, Python programming language was used through the COM interface in VISSIM. One-way analysis of variance (ANOVA) and post-hoc testing results indicate that implementing any suggested alternative would substantially decrease the mean vehicular travel time compared to the fixed signal control strategy currently implemented. Specifically, the ATSC-CVbased systems yielded notable travel time reductions ranging from 9.5% to 21.3%. Also, ANOVA results revealed that the highest benefit-to-cost ratio among all alternatives belonged to scenarios in which the MPRs of CVs were 100%. It was also found that ATSC-CV-based systems with MPRs of 25% and 50% would be as feasible as converting signalized intersections to underpass interchanges.","PeriodicalId":275688,"journal":{"name":"Azerbaijan Journal of High Performance Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125638184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

PREDICTION OF CARDIOVASCULAR DISEASES (CVDS) USING MACHINE LEARNING TECHNIQUES IN HEALTH CARE CENTERS 在医疗中心使用机器学习技术预测心血管疾病(cvd)

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.267.279

Hafiz Gulfam Ahmad, Muhammad Jasim Shah

{"title":"PREDICTION OF CARDIOVASCULAR DISEASES (CVDS) USING MACHINE LEARNING TECHNIQUES IN HEALTH CARE CENTERS","authors":"Hafiz Gulfam Ahmad, Muhammad Jasim Shah","doi":"10.32010/26166127.2021.4.2.267.279","DOIUrl":"https://doi.org/10.32010/26166127.2021.4.2.267.279","url":null,"abstract":"Cardiovascular Diseases (CVDs) are one of the most common health problems nowadays. Early diagnosis of heart disease is a significant concern for health professionals in medical centers. An incorrect forecast is more likely to have negative effects, such as disability or even death. Our research is motivated by the desire to predict cardiovascular diseases based on data mining that can be valuable to medical centers. Various data mining approaches are used for the early detection of cardiac diseases. This paper examines several research publications that work on various heart diseases. We compare and contrast several machine learning methods, such as KNN, ANN, Decision Tree, SVM, and Random Forest. We looked at 918 observations with several features related to heart disease. A comparative study with age and sex is established to predict cardiac disease using the decision tree approach. Our dataset contains 11 features that are used to forecast possible heart disease. One of the attributes indicates that the age factor has the most significant impact on heart disease. According to our findings, heart attacks cause four out of every five CVD deaths, with one-third of these deaths occurring suddenly in those under 70.","PeriodicalId":275688,"journal":{"name":"Azerbaijan Journal of High Performance Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122937435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A SURVEY OF RETRIEVAL ALGORITHMS AND THEIR PARALLELIZATION IN LARGE-SCALE SYSTEMS 大规模系统中检索算法及其并行化研究综述

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.263.266

Suleyman Suleymanzade

引用次数: 0

A REVIEW ON IMAGE PROCESSING TECHNIQUES FOR BREAST CANCER ANALYSIS 乳腺癌图像处理技术研究进展

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.206.231

Iqra Rashid, Javeria Naz

引用次数: 0

END-TO-END RELATION EXTRACTION ON CLINICAL TEXT DATA USING NATURAL LANGUAGE PROCESSING 基于自然语言处理的临床文本数据端到端关系提取

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.232.241

Naveen S Pagad, Pradeep N

引用次数: 0

ExaLazy: A Model for Lazy-Copy Migration Mechanism to Support Distributed Exascale System 一种支持分布式百亿亿级系统的延迟复制迁移机制模型

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.170.187

Ehsan Mousavi Khaneghah, Tayebeh Khoshrooynemati, Azar Feyziyev

{"title":"ExaLazy: A Model for Lazy-Copy Migration Mechanism to Support Distributed Exascale System","authors":"Ehsan Mousavi Khaneghah, Tayebeh Khoshrooynemati, Azar Feyziyev","doi":"10.32010/26166127.2021.4.2.170.187","DOIUrl":"https://doi.org/10.32010/26166127.2021.4.2.170.187","url":null,"abstract":"There is a possibility of dynamic and interactive nature occurring at any moment of the scientific program implementation process in the computing system. While affecting the computational processes in the system, dynamic and interactive occurrence also affects the function of the elements that make up the management element of the computing system. The effect of dynamic and interactive events on the function of the elements that make up the management element of the computing system causes the time required to run the user program to increase or the function of these elements to change. These changes either increase the execution time of the scientific program or make the system incapable of executing the program. The occurrence of dynamic and interactive nature creates new situations in the computing system that the mechanisms to deal with when designing the computing system are not defined and considered. In this paper, the Lazy-Copy process migration management mechanism, specifically the Lazy-Copy mechanism in distributed large-scale systems, the effects of dynamic and interactive occurrence in the computational system investigate, and the effects of dynamic and interactive occurrence on the system investigate. Computational processes on the migration process and vector algebras try to analyze and enable the Lazy-Copy process migration mechanism in support of distributed largescale systems despite dynamic and interactive events","PeriodicalId":275688,"journal":{"name":"Azerbaijan Journal of High Performance Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130256192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industry 4.0 in Cosmetic Companies 化妆品公司的工业4.0

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.135.154

Z. Pólkowski, A. Wierzbicka

引用次数: 0

CHALLENGES OF USING THE FUZZY APPROACH IN EXASCALE COMPUTING SYSTEMS 在百亿亿次计算系统中使用模糊方法的挑战

Azerbaijan Journal of High Performance Computing Pub Date : 2021-12-31 DOI: 10.32010/26166127.2021.4.2.198.205

N. Ismayilova

引用次数: 0

SENTIMENT ANALYSIS OF CUSTOMER REVIEWS 客户评论的情感分析

Azerbaijan Journal of High Performance Computing Pub Date : 2021-06-30 DOI: 10.32010/26166127.2021.4.1.113.125

Syed Rashiq Nazar, T. Bhattasali

{"title":"SENTIMENT ANALYSIS OF CUSTOMER REVIEWS","authors":"Syed Rashiq Nazar, T. Bhattasali","doi":"10.32010/26166127.2021.4.1.113.125","DOIUrl":"https://doi.org/10.32010/26166127.2021.4.1.113.125","url":null,"abstract":"Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.","PeriodicalId":275688,"journal":{"name":"Azerbaijan Journal of High Performance Computing","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134454082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TRUST BASE JOB SCHEDULING IN CLOUD COMPUTING 云计算中基于信任的作业调度

Azerbaijan Journal of High Performance Computing Pub Date : 2021-06-30 DOI: 10.32010/26166127.2021.4.1.39.47

F. Rezaei, Shamsollah Ghanbari

{"title":"TRUST BASE JOB SCHEDULING IN CLOUD COMPUTING","authors":"F. Rezaei, Shamsollah Ghanbari","doi":"10.32010/26166127.2021.4.1.39.47","DOIUrl":"https://doi.org/10.32010/26166127.2021.4.1.39.47","url":null,"abstract":"Cloud computing is a new technology recently being developed seriously. Scheduling is an essential issue in the area of cloud computing. There is an extensive literature concerning scheduling in the area of distributed systems. Some of them are applicable for cloud computing. Traditional scheduling methods are unable to provide scheduling in cloud environments. According to a simple classification, scheduling algorithms in the cloud environment are divided into two main groups: batch mode and online heuristics scheduling. This paper focuses on the trust of cloud-based scheduling algorithms. According to the literature, the existing algorithm examinee latest algorithm is related to an algorithm trying to optimize scheduling using the Trust method. The existing algorithm has some drawbacks, including the additional overhead and inaccessibility to the past transaction data. This paper is an improvement of the trust-based algorithm to reduce the drawbacks of the existing algorithms. Experimental results indicate that the proposed method can execute better than the previous method. The efficiency of this method depends on the number of nods and tasks. The more trust in the number of nods and tasks, the more the performance improves when the time cost increases","PeriodicalId":275688,"journal":{"name":"Azerbaijan Journal of High Performance Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132760925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0