Journal of Information Science and Engineering最新文献

An Approach to Monitor Vaccine Quality During Distribution Using Internet of Things 基于物联网的疫苗配送质量监控方法

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2022-01-01 DOI: 10.6688/JISE.202209_38(5).0005

I. K. A. Enriko, Fariz Alemuda, Daniel Adrianto

{"title":"An Approach to Monitor Vaccine Quality During Distribution Using Internet of Things","authors":"I. K. A. Enriko, Fariz Alemuda, Daniel Adrianto","doi":"10.6688/JISE.202209_38(5).0005","DOIUrl":"https://doi.org/10.6688/JISE.202209_38(5).0005","url":null,"abstract":"Vaccines containing living entities must be stored in a strictly controlled environment;otherwise, the vaccine would be obsolete if the criteria did not occur. Hence, the current distribution vaccine is only implemented in the local system. There is no interconnection between the temperature sensor to the command center. Only the local staff can know the status while they did not maintain it continuously. Moreover, the system relies on a paper -based report, so there is no prevention system to mitigate any potential failure. This research proposes an IoT-based vaccine monitoring system to help stakeholders maintain vaccine distribution. This research focuses on the distribution of Sinovac as the most extensive and most-ready stock. The overall system consists of devices, networks, and an application. Devices reside either in a static environment or a mobile environment. Network connectivity relies on LoRaWAN, and GSM depends on the actual availabilities. Application is responsible for displaying, track, and notifying the status of the vaccines. Furthermore, this research discusses the measurement method and testing method.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90286876","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 Optimized Modelling and Simulation on Task Scheduling for Multi-Processor System using Hybridized ACO-CVOA 基于杂交ACO-CVOA的多处理器系统任务调度优化建模与仿真

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2022-01-01 DOI: 10.6688/JISE.202209_38(5).0001

A. Priya, S. Sahana

{"title":"An Optimized Modelling and Simulation on Task Scheduling for Multi-Processor System using Hybridized ACO-CVOA","authors":"A. Priya, S. Sahana","doi":"10.6688/JISE.202209_38(5).0001","DOIUrl":"https://doi.org/10.6688/JISE.202209_38(5).0001","url":null,"abstract":"Task allocation on the multi-processor system distributes the task according to capacity of each processor that optimally selects the best. The optimal selection of processor leads to increase performance and this also impact the makespan. In task scheduling, most of the research work focused on the objective of managing the power consumption and time complexity due to improper selection of processors for the given task items. This paper mainly focusses on the modelling of the optimal task allocation using a novel hybridization method of Ant Colony Optimization (ACO) with Corona Virus Optimization Algorithm (CVOA). There are several other methods that estimate the weight value of processors and find the best match to the task by using the traditional distance estimation method or by using standard rule-based validation. The proposed algorithm searches the best selection of machines for the corresponding parameters and weight value iteratively and finally recognizes the capacity of it. The performance of proposed method is evaluated on the parameters of elapsed time, throughput and compared with the state-of-art methods. © 2022 Institute of Information Science. All rights reserved.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90161129","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

MedCheX: An Efficient COVID-19 Detection Model for Clinical Usage MedCheX:一种高效的COVID-19临床检测模型

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2022-01-01 DOI: 10.6688/jise.202207_38

Chi-Shiang Wang, Fang-Yi Su, J. Chiang

引用次数: 1

Spatiotemporal Data Warehousing for Event Tracking Applications 事件跟踪应用的时空数据仓库

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2022-01-01 DOI: 10.6688/JISE.202211_38(6).0007

F. S. Tseng, Annie Y. H. Chou

引用次数: 0

Data Science Projects in Pharmaceutical Industry 制药行业的数据科学项目

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-09-01 DOI: 10.6688/JISE.202109_37(5).0010

A. Pesqueira, M. Sousa, Pere Mercadé Melé, Á. Rocha, Miguel Sousa, Renato Lopes da Costa

{"title":"Data Science Projects in Pharmaceutical Industry","authors":"A. Pesqueira, M. Sousa, Pere Mercadé Melé, Á. Rocha, Miguel Sousa, Renato Lopes da Costa","doi":"10.6688/JISE.202109_37(5).0010","DOIUrl":"https://doi.org/10.6688/JISE.202109_37(5).0010","url":null,"abstract":"The purpose of this paper is to discuss the relevance of data science in Medical Affairs (MA) functions in the pharmaceutical industry, where data is becoming more important for the execution of activities and strategic planning in the health industry. This study analyses pharmaceutical companies who have a data science strategy and the variables that can influence the definition of a data science strategy in pharma companies in opposite to other pharmaceutical companies without a data science strategy. The current paper is empirical and the research approach consists of verifying the characteristics and differences between those two types of pharmaceutical companies. A questionnaire specifically for this research was developed and applied to a sample of 280 pharma companies. The development and analysis of the questionnaire was based on a Systematic Literature Review of studies published up to (and including) 2017 through a database search and backward and forward snowballing. In total, we evaluated 2247 papers, of which 11 included specific data science methodologies criteria used in medical affairs departments. It was also made a quantitative analysis based on data from a questionnaire applied to a Pharma organization. The findings indicate that there is good evidence in the empirical relation between Data Science and the strategies of the organization.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85549742","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

Deep Learning based Automated Fruit Nutrients Deficiency Recognition System 基于深度学习的水果营养缺乏自动识别系统

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-09-01 DOI: 10.6688/JISE.202109_37(5).0011

Ashwani Kumar Dubey, Yogesh Kumar, Rajeev Ratan, Á. Rocha

{"title":"Deep Learning based Automated Fruit Nutrients Deficiency Recognition System","authors":"Ashwani Kumar Dubey, Yogesh Kumar, Rajeev Ratan, Á. Rocha","doi":"10.6688/JISE.202109_37(5).0011","DOIUrl":"https://doi.org/10.6688/JISE.202109_37(5).0011","url":null,"abstract":"The recent development in deep learning allows us to develop a computer vision-based system for recognition, detection, and localization of nutrients deficiency in fruits. Due to the time constraints, it is important to use an optimized and fast system for fruit quality inspection. In this paper, the input is taken as an image. A deep learning-based method extracts low level and high-level features such as edges, geometrical, statistical, texture, intensity, etc. After validation of the system with the test data, the output is predicted by the system. The processing time is optimized by avoiding fully connected layers which further minimize the requirement of neurons in the network. The convolutional neural network extracts the features of the fruits, Rectified Linear Unit (ReLu) removes the non-fruit pixels. Pooling shrinks, the image by selecting the maximum value of the pixel. The process is repeated until the size of the image is at the desired level. The aim is to identify the objects and recognize them. The foreground region objects are of our interest and being segmented for higher-level image processing. The proposed system attains the accuracy of 99.30 % with the processing time of 3.207 sec.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76803792","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: 2

Big Data Based Knowledge Management vs. Traditional Knowledge Management: A People, Process and Technology Perspective 基于大数据的知识管理与传统知识管理:人、流程和技术视角

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-09-01 DOI: 10.6688/JISE.202109_37(5).0005

M. S. Sumbal, Murad Ali, U. Sahibzada, F. Nawaz, Adeel Tariq, H. Munir

{"title":"Big Data Based Knowledge Management vs. Traditional Knowledge Management: A People, Process and Technology Perspective","authors":"M. S. Sumbal, Murad Ali, U. Sahibzada, F. Nawaz, Adeel Tariq, H. Munir","doi":"10.6688/JISE.202109_37(5).0005","DOIUrl":"https://doi.org/10.6688/JISE.202109_37(5).0005","url":null,"abstract":"Value creation is one of the core aspects of Big Data. This concept of value creation can be linked to the efficient knowledge management within the organizations, in terms of knowledge creation, sharing and application, through which organizations can enhance their organizational performance. Little work has been done on the linkage of value creation from big data and the knowledge management capability of the organizations in terms of people, processes and technology which play a crucial role in effective knowledge management. This study contributes towards the existing body of knowledge by exploring this linkage of people, process and technology in relation to big data through the lens of knowledge management, by conducting a qualitative study in the oil and gas industry. The findings reveal that the KM capability of the organizations through big data can be explained through the Complex domain of Cynefin framework which involves probing, sensing and responding in which there are no right answers and instructive patterns (predictive knowledge) emerging from big data could be right or wrong depending upon the complexity of the situation. The useful and tested predictive knowledge by experts (people) can then emerge as good or best practice falling into complicated and simple domains of Cynefin framework.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80550156","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: 4

High Performance Post-Quantum Key Exchange on FPGAs fpga上的高性能后量子密钥交换

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-09-01 DOI: 10.6688/JISE.202109_37(5).0015

Po-Chun Kuo, Yu-Wei Chen, Yuan-Che Hsu, Chen-Mou Cheng, Wen-Ding Li, Bo-Yin Yang

引用次数: 46

Ensemble Case based Reasoning Imputation in Breast Cancer Classification 基于集成案例的推理归算在乳腺癌分类中的应用

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-09-01 DOI: 10.6688/JISE.202109_37(5).0004

Imane Chlioui, A. Idri, Ibtissam Abnane, M. Ezzat

{"title":"Ensemble Case based Reasoning Imputation in Breast Cancer Classification","authors":"Imane Chlioui, A. Idri, Ibtissam Abnane, M. Ezzat","doi":"10.6688/JISE.202109_37(5).0004","DOIUrl":"https://doi.org/10.6688/JISE.202109_37(5).0004","url":null,"abstract":"Missing Data (MD) is a common drawback that affects breast cancer classification. Thus, handling missing data is primordial before building any breast cancer classifier. This paper presents the impact of using ensemble Case-Based Reasoning (CBR) imputation on breast cancer classification. Thereafter, we evaluated the influence of CBR using parameter tuning and ensemble CBR (E-CBR) with three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random and NMAR: not missing at random) and nine percentages (10% to 90%) on the accuracy rates of five classifiers: Decision trees, Random forest, K-nearest neighbor, Support vector machine and Multi-layer perceptron over two Wisconsin breast cancer datasets. All experiments were implemented using Weka JAVA API code 3.8; SPSS v20 was used for statistical tests. The findings confirmed that E-CBR yields to better results compared to CBR for the five classifiers. The MD percentage affects negatively the classifier performance: as the MD percentage increases, the accuracy rates of the classifier decrease regardless the MD mechanism and technique. RF with E-CBR outperformed all the other combinations (MD technique, classifier) with 89.72% for MCAR, 87.08% for MAR and 86.84% for NMAR.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78162520","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: 3

Exploiting Machine Learning and Feature Selection Algorithms to Predict Instructor Performance in Higher Education 利用机器学习和特征选择算法预测高等教育教师的表现

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-09-01 DOI: 10.6688/JISE.202109_37(5).0001

Ravinder Ahuja, S. C. Sharma

{"title":"Exploiting Machine Learning and Feature Selection Algorithms to Predict Instructor Performance in Higher Education","authors":"Ravinder Ahuja, S. C. Sharma","doi":"10.6688/JISE.202109_37(5).0001","DOIUrl":"https://doi.org/10.6688/JISE.202109_37(5).0001","url":null,"abstract":"Machine learning has emerged as the most important and widely used tool in resolving the administrative and other educational related problems. Most of the research in the educational field centers on demonstrating the student's potential rather than focusing on faculty quality. In this paper the performance of the instructor is evaluated through feedback collected from students in the questionnaire form. The unlabelled dataset is taken from UCI machine learning repository consisting of 5820 records with 33 attributes. Firstly, the dataset is labelled(three labels) using agglomerative clustering and the k-means algorithms. Further, five feature selection techniques (Random Forest,Principal Component Analysis, Recursive Feature Selection, Univariate Feature Selection, and Genetic Algorithm) are applied to extract essential features. After feature selection, twelve classification algorithms (K Nearest Neighbor, XGBoost, Multi-Layer Perceptron, AdaBoost, Random Forest, Logistic Regression, Decision Tree, Bagging, LightGBM, Support Vector Machine, Extra Tree and Naive Bayes) are applied using Python language. Out of all algorithms applied, Support Vector Machine with PCA feature selection technique has given the highest accuracy value 99.66%, recall value 99.66%, precision value 99.67%, and f-score value 99.67%. To prove that results are statistically different, we have applied ANOVA one way test.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86839919","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: 4