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":"1981 1","pages":"951-962"},"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

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

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":"27 1","pages":"1039-1051"},"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

Data Science Applied to Marketing: A Literature Review 数据科学在市场营销中的应用:文献综述

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).0006

A. Rosário, Luís Bettencourt Moniz, Rui Cruz

引用次数: 5

Using Artificial Intelligence in IC Substrate Production Predicting 人工智能在IC衬底生产预测中的应用

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-07-19 DOI: 10.21203/rs.3.rs-552378/v1

Zhifang Liu

{"title":"Using Artificial Intelligence in IC Substrate Production Predicting","authors":"Zhifang Liu","doi":"10.21203/rs.3.rs-552378/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-552378/v1","url":null,"abstract":"\u0000 Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"79 1","pages":"637-654"},"PeriodicalIF":1.1,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73334808","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

Anomaly Chicken Cell Identification Using Deep Learning Techniques 利用深度学习技术识别异常鸡细胞

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-07-01 DOI: 10.6688/JISE.202107_37(4).0006

Natinai Jinsakul, Cheng-Fa Tsai, Chia-En Tsai

引用次数: 0

Design of a Lightweight Palmf-Vein Authentication System Based on Model Compression 基于模型压缩的轻量级掌纹认证系统设计

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-07-01 DOI: 10.6688/JISE.202107_37(4).0005

Zih-Ching Chen, Sin-Ye Jhong, Chin-Hsien Hsia

{"title":"Design of a Lightweight Palmf-Vein Authentication System Based on Model Compression","authors":"Zih-Ching Chen, Sin-Ye Jhong, Chin-Hsien Hsia","doi":"10.6688/JISE.202107_37(4).0005","DOIUrl":"https://doi.org/10.6688/JISE.202107_37(4).0005","url":null,"abstract":"Palm-vein authentication is a secure and highly accurate vein feature authentication technology that has recently gained a lot of attention. Convolutional neural networks (CNNs) provide relatively high performance in the field of image processing, computer vision, and have been adapted for feature learning of palm-vein images. However, they often require high computation that not only are infeasible for real-time vein verification but also a challenge to apply on mobile devices. To address this limitation, we proposed a lightweight MobileNet based deep learning (DL) architecture with depthwise separable convolution (DSC) and adopt a knowledge distillation (KD) method to learn the knowledge from the more complex CNN, which makes it small but effective. Through the depth of separable convolution, the number of model parameters is significantly decreased, while still remaining high accuracy and stable performance. Experiments demonstrated that the size of the proposed model is 100 times less than the Inception_v3 model, while the performance can go beyond 98% correct identification rate (CIR) for the CASIA database.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"16 1","pages":"809-825"},"PeriodicalIF":1.1,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77644310","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

Residual Network for Deep Reinforcement Learning with Attention Mechanism 基于注意机制的深度强化学习残差网络

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-05-01 DOI: 10.6688/JISE.202105_37(3).0002

Hanhua Zhu, Tomoyuki Kaneko

引用次数: 0

Optimizing Read Operations of Hadoop Distributed File System on Heterogeneous Storages Hadoop分布式文件系统在异构存储上的读操作优化

IF 1.1 4区计算机科学

Journal of Information Science and Engineering Pub Date : 2021-05-01 DOI: 10.6688/JISE.202105_37(3).0013

Jongbaeg Lee, Jong-Woo Lee, Sang-Won Lee

引用次数: 0