Acta Informatica Pragensia最新文献_第5页

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data 基于阿尔茨海默病数据的机器学习分类器性能指标的比较分析

Acta Informatica Pragensia Pub Date : 2022-11-03 DOI: 10.18267/j.aip.198

Sivakani Rajayyan, Syed Masood Mohamed Mustafa

{"title":"Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data","authors":"Sivakani Rajayyan, Syed Masood Mohamed Mustafa","doi":"10.18267/j.aip.198","DOIUrl":"https://doi.org/10.18267/j.aip.198","url":null,"abstract":"Alzheimer's disease is a brain memory loss disease. Usually, it will affect persons over 60 years of age. The literature has revealed that it is quite difficult to diagnose the disease, so researchers are trying to predict the disease in the early stage. This paper proposes a framework to classify Alzheimer's patients and to predict the best classification algorithm. The Bestfirst and CfssubsetEval methods are used for feature selection. A multi-class classification is done using machine learning algorithms, namely the naïve Bayes algorithm, the logistic algorithm, the SMO/SMV algorithm and the random forest algorithm. The classification accuracy of the algorithms is 67.68%, 84.58%, 87.42%, and 88.90% respectively. The validation applied is 10-fold cross-validation. Then, a confusion matrix is generated and class-wise performance is analysed to find the best algorithm. The ADNI database is used for the implementation process. To compare the performance of the proposed model, the OASIS dataset is applied to the model with the same algorithms and the accuracy of the algorithms is 98%, 99%, 99% and 100% respectively. Also, the time for the model construction is compared for both datasets. The proposed work is compared with existing studies to check the efficiency of the proposed model.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48526266","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

Classification of Handwritten Text Signatures by Person and Gender: A Comparative Study of Transfer Learning Methods 手写体文本签名的人与性别分类:迁移学习方法的比较研究

Acta Informatica Pragensia Pub Date : 2022-11-02 DOI: 10.18267/j.aip.197

Sidar Agduk, Emrah Aydemir

引用次数: 2

Data Analytics Approach for Short-term Sales Forecasts Using Limited Information in E-commerce Marketplace 基于有限信息的电子商务市场短期销售预测的数据分析方法

Acta Informatica Pragensia Pub Date : 2022-11-01 DOI: 10.18267/j.aip.196

Christopher Chin Fung Chee, Kang Leng Chiew, I. N. Sarbini, Eileen Kho Huei Jing

{"title":"Data Analytics Approach for Short-term Sales Forecasts Using Limited Information in E-commerce Marketplace","authors":"Christopher Chin Fung Chee, Kang Leng Chiew, I. N. Sarbini, Eileen Kho Huei Jing","doi":"10.18267/j.aip.196","DOIUrl":"https://doi.org/10.18267/j.aip.196","url":null,"abstract":"E-commerce has become very important in our daily lives. Many business transactions are made easier on this platform. Sellers and consumers are the two main parties that gain a lot of benefits from it. Although many sellers are attracted to set up their businesses on this online platform, it also causes challenges such as a highly competitive business environment and unpredictable sales. Thus, we propose a data analytics approach for short-term sales forecasts using limited information in the e-commerce marketplace. Product details are scraped from the e-commerce marketplace using a content scraping tool. Since the information in the e-commerce marketplace is limited and essential, scraped product details are pre-processed and constructed into meaningful data. These data are used in the computation of the forecasting methods. Three types of quantitative forecasting methods are computed and compared. These are simple moving average, dynamic linear regression and exponential smoothing. Three different evaluation metrics, namely mean absolute deviation, mean absolute percentage error and mean squared error, are used for the performance evaluation in order to determine the most suitable forecasting method. In our experiment, we found that the simple moving average has the best forecasting accuracy among other forecasting methods. Therefore, the application of the simple moving average forecasting method is suitable and can be used in the e-commerce marketplace for sales forecasting.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45900827","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: 1

Use of Intelligent Navigation and Crowd Collaboration for Automated Collection of Data on Transport Infrastructure 使用智能导航和人群协作自动收集交通基础设施数据

Acta Informatica Pragensia Pub Date : 2022-10-18 DOI: 10.18267/j.aip.195

T. Tvrzský

{"title":"Use of Intelligent Navigation and Crowd Collaboration for Automated Collection of Data on Transport Infrastructure","authors":"T. Tvrzský","doi":"10.18267/j.aip.195","DOIUrl":"https://doi.org/10.18267/j.aip.195","url":null,"abstract":"The article briefly presents the main results of an applied research project to the professional public. The project output is a solution that enables the recognition of selected types of traffic signs using artificial intelligence for image recognition. This computationally intensive process is implemented in mobile phones. In order to achieve the involvement of the general public in the collection of data on transport infrastructure, the entire solution is part of navigation for mobile phones and supported by two functions that motivate users to collect data, i.e., scan the area in front of the vehicle with the phone's camera. The first function is the projection of the route into the real environment (the so-called augmented reality mode), and the second function is the possibility of video recording the drive. The video recording is cryptographically signed to ensure authenticity in administrative or judicial proceedings, e.g., when proving the course and circumstances of a traffic accident. The collection of data on transport infrastructure is completely anonymous in compliance with applicable laws. The data about recognized traffic signs will not only serve the navigation provider to improve the user experience but the processed data will also be exported to community-created world maps (project OpenStreetMap).","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43073327","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

Survey on Electronic Health Record Management Using Amalgamation of Artificial Intelligence and Blockchain Technologies 人工智能与区块链技术相结合的电子病历管理研究

Acta Informatica Pragensia Pub Date : 2022-09-25 DOI: 10.18267/j.aip.194

K. P. Rao, S. Manvi

{"title":"Survey on Electronic Health Record Management Using Amalgamation of Artificial Intelligence and Blockchain Technologies","authors":"K. P. Rao, S. Manvi","doi":"10.18267/j.aip.194","DOIUrl":"https://doi.org/10.18267/j.aip.194","url":null,"abstract":"In the present times, the healthcare sector has seen an enormous growth in the usage of technology ranging from EHRs (electronic health records) to personal health trackers. Currently, there is a need for managing EHRs effectively with respect to storage, privacy and security measures. State-of-art technologies such as blockchain and artificial intelligence (AI) are applied in the healthcare domain. Innovation in AI is steadily advancing and is finding its place in different industries. The integration of blockchain and AI looks promising as there are several benefits. Blockchain can make the AI more secure and autonomous whereas AI can drive the blockchain with intelligence. The objective of this article is to explore the uses of blockchain as well as AI technology in the field of healthcare. We aim to survey the advantages, issues and challenges of integrating blockchain with AI technology, including future research directions in the healthcare domain. In this study, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) rules and an efficient searching protocol were used to examine several scientific databases to recognize and investigate every important publication. A solid systematic review was carried out on integration of blockchain and AI in the healthcare domain to identify existing challenges and benefits of integrating these two technologies in healthcare. Our study found that the integration of AI and blockchain technology has a potential to provide several benefits in terms of performance and security which conventional EHRs lack. The inherent benefits of blockchain and AI together are limitless, but the bare outcomes based on blockchain powered by AI technology are yet to be obtained. In addition, the outcome of our detailed study may aid researchers to carry out further research.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43358988","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

A Novel Automatic Relational Database Normalization Method 一种新的关系数据库自动规范化方法

Acta Informatica Pragensia Pub Date : 2022-09-09 DOI: 10.18267/j.aip.193

Emre Akadal, Mehmet Hakan Satman

引用次数: 0

Service Desk Onboarding Training Environment 服务台入职培训环境

Acta Informatica Pragensia Pub Date : 2022-08-19 DOI: 10.18267/j.aip.188

Michal Dostál

引用次数: 0

Increasing Efficiency in Inventory Control of Products with Sporadic Demand Using Simulation 利用仿真方法提高零星需求产品库存控制效率

Acta Informatica Pragensia Pub Date : 2022-08-19 DOI: 10.18267/j.aip.184

K. Hušková, J. Dyntar

{"title":"Increasing Efficiency in Inventory Control of Products with Sporadic Demand Using Simulation","authors":"K. Hušková, J. Dyntar","doi":"10.18267/j.aip.184","DOIUrl":"https://doi.org/10.18267/j.aip.184","url":null,"abstract":"The goal of this paper is to examine whether, in Q-system inventory control policy, a combination of the reorder point exceeding order quantity leads to minimal holding and ordering costs when dealing with sporadic demand. For this purpose, a past stock movement simulation is applied to a set of randomly generated data with different numbers of zero demand periods ranging from 10 to 90%. The outputs of the simulation prove that in situations where stock holding costs are too high, the simulation tends to reduce average stock by overcoming periods between two demand peaks with an increase in the numbers of small replenishment orders and reaches lower stock holding and ordering costs. Furthermore, the correlation analysis proves that there is a statistically significant relationship (r = .847, p = .004) between the number of time series that reach minimal holding and ordering costs under the control of reorder point (replenishment order) and the demand standard deviation affected by the evolving sporadicity. These findings can support decision making linked with inventory management of products with sporadic demand and contribute to development of business information systems.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43622899","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: 1

Current Status and Plans for Further Development of Acta Informatica Pragensia 《布拉格信息学报》的现状与进一步发展计划

Acta Informatica Pragensia Pub Date : 2022-08-19 DOI: 10.18267/j.aip.191

Zdenek Smutný, S. Mildeová

引用次数: 0

Classification of Eye Images by Personal Details With Transfer Learning Algorithms 基于迁移学习算法的人眼图像个人细节分类

Acta Informatica Pragensia Pub Date : 2022-08-19 DOI: 10.18267/j.aip.190

Cemal Aktürk, Emrah Aydemir, Yasr Mahdi Hama Rashid

{"title":"Classification of Eye Images by Personal Details With Transfer Learning Algorithms","authors":"Cemal Aktürk, Emrah Aydemir, Yasr Mahdi Hama Rashid","doi":"10.18267/j.aip.190","DOIUrl":"https://doi.org/10.18267/j.aip.190","url":null,"abstract":"Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer learning. In this study, a total of 1980 eye contour images of 96 different people were collected in order to solve the problem of recognizing people from their eye images. These collected data were classified in terms of person, age and gender. In the classification made for eye recognition, feature extraction was performed with 32 different transfer learning algorithms in the Python program and classified using the RandomForest algorithm for person estimation. According to the results of the research, 30 different classification algorithms were used, with the ResNet50 algorithm being the most successful, and the data were also classified in terms of age and gender. Thus, the highest success rates of 83.52%, 96.41% and 77.56% were obtained in person, age and gender classification, respectively. The study shows that people can be identified only by eye images obtained from a smartphone without using any special equipment, and even the characteristics of people such as age and gender can be determined. In addition, it has been concluded that eye images can be used in a more efficient and practical biometric recognition system than iris recognition.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46250008","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: 1