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Autonomous Last Mile Shuttle ISEAUTO for Education and Research 用于教育和研究的自主最后一英里班车ISEAUTO
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-01-01 DOI: 10.4018/ijaiml.2020010102
R. Sell, Mairo Leier, A. Rassõlkin, J. Ernits
{"title":"Autonomous Last Mile Shuttle ISEAUTO for Education and Research","authors":"R. Sell, Mairo Leier, A. Rassõlkin, J. Ernits","doi":"10.4018/ijaiml.2020010102","DOIUrl":"https://doi.org/10.4018/ijaiml.2020010102","url":null,"abstract":"The article introduces an educational and research project ISEAUTO, targeted to using self-driving cars to solve urban mobility issues. The project focusses on the design and development of an autonomous shuttle as a collaboration between academic staff of the university, students, and a partner company. The article presents an account of the experience of developing vehicle from scratch in one year using a stock electric vehicle, widely available sensors and open source software. Technical solutions based on the latest trends in autonomous mobility are conferred, special attention is given to control and software architectures. In addition to reaching the goal of making the shuttle drive autonomously by the end of the first year of the project, it was possible to combine various tasks with teaching and award more than 460 ECTS to participating students. The project continues and a commercial version of the vehicle is in development.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126568972","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 Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques 时间序列基序发现技术在心电信号分类中的应用综述:利用时间序列基序发现技术对心电信号进行分类
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2019-07-01 DOI: 10.4018/ijaiml.2019070103
E. Ramanujam, S. Padmavathi
{"title":"A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques","authors":"E. Ramanujam, S. Padmavathi","doi":"10.4018/ijaiml.2019070103","DOIUrl":"https://doi.org/10.4018/ijaiml.2019070103","url":null,"abstract":"Cardiovascular disease diagnosis from an ECG signal plays an important and significant role in the health care system. Recently, numerous researchers have developed an automatic time series-based multi-step diagnosis system for the fast and accurate diagnosis of ECG abnormalities. The multi-step procedure involves ECG signal acquisition, signal pre-processing, feature extraction, and classification. Among which, the feature extraction plays a vital role in the field of accurate diagnosis. The features may be different types such as statistical, morphological, wavelet or any other signal-based approach. This article discusses various time series motif-based feature extraction techniques with respect to a different dimension of ECG signal.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130473480","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: 2
Intelligent System for Credit Risk Management in Financial Institutions 金融机构信用风险管理智能系统
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2019-07-01 DOI: 10.4018/ijaiml.2019070104
Philip Sarfo-Manu, Gifty Siaw, Peter Appiahene
{"title":"Intelligent System for Credit Risk Management in Financial Institutions","authors":"Philip Sarfo-Manu, Gifty Siaw, Peter Appiahene","doi":"10.4018/ijaiml.2019070104","DOIUrl":"https://doi.org/10.4018/ijaiml.2019070104","url":null,"abstract":"Credit crunch is an alarming challenge facing financial institutions in Ghana due to their inability to manage credit risk. Failure to manage credit risk may lead to customers defaulting and institutions becoming bankrupt, making it a major concern for financial institutions and the government. The assessment and evaluation of loan applications based on a loan officer's subjective assessment and human judgment is inefficient, inconsistent, non-uniform, and time consuming. Therefore, a knowledge discovery tool is required to help in decision making regarding the approval of loan application. The aim of this project is to develop an intelligent system based on a decision tree model to manage credit risk. Data was obtained from the bank loan histories. The data is comprised of four hundred observations with seven variables: client age, amount requested, dependents, collateral value, employment sector, employment type, and results. The results of study suggest that the proposed system can be used to predict client eligibility for loans with an accuracy rate of 70%.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133891207","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
Multilayer Neural Network Technique for Parsing the Natural Language Sentences 自然语言句子分析的多层神经网络技术
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2019-07-01 DOI: 10.4018/ijaiml.2019070102
M. Singh, Sukrati Chaturvedi, Deepak Shudhalwar
{"title":"Multilayer Neural Network Technique for Parsing the Natural Language Sentences","authors":"M. Singh, Sukrati Chaturvedi, Deepak Shudhalwar","doi":"10.4018/ijaiml.2019070102","DOIUrl":"https://doi.org/10.4018/ijaiml.2019070102","url":null,"abstract":"In this article is presented an approach for parsing natural language sentences using neural networks. The pre-processing technique is applied to code the sentences into string of bits and after the training process is started, is formed into patterns available in the form of coded information. The multilayer feed forward networks are used here for training to classify the words into appropriate syntactical categories. The classified words represent the parsed information of the given sentences. The main function of the network is to assign the respective syntactical categories to each word of a sentence with a minimal error rate. The comparison between the two popular neural network approaches i.e. feed forward neural network and radial basis neural network is presented to analyze performance for the new and unknown sentences.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132783604","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: 2
Multi-Objective Materialized View Selection Using Improved Strength Pareto Evolutionary Algorithm 基于改进强度Pareto进化算法的多目标物化视图选择
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2019-07-01 DOI: 10.4018/ijaiml.2019070101
J. Prakash, T. Kumar
{"title":"Multi-Objective Materialized View Selection Using Improved Strength Pareto Evolutionary Algorithm","authors":"J. Prakash, T. Kumar","doi":"10.4018/ijaiml.2019070101","DOIUrl":"https://doi.org/10.4018/ijaiml.2019070101","url":null,"abstract":"A data warehouse system uses materialized views extensively in order to speedily tackle analytical queries. Considering that all possible views cannot be materialized due to maintenance cost and storage constraints, the selection of an appropriate set of views to materialize that achieve an optimal trade-off among query response time, maintenance cost, and the storage constraint becomes an essential necessity. The selection of such an appropriate set of views for materialization is referred to as the materialized views selection problem, which is an NP-Complete problem. In the last two decades, several new selection approaches, based on heuristics, have been proposed. Most of these have used a single objective or weighted sum approach to address the various constraints. In this article, an attempt has been made to address the bi-objective materialized view selection problem, where the objective is to minimize the view evaluation cost of materialized views and the view evaluation cost of the non-materialized views, using the Improved Strength Pareto Evolutionary Algorithm. The experimental results show that the proposed multi-objective view selection algorithm is able to select the Top-K views that achieves a reasonable trade-off between the two objectives. Materializing these selected views would reduce the query response times for analytical queries and thereby facilitates the decision-making process.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131427352","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: 9
Quorum Sensing Digital Simulations for the Emergence of Scalable and Cooperative Artificial Networks 群体感应数字模拟可扩展和协作人工网络的出现
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2019-01-01 DOI: 10.4018/IJAIML.2019010102
Nedjma Djezzar, Iñaki Fernández Pérez, Noureddine Djedi, Y. Duthen
{"title":"Quorum Sensing Digital Simulations for the Emergence of Scalable and Cooperative Artificial Networks","authors":"Nedjma Djezzar, Iñaki Fernández Pérez, Noureddine Djedi, Y. Duthen","doi":"10.4018/IJAIML.2019010102","DOIUrl":"https://doi.org/10.4018/IJAIML.2019010102","url":null,"abstract":"This article proposes digital simulations of a bacterial communication system termed quorum sensing, and investigates the design of artificial networks build on the behavior of bacteria societies that tweet using quorum sensing signals. To this end, this article proposes a cell-based model that uses a “bottom-up” agent-based model coupled with ordinary differential equations, and develops the abstraction of intracellular dynamics as a basis underlying cooperative artificial network formation. Results show the emergence of self-sustainable behaviors thanks to the proposed model of metabolism that permits bacteria to grow, reproduce, interact, and coordinate at the population level to exhibit near-perfect bioluminescence behaviors. Moreover, the evolution of cooperation in the subsequent artificial network leads to the emergence of non-predicted coercive strategies. Coercion has been shown to be beneficial to share common interests between variants of cooperators leading the entire population of cells to be networked.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125178521","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
Early Warning System Framework Proposal, Based on Big Data Environment 基于大数据环境的预警系统框架方案
Int. J. Artif. Intell. Mach. Learn. Pub Date : 2019-01-01 DOI: 10.4018/IJAIML.2019010103
G. Klepac, R. Kopal, Leo Mršić
{"title":"Early Warning System Framework Proposal, Based on Big Data Environment","authors":"G. Klepac, R. Kopal, Leo Mršić","doi":"10.4018/IJAIML.2019010103","DOIUrl":"https://doi.org/10.4018/IJAIML.2019010103","url":null,"abstract":"Early warning systems are made with the purpose to efficiently recognize deviant and potentially dangerous trends related to company business as early as possible. The Big Data environment gives new opportunities and new approaches in analytical processes. There are numerous ways how to set up early warning systems within a company. The Big Data environment forces companies to apply new ways of thinking and use new disposable data sources. This article gives a novel concept for an early warning system design within a company, which is applicable in different industries. The core of the proposed framework is a hybrid fuzzy expert system which can contain a variety of data mining predictive models responsible for some specific areas as addition to traditional rule blocks. It can also include social network analysis metrics based on linguistic variables and incorporated within the rule blocks. As a part of this framework, SNA methods are also explained and introduced as powerful and unique tool to be used in modern early warning systems.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"36 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115050550","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
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