智能学习系统与应用(英文)最新文献_第4页

Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads 模糊规则改进城市道路红绿灯决策

智能学习系统与应用(英文) Pub Date : 2018-05-07 DOI: 10.4236/jilsa.2018.102003

J. A. C. Rocha, S. I. Martínez, J. L. Menchaca, J. D. T. Villanueva, M. G. T. Berrones, J. P. Cobos, D. U. Agundis

{"title":"Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads","authors":"J. A. C. Rocha, S. I. Martínez, J. L. Menchaca, J. D. T. Villanueva, M. G. T. Berrones, J. P. Cobos, D. U. Agundis","doi":"10.4236/jilsa.2018.102003","DOIUrl":"https://doi.org/10.4236/jilsa.2018.102003","url":null,"abstract":"Many researchers around the world are looking for developing techniques or technologies that cover traditional and recent constraints in urban traffic con-trol. Normally, such traffic devices are facing with a large scale of input data when they must to response in a reliable, suitable and fast way. Because of such statement, the paper is devoted to introduce a proposal for enhancing the traffic light decisions. The principal goal is that a semaphore can provide a correct and fluent vehicular mobility. However, the traditional semaphore operative ways are outdated. We present in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of a fuzzy inference system helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using a fuzzy inference approach the decisions of the system improve almost 18% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"10 1","pages":"36-45"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733107","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

Interval-Based Out-of-Order Event Processing in Intelligent Manufacturing 智能制造中基于区间的无序事件处理

智能学习系统与应用(英文) Pub Date : 2018-05-07 DOI: 10.4236/JILSA.2018.102002

Chunjie Zhou, Pengfei Dai, Zhenxing Zhang, Tong Liu

引用次数: 0

BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data BioBroker:异构生物医学本体和数据的知识发现框架

智能学习系统与应用(英文) Pub Date : 2018-03-20 DOI: 10.4236/JILSA.2018.101001

F. Shen, Yugyung Lee

{"title":"BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data","authors":"F. Shen, Yugyung Lee","doi":"10.4236/JILSA.2018.101001","DOIUrl":"https://doi.org/10.4236/JILSA.2018.101001","url":null,"abstract":"A large number of ontologies have been introduced by the biomedical community in recent years. Knowledge discovery for entity identification from ontology has become an important research area, and it is always interesting to discovery how associations are established to connect concepts in a single ontology or across multiple ontologies. However, due to the exponential growth of biomedical big data and their complicated associations, it becomes very challenging to detect key associations among entities in an inefficient dynamic manner. Therefore, there exists a gap between the increasing needs for association detection and large volume of biomedical ontologies. In this paper, to bridge this gap, we presented a knowledge discovery framework, the BioBroker, for grouping entities to facilitate the process of biomedical knowledge discovery in an intelligent way. Specifically, we developed an innovative knowledge discovery algorithm that combines a graph clustering method and an indexing technique to discovery knowledge patterns over a set of interlinked data sources in an efficient way. We have demonstrated capabilities of the BioBroker for query execution with a use case study on a subset of the Bio2RDF life science linked data.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"10 1","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2018-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43891812","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: 8

Application of Union of Fuzzy Automata on Target Tracking 模糊自动机联合在目标跟踪中的应用

智能学习系统与应用(英文) Pub Date : 2017-09-26 DOI: 10.4236/JILSA.2017.94005

Qing-E Wu, Zhenyu Han, Qinge Wu

引用次数: 0

A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition 基于特征选择的手写数字识别最小特征子集

智能学习系统与应用(英文) Pub Date : 2017-09-26 DOI: 10.4236/JILSA.2017.94006

Areej Alsaafin, Ashraf Elnagar

{"title":"A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition","authors":"Areej Alsaafin, Ashraf Elnagar","doi":"10.4236/JILSA.2017.94006","DOIUrl":"https://doi.org/10.4236/JILSA.2017.94006","url":null,"abstract":"Many systems of handwritten digit recognition built using the complete set of features in order to enhance the accuracy. However, these systems lagged in terms of time and memory. These two issues are very critical issues especially for real time applications. Therefore, using Feature Selection (FS) with suitable machine learning technique for digit recognition contributes to facilitate solving the issues of time and memory by minimizing the number of features used to train the model. This paper examines various FS methods with several classification techniques using MNIST dataset. In addition, models of different algorithms (i.e. linear, non-linear, ensemble, and deep learning) are implemented and compared in order to study their suitability for digit recognition. The objective of this study is to identify a subset of relevant features that provides at least the same accuracy as the complete set of features in addition to reducing the required time, computational complexity, and required storage for digit recognition. The experimental results proved that 60% of the complete set of features reduces the training time up to third of the required time using the complete set of features. Moreover, the classifiers trained using the proposed subset achieve the same accuracy as the classifiers trained using the complete set of features.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"09 1","pages":"55-68"},"PeriodicalIF":0.0,"publicationDate":"2017-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46188246","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: 13

Classification Based on Invariants of the Data Matrix 基于数据矩阵不变量的分类

智能学习系统与应用(英文) Pub Date : 2017-08-23 DOI: 10.4236/JILSA.2017.93004

V. Shats

{"title":"Classification Based on Invariants of the Data Matrix","authors":"V. Shats","doi":"10.4236/JILSA.2017.93004","DOIUrl":"https://doi.org/10.4236/JILSA.2017.93004","url":null,"abstract":"The paper proposes a solution to the problem classification by calculating the sequence of matrices of feature indices that approximate invariants of the data matrix. Here the feature index is the index of interval for feature values, and the number of intervals is a parameter. Objects with the equal indices form granules, including information granules, which correspond to the objects of the training sample of a certain class. From the ratios of the information granules lengths, we obtain the frequency intervals of any feature that are the same for the appropriate objects of the control sample. Then, for an arbitrary object, we find object probability estimation in each class and then the class of object that corresponds to the maximum probability. For a sequence of the parameter values, we find a converging sequence of error rates. An additional effect is created by the parameters aimed at increasing the data variety and compressing rare data. The high accuracy and stability of the results obtained using this method have been confirmed for nine data set from the UCI repository. The proposed method has obvious advantages over existing ones due to the algorithm’s simplicity and universality, as well as the accuracy of the solutions.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"09 1","pages":"35-46"},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45631676","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

MCS HOG Features and SVM Based Handwritten Digit Recognition System 基于MCS HOG特征和SVM的手写数字识别系统

智能学习系统与应用(英文) Pub Date : 2017-05-12 DOI: 10.4236/JILSA.2017.92003

H. A. Khan

{"title":"MCS HOG Features and SVM Based Handwritten Digit Recognition System","authors":"H. A. Khan","doi":"10.4236/JILSA.2017.92003","DOIUrl":"https://doi.org/10.4236/JILSA.2017.92003","url":null,"abstract":"Digit Recognition is an essential element of the process of scanning and converting documents into electronic format. In this work, a new Multiple-Cell Size (MCS) approach is being proposed for utilizing Histogram of Oriented Gradient (HOG) features and a Support Vector Machine (SVM) based classifier for efficient classification of Handwritten Digits. The HOG based technique is sensitive to the cell size selection used in the relevant feature extraction computations. Hence a new MCS approach has been used to perform HOG analysis and compute the HOG features. The system has been tested on the Benchmark MNIST Digit Database of handwritten digits and a classification accuracy of 99.36% has been achieved using an Independent Test set strategy. A Cross-Validation analysis of the classification system has also been performed using the 10-Fold Cross-Validation strategy and a 10-Fold classification accuracy of 99.26% has been obtained. The classification performance of the proposed system is superior to existing techniques using complex procedures since it has achieved at par or better results using simple operations in both the Feature Space and in the Classifier Space. The plots of the system’s Confusion Matrix and the Receiver Operating Characteristics (ROC) show evidence of the superior performance of the proposed new MCS HOG and SVM based digit classification system.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"09 1","pages":"21-33"},"PeriodicalIF":0.0,"publicationDate":"2017-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46676772","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: 17

Survey of Machine Learning Algorithms for Disease Diagnostic 疾病诊断机器学习算法研究综述

智能学习系统与应用(英文) Pub Date : 2017-01-24 DOI: 10.4236/JILSA.2017.91001

M. Fatima, M. Pasha

{"title":"Survey of Machine Learning Algorithms for Disease Diagnostic","authors":"M. Fatima, M. Pasha","doi":"10.4236/JILSA.2017.91001","DOIUrl":"https://doi.org/10.4236/JILSA.2017.91001","url":null,"abstract":"In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of research. In recent years, significant attempts are made for the enhancement of computer aided diagnosis applications because errors in medical diagnostic systems can result in seriously misleading medical treatments. Machine learning is important in Computer Aided Diagnosis. After using an easy equation, objects such as organs may not be indicated accurately. So, pattern recognition fundamentally involves learning from examples. In the field of bio-medical, pattern recognition and machine learning promise the improved accuracy of perception and diagnosis of disease. They also promote the objectivity of decision-making process. For the analysis of high-dimensional and multimodal bio-medical data, machine learning offers a worthy approach for making classy and automatic algorithms. This survey paper provides the comparative analysis of different machine learning algorithms for diagnosis of different diseases such as heart disease, diabetes disease, liver disease, dengue disease and hepatitis disease. It brings attention towards the suite of machine learning algorithms and tools that are used for the analysis of diseases and decision-making process accordingly.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"09 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2017-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43470925","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: 458

Text-Based Intelligent Learning Emotion System 基于文本的智能学习情感系统

智能学习系统与应用(英文) Pub Date : 2017-01-24 DOI: 10.4236/JILSA.2017.91002

M. A. Razek, C. Frasson

引用次数: 15

Improved Term Weighting Technique for Automatic Web Page Classification 网页自动分类的改进词权加权技术

智能学习系统与应用(英文) Pub Date : 2016-09-27 DOI: 10.4236/JILSA.2016.84006

Kathirvalavakumar Thangairulappan, Arun Kanagavel

引用次数: 3