Pub Date : 2016-09-27DOI: 10.4236/JILSA.2016.84007
Y. Yahya, Ai Qian, Adel Yahya
This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.
{"title":"Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems","authors":"Y. Yahya, Ai Qian, Adel Yahya","doi":"10.4236/JILSA.2016.84007","DOIUrl":"https://doi.org/10.4236/JILSA.2016.84007","url":null,"abstract":"This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"08 1","pages":"77-91"},"PeriodicalIF":0.0,"publicationDate":"2016-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330852","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}
Pub Date : 2016-08-03DOI: 10.4236/JILSA.2016.83005
Waheeda Almayyan
This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.
{"title":"Lymph Diseases Prediction Using Random Forest and Particle Swarm Optimization","authors":"Waheeda Almayyan","doi":"10.4236/JILSA.2016.83005","DOIUrl":"https://doi.org/10.4236/JILSA.2016.83005","url":null,"abstract":"This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"08 1","pages":"51-62"},"PeriodicalIF":0.0,"publicationDate":"2016-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330765","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}
Pub Date : 2016-05-30DOI: 10.4236/JILSA.2016.82004
Stephane Kouamo, C. Tangha
Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.
{"title":"Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning","authors":"Stephane Kouamo, C. Tangha","doi":"10.4236/JILSA.2016.82004","DOIUrl":"https://doi.org/10.4236/JILSA.2016.82004","url":null,"abstract":"Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"08 1","pages":"39-49"},"PeriodicalIF":0.0,"publicationDate":"2016-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330714","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}
Pub Date : 2016-02-19DOI: 10.4236/JILSA.2016.81003
H. Ho, Woody Jann-Der Fann, Hsiu-Jye Chiang, Phung-Tuyen Nguyen, Duc-Hieu Pham, Phuoc-Hai Nguyen, M. Nagai
Introduction to education is one of the basic courses in teacher education professional education, it covers a wide range of subjects. Thus, in order to practice the management teaching goals, the interdisciplinary developed mathematical tools are applied for the study. The participants of this study are students in course of introduction to education, and the research instruments applied are rough set, grey structural modeling (GSM), and matrix based-structural modeling (MSM). The purposes of this paper are: 1) To logically analyze educational datasets to practice the scientific traits in education; 2) To benefit from directed hierarchical analysis to identify and propose action planning; 3) To construct core-oriented educational structure as the criterion-reference for one-lesson-multiple-design and to provide the whole scope and visualized analysis with GSM and MSM.
{"title":"Application of Rough Set, GSM and MSM to Analyze Learning Outcome—An Example of Introduction to Education","authors":"H. Ho, Woody Jann-Der Fann, Hsiu-Jye Chiang, Phung-Tuyen Nguyen, Duc-Hieu Pham, Phuoc-Hai Nguyen, M. Nagai","doi":"10.4236/JILSA.2016.81003","DOIUrl":"https://doi.org/10.4236/JILSA.2016.81003","url":null,"abstract":"Introduction to education is one of the \u0000basic courses in teacher education professional education, it covers a wide \u0000range of subjects. Thus, in order to practice the management teaching goals, \u0000the interdisciplinary developed mathematical tools are applied for the study. \u0000The participants of this study are students in course of introduction to \u0000education, and the research instruments applied are rough set, grey structural \u0000modeling (GSM), and matrix based-structural modeling (MSM). The purposes of \u0000this paper are: 1) To logically analyze educational datasets to practice the \u0000scientific traits in education; 2) To benefit from directed hierarchical \u0000analysis to identify and propose action planning; 3) To construct core-oriented \u0000educational structure as the criterion-reference for one-lesson-multiple-design and to provide the whole scope and visualized analysis \u0000with GSM and MSM.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"08 1","pages":"23-38"},"PeriodicalIF":0.0,"publicationDate":"2016-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330703","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}
Pub Date : 2016-01-01DOI: 10.4236/JILSA.2016.81002
M. Yousef, J. Allmer, Waleed Khalifa
MicroRNAs (miRNAs) are short (~21 nt) nucleotide sequences that are either co-transcribed during the production of mRNA or are organized in intergenic regions transcribed by RNA polymerase II. In animals, Drosha, and in plants DCL1 recognize pre-miRNAs which set themselves apart by their characteristic stem loop (hairpin) structure. This structure appears important for their recognition during the process of maturation leading to functioning mature miRNAs. A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. For the prediction of pre-miRNAs, usually machine learning approaches are employed. Therefore, it is necessary to convert the pre-miRNAs into a set of features that can be calculated and many such features have been described. We here select a subset of the previously described features and add sequence motifs as new features. The resulting model which we called MotifmiRNAPred was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field. With an accuracy of 99.95% for the generalized plant model, it distinguishes itself from previously published results which reach an average accuracy between 74% and 98%. We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment.
{"title":"Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features","authors":"M. Yousef, J. Allmer, Waleed Khalifa","doi":"10.4236/JILSA.2016.81002","DOIUrl":"https://doi.org/10.4236/JILSA.2016.81002","url":null,"abstract":"MicroRNAs (miRNAs) are short (~21 nt) nucleotide sequences that are either co-transcribed during the production of mRNA or are organized in intergenic regions transcribed by RNA polymerase II. In animals, Drosha, and in plants DCL1 recognize pre-miRNAs which set themselves apart by their characteristic stem loop (hairpin) structure. This structure appears important for their recognition during the process of maturation leading to functioning mature miRNAs. A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. For the prediction of pre-miRNAs, usually machine learning approaches are employed. Therefore, it is necessary to convert the pre-miRNAs into a set of features that can be calculated and many such features have been described. We here select a subset of the previously described features and add sequence motifs as new features. The resulting model which we called MotifmiRNAPred was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field. With an accuracy of 99.95% for the generalized plant model, it distinguishes itself from previously published results which reach an average accuracy between 74% and 98%. We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"08 1","pages":"9-22"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330671","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}
A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.
{"title":"Ensemble Neural Network in Classifying Handwritten Arabic Numerals","authors":"Kathirvalavakumar Thangairulappan, Palaniappan Rathinasamy","doi":"10.4236/JILSA.2016.81001","DOIUrl":"https://doi.org/10.4236/JILSA.2016.81001","url":null,"abstract":"A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"08 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330655","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}
Pub Date : 2015-09-29DOI: 10.4236/JILSA.2015.74009
Ashraf Elnagar, Rahima Bentrcia
Segmenting Arabic handwritings had been one of the subjects of research in the field of Arabic character recognition for more than 25 years. The majority of reported segmentation techniques share a critical shortcoming, which is over-segmentation. The aim of segmentation is to produce the letters (segments) of a handwritten word. When a resulting letter (segment) is made of more than one piece (stroke) instead of one, this is called over-segmentation. Our objective is to overcome this problem by using an Artificial Neural Networks (ANN) to verify the resulting segment. We propose a set of heuristic-based rules to assemble strokes in order to report the precise segmented letters. Preprocessing phases that include normalization and feature extraction are required as a prerequisite step for the ANN system for recognition and verification. In our previous work [1], we did achieve a segmentation success rate of 86% but without recognition. In this work, our experimental results confirmed a segmentation success rate of no less than 95%.
{"title":"A Recognition-Based Approach to Segmenting Arabic Handwritten Text","authors":"Ashraf Elnagar, Rahima Bentrcia","doi":"10.4236/JILSA.2015.74009","DOIUrl":"https://doi.org/10.4236/JILSA.2015.74009","url":null,"abstract":"Segmenting Arabic handwritings had been one of the subjects of research in the field of Arabic character recognition for more than 25 years. The majority of reported segmentation techniques share a critical shortcoming, which is over-segmentation. The aim of segmentation is to produce the letters (segments) of a handwritten word. When a resulting letter (segment) is made of more than one piece (stroke) instead of one, this is called over-segmentation. Our objective is to overcome this problem by using an Artificial Neural Networks (ANN) to verify the resulting segment. We propose a set of heuristic-based rules to assemble strokes in order to report the precise segmented letters. Preprocessing phases that include normalization and feature extraction are required as a prerequisite step for the ANN system for recognition and verification. In our previous work [1], we did achieve a segmentation success rate of 86% but without recognition. In this work, our experimental results confirmed a segmentation success rate of no less than 95%.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"07 1","pages":"93-103"},"PeriodicalIF":0.0,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330545","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}
Pub Date : 2015-09-29DOI: 10.4236/JILSA.2015.74010
M. Beckmann, N. Ebecken, B. D. Lima
In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery.
{"title":"A KNN Undersampling Approach for Data Balancing","authors":"M. Beckmann, N. Ebecken, B. D. Lima","doi":"10.4236/JILSA.2015.74010","DOIUrl":"https://doi.org/10.4236/JILSA.2015.74010","url":null,"abstract":"In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"7 1","pages":"104-116"},"PeriodicalIF":0.0,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330594","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}
Pub Date : 2015-09-29DOI: 10.4236/JILSA.2015.74008
J. Rosales-Huamaní, J. Castillo-Sequera, Fabricio Puente-Mansilla, Gustavo Boza-Quispe
In the world, 10% of the world population suffer with some type of disability, however the fast technological development can originate some barriers that these people have to face if they want to access to technology. This is particularly true in the case of visually impaired users, as they require special assistance when they use any computer system and also depend on the audio for navigation tasks. Therefore, this paper is focused on making a prototype of a semantic platform with web accessibility for blind people. We propose a method to interaction with user through voice commands, allowing the direct communication with the platform. The proposed platform will be implemented using Semantic Web tools, because we intend to facilitate the search and retrieval of information in a more efficient way and offer a personalized learning. Also, Google APIs (STT (Speech to Text) and TTS (Text to Speech)) and Raspberry Pi board will be integrated in a speech recognition module.
{"title":"A Prototype of a Semantic Platform with a Speech Recognition System for Visual Impaired People","authors":"J. Rosales-Huamaní, J. Castillo-Sequera, Fabricio Puente-Mansilla, Gustavo Boza-Quispe","doi":"10.4236/JILSA.2015.74008","DOIUrl":"https://doi.org/10.4236/JILSA.2015.74008","url":null,"abstract":"In the world, 10% of the world population suffer with some type of disability, however the fast technological development can originate some barriers that these people have to face if they want to access to technology. This is particularly true in the case of visually impaired users, as they require special assistance when they use any computer system and also depend on the audio for navigation tasks. Therefore, this paper is focused on making a prototype of a semantic platform with web accessibility for blind people. We propose a method to interaction with user through voice commands, allowing the direct communication with the platform. The proposed platform will be implemented using Semantic Web tools, because we intend to facilitate the search and retrieval of information in a more efficient way and offer a personalized learning. Also, Google APIs (STT (Speech to Text) and TTS (Text to Speech)) and Raspberry Pi board will be integrated in a speech recognition module.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"07 1","pages":"87-92"},"PeriodicalIF":0.0,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330506","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}
Pub Date : 2015-09-29DOI: 10.4236/JILSA.2015.74011
Waleed Ali, S. Shamsuddin
One of commonly used approach to enhance the Web performance is Web proxy caching technique. In Web proxy caching, Least-Frequently-Used-Dynamic-Aging (LFU-DA) is one of the common proxy cache replacement methods, which is widely used in Web proxy cache management. LFU-DA accomplishes a superior byte hit ratio compared to other Web proxy cache replacement algorithms. However, LFU-DA may suffer in hit ratio measure. Therefore, in this paper, LFU-DA is enhanced using popular supervised machine learning techniques such as a support vector machine (SVM), a naive Bayes classifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from Web proxy logs files and then intelligently incorporated with LFU-DA to form Intelligent Dynamic- Aging (DA) approaches. The simulation results revealed that the proposed intelligent Dynamic- Aging approaches considerably improved the performances in terms of hit and byte hit ratio of the conventional LFU-DA on a range of real datasets.
提高Web性能的常用方法之一是Web代理缓存技术。在Web代理缓存中，LFU-DA (least - frequency - used - dynamic - aging)是一种常用的代理缓存替换方法，广泛应用于Web代理缓存管理中。与其他Web代理缓存替换算法相比，LFU-DA实现了更高的字节命中率。然而，LFU-DA在命中率测量方面可能会受到影响。因此，在本文中，使用流行的监督机器学习技术(如支持向量机(SVM)，朴素贝叶斯分类器(NB)和决策树(C4.5)来增强LFU-DA。SVM、NB和C4.5从Web代理日志文件中进行训练，然后与LFU-DA智能结合，形成智能动态老化(Intelligent Dynamic- Aging, DA)方法。仿真结果表明，所提出的智能动态老化方法在命中率和字节命中率方面显著提高了传统LFU-DA在一系列真实数据集上的性能。
{"title":"Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement","authors":"Waleed Ali, S. Shamsuddin","doi":"10.4236/JILSA.2015.74011","DOIUrl":"https://doi.org/10.4236/JILSA.2015.74011","url":null,"abstract":"One of commonly used approach to enhance \u0000the Web performance is Web proxy caching technique. In Web proxy caching, \u0000Least-Frequently-Used-Dynamic-Aging (LFU-DA) is one of the common proxy cache \u0000replacement methods, which is widely used in Web proxy cache management. LFU-DA \u0000accomplishes a superior byte hit ratio compared to other Web proxy cache \u0000replacement algorithms. However, LFU-DA may suffer in hit ratio measure. \u0000Therefore, in this paper, LFU-DA is enhanced using popular supervised machine \u0000learning techniques such as a support vector machine (SVM), a naive Bayes \u0000classifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from \u0000Web proxy logs files and then intelligently incorporated with LFU-DA to form \u0000Intelligent Dynamic- Aging (DA) approaches. The simulation results revealed \u0000that the proposed intelligent Dynamic- Aging approaches considerably improved \u0000the performances in terms of hit and byte hit ratio of the conventional LFU-DA \u0000on a range of real datasets.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"30 1","pages":"117-127"},"PeriodicalIF":0.0,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330609","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}
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