智能学习系统与应用(英文)最新文献_第6页

Disparity in Intelligent Classification of Data Sets Due to Dominant Pattern Effect (DPE) 基于优势模式效应(DPE)的数据集智能分类差异

智能学习系统与应用(英文) Pub Date : 2015-07-10 DOI: 10.4236/JILSA.2015.73007

M. Iskandarani

引用次数: 0

Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers 使用结构化训练实例和分类器集合对非结构化文本进行分类

智能学习系统与应用(英文) Pub Date : 2015-05-26 DOI: 10.4236/JILSA.2015.72006

A. Lianos, Yanyan Yang

引用次数: 2

An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing 基于位置敏感哈希的资源分配网络在线恶意垃圾邮件检测系统

智能学习系统与应用(英文) Pub Date : 2015-04-15 DOI: 10.4236/JILSA.2015.72005

Siti-Hajar-Aminah Ali, S. Ozawa, J. Nakazato, Tao Ban, Jumpei Shimamura

{"title":"An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing","authors":"Siti-Hajar-Aminah Ali, S. Ozawa, J. Nakazato, Tao Ban, Jumpei Shimamura","doi":"10.4236/JILSA.2015.72005","DOIUrl":"https://doi.org/10.4236/JILSA.2015.72005","url":null,"abstract":"In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330295","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: 10

Iterated Function System-Based Crossover Operation for Real-Coded Genetic Algorithm 基于迭代函数系统的实编码遗传算法交叉运算

智能学习系统与应用(英文) Pub Date : 2015-04-15 DOI: 10.4236/JILSA.2015.72004

S. Ling

引用次数: 4

Experimental Design and Its Posterior Efficiency for the Calibration of Wearable Sensors 可穿戴传感器标定的实验设计及其后验效率

智能学习系统与应用(英文) Pub Date : 2015-01-22 DOI: 10.4236/JILSA.2015.71002

Lin Ye, S. Su

引用次数: 1

A Novel Fuzzy Membership Partitioning for Improved Voting in Fault Tolerant System 一种改进容错系统投票的模糊隶属度划分方法

智能学习系统与应用(英文) Pub Date : 2015-01-22 DOI: 10.4236/JILSA.2015.71001

Akhilesh Pathak, Tarang Agarwal, A. Mohan

引用次数: 2

Applying DNA Computation to Error Detection Problem in Rule-Based Systems DNA计算在基于规则系统错误检测中的应用

智能学习系统与应用(英文) Pub Date : 2015-01-22 DOI: 10.4236/JILSA.2015.71003

Behrouz Madahian, A. Salighehdar, R. Amini

{"title":"Applying DNA Computation to Error Detection Problem in Rule-Based Systems","authors":"Behrouz Madahian, A. Salighehdar, R. Amini","doi":"10.4236/JILSA.2015.71003","DOIUrl":"https://doi.org/10.4236/JILSA.2015.71003","url":null,"abstract":"As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman’s operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n*(Max{q, K, z})), in which n is the number of fact clauses; q is the number of rules in the longest inference chain; K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes; and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330217","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: 4

Computational Approaches for Biomarker Discovery 生物标志物发现的计算方法

智能学习系统与应用(英文) Pub Date : 2014-10-15 DOI: 10.4236/JILSA.2014.64012

M. Yousef, N. Najami, Loai AbedAllah, Waleed Khalifa

引用次数: 9

A Reinforcement Learning System to Dynamic Movement and Multi-Layer Environments 动态运动和多层环境下的强化学习系统

智能学习系统与应用(英文) Pub Date : 2014-10-15 DOI: 10.4236/JILSA.2014.64014

Uthai Phommasak, D. Kitakoshi, H. Shioya, Junji Maeda

{"title":"A Reinforcement Learning System to Dynamic Movement and Multi-Layer Environments","authors":"Uthai Phommasak, D. Kitakoshi, H. Shioya, Junji Maeda","doi":"10.4236/JILSA.2014.64014","DOIUrl":"https://doi.org/10.4236/JILSA.2014.64014","url":null,"abstract":"There are many proposed policy-improving systems of Reinforcement Learning (RL) agents which are effective in quickly adapting to environmental change by using many statistical methods, such as mixture model of Bayesian Networks, Mixture Probability and Clustering Distribution, etc. However such methods give rise to the increase of the computational complexity. For another method, the adaptation performance to more complex environments such as multi-layer environments is required. In this study, we used profit-sharing method for the agent to learn its policy, and added a mixture probability into the RL system to recognize changes in the environment and appropriately improve the agent’s policy to adjust to the changing environment. We also introduced a clustering that enables a smaller, suitable selection in order to reduce the computational complexity and simultaneously maintain the system’s performance. The results of experiments presented that the agent successfully learned the policy and efficiently adjusted to the changing in multi-layer environment. Finally, the computational complexity and the decline in effectiveness of the policy improvement were controlled by using our proposed system.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330156","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

Application of the Adaptive Neuro-Fuzzy Inference System for Optimal Design of Reinforced Concrete Beams 自适应神经模糊推理系统在钢筋混凝土梁优化设计中的应用

智能学习系统与应用(英文) Pub Date : 2014-10-15 DOI: 10.4236/JILSA.2014.64013

J. Yeh, Renxu Yang

{"title":"Application of the Adaptive Neuro-Fuzzy Inference System for Optimal Design of Reinforced Concrete Beams","authors":"J. Yeh, Renxu Yang","doi":"10.4236/JILSA.2014.64013","DOIUrl":"https://doi.org/10.4236/JILSA.2014.64013","url":null,"abstract":"Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel; design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam; its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330113","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: 4