Journal of Artificial Intelligence and Data Mining最新文献_第6页

Probabilistic Reasoning and Markov Chains as Means to Improve Performance of Tuning Decisions under Uncertainty 概率推理和马尔可夫链作为提高不确定性下调整决策性能的手段

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-09-09 DOI: 10.22044/JADM.2020.8920.2027

A. Omondi, I. A. Lukandu, G. Wanyembi

{"title":"Probabilistic Reasoning and Markov Chains as Means to Improve Performance of Tuning Decisions under Uncertainty","authors":"A. Omondi, I. A. Lukandu, G. Wanyembi","doi":"10.22044/JADM.2020.8920.2027","DOIUrl":"https://doi.org/10.22044/JADM.2020.8920.2027","url":null,"abstract":"Variable environmental conditions and runtime phenomena require developers of complex business information systems to expose configuration parameters to system administrators. This allows system administrators to intervene by tuning the bottleneck configuration parameters in response to current changes or in anticipation of future changes in order to maintain the system’s performance at an optimum level. However, these manual performance tuning interventions are prone to error and lack of standards due to fatigue, varying levels of expertise and over-reliance on inaccurate predictions of future states of a business information system. As a result, the purpose of this research is to investigate on how the capacity of probabilistic reasoning to handle uncertainty can be combined with the capacity of Markov chains to map stochastic environmental phenomena to ideal self-optimization actions. This was done using a comparative experimental research design that involved quantitative data collection through simulations of different algorithm variants. This provided compelling results that indicate that applying the algorithm in a distributed database system improves performance of tuning decisions under uncertainty. The improvement was quantitatively measured by a response-time latency that was 27% lower than average and a transaction throughput that was 17% higher than average.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43465033","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

A New Incentive Mechanism to Detect and Restrict Sybil Nodes in P2P File-Sharing Networks with a Heterogeneous Bandwidth 异构带宽P2P文件共享网络中Sybil节点检测与约束的新激励机制

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-21 DOI: 10.22044/JADM.2020.9063.2049

M. Shareh, H. Navidi, H. Javadi, M. Hosseinzadeh

{"title":"A New Incentive Mechanism to Detect and Restrict Sybil Nodes in P2P File-Sharing Networks with a Heterogeneous Bandwidth","authors":"M. Shareh, H. Navidi, H. Javadi, M. Hosseinzadeh","doi":"10.22044/JADM.2020.9063.2049","DOIUrl":"https://doi.org/10.22044/JADM.2020.9063.2049","url":null,"abstract":"In cooperative P2P networks, there are two kinds of illegal users, namely free riders and Sybils. Free riders are those who try to receive services without any sort of cost. Sybil users are rational peers which have multiple fake identities. There are some techniques to detect free riders and Sybil users which have previously been proposed by a number of researchers such as the Tit-for-tat and Sybil guard techniques. Although such previously proposed techniques were quite successful in detecting free riders and Sybils individually, there is no technique capable of detecting both these riders simultaneously. Therefore, the main objective of this research is to propose a single mechanism to detect both kinds of these illegal users based on Game theory. Obtaining new centrality and bandwidth contribution formulas with an incentive mechanism approach is the basic idea of the present research’s proposed solution. The result of this paper shows that as the life of the network passes, free riders are identified, and through detecting Sybil nodes, the number of services offered to them will be decreased.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43514905","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

A Combinatorial Algorithm for Fuzzy Parameter Estimation with Application to Uncertain Measurements 模糊参数估计的组合算法及其在不确定测量中的应用

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-21 DOI: 10.22044/JADM.2020.8610.1996

M. Danesh, S. Danesh

{"title":"A Combinatorial Algorithm for Fuzzy Parameter Estimation with Application to Uncertain Measurements","authors":"M. Danesh, S. Danesh","doi":"10.22044/JADM.2020.8610.1996","DOIUrl":"https://doi.org/10.22044/JADM.2020.8610.1996","url":null,"abstract":"This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where the variation around the mean is not determinate. However, to provide unbiased and precise estimations, the predictions are required to be correct on average and the spread of date be specified. To address this issue, we proposed a method based on the fuzzy inference system, and genetic and linear programming algorithms. We consider the crisp inputs and the symmetrical triangular fuzzy output. The proposed algorithm is applied to fit the fuzzy regression model. In addition, we apply a simulation example and a practical example in the field of machining process to assess the performance of the proposed method in dealing with practical problems in which the output variables have the nature of uncertainty and impression. Finally, we compare the performance of the suggested method with other methods. Based on the examples, the proposed method is verified for prediction. The results show that the proposed method reduces the error values to a minimum level and is more accurate than the Linear Programming (LP) and fuzzy weights with linear programming (FWLP) methods.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41726480","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

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data 模糊数据聚类的模糊C均值算法及其在不完全数据聚类中的应用

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-13 DOI: 10.22044/JADM.2020.9021.2038

J. Tayyebi, E. Hosseinzadeh

引用次数: 3

A Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters 一种基于蒙特卡罗的性能调整参数降维搜索策略

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-05 DOI: 10.22044/JADM.2020.9403.2076

A. Omondi, I. A. Lukandu, G. Wanyembi

{"title":"A Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters","authors":"A. Omondi, I. A. Lukandu, G. Wanyembi","doi":"10.22044/JADM.2020.9403.2076","DOIUrl":"https://doi.org/10.22044/JADM.2020.9403.2076","url":null,"abstract":"Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of relevant features and the exploration of features that have the potential to be relevant. In doing so, the study evaluated how effective the manipulation of the search component in feature selection is on achieving high accuracy with reduced dimensions. A control group experimental design was used to observe factual evidence. The context of the experiment was the high dimensional data experienced in performance tuning of complex database systems. The Wilcoxon signed-rank test at .05 level of significance was used to compare repeated classification accuracy measurements on the independent experiment and control group samples. Encouraging results with a p-value < 0.05 were recorded and provided evidence to reject the null hypothesis in favour of the alternative hypothesis which states that meta-heuristic search approaches are effective in achieving high accuracy with reduced dimensions depending on the outcome variable under investigation.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42690453","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

Development of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability 用于预测乳腺癌生存能力的集成多阶段机器的开发

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-01 DOI: 10.22044/JADM.2020.8406.1978

M. Salehi, J. Razmara, S. Lotfi

{"title":"Development of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability","authors":"M. Salehi, J. Razmara, S. Lotfi","doi":"10.22044/JADM.2020.8406.1978","DOIUrl":"https://doi.org/10.22044/JADM.2020.8406.1978","url":null,"abstract":"Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. ‎In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set‎. ‎Therefore‎, ‎developing a machine for prediction of survivability‎, ‎which ignores these features for simple cases and yields an acceptable prediction accuracy‎, ‎has turned into a challenge for researchers‎. ‎In this paper‎, ‎we have developed an ensemble multi-stage machine for survivability prediction which ignores difficult features for simple cases‎. ‎The machine employs three basic learners‎, ‎namely multilayer perceptron (MLP), ‎ support vector machine (SVM), and decision tree (DT)‎, ‎in the first stage to predict survivability using simple features‎. ‎If the learners agree on the output‎, ‎the machine makes the final decision in the first stage‎. Otherwise, ‎for difficult cases where the output of learners is different‎, ‎the machine makes decision in the second stage using SVM over all features‎. The developed model was evaluated using the Surveillance, Epidemiology, and End Results (SEER) database. The experimental results revealed that ‎the developed machine obtains considerable accuracy while it ignores difficult features for most of the input samples‎‎.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"8 1","pages":"371-378"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43310037","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: 5

VHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine 谷歌地球引擎上基于随机森林分类的VHR语义标注及光谱与空间特征融合

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-01 DOI: 10.22044/JADM.2020.8252.1964

M. Kakooei, Y. Baleghi

{"title":"VHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine","authors":"M. Kakooei, Y. Baleghi","doi":"10.22044/JADM.2020.8252.1964","DOIUrl":"https://doi.org/10.22044/JADM.2020.8252.1964","url":null,"abstract":"Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial features are fused into a Heterogeneous Feature Map to train the classifier. Evaluation database classes are impervious surface, building, low vegetation, tree, car, and background. The proposed method is implemented on Google Earth Engine. The method consists of several levels. First, Principal Component Analysis is applied to vegetation indexes to find maximum separable color space between vegetation and non-vegetation area. Gray Level Co-occurrence Matrix is computed to provide texture information as spatial features. Several Random Forests are trained with automatically selected train dataset. Several spatial operators follow the classification to refine the result. Leaf-Less-Tree feature is used to solve the underestimation problem in tree detection. Area, major and, minor axis of connected components are used to refine building and car detection. Evaluation shows significant improvement in tree, building, and car accuracy. Overall accuracy and Kappa coefficient are appropriate.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"8 1","pages":"357-370"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47544546","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

Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification 基于尺寸估计和词袋分类的车型识别

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-01 DOI: 10.22044/JADM.2020.8375.1975

R. A. Dehkordi, H. Khosravi

{"title":"Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification","authors":"R. A. Dehkordi, H. Khosravi","doi":"10.22044/JADM.2020.8375.1975","DOIUrl":"https://doi.org/10.22044/JADM.2020.8375.1975","url":null,"abstract":"Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce erroneous results. This paper presents a methodology that uses two criteria to identify common vehicle types. The first criterion is feature extraction and classification and the second criterion is to use the dimensions of car for classification. This method consists of three phases. In the first phase, the coordinates of the vanishing points are obtained. In the second phase, the bounding box and dimensions are calculated for each passing vehicle. Finally, in the third phase, the exact vehicle type is determined by combining the results of the first and second criteria. To evaluate the proposed method, a dataset of images and videos, prepared by the authors, has been used. This dataset is recorded from places similar to those of a roadside camera. Most existing methods use high-quality images for evaluation and are not applicable in the real world, but in the proposed method real-world video frames are used to determine the exact type of vehicle, and the accuracy of 89.5% is achieved, which represents a good performance.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"8 1","pages":"427-438"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45283578","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

Improving Accuracy of Recommender Systems using Social Network Information and Longitudinal Data 利用社会网络信息和纵向数据提高推荐系统的准确性

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-01 DOI: 10.22044/JADM.2020.7326.1871

B. Hassanpour, N. Abdolvand, S. R. Harandi

引用次数: 0

Chaotic-based Particle Swarm Optimization with Inertia Weight for Optimization Tasks 基于混沌的惯性权粒子群优化算法

Journal of Artificial Intelligence and Data Mining Pub Date : 2020-07-01 DOI: 10.22044/JADM.2020.8594.1993

N. Mobaraki, R. Boostani, M. Sabeti

{"title":"Chaotic-based Particle Swarm Optimization with Inertia Weight for Optimization Tasks","authors":"N. Mobaraki, R. Boostani, M. Sabeti","doi":"10.22044/JADM.2020.8594.1993","DOIUrl":"https://doi.org/10.22044/JADM.2020.8594.1993","url":null,"abstract":"Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may be trapped in a local minimum through a limited number of iterations. To increase its diversity as well as enhancing its exploration ability, this paper inserts a chaotic factor, generated by three chaotic systems, along with a perturbation stage into AIW-PSO to avoid premature convergence, especially in complex nonlinear problems. To assess the proposed method, a known optimization benchmark containing nonlinear complex functions was selected and its results were compared to that of standard PSO, AIW-PSO and genetic algorithm (GA). The empirical results demonstrate the superiority of the proposed chaotic AIW-PSO to the counterparts over 21 functions, which confirms the promising role of inserting the randomness into the AIW-PSO. The behavior of error through the epochs show that the proposed manner can smoothly find proper minimums in a timely manner without encountering with premature convergence.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":"8 1","pages":"303-312"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49397147","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