Journal of Artificial Intelligence and Data Mining最新文献_第2页

Data Mining-based Structural Damage Identification of Composite Bridge using Support Vector Machine 基于数据挖掘的支持向量机组合梁结构损伤识别

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-07-24 DOI: 10.22044/JADM.2021.10430.2182

M. Gordan, S. Sabbagh-Yazdi, Z. Ismail, Khaled Ghaedi, H. H. Ghayeb

{"title":"Data Mining-based Structural Damage Identification of Composite Bridge using Support Vector Machine","authors":"M. Gordan, S. Sabbagh-Yazdi, Z. Ismail, Khaled Ghaedi, H. H. Ghayeb","doi":"10.22044/JADM.2021.10430.2182","DOIUrl":"https://doi.org/10.22044/JADM.2021.10430.2182","url":null,"abstract":"A structural health monitoring system contains two components, i.e. a data collection approach comprising a network of sensors for recording the structural responses as well as an extraction methodology in order to achieve beneficial information on the structural health condition. In this regard, data mining which is one of the emerging computer-based technologies, can be employed for extraction of valuable information from obtained sensor databases. On the other hand, data inverse analysis scheme as a problem-based procedure has been developing rapidly. Therefore, the aforesaid scheme and data mining should be combined in order to satisfy increasing demand of data analysis, especially in complex systems such as bridges. Consequently, this study develops a damage detection methodology based on these strategies. To this end, an inverse analysis approach using data mining is applied for a composite bridge. To aid the aim, the support vector machine (SVM) algorithm is utilized to generate the patterns by means of vibration characteristics dataset. To compare the robustness and accuracy of the predicted outputs, four kernel functions, including linear, polynomial, sigmoid, and radial basis function (RBF) are applied to build the patterns. The results point out the feasibility of the proposed method for detecting damage in composite slab-on-girder bridges.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49404707","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: 7

Water Meter Replacement Recommendation for Municipal Water Distribution Networks using Ensemble Outlier Detection Methods 使用集合异常值检测方法的城市配水网络水表更换建议

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-07-24 DOI: 10.22044/JADM.2021.10672.2202

F. Kaveh-Yazdy, S. Zarifzadeh

{"title":"Water Meter Replacement Recommendation for Municipal Water Distribution Networks using Ensemble Outlier Detection Methods","authors":"F. Kaveh-Yazdy, S. Zarifzadeh","doi":"10.22044/JADM.2021.10672.2202","DOIUrl":"https://doi.org/10.22044/JADM.2021.10672.2202","url":null,"abstract":"Due to their structure and usage condition, water meters face degradation, breaking, freezing, and leakage problems. There are various studies intended to determine the appropriate time to replace degraded ones. Earlier studies have used several features, such as user meteorological parameters, usage conditions, water network pressure, and structure of meters to detect failed water meters. This article proposes a recommendation framework that uses registered water consumption values as input data and provides meter replacement recommendations. This framework takes time series of registered consumption values and preprocesses them in two rounds to extract effective features. Then, multiple un-/semi-supervised outlier detection methods are applied to the processed data and assigns outlier/normal labels to them. At the final stage, a hypergraph-based ensemble method receives the labels and combines them to discover the suitable label. Due to the unavailability of ground truth labeled data for meter replacement, we compare our method with respect to its FPR and two internal metrics: Dunn index and Davies-Bouldin Index. Results of our comparative experiments show that the proposed framework detects more compact clusters with smaller variance.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47304179","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

Detecting Breast Cancer through Blood Analysis Data using Classification Algorithms 基于分类算法的血液分析数据检测癌症

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-07-10 DOI: 10.22044/JADM.2021.9839.2116

Oladosu Oyebisi Oladimeji, O. Oladimeji

{"title":"Detecting Breast Cancer through Blood Analysis Data using Classification Algorithms","authors":"Oladosu Oyebisi Oladimeji, O. Oladimeji","doi":"10.22044/JADM.2021.9839.2116","DOIUrl":"https://doi.org/10.22044/JADM.2021.9839.2116","url":null,"abstract":"Breast cancer is the second major cause of death and accounts for 16% of all cancer deaths worldwide. Most of the methods of detecting breast cancer are very expensive and difficult to interpret such as mammography. There are also limitations such as cumulative radiation exposure, over-diagnosis, false positives and negatives in women with a dense breast which pose certain uncertainties in high-risk population. The objective of this study is Detecting Breast Cancer Through Blood Analysis Data Using Classification Algorithms. This will serve as a complement to these expensive methods. High ranking features were extracted from the dataset. The KNN, SVM and J48 algorithms were used as the training platform to classify 116 instances. Furthermore, 10-fold cross validation and holdout procedures were used coupled with changing of random seed. The result showed that KNN algorithm has the highest and best accuracy of 89.99% and 85.21% for cross validation and holdout procedure respectively. This is followed by the J48 with 84.65% and 75.65% for the two procedures respectively. SVM had 77.58% and 68.69% respectively. Although it was also discovered that Blood Glucose level is a major determinant in detecting breast cancer, it has to be combined with other attributes to make decision as a result of other health issues like diabetes. With the result obtained, women are advised to do regular check-ups including blood analysis in order to know which of the blood components need to be worked on to prevent breast cancer based on the model generated in this study.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43191614","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

Investigating Changes in Household Consumable Market Using Data Mining Techniques 利用数据挖掘技术调查家庭消费品市场的变化

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-06-30 DOI: 10.22044/JADM.2021.10024.2139

A. Hasan-Zadeh, F. Asadi, N. Garbazkar

引用次数: 1

Hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation 基于类比的软件项目工作量估计中特征加权的PSO-SA混合方法

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-06-27 DOI: 10.22044/JADM.2021.10119.2152

Z. Shahpar, V. Khatibi, A. K. Bardsiri

引用次数: 7

A Mobile Charger based on Wireless Power Transfer Technologies: A Survey of Concepts, Techniques, Challenges, and Applications on Rechargeable Wireless Sensor Networks 基于无线功率传输技术的移动充电器：可充电无线传感器网络的概念、技术、挑战和应用综述

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-06-15 DOI: 10.22044/JADM.2021.9936.2127

N. Nowrozian, F. Tashtarian

{"title":"A Mobile Charger based on Wireless Power Transfer Technologies: A Survey of Concepts, Techniques, Challenges, and Applications on Rechargeable Wireless Sensor Networks","authors":"N. Nowrozian, F. Tashtarian","doi":"10.22044/JADM.2021.9936.2127","DOIUrl":"https://doi.org/10.22044/JADM.2021.9936.2127","url":null,"abstract":"Battery power limitation of sensor nodes (SNs) is a major challenge for wireless sensor networks (WSNs) which affects network survival. Thus, optimizing the energy consumption of the SNs as well as increasing the lifetime of the SNs and thus, extending the lifetime of WSNs are of crucial importance in these types of networks. Mobile chargers (MCs) and wireless power transfer (WPT) technologies have played an important long role in WSNs, and much research has been done on how to use the MC to enhance the performance of WSNs in recent decades. In this paper, we first review the application of MCs and WPT technologies in WSNs. Then, forwarding issues the MC has been considered in the role of power transmitter in WSNs and the existing approaches are categorized, with the purposes and limitations of MC dispatching studied. Then an overview of the existing articles is presented and to better understand the contents, tables and figures are offered that summarize the existing methods. We examine them in different dimensions such as advantages and disadvantages etc. Finally, the future prospects of MC are discussed.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44424292","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

An Efficient Approach to Solve Software-defined Networks based Virtual Machines Placement Problem using Moth-Flame Optimization in the Cloud Computing Environment 云计算环境下基于软件定义网络的虚拟机布局问题的蛾焰优化方法

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-05-29 DOI: 10.22044/JADM.2021.9737.2106

A. H. Safari-Bavil, S. Jabbehdari, Mostafa Ghobaei-Arani

引用次数: 0

Camera Arrangement using Geometric Optimization to Minimize Localization Error in Stereo-vision Systems 利用几何优化最小化立体视觉系统定位误差的摄像机排列

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-05-25 DOI: 10.22044/JADM.2021.9855.2117

H. K. Ardakani, Seyed A. Mousavinia, F. Safaei

{"title":"Camera Arrangement using Geometric Optimization to Minimize Localization Error in Stereo-vision Systems","authors":"H. K. Ardakani, Seyed A. Mousavinia, F. Safaei","doi":"10.22044/JADM.2021.9855.2117","DOIUrl":"https://doi.org/10.22044/JADM.2021.9855.2117","url":null,"abstract":"Stereo machine vision can be used as a Space Sampling technique and the cameras parameters and configuration can effectively change the number of Samples in each Volume of space called Space Sampling Density (SSD). Using the concept of Voxels, this paper presents a method to optimize the geometric configuration of the cameras to maximize the SSD which means minimizing the Voxel volume and reducing the uncertainty in localizing an object in 3D space. Each pixel’s field of view (FOV) is considered as a skew pyramid. The uncertainty region will be created from the intersection of two pyramids associated with any of the cameras. Then, the mathematical equation of the uncertainty region is developed based on the correspondence field as a criterion for the localization error, including depth error as well as X and Y axes error. This field is completely dependent on the internal and external parameters of the cameras. Given the mathematical equation of localization error, the camera’s configuration optimization is addressed in a stereo vision system. Finally, the validity of the proposed method is examined by simulation and empirical results. These results show that the localization error will be significantly decreased in the optimized camera configuration.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41319431","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

Sequential Multi-objective Genetic Algorithm 序列多目标遗传算法

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-05-25 DOI: 10.22044/JADM.2021.9598.2092

L. Falahiazar, V. Seydi, M. Mirzarezaee

{"title":"Sequential Multi-objective Genetic Algorithm","authors":"L. Falahiazar, V. Seydi, M. Mirzarezaee","doi":"10.22044/JADM.2021.9598.2092","DOIUrl":"https://doi.org/10.22044/JADM.2021.9598.2092","url":null,"abstract":"Many of the real-world issues have multiple conflicting objectives that the optimization between contradictory objectives is very difficult. In recent years, the Multi-objective Evolutionary Algorithms (MOEAs) have shown great performance to optimize such problems. So, the development of MOEAs will always lead to the advancement of science. The Non-dominated Sorting Genetic Algorithm II (NSGAII) is considered as one of the most used evolutionary algorithms, and many MOEAs have emerged to resolve NSGAII problems, such as the Sequential Multi-Objective Algorithm (SEQ-MOGA). SEQ-MOGA presents a new survival selection that arranges individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this study, the Archive Sequential Multi-Objective Algorithm (ASMOGA) is proposed to develop and improve SEQ-MOGA. ASMOGA uses the archive technique to save the history of the search procedure, so that the maintenance of the diversity in the decision space is satisfied adequately. To demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing benchmark functions and designing the I-Beam problem. The optimization results are evaluated by Performance Metrics such as hypervolume, Generational Distance, Spacing, and the t-test (a statistical test); based on the results, the superiority of the proposed algorithm is identified clearly.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47190782","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

A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks 基于模糊神经网络和深度神经网络的混合人格预测框架

Journal of Artificial Intelligence and Data Mining Pub Date : 2021-05-23 DOI: 10.22044/JADM.2021.10583.2197

Nazila Taghvaei, B. Masoumi, M. Keyvanpour

{"title":"A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks","authors":"Nazila Taghvaei, B. Masoumi, M. Keyvanpour","doi":"10.22044/JADM.2021.10583.2197","DOIUrl":"https://doi.org/10.22044/JADM.2021.10583.2197","url":null,"abstract":"In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.","PeriodicalId":32592,"journal":{"name":"Journal of Artificial Intelligence and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44835455","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