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Articles 1 - 30 of 38
Full-Text Articles in Engineering
Spatio-Temporal Association Rule Mining Of Traffic Congestion In A Large-Scale Road Network Based On Trajectory Data, Qifan Zhou, Haixu Liu, Zhipeng Dong, Yin Xu
Spatio-Temporal Association Rule Mining Of Traffic Congestion In A Large-Scale Road Network Based On Trajectory Data, Qifan Zhou, Haixu Liu, Zhipeng Dong, Yin Xu
Journal of System Simulation
Abstract: A K neighbor-RElim (KNR) algorithm and a sequential KNbr-RElim (SKNR) algorithm are proposed to mine traffic congestion association rules and congestion propagation spatio-temporal association rules by vehicle trajectory data in a large-scale road network. The KNR algorithm extends the spatial topology constraint based on the RElim algorithm. The KNR can be used to mine the road links prone to congestion from the large-scale trajectory dataset in a large-scale road network and quantify the strength of association for congested road links. The SKNR algorithm expands the time dimension in the form of sliding window and can be applied for mining …
Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi
Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi
Journal of Aviation Technology and Engineering
Weather is responsible for approximately 70% of air transportation delays in the National Airspace System, and delays resulting from convective weather alone cost airlines and passengers millions of dollars each year due to delays that could be avoided. This research sought to establish relationships between environmental variables and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered from six out of ten airports using various machine learning methods within an overarching data mining protocol, and the developed models were tested using historical data.
Modeling Airport Catchment Areas: Using Spatial Analysis Approach, Sitong Chen
Modeling Airport Catchment Areas: Using Spatial Analysis Approach, Sitong Chen
The Journal of Purdue Undergraduate Research
Th e airport catchment area is the geographic area from which an airport can reasonably expect to draw commercial air service passengers. Th e purpose of this interdisciplinary research is to estimate airport catchment areas using a spatial analysis method for informed airport management. In order to ensure the comprehensiveness and reliability of the research, we chose to analyze the catchment areas for five airports of different sizes and in different geographic locations in the United States. The Huff model, which is usually used in marketing, economics, and retail research, was adopted in this study. We applied this model in …
Design Lifecycle Management Of Technological Processes, Yusuf Shodiyevich Avazov
Design Lifecycle Management Of Technological Processes, Yusuf Shodiyevich Avazov
Chemical Technology, Control and Management
The issues of life cycle management of design processes of complex technological processes are considered. An analysis of works devoted to the automated design of technological processes and industries, product life cycle management, and the use of predominant modeling in design is presented. A structure for the implementation of the design of technological processes is proposed and it’s components are described. An algorithm is proposed to reduce the life cycle of the design of a technological process. The sequence of selection of the most optimal project options with the help of the proposed algorithm is described. It is shown that …
Research On Assocoation Information Mining Of Space Reconnaissance Equipment System Index, Han Chi, Xiong Wei
Research On Assocoation Information Mining Of Space Reconnaissance Equipment System Index, Han Chi, Xiong Wei
Journal of System Simulation
Abstract: The system effectiveness and system contribution rate of the Space Reconnaissance Equipment System (SRES) has a large number of mutally associated indicators. How to identify relationships the association, select the key indicators and clarify the assocition between core indicators and system contribution rate are the key of the evaluation of system effectiveness and contribution rate. Through the joint simulation of MATLAB and STK, the underlying index data of SRES is obtained. Based on the Frequent Pattern-Tree (FP-Tree) algorithm, the assocition information is discovered, the redundancy is removed and the type of indicator assocition is determined, and an optimization model …
Aircraft Exhaust Gas Temperature Value Mining With Rough Set Method, Mustagime Tülin Yıldırım Asst. Prof., Mehtap Taşcı
Aircraft Exhaust Gas Temperature Value Mining With Rough Set Method, Mustagime Tülin Yıldırım Asst. Prof., Mehtap Taşcı
International Journal of Aviation, Aeronautics, and Aerospace
Aircrafts are one of the most important means of transportation today. For aircrafts to be able to serve safely, their maintenance must be done in a timely and complete manner. In addition to regular maintenance, it may appear suddenly; there is also irregular maintenance performed in cases such as lightning strikes, bird strikes, and hard landings. Engine failures and maintenance has great importance in aircraft maintenance. Using the data recorded during the flight by flight data recorder, the engine health condition is monitored and the necessary maintenance procedures are carried out. In this study, the exhaust gas temperature was estimated …
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n
Turkish Journal of Electrical Engineering and Computer Sciences
Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …
Recognition Via Morphological Features Of Tulip By The Algorithms For Calculating Estimates, Mirzayan Kamilov, Mirzaakbar Hudayberdiev
Recognition Via Morphological Features Of Tulip By The Algorithms For Calculating Estimates, Mirzayan Kamilov, Mirzaakbar Hudayberdiev
Chemical Technology, Control and Management
The paper discusses two problem, the firth, possibilities of improving the quality of the recognition algorithm based on partial precedent, by the original pre-training procedures. And second finding the optimal procedure for constructing improved results in some sense, the algorithms for calculating estimates. The peculiarity of this algorithm is that as precedents only such "anchor points" of a pattern that ensuring the following conditions are left: the distance from any point on the training set of i-th pattern to their nearest precedent is less than the distance to the nearest precedent of another pattern. This set of precedents provides unmistakable …
Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse, Jingbing Wu, Hanqing Tang, Xu Jun
Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse, Jingbing Wu, Hanqing Tang, Xu Jun
Journal of System Simulation
Abstract: Because the traditional methods can hardly analyze the complex combustion characteristics of cement kiln mixed with domestic refuse, a data mining technology is introduced. A domestic cement plant is selected as the object, and its operating data and relevant parameters are collected. The influence coefficient of each parameter on coal consumption and NOx emission is analyzed by using Stability Selection algorithm. The mathematical model of coal consumption and NOx emission is established with Random Forest algorithm, and the key optimization parameters and their optimal values are obtained by K-means clustering algorithm. The result shows that this method …
Multitask-Based Association Rule Mining, Peli̇n Yildirim Taşer, Kökten Ulaş Bi̇rant, Derya Bi̇rant
Multitask-Based Association Rule Mining, Peli̇n Yildirim Taşer, Kökten Ulaş Bi̇rant, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and …
Energy Efficiency Data Mining And Scheduling Optimization Of Discrete Workshop, Yugu Lin, Wang Yan
Energy Efficiency Data Mining And Scheduling Optimization Of Discrete Workshop, Yugu Lin, Wang Yan
Journal of System Simulation
Abstract: This paper addresses the optimization of energy consumption in discrete workshops and establishes the energy efficiency optimization model of discrete workshops. The relationship between data mining and knowledge discovery is established. Through scheduling data preprocessing and C4.5 decision tree learning algorithm, the discovery of scheduling knowledge is realized. Energy efficiency optimization calculation is achieved in discrete workshops by the combination of scheduling knowledge and improved differential evolution algorithm (IDE). By comparing with TLBO, GA and PSO, the feasibility of IDE algorithm is verified.
Design And Implementation Of Information Management Platform For Big Data Of Uranium, Zhou Xiaoxi, Deng Fan, Wan Lin, Yang Jun
Design And Implementation Of Information Management Platform For Big Data Of Uranium, Zhou Xiaoxi, Deng Fan, Wan Lin, Yang Jun
Coal Geology & Exploration
In order to integrate the borehole data and geological survey data of sandstone-type uranium deposits, the unified management of the borehole database was implemented, and the efficiency of integrated application of the geological data of borehole data was improved. A comprehensive information management platform for uranium was designed and implemented. For big data platform, the four-layer framework composed of the base installation, information resources, application service, user interaction was proposed. Techniques such as virtualization of cloud computing, distributive storage and parallel computing were adopted to set up the basic environment of the big data of uranium and improve the unified …
Optimization Of Material Release For Printed Circuit Board Template Based On Data Mining, Shengping Lü, Qiangsheng Yue, Liu Tao
Optimization Of Material Release For Printed Circuit Board Template Based On Data Mining, Shengping Lü, Qiangsheng Yue, Liu Tao
Journal of System Simulation
Abstract: Data mining were employed for the optimization of material release of PCB (Printed Circuit Board) template. PCB scrap ratio related parameters were specified and prediction model variables were chosen according to hypothesis test. Multiple linear regression (MLR), Chi-squared automatic interaction detector, artificial neural network and support vector machine approaches for the prediction of scrap ratio were employed. Evaluation indictors called as superfluous ratio, supplement release ratio and weighted sum of the two were presented; the material release simulation was conducted and then the four approaches were compared and MLR was taken as the preferred one. Adjust coefficient …
Mining And Validation Of Attacking Behavior In The Robocup 2d Simulation, Chen Bing, Zhang Heng, Zekai Cheng, Dong Peng, Lin Chao
Mining And Validation Of Attacking Behavior In The Robocup 2d Simulation, Chen Bing, Zhang Heng, Zekai Cheng, Dong Peng, Lin Chao
Journal of System Simulation
Abstract: Robocup is an international academic competition which focuses on artificial intelligence and robotics. The 2D simulation is one of the earliest and most influential projects in Robocup. Attacking is the core behaviour of the simulated football game, as well as the attack recognition is considered as an important part in team-confrontations. This paper selects some active and contribution index of attacking, extracts lots of attacking behaviour data of the key agents, proposes two kinds of attacking patterns of 2D simulation, as ‘separate attack’ and ‘cooperative attack’, according to the human-player actions. The following simulation tests give the accuracy of …
Data Analysis Through Social Media According To The Classified Crime, Serkan Savaş, Nuretti̇n Topaloğlu
Data Analysis Through Social Media According To The Classified Crime, Serkan Savaş, Nuretti̇n Topaloğlu
Turkish Journal of Electrical Engineering and Computer Sciences
The amount and variety of data generated through social media sites has increased along with the widespread use of social media sites. In addition, the data production rate has increased in the same way. The inclusion of personal information within these data makes it important to process the data and reach meaningful information within it. This process can be called intelligence and this meaningful information may be for commercial, academic, or security purposes. An example application is developed in this study for intelligence on Twitter. Crimes in Turkey are classified according to Turkish Statistical Institute criminal data and keywords are …
Heart Attack Mortality Prediction: An Application Of Machine Learning Methods, Issam Salman
Heart Attack Mortality Prediction: An Application Of Machine Learning Methods, Issam Salman
Turkish Journal of Electrical Engineering and Computer Sciences
The heart is an important organ in the human body, and acute myocardial infarction (AMI) is the leading cause of death in most countries. Researchers are doing a lot of data analysis work to assist doctors in predicting the heart problem. An analysis of the data related to different health problems and its functions can help in predicting the wellness of this organ with a degree of certainty. Our research reported in this paper consists of two main parts. In the first part of the paper, we compare different predictive models of hospital mortality for patients with AMI. All results …
Building Indicators Of Sustainable Development With The Use Of Multi-Criteria Decision Making Methods., V.B Britkov, R.D Zaitsev, R.A Perelet, G.V Royzenson
Building Indicators Of Sustainable Development With The Use Of Multi-Criteria Decision Making Methods., V.B Britkov, R.D Zaitsev, R.A Perelet, G.V Royzenson
Chemical Technology, Control and Management
An approach for analyzing big data is proposed with reference to sustainable development indicators (SDI), based on the application of combinations of various mathematical techniques. In particular, methods of multi-criteria decision-making for developing the SDI are described in the article. Examples of constructing UN SDI are given.
Clustering Method Based On Graph Data Model And Reliability Detection, Yanyun Cheng, Huisong Bian, Changsheng Bian
Clustering Method Based On Graph Data Model And Reliability Detection, Yanyun Cheng, Huisong Bian, Changsheng Bian
Journal of System Simulation
Abstract: For the data in feature space, traditional clustering algorithm can take clustering analysis directly. High-dimensional spatial data cannot achieve intuitive and effective graphical visualization of clustering results in 2D plane. Graph data can clearly reflect the similarity relationship between objects. According to the distance of the data objects, the feature space data are modeled as graph data by iteration. Cluster analysis based on modularity is carried out on the modeling graph data. The two-dimensional visualization of non-spherical-shape distribution data cluster and result is achieved. The concept of credibility of the clustering result is proposed, and a method is proposed, …
Real-Time Power System Dynamic Security Assessment Based On Advanced Feature Selection For Decision Tree Classifiers, Qusay Al-Gubri, Mohd Aifaa Mohd Ariff
Real-Time Power System Dynamic Security Assessment Based On Advanced Feature Selection For Decision Tree Classifiers, Qusay Al-Gubri, Mohd Aifaa Mohd Ariff
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a novel algorithm based on an advanced feature selection technique for the decision tree (DT) classifier to assess the dynamic security in a power system. The proposed methodology utilizes symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier-based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of the DT classifier. The effectiveness of the proposed technique is demonstrated on the modified IEEE 30-bus test system model. The results show that the DT classifier with …
Development Of An Enhanced Generic Data Mining Life Cycle (Dmlc), Markus Hofmann, Brendan Tierney
Development Of An Enhanced Generic Data Mining Life Cycle (Dmlc), Markus Hofmann, Brendan Tierney
The ITB Journal
Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve …
Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant
Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
A number of recent studies have used a decision tree approach as a data mining technique; some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. The main objective of this study is to compute the similarity of decision trees using data mining techniques. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and …
Discovering The Relationships Between Yarn And Fabric Properties Using Association Rule Mining, Peli̇n Yildirim, Derya Bi̇rant, Tuba Alpyildiz
Discovering The Relationships Between Yarn And Fabric Properties Using Association Rule Mining, Peli̇n Yildirim, Derya Bi̇rant, Tuba Alpyildiz
Turkish Journal of Electrical Engineering and Computer Sciences
Investigation of the effects of yarn parameters on fabric quality and finding important parameters to achieve desired fabric properties are important issues for the design process with the aim to meet the needs of the textile industry and the consumer for complex and specific requirements of functionality. Despite many statistical and mathematical studies that predict and reveal specific properties of utilized yarn and fabric materials, a number of challenges continue to exist when evaluated in many perspectives, such as discovering complex relationships among material properties in data. Data mining plays an important role in discovering hidden patterns from fabric data …
An Ant Colony Optimization Algorithm-Based Classification For The Diagnosis Of Primary Headaches Using A Website Questionnaire Expert System, Ufuk Çeli̇k, Ni̇lüfer Yurtay
An Ant Colony Optimization Algorithm-Based Classification For The Diagnosis Of Primary Headaches Using A Website Questionnaire Expert System, Ufuk Çeli̇k, Ni̇lüfer Yurtay
Turkish Journal of Electrical Engineering and Computer Sciences
The purpose of this research was to evaluate the classification accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified patients with 96.9412% overall accuracy. …
Proposing A New Clustering Method To Detect Phishing Websites, Morteza Arab, Mohammad Karim Sohrabi
Proposing A New Clustering Method To Detect Phishing Websites, Morteza Arab, Mohammad Karim Sohrabi
Turkish Journal of Electrical Engineering and Computer Sciences
Phishing websites are fake ones that are developed by ill-intentioned people to imitate real and legal websites. Most of these types of web pages have high visual similarities to hustle the victims. The victims of phishing websites may give their bank accounts, passwords, credit card numbers, and other important information to the designers and owners of phishing websites. The increasing number of phishing websites has become a great challenge in e-business in general and in electronic banking specifically. In the present study, a novel framework based on model-based clustering is introduced to fight against phishing websites. First, a model is …
Prediction And Recommendations On The It Leaners' Learning Path As A Collective Intelligence Using A Data Mining Technique, Seong-Yong Hong, Juyun Cho, Yonghyun Hwang
Prediction And Recommendations On The It Leaners' Learning Path As A Collective Intelligence Using A Data Mining Technique, Seong-Yong Hong, Juyun Cho, Yonghyun Hwang
Journal of International Technology and Information Management
With the recent advances in computer technology along with pervasive internet accesses, data analytics is getting more attention than ever before. In addition, research areas on data analysis are diverging and integrating lots of different fields such as a business and social sector. Especially, recent researches focus on the data analysis for a better intelligent decision making and prediction system. This paper analyzes data collected from current IT learners who have already studied various IT subjects to find the IT learners’ learning patterns. The most popular learning patterns are identified through an association rule data mining using an arules package …
Efficiently Mining Frequent Itemsets In Transactional Databases, Salah Alghyaline, Jun-Wei Hsieh, Jim Z. C Lai
Efficiently Mining Frequent Itemsets In Transactional Databases, Salah Alghyaline, Jun-Wei Hsieh, Jim Z. C Lai
Journal of Marine Science and Technology
Discovering frequent itemsets is an essential task in association rules mining and it is considered to be computationally expensive. To find the frequent itemsets, the algorithm of frequent pattern growth (FP-growth) is one of the best algorithms for mining frequent patterns. However, many experimental results have shown that building conditional FP-trees during mining data using this FP-growth method will consume most of CPU time. In addition, it requires a lot of space to save the FP-trees. This paper presents a new approach for mining frequent item sets from a transactional database without building the conditional FP-trees. Thus, lots of computing …
Evaluation Of Classification And Ensemble Algorithms For Bank Customer Marketing Response Prediction, Olatunji Apampa
Evaluation Of Classification And Ensemble Algorithms For Bank Customer Marketing Response Prediction, Olatunji Apampa
Journal of International Technology and Information Management
This article attempts to improve the performance of classification algorithms used in the bank customer marketing response prediction of an unnamed Portuguese bank using the Random Forest ensemble. A thorough exploratory data analysis (EDA) was conducted on the data in order to ascertain the presence of anomalies such as outliers and extreme values. The EDA revealed that the bank data had 45, 211 instances and 17 features, with 11.7% positive responses. This was in addition to the detection of outliers and extreme values. Classification algorithms used for modelling the bank dataset include; Logistic Regression, Decision Tree, Naïve Bayes and the …
Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker
Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker
Turkish Journal of Electrical Engineering and Computer Sciences
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation …
Application Of A Decision Tree Method With A Spatiotemporal Object Database For Pavement Maintenance And Management, Chien-Ta Chen, Chia-Tse Hung, Jyh-Dong Lin, Po-Hsun Sung
Application Of A Decision Tree Method With A Spatiotemporal Object Database For Pavement Maintenance And Management, Chien-Ta Chen, Chia-Tse Hung, Jyh-Dong Lin, Po-Hsun Sung
Journal of Marine Science and Technology
In recent years, pavement engineering has gradually shifted from new construction work to pavement maintenance and management. Since pavement engineers of the Taipei City Government change frequently, objective data is used to make decisions pertaining to road maintenance in Taipei City instead of relying on engineers' experience. In this study, three methods (ID3, C5.0 and SVM) have been chosen to test for use in the decision-making process related to road maintenance of Taipei City. The results show the correct classification rates of the decision trees are 76.67% (C5.0), 64.52% (ID3), and 66.67% (SVM). The decision tree of C5.0 was compared …
Predicting Cross-Gaming Propensity Using E-Chaid Analysis, Eunju Suh, Matt Alhaery
Predicting Cross-Gaming Propensity Using E-Chaid Analysis, Eunju Suh, Matt Alhaery
UNLV Gaming Research & Review Journal
Cross-selling different types of games could provide an opportunity for casino operators to generate additional time and money spent on gaming from existing patrons. One way to identify the patrons who are likely to cross-play is mining individual players’ gaming data using predictive analytics. Hence, this study aims to predict casino patrons’ propensity to play both slots and table games, also known as cross-gaming, by applying a data-mining algorithm to patrons’ gaming data. The Exhaustive Chi-squared Automatic Interaction Detector (E-CHAID) method was employed to predict cross-gaming propensity. The E-CHAID models based on the gaming-related behavioral data produced actionable model accuracy …