H.3. Artificial Intelligence
Sajjad Alizadeh Fard; Hossein Rahmani
Abstract
Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly ...
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Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly impacts model accuracy and execution time. In this paper, we introduce an ensemble-based, explainable feature selection framework founded on SHAP and LIME algorithms, called "X-SHAoLIM". We applied our framework to diverse combinations of the best models from previous studies, conducting both quantitative and qualitative comparisons with other feature selection methods. The quantitative evaluation of the "X-SHAoLIM" framework across various model combinations revealed consistent accuracy improvements on average, including increases in Precision (+5.6), Recall (+1.5), F1-Score (+3.5), and AUC-PR (+6.75). Beyond enhanced accuracy, our proposed framework, leveraging explainable algorithms like SHAP and LIME, provides a deeper understanding of features' importance in model predictions, delivering effective explanations to system users.
H.6.3.3. Pattern analysis
Meysam Roostaee; Razieh Meidanshahi
Abstract
In this study, we sought to minimize the need for redundant blood tests in diagnosing common diseases by leveraging unsupervised data mining techniques on a large-scale dataset of over one million patients' blood test results. We excluded non-numeric and subjective data to ensure precision. To identify ...
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In this study, we sought to minimize the need for redundant blood tests in diagnosing common diseases by leveraging unsupervised data mining techniques on a large-scale dataset of over one million patients' blood test results. We excluded non-numeric and subjective data to ensure precision. To identify relationships between attributes, we applied a suite of unsupervised methods including preprocessing, clustering, and association rule mining. Our approach uncovered correlations that enable healthcare professionals to detect potential acute diseases early, improving patient outcomes and reducing costs. The reliability of our extracted patterns also suggest that this approach can lead to significant time and cost savings while reducing the workload for laboratory personnel. Our study highlights the importance of big data analytics and unsupervised learning techniques in increasing efficiency in healthcare centers.
Mohammad Reza Keyvanpour; Zahra Karimi Zandian; Nasrin Mottaghi
Abstract
Regression testing reduction is an essential phase in software testing. In this step, the redundant and unnecessary cases are eliminated, whereas software accuracy and performance are not degraded. So far, various researches have been proposed in regression testing reduction field. The main challenge ...
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Regression testing reduction is an essential phase in software testing. In this step, the redundant and unnecessary cases are eliminated, whereas software accuracy and performance are not degraded. So far, various researches have been proposed in regression testing reduction field. The main challenge in this area is to provide a method that maintain fault-detection capability while reducing test suites. In this paper, a new test suite reduction technique is proposed based on data mining. In this method, in addition to test suite reduction, its fault-detection capability is preserved using both clustering and classification. In this approach, regression test cases are reduced using a bi-criteria data mining-based method in two levels. In each level, the different and useful coverage criteria and clustering algorithms are used to establish a better compromise between test suite size and the ability of reduced test suite fault detection. The results of the proposed method have been compared to the effects of five other methods based on PSTR and PFDL. The experiments show the efficiency of the proposed method in the test suite reduction in maintaining its capability in fault detection.
F.4.18. Time series analysis
Ali Ghorbanian; Hamideh Razavi
Abstract
In time series clustering, features are typically extracted from the time series data and used for clustering instead of directly clustering the data. However, using the same set of features for all data sets may not be effective. To overcome this limitation, this study proposes a five-step algorithm ...
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In time series clustering, features are typically extracted from the time series data and used for clustering instead of directly clustering the data. However, using the same set of features for all data sets may not be effective. To overcome this limitation, this study proposes a five-step algorithm that extracts a complete set of features for each data set, including both direct and indirect features. The algorithm then selects essential features for clustering using a genetic algorithm and internal clustering criteria. The final clustering is performed using a hierarchical clustering algorithm and the selected features. Results from applying the algorithm to 81 data sets indicate an average Rand index of 72.16%, with 38 of the 78 extracted features, on average, being selected for clustering. Statistical tests comparing this algorithm to four others in the literature confirm its effectiveness.
Seyed Mahdi Sadatrasoul; Omid Mahdi Ebadati; Amir Amirzadeh Irani
Abstract
Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s ...
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Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s incomes, costs, expenses, assets and liabilities. Smoothing can affect credit scoring models reliability, it can cause to providing/not providing facilities to a non-worthy/worthy organization orderly, which are both known as decision errors and are reported as “type I” and “type II” errors, which are very important for Banks Loan portfolio. This paper investigates this issue for the first time in credit scoring studies on the authors knowledge and searches. The data of companies associated with a major Asian Bank are first applied using logistic regression. Different smoothing scenarios are tested, using wilcoxon statistic indicated that traditional credit scoring models have significant errors when smoothing procedures have more than 20% change in adjusting company’s financial statements and balance sheets parameters.
J. Barazande; N. Farzaneh
Abstract
One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities ...
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One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities of the people inside them. Detecting the activities of the smart homes’ users may include the detection of activities such as making food or watching TV. Detecting the activities of residents of smart homes can tremendously help the elderly or take care of the kids or, even, promote security issues. The information collected by the sensors could be used for detecting the kind of activities; however, the main challenge is the poor precision of most of the activity detection methods. In the proposed method, for reducing the clustering error of the data mining techniques, a hybrid learning approach is presented using Water Strider Algorithm. In the proposed method, Water Strider Algorithm can be used in the feature extraction phase and exclusively extract the main features for machine learning. The analysis of the proposed method shows that it has precision of 97.63 %, accuracy of 97. 12 %, and F1 index of 97.45 %. It, in comparison with similar algorithms (such as Butterfly Optimization Algorithm, Harris Hawks Optimization Algorithm, and Black Widow Optimization Algorithm), has higher precision while detecting the users’ activities.
M. Gordan; Saeed R. Sabbagh-Yazdi; Z. Ismail; Kh. Ghaedi; H. Hamad Ghayeb
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 ...
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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.
A. Hasan-Zadeh; F. Asadi; N. Garbazkar
Abstract
For an economic review of food prices in May 2019 to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time. The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical ...
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For an economic review of food prices in May 2019 to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time. The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical analysis. Among the various methods of modelling and statistical prediction, and in a new approach, we modeled the data using data mining techniques consisting of decision tree methods, associative rules, and Bayesian law. Then, prediction, validation, and standardization of the accuracy of the validation are performed on them. Results of data validation in the urban and national area and the results of the standardization of the accuracy of validation in the urban and national area are presented with the desired accuracy.
Oladosu Oladimeji; Olayanju Oladimeji
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 ...
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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.
H.3.14. Knowledge Management
A. Soltani; M. Soltani
Abstract
High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its ...
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High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its utility measurement and utility values tend to become higher for itemsets containing more items. Hence, HUIM algorithms discover a huge enormous number of long patterns. High average-utility itemset mining (HAUIM) is a variation of HUIM that selects patterns by considering both their utilities and lengths. In the last decades, several algorithms have been introduced to mine high average-utility itemsets. To speed up the HAUIM process, here a new algorithm is proposed which uses a new list structure and pruning strategy. Several experiments performed on real and synthetic datasets show that the proposed algorithm outperforms the state-of-the-art HAUIM algorithms in terms of runtime and memory consumption.
D. Data
S. Taherian Dehkordi; A. Khatibi Bardsiri; M. H. Zahedi
Abstract
Data mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigating ...
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Data mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigating patients' records. Early diagnosis of diabetes can reduce the effects of this devastating disease. A common way to diagnose this disease is performing a blood test, which, despite its high precision, has some disadvantages such as: pain, cost, patient stress, lack of access to a laboratory, and so on. Diabetic patients’ information has hidden patterns, which can help you investigate the risk of diabetes in individuals, without performing any blood tests. Use of neural networks, as powerful data mining tools, is an appropriate method to discover hidden patterns in diabetic patients’ information. In this paper, in order to discover the hidden patterns and diagnose diabetes, a water wave optimization(WWO) algorithm; as a precise metaheuristic algorithm, was used along with a neural network to increase the precision of diabetes prediction. The results of our implementation in the MATLAB programming environment, using the dataset related to diabetes, indicated that the proposed method diagnosed diabetes at a precision of 94.73%,sensitivity of 94.20%, specificity of 93.34%, and accuracy of 95.46%, and was more sensitive than methods such as: support vector machines, artificial neural networks, and decision trees.
H.3. Artificial Intelligence
A.R. Hatamlou; M. Deljavan
Abstract
Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase ...
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Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase of forecast accuracy. In this paper, different factors were studied in comparison to the previous studies on gold price forecast. In terms of time span, the collected data were divided into three groups of daily, monthly and annually. The conducted tests using new factors indicate accuracy improvement up to 2% in neural networks methods, 7/3% in time series method and 5/6% in linear regression method.
G.3.9. Database Applications
M. Shamsollahi; A. Badiee; M. Ghazanfari
Abstract
Heart disease is one of the major causes of morbidity in the world. Currently, large proportions of healthcare data are not processed properly, thus, failing to be effectively used for decision making purposes. The risk of heart disease may be predicted via investigation of heart disease risk factors ...
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Heart disease is one of the major causes of morbidity in the world. Currently, large proportions of healthcare data are not processed properly, thus, failing to be effectively used for decision making purposes. The risk of heart disease may be predicted via investigation of heart disease risk factors coupled with data mining knowledge. This paper presents a model developed using combined descriptive and predictive techniques of data mining that aims to aid specialists in the healthcare system to effectively predict patients with Coronary Artery Disease (CAD). To achieve this objective, some clustering and classification techniques are used. First, the number of clusters are determined using clustering indexes. Next, some types of decision tree methods and Artificial Neural Network (ANN) are applied to each cluster in order to predict CAD patients. Finally, results obtained show that the C&RT decision tree method performs best on all data used in this study with 0.074 error. All data used in this study are real and are collected from a heart clinic database.
D. Data
M. Abdar; M. Zomorodi-Moghadam
Abstract
In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, ...
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In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, different kernel functions and C parameters have been used and our results show that SVM with C parameter (C-SVM) with average of 99.18% accuracy with Polynomial Kernel function in testing step, has better performance compared to the other Kernel functions such as RBF and Sigmoid as well as Bayesian Network algorithm. It is also shown that ten important factors in SVM algorithm are Jitter (Abs), Subject #, RPDE, PPE, Age, NHR, Shimmer APQ 11, NHR, Total-UPDRS, Shimmer (dB) and Shimmer. We also prove that the accuracy of our proposed C-SVM and RBF approaches is in direct proportion to the value of C parameter such that with increasing the amount of C, accuracy in both Kernel functions is increased. But unlike Polynomial and RBF, Sigmoid has an inverse relation with the amount of C. Indeed, by using these methods, we can find the most effective factors common in both genders (male and female). To the best of our knowledge there is no study on Parkinson's disease for identifying the most effective factors which are common in both genders.
H.3.2.3. Decision support
F. Moslehi; A.R. Haeri; A.R. Moini
Abstract
In today's world, most financial transactions are carried out using done through electronic instruments and in the context of the Information Technology and Internet. Disregarding the application of new technologies at this field and sufficing to traditional ways, will result in financial loss and customer ...
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In today's world, most financial transactions are carried out using done through electronic instruments and in the context of the Information Technology and Internet. Disregarding the application of new technologies at this field and sufficing to traditional ways, will result in financial loss and customer dissatisfaction. The aim of the present study is surveying and analyzing the use of electronic payment instruments in banks across the country using statistics and information retrieved from the Central Bank and data mining techniques. For this purpose, firstly, according to the volume of transactions carried out and with the help of using the K-Means algorithm, a label was dedicated to any record; then hidden patterns of the E-payment instruments transaction were detected using the CART algorithm. The obtained results of this study enable banks administrators to balance their future policies in the field of E-payment and in the bank and customers’ interest's direction based on detected patterns and provide higher quality services to their customers.
H.3.2.5. Environment
M. T. Sattari; M. Pal; R. Mirabbasi; J. Abraham
Abstract
This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making ...
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This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace methods are used. The dataset, which consisted of 488 samples with nine input parameters were obtained from the Barandoozchay River in West Azerbaijan province, Iran. Three evaluation criteria: correlation coefficient, root mean square error and mean absolute error were used to judge the accuracy of different ensemble models. In addition to the use of M5 model tree to predict the SAR values, a wrapper-based variable selection approach using a M5 model tree as the learning algorithm and a genetic algorithm, was also used to select useful input variables. The encouraging performance motivates the use of this technique to predict SAR values.
H.5.10. Applications
S. Shoorabi Sani
Abstract
In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a ...
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In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a conventional medium sized bridge (length of 50 meters, height of 10 meters, and with 2 piers) was examined for the purpose of this study. This method includes installing two sensor nodes with the ability of measuring temperature and humidity on both side of the bridge deck. The data collected by the system including temperature and humidity values are received by a LABVIEW-based software to be analyzed and stored in a database. Proposed SHM monitoring system is equipped by a novel method of using data mining techniques on the database of climatic conditions of past few years related to the location of the bridge to predict the occurrence and severity of future damages. In addition, this system has several alarm levels which are based on analysis of bridge conditions with fuzzy inference method, so it can issue proactive and precise warnings and alarms in terms of place of occurrence and severity of possible damages in the bridge deck to ensure total productive (TPM) and proactive maintenance. Very low costs, increased efficiency of the bridge service, and reduced maintenance costs makes this SHM system a practical and applicable system. The data and results related to all mentioned subjects were thoroughly discussed .
H.4.6. Computational Geometry and Object Modeling
A. Mousavi; A. Sheikh Mohammad Zadeh; M. Akbari; A. Hunter
Abstract
Mobile technologies have deployed a variety of Internet–based services via location based services. The adoption of these services by users has led to mammoth amounts of trajectory data. To use these services effectively, analysis of these kinds of data across different application domains is required ...
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Mobile technologies have deployed a variety of Internet–based services via location based services. The adoption of these services by users has led to mammoth amounts of trajectory data. To use these services effectively, analysis of these kinds of data across different application domains is required in order to identify the activities that users might need to do in different places. Researchers from different communities have developed models and techniques to extract activity types from such data, but they mainly have focused on the geometric properties of trajectories and do not consider the semantic aspect of moving objects. This work proposes a new ontology-based approach so as to recognize human activity from GPS data for understanding and interpreting mobility data. The performance of the approach was tested and evaluated using a dataset, which was acquired by a user over a year within the urban area in the City of Calgary in 2010. It was observed that the accuracy of the results was related to the availability of the points of interest around the places that the user had stopped. Moreover, an evaluation experiment was done, which revealed the effectiveness of the proposed method with an improvement of 50 % performance with complexity trend of an O(n).
J.10.3. Financial
G. Ozdagoglu; A. Ozdagoglu; Y. Gumus; G. Kurt Gumus
Abstract
Predicting financially false statements to detect frauds in companies has an increasing trend in recent studies. The manipulations in financial statements can be discovered by auditors when related financial records and indicators are analyzed in depth together with the experience of auditors in order ...
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Predicting financially false statements to detect frauds in companies has an increasing trend in recent studies. The manipulations in financial statements can be discovered by auditors when related financial records and indicators are analyzed in depth together with the experience of auditors in order to create knowledge to develop a decision support system to classify firms. Auditors may annotate the firms’ statements as “correct” or “incorrect” to add their experience, and then these annotations with related indicators can be used for the learning process to generate a model. Once the model is learned and tested for validation, it can be used for new firms to predict their class values. In this research, we attempted to reveal this benefit in the framework of Turkish firms. In this regard, the study aims at classifying financially correct and false statements of Turkish firms listed on Borsa İstanbul, using their particular financial ratios as indicators of a success or a manipulation. The dataset was selected from a particular period after the crisis (2009 to 2013). Commonly used three classification methods in data mining were employed for the classification: decision tree, logistic regression, and artificial neural network, respectively. According to the results, although all three methods are performed well, the latter had the best performance, and it outperforms other two classical methods. The common ground of the selected methods is that they pointed out the Z-score as the first distinctive indicator for classifying financial statements under consideration.
H.3.14. Knowledge Management
M. Sakenian Dehkordi; M. Naderi Dehkordi
Abstract
Due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. Association Rules Mining is one of the data mining techniques to extract useful patterns in the form of association rules. One of the main problems in applying this technique on ...
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Due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. Association Rules Mining is one of the data mining techniques to extract useful patterns in the form of association rules. One of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. Hiding the association rules is one of the methods to preserve privacy and it is a main subject in the field of data mining and database security, for which several algorithms with different approaches are presented so far. An algorithm to hide sensitive association rules with a heuristic approach is presented in this article, where the Perturb technique based on reducing confidence or support rules is applied with the attempt to remove the considered item from a transaction with the highest weight by allocating weight to the items and transactions. Efficiency is measured by the failure criteria of hiding, number of lost rules and ghost rules, and execution time. The obtained results of this study are assessed and compared with two known FHSAR and RRLR algorithms, based on two real databases (dense and sparse). The results indicate that the number of lost rules in all experiments are reduced by 47% in comparison with RRLR and reduced by 23% in comparison with FHSAR. Moreover, the other undesirable side effects, in this proposed algorithm in the worst case are equal to that of the base algorithms.
Farzaneh Zahedi; Mohammad-Reza Zare-Mirakabad
Abstract
Drug addiction is a major social, economic, and hygienic challenge that impacts on all the community and needs serious threat. Available treatments are successful only in short-term unless underlying reasons making individuals prone to the phenomenon are not investigated. Nowadays, there are some treatment ...
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Drug addiction is a major social, economic, and hygienic challenge that impacts on all the community and needs serious threat. Available treatments are successful only in short-term unless underlying reasons making individuals prone to the phenomenon are not investigated. Nowadays, there are some treatment centers which have comprehensive information about addicted people. Therefore, given the huge data sources, data mining can be used to explore knowledge implicit in them, their results can be employed as a knowledge base of decision support systems to make decisions regarding addiction prevention and treatment. We studied participants of such clinics including 471 participants, where 86.2% were male and 13.8% were female. The study aimed to extract rules from the collected data by using association models. Results can be used by rehab clinics to give more knowledge regarding relationships between various parameters and help them for better and more effective treatments. E.g. according to the findings of the study, there is a relationship between individual characteristics and LSD abuse, individual characteristics, the kind of narcotics taken, and committing crimes, family history of drug addiction and family member drug addiction.
Amir Mosavi
Abstract
Often in modeling the engineering optimization design problems, the value of objective function(s) is not clearly defined in terms of design variables. Instead it is obtained by some numerical analysis such as FE structural analysis, fluid mechanic analysis, and thermodynamic analysis, etc. Yet, the ...
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Often in modeling the engineering optimization design problems, the value of objective function(s) is not clearly defined in terms of design variables. Instead it is obtained by some numerical analysis such as FE structural analysis, fluid mechanic analysis, and thermodynamic analysis, etc. Yet, the numerical analyses are considerably time consuming to obtain the final value of objective function(s). For the reason of reducing the number of analyses as few as possible our methodology works as a supporting tool to the meta-models. The research in meta-modeling for multiobjective optimization are relatively young and there is still much to do. Here is shown that visualizing the problem on the basis of the randomly sampled geometrical big-data of computer aided design (CAD) and computer aided engineering (CAE) simulation results, combined with utilizing classification tool of data mining could be effective as a supporting system to the available meta-modeling approaches. To evaluate the effectiveness of the proposed method a study case in 3D wing optimal design is given. Along with the study case, it is discussed that how effective the proposed methodology could be in further practical engineering design problems.
Seyed Mahdi sadatrasoul; Mohammadreza gholamian; Mohammad Siami; Zeynab Hajimohammadi
Abstract
This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates ...
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This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The articles are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques is also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because it’s a major issue in credit scoring problem. The findings of the review reveals that data mining techniques are mostly applied to individual credit score and there are a few researches on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Hybrid methods are investigated in four categories and two of them which are “classification and classification” and “clustering and classification” combinations are used more. Paper analysis provides a guide to future researches and concludes with several suggestions for further studies.
