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decision tree</title> <meta name="description" content="Search results for: algorithms decision tree"> <meta name="keywords" content="algorithms decision tree"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="algorithms decision tree"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6398</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: algorithms decision tree</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6398</span> Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Attah"> B. Attah</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Misra"> S. Misra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20algorithms" title=" decision tree algorithms"> decision tree algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus" title=" diabetes mellitus"> diabetes mellitus</a> </p> <a href="https://publications.waset.org/abstracts/35949/performance-analysis-of-artificial-neural-network-with-decision-tree-in-prediction-of-diabetes-mellitus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35949.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">408</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6397</span> Understanding Farmers’ Perceptions Towards Agrivoltaics Using Decision Tree Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayuri%20Roy%20Choudhury">Mayuri Roy Choudhury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent times the concept of agrivoltaics has gained popularity due to the dual use of land and the added value provided by photovoltaics in terms of renewable energy and crop production on farms. However, the transition towards agrivoltaics has been slow, and our research tries to investigate the obstacles leading towards the slow progress of agrivoltaics. We applied data science decision tree algorithms to quantify qualitative perceptions of farmers in the United States for agrivoltaics. To date, there has not been much research that mentions farmers' perceptions, as most of the research focuses on the benefits of agrivoltaics. Our study adds value by putting forward the voices of farmers, which play a crucial towards the transition to agrivoltaics in the future. Our results show a mixture of responses in favor of agrivoltaics. Furthermore, it also portrays significant concerns of farmers, which is useful for decision-makers when it comes to formulating policies for agrivoltaics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agrivoltaics" title="agrivoltaics">agrivoltaics</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-tree%20algorithms" title=" decision-tree algorithms"> decision-tree algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=farmers%20perception" title=" farmers perception"> farmers perception</a>, <a href="https://publications.waset.org/abstracts/search?q=transition" title=" transition"> transition</a> </p> <a href="https://publications.waset.org/abstracts/139772/understanding-farmers-perceptions-towards-agrivoltaics-using-decision-tree-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139772.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6396</span> Data Mining Algorithms Analysis: Case Study of Price Predictions of Lands</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Julio%20Albuja">Julio Albuja</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Zaldumbide"> David Zaldumbide</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=data" title=" data"> data</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation" title=" transformation"> transformation</a> </p> <a href="https://publications.waset.org/abstracts/76382/data-mining-algorithms-analysis-case-study-of-price-predictions-of-lands" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6395</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6394</span> Complex Decision Rules in the Form of Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Avinash%20S.%20Jagtap">Avinash S. Jagtap</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharad%20D.%20Gore"> Sharad D. Gore</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20G.%20Gurao"> Rajendra G. Gurao </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Decision rules become more and more complex as the number of conditions increase. As a consequence, the complexity of the decision rule also influences the time complexity of computer implementation of such a rule. Consider, for example, a decision that depends on four conditions A, B, C and D. For simplicity, suppose each of these four conditions is binary. Even then the decision rule will consist of 16 lines, where each line will be of the form: If A and B and C and D, then action 1. If A and B and C but not D, then action 2 and so on. While executing this decision rule, each of the four conditions will be checked every time until all the four conditions in a line are satisfied. The minimum number of logical comparisons is 4 whereas the maximum number is 64. This paper proposes to present a complex decision rule in the form of a decision tree. A decision tree divides the cases into branches every time a condition is checked. In the form of a decision tree, every branching eliminates half of the cases that do not satisfy the related conditions. As a result, every branch of the decision tree involves only four logical comparisons and hence is significantly simpler than the corresponding complex decision rule. The conclusion of this paper is that every complex decision rule can be represented as a decision tree and the decision tree is mathematically equivalent but computationally much simpler than the original complex decision rule <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strategic" title="strategic">strategic</a>, <a href="https://publications.waset.org/abstracts/search?q=tactical" title=" tactical"> tactical</a>, <a href="https://publications.waset.org/abstracts/search?q=operational" title=" operational"> operational</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive" title=" adaptive"> adaptive</a>, <a href="https://publications.waset.org/abstracts/search?q=innovative" title=" innovative"> innovative</a> </p> <a href="https://publications.waset.org/abstracts/77189/complex-decision-rules-in-the-form-of-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6393</span> Amharic Text News Classification Using Supervised Learning </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Misrak%20Assefa">Misrak Assefa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20categorization" title="text categorization">text categorization</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20machine%20learning" title=" supervised machine learning"> supervised machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20Bayes" title=" naive Bayes"> naive Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/124249/amharic-text-news-classification-using-supervised-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124249.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">209</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6392</span> A Novel PSO Based Decision Tree Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Farzan">Ali Farzan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification of data objects or patterns is a major part in most of Decision making systems. One of the popular and commonly used classification methods is Decision Tree (DT). It is a hierarchical decision making system by which a binary tree is constructed and starting from root, at each node some of the classes is rejected until reaching the leaf nods. Each leaf node is a representative of one specific class. Finding the splitting criteria in each node for constructing or training the tree is a major problem. Particle Swarm Optimization (PSO) has been adopted as a metaheuristic searching method for finding the best splitting criteria. Result of evaluating the proposed method over benchmark datasets indicates the higher accuracy of the new PSO based decision tree. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=splitting%20criteria" title=" splitting criteria"> splitting criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a> </p> <a href="https://publications.waset.org/abstracts/32425/a-novel-pso-based-decision-tree-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32425.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6391</span> Commuters Trip Purpose Decision Tree Based Model of Makurdi Metropolis, Nigeria and Strategic Digital City Project</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Okechukwu%20Nwafor">Emmanuel Okechukwu Nwafor</a>, <a href="https://publications.waset.org/abstracts/search?q=Folake%20Olubunmi%20Akintayo"> Folake Olubunmi Akintayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Denis%20Alcides%20Rezende"> Denis Alcides Rezende</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20algorithm" title="decision tree algorithm">decision tree algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=trip%20purpose" title=" trip purpose"> trip purpose</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transport" title=" intelligent transport"> intelligent transport</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20digital%20city" title=" strategic digital city"> strategic digital city</a>, <a href="https://publications.waset.org/abstracts/search?q=travel%20pattern" title=" travel pattern"> travel pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20transport" title=" sustainable transport"> sustainable transport</a> </p> <a href="https://publications.waset.org/abstracts/191019/commuters-trip-purpose-decision-tree-based-model-of-makurdi-metropolis-nigeria-and-strategic-digital-city-project" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191019.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6390</span> Predication Model for Leukemia Diseases Based on Data Mining Classification Algorithms with Best Accuracy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fahd%20Sabry%20Esmail">Fahd Sabry Esmail</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Badr%20Senousy"> M. Badr Senousy</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ragaie"> Mohamed Ragaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20techniques" title=" classification techniques"> classification techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20rule" title=" classification rule"> classification rule</a>, <a href="https://publications.waset.org/abstracts/search?q=leukemia%20diseases" title=" leukemia diseases"> leukemia diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray%20data" title=" microarray data"> microarray data</a> </p> <a href="https://publications.waset.org/abstracts/44376/predication-model-for-leukemia-diseases-based-on-data-mining-classification-algorithms-with-best-accuracy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44376.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6389</span> Decision Tree Based Scheduling for Flexible Job Shops with Multiple Process Plans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.-H.%20Doh">H.-H. Doh</a>, <a href="https://publications.waset.org/abstracts/search?q=J.-M.%20Yu"> J.-M. Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.-J.%20Kwon"> Y.-J. Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=J.-H.%20Shin"> J.-H. Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=H.-W.%20Kim"> H.-W. Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=S.-H.%20Nam"> S.-H. Nam</a>, <a href="https://publications.waset.org/abstracts/search?q=D.-H.%20Lee"> D.-H. Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i. e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling" title="flexible job shop scheduling">flexible job shop scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=priority%20rules" title=" priority rules"> priority rules</a>, <a href="https://publications.waset.org/abstracts/search?q=case%20study" title=" case study"> case study</a> </p> <a href="https://publications.waset.org/abstracts/6996/decision-tree-based-scheduling-for-flexible-job-shops-with-multiple-process-plans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6996.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6388</span> Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Ahirwar">Neha Ahirwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=efficient%20credit%20card%20fraud%20detection" title="efficient credit card fraud detection">efficient credit card fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title=" XGBoost"> XGBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/179778/efficient-credit-card-fraud-detection-based-on-multiple-ml-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179778.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6387</span> Model for Introducing Products to New Customers through Decision Tree Using Algorithm C4.5 (J-48)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komol%20Phaisarn">Komol Phaisarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Anuphan%20Suttimarn"> Anuphan Suttimarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Vitchanan%20Keawtong"> Vitchanan Keawtong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kittisak%20Thongyoun"> Kittisak Thongyoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Chaiyos%20Jamsawang"> Chaiyos Jamsawang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=customers" title=" customers"> customers</a>, <a href="https://publications.waset.org/abstracts/search?q=life%20insurance%20pay%20package" title=" life insurance pay package"> life insurance pay package</a> </p> <a href="https://publications.waset.org/abstracts/11724/model-for-introducing-products-to-new-customers-through-decision-tree-using-algorithm-c45-j-48" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11724.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">427</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6386</span> Enunciation on Complexities of Selected Tree Searching Algorithms </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parag%20Bhalchandra">Parag Bhalchandra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20D.%20Khamitkar"> S. D. Khamitkar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching trees is a most interesting application of Artificial Intelligence. Over the period of time, many innovative methods have been evolved to better search trees with respect to computational complexities. Tree searches are difficult to understand due to the exponential growth of possibilities when increasing the number of nodes or levels in the tree. Usually it is understood when we traverse down in the tree, traverse down to greater depth, in the search of a solution or a goal. However, this does not happen in reality as explicit enumeration is not a very efficient method and there are many algorithmic speedups that will find the optimal solution without the burden of evaluating all possible trees. It was a common question before all researchers where they often wonder what algorithms will yield the best and fastest result The intention of this paper is two folds, one to review selected tree search algorithms and search strategies that can be applied to a problem space and the second objective is to stimulate to implement recent developments in the complexity behavior of search strategies. The algorithms discussed here apply in general to both brute force and heuristic searches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trees%20search" title="trees search">trees search</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20complexity" title=" asymptotic complexity"> asymptotic complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=brute%20force" title=" brute force"> brute force</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics%20algorithms" title=" heuristics algorithms"> heuristics algorithms</a> </p> <a href="https://publications.waset.org/abstracts/13407/enunciation-on-complexities-of-selected-tree-searching-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13407.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6385</span> The Best Prediction Data Mining Model for Breast Cancer Probability in Women Residents in Kabul</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Jafari">Mina Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Kobra%20Hamraee"> Kobra Hamraee</a>, <a href="https://publications.waset.org/abstracts/search?q=Saied%20Hossein%20Hosseini"> Saied Hossein Hosseini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=probability" title=" probability"> probability</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/128692/the-best-prediction-data-mining-model-for-breast-cancer-probability-in-women-residents-in-kabul" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6384</span> Semi-Supervised Hierarchical Clustering Given a Reference Tree of Labeled Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Zhao">Ying Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingyan%20Bin"> Xingyan Bin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20clustering" title="semi-supervised clustering">semi-supervised clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%0D%0Aagglomerative%20clustering" title=" hierarchical agglomerative clustering"> hierarchical agglomerative clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20trees" title=" reference trees"> reference trees</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20constraints" title=" distance constraints "> distance constraints </a> </p> <a href="https://publications.waset.org/abstracts/19478/semi-supervised-hierarchical-clustering-given-a-reference-tree-of-labeled-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19478.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6383</span> Artificial Neural Networks with Decision Trees for Diagnosis Issues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Kourd">Y. Kourd</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Lefebvre"> D. Lefebvre</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Guersi"> N. Guersi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=behaviors" title=" behaviors"> behaviors</a> </p> <a href="https://publications.waset.org/abstracts/8203/artificial-neural-networks-with-decision-trees-for-diagnosis-issues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">505</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6382</span> A Comparison of Single of Decision Tree, Decision Tree Forest and Group Method of Data Handling to Evaluate the Surface Roughness in Machining Process </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The machinability of workpieces (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron) in turning operation has been carried out using different types of cutting tool (conventional, cutting tool with holes in toolholder and cutting tool filled up with composite material) under dry conditions on a turning machine at different stages of spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). Experimentation was performed as per Taguchi’s orthogonal array. To evaluate the relative importance of factors affecting surface roughness the single decision tree (SDT), Decision tree forest (DTF) and Group method of data handling (GMDH) were applied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20forest" title="decision tree forest">decision tree forest</a>, <a href="https://publications.waset.org/abstracts/search?q=GMDH" title=" GMDH"> GMDH</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=turning%20process" title=" turning process"> turning process</a> </p> <a href="https://publications.waset.org/abstracts/66804/a-comparison-of-single-of-decision-tree-decision-tree-forest-and-group-method-of-data-handling-to-evaluate-the-surface-roughness-in-machining-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66804.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6381</span> Patient-Specific Modeling Algorithm for Medical Data Based on AUC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guilherme%20Ribeiro">Guilherme Ribeiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Oliveira"> Alexandre Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Ferreira"> Antonio Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyam%20Visweswaran"> Shyam Visweswaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Gregory%20Cooper"> Gregory Cooper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approach%20instance-based" title="approach instance-based">approach instance-based</a>, <a href="https://publications.waset.org/abstracts/search?q=area%20under%20the%20ROC%20curve" title=" area under the ROC curve"> area under the ROC curve</a>, <a href="https://publications.waset.org/abstracts/search?q=patient-specific%20decision%20path" title=" patient-specific decision path"> patient-specific decision path</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20predictions" title=" clinical predictions"> clinical predictions</a> </p> <a href="https://publications.waset.org/abstracts/35519/patient-specific-modeling-algorithm-for-medical-data-based-on-auc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35519.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6380</span> Data Mining in Medicine Domain Using Decision Trees and Vector Support Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Djamila%20Benhaddouche">Djamila Benhaddouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomedical%20data" title="biomedical data">biomedical data</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classifier" title=" classifier"> classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms%20decision%20tree" title=" algorithms decision tree"> algorithms decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a> </p> <a href="https://publications.waset.org/abstracts/15138/data-mining-in-medicine-domain-using-decision-trees-and-vector-support-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15138.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">559</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6379</span> Determining of the Performance of Data Mining Algorithm Determining the Influential Factors and Prediction of Ischemic Stroke: A Comparative Study in the Southeast of Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Mehdipour">Y. Mehdipour</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ebrahimi"> S. Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Jahanpour"> A. Jahanpour</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Seyedzaei"> F. Seyedzaei</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Sabayan"> B. Sabayan</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Karimi"> A. Karimi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Amirifard"> H. Amirifard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=ischemic%20stroke" title=" ischemic stroke"> ischemic stroke</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</a> </p> <a href="https://publications.waset.org/abstracts/82771/determining-of-the-performance-of-data-mining-algorithm-determining-the-influential-factors-and-prediction-of-ischemic-stroke-a-comparative-study-in-the-southeast-of-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6378</span> Using Risk Management Indicators in Decision Tree Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Ali%20Elshaibani">Adel Ali Elshaibani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Risk management indicators augment the reporting infrastructure, particularly for the board and senior management, to identify, monitor, and manage risks. This enhancement facilitates improved decision-making throughout the banking organization. Decision tree analysis is a tool that visually outlines potential outcomes, costs, and consequences of complex decisions. It is particularly beneficial for analyzing quantitative data and making decisions based on numerical values. By calculating the expected value of each outcome, decision tree analysis can help assess the best course of action. In the context of banking, decision tree analysis can assist lenders in evaluating a customer’s creditworthiness, thereby preventing losses. However, applying these tools in developing countries may face several limitations, such as data availability, lack of technological infrastructure and resources, lack of skilled professionals, cultural factors, and cost. Moreover, decision trees can create overly complex models that do not generalize well to new data, known as overfitting. They can also be sensitive to small changes in the data, which can result in different tree structures and can become computationally expensive when dealing with large datasets. In conclusion, while risk management indicators and decision tree analysis are beneficial for decision-making in banks, their effectiveness is contingent upon how they are implemented and utilized by the board of directors, especially in the context of developing countries. It’s important to consider these limitations when planning to implement these tools in developing countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=risk%20management%20indicators" title="risk management indicators">risk management indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20analysis" title=" decision tree analysis"> decision tree analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=developing%20countries" title=" developing countries"> developing countries</a>, <a href="https://publications.waset.org/abstracts/search?q=board%20of%20directors" title=" board of directors"> board of directors</a>, <a href="https://publications.waset.org/abstracts/search?q=bank%20performance" title=" bank performance"> bank performance</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management%20strategy" title=" risk management strategy"> risk management strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=banking%20institutions" title=" banking institutions"> banking institutions</a> </p> <a href="https://publications.waset.org/abstracts/176212/using-risk-management-indicators-in-decision-tree-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">60</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6377</span> Heart Failure Identification and Progression by Classifying Cardiac Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Saqlain">Muhammad Saqlain</a>, <a href="https://publications.waset.org/abstracts/search?q=Nazar%20Abbas%20Saqib"> Nazar Abbas Saqib</a>, <a href="https://publications.waset.org/abstracts/search?q=Muazzam%20A.%20Khan"> Muazzam A. Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20failure" title=" heart failure"> heart failure</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20model" title=" classification model"> classification model</a> </p> <a href="https://publications.waset.org/abstracts/62215/heart-failure-identification-and-progression-by-classifying-cardiac-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62215.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6376</span> Machine Learning Predictive Models for Hydroponic Systems: A Case Study Nutrient Film Technique and Deep Flow Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kritiyaporn%20Kunsook">Kritiyaporn Kunsook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20Bayes" title=" naïve Bayes"> naïve Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20classifier%20by%20voting" title=" ensemble classifier by voting"> ensemble classifier by voting</a> </p> <a href="https://publications.waset.org/abstracts/91070/machine-learning-predictive-models-for-hydroponic-systems-a-case-study-nutrient-film-technique-and-deep-flow-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91070.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6375</span> An Alternative Approach for Assessing the Impact of Cutting Conditions on Surface Roughness Using Single Decision Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghorbani">S. Ghorbani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20I.%20Polushin"> N. I. Polushin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutting%20condition" title="cutting condition">cutting condition</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=CART%20algorithm" title=" CART algorithm"> CART algorithm</a> </p> <a href="https://publications.waset.org/abstracts/70715/an-alternative-approach-for-assessing-the-impact-of-cutting-conditions-on-surface-roughness-using-single-decision-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70715.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6374</span> Feature Weighting Comparison Based on Clustering Centers in the Detection of Diabetic Retinopathy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat">Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20weighting" title=" data weighting"> data weighting</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/51496/feature-weighting-comparison-based-on-clustering-centers-in-the-detection-of-diabetic-retinopathy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51496.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6373</span> About the Case Portfolio Management Algorithms and Their Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Chumburidze">M. Chumburidze</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Salia"> N. Salia</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Namchevadze"> T. Namchevadze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20network" title="credit network">credit network</a>, <a href="https://publications.waset.org/abstracts/search?q=case%20portfolio" title=" case portfolio"> case portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20tree" title=" binary tree"> binary tree</a>, <a href="https://publications.waset.org/abstracts/search?q=priority%20queue" title=" priority queue"> priority queue</a>, <a href="https://publications.waset.org/abstracts/search?q=stack" title=" stack"> stack</a> </p> <a href="https://publications.waset.org/abstracts/168639/about-the-case-portfolio-management-algorithms-and-their-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168639.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6372</span> Angular-Coordinate Driven Radial Tree Drawing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farshad%20Ghassemi%20Toosi">Farshad Ghassemi Toosi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikola%20S.%20Nikolov"> Nikola S. Nikolov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a visualization technique for radial drawing of trees consisting of two slightly different algorithms. Both of them make use of node-link diagrams for visual encoding. This visualization creates clear drawings without edge crossing. One of the algorithms is suitable for real-time visualization of large trees, as it requires minimal recalculation of the layout if leaves are inserted or removed from the tree; while the other algorithm makes better utilization of the drawing space. The algorithms are very similar and follow almost the same procedure but with different parameters. Both algorithms assign angular coordinates for all nodes which are then converted into 2D Cartesian coordinates for visualization. We present both algorithms and discuss how they compare to each other. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radial%20drawing" title="Radial drawing">Radial drawing</a>, <a href="https://publications.waset.org/abstracts/search?q=Visualization" title=" Visualization"> Visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=Algorithm" title=" Algorithm"> Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Use%20of%20node-link%20diagrams" title=" Use of node-link diagrams"> Use of node-link diagrams</a> </p> <a href="https://publications.waset.org/abstracts/2184/angular-coordinate-driven-radial-tree-drawing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2184.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6371</span> Experimental Evaluation of Succinct Ternary Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dmitriy%20Kuptsov">Dmitriy Kuptsov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tree data structures, such as binary or in general k-ary trees, are essential in computer science. The applications of these data structures can range from data search and retrieval to sorting and ranking algorithms. Naive implementations of these data structures can consume prohibitively large volumes of random access memory limiting their applicability in certain solutions. Thus, in these cases, more advanced representation of these data structures is essential. In this paper we present the design of the compact version of ternary tree data structure and demonstrate the results for the experimental evaluation using static dictionary problem. We compare these results with the results for binary and regular ternary trees. The conducted evaluation study shows that our design, in the best case, consumes up to 12 times less memory (for the dictionary used in our experimental evaluation) than a regular ternary tree and in certain configuration shows performance comparable to regular ternary trees. We have evaluated the performance of the algorithms using both 32 and 64 bit operating systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20structures" title="data structures">data structures</a>, <a href="https://publications.waset.org/abstracts/search?q=succinct%20ternary%20tree" title="succinct ternary tree">succinct ternary tree</a>, <a href="https://publications.waset.org/abstracts/search?q=per-%20formance%20evaluation" title="per- formance evaluation">per- formance evaluation</a> </p> <a href="https://publications.waset.org/abstracts/144336/experimental-evaluation-of-succinct-ternary-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144336.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6370</span> Real-Time Classification of Marbles with Decision-Tree Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Parlak">K. S. Parlak</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Turan"> E. Turan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=marble%20classification" title=" marble classification"> marble classification</a> </p> <a href="https://publications.waset.org/abstracts/76038/real-time-classification-of-marbles-with-decision-tree-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76038.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6369</span> Analysis on Thermococcus achaeans with Frequent Pattern Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeongyeob%20Hong">Jeongyeob Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Myeonghoon%20Park"> Myeonghoon Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Taeson%20Yoon"> Taeson Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> After the advent of Achaeans which utilize different metabolism pathway and contain conspicuously different cellular structure, they have been recognized as possible materials for developing quality of human beings. Among diverse Achaeans, in this paper, we compared 16s RNA Sequences of four different species of Thermococcus: Achaeans genus specialized in sulfur-dealing metabolism. Four Species, Barophilus, Kodakarensis, Hydrothermalis, and Onnurineus, live near the hydrothermal vent that emits extreme amount of sulfur and heat. By comparing ribosomal sequences of aforementioned four species, we found similarities in their sequences and expressed protein, enabling us to expect that certain ribosomal sequence or proteins are vital for their survival. Apriori algorithms and Decision Tree were used. for comparison. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Achaeans" title="Achaeans">Achaeans</a>, <a href="https://publications.waset.org/abstracts/search?q=Thermococcus" title=" Thermococcus"> Thermococcus</a>, <a href="https://publications.waset.org/abstracts/search?q=apriori%20algorithm" title=" apriori algorithm"> apriori algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/30046/analysis-on-thermococcus-achaeans-with-frequent-pattern-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30046.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">290</span> </span> </div> </div> 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