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trees</title> <meta name="description" content="Search results for: decision trees"> <meta name="keywords" content="decision trees"> <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 d-lg-none navbar-toggler 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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="decision trees"> <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> 4467</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: decision trees</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4467</span> Spatial Data Mining by Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sihem%20Oujdi">Sihem Oujdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafida%20Belbachir"> Hafida Belbachir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=C4.5%20algorithm" title="C4.5 algorithm">C4.5 algorithm</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=S-CART" title=" S-CART"> S-CART</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20mining" title=" spatial data mining"> spatial data mining</a> </p> <a href="https://publications.waset.org/abstracts/11935/spatial-data-mining-by-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11935.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">612</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">4466</span> Determining Optimal Number of Trees in Random Forests</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Songul%20Cinaroglu">Songul Cinaroglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20methods" title="classification methods">classification methods</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=number%20of%20trees" title=" number of trees"> number of trees</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/30675/determining-optimal-number-of-trees-in-random-forests" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30675.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">395</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">4465</span> Performance Analysis of Search Medical Imaging Service on Cloud Storage Using Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gonz%C3%A1lez%20A.%20Julio">González A. Julio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ram%C3%ADrez%20L.%20Leonardo"> Ramírez L. Leonardo</a>, <a href="https://publications.waset.org/abstracts/search?q=Puerta%20A.%20Gabriel"> Puerta A. Gabriel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Telemedicine services use a large amount of data, most of which are diagnostic images in Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7) formats. Metadata is generated from each related image to support their identification. This study presents the use of decision trees for the optimization of information search processes for diagnostic images, hosted on the cloud server. To analyze the performance in the server, the following quality of service (QoS) metrics are evaluated: delay, bandwidth, jitter, latency and throughput in five test scenarios for a total of 26 experiments during the loading and downloading of DICOM images, hosted by the telemedicine group server of the Universidad Militar Nueva Granada, Bogotá, Colombia. By applying decision trees as a data mining technique and comparing it with the sequential search, it was possible to evaluate the search times of diagnostic images in the server. The results show that by using the metadata in decision trees, the search times are substantially improved, the computational resources are optimized and the request management of the telemedicine image service is improved. Based on the experiments carried out, search efficiency increased by 45% in relation to the sequential search, given that, when downloading a diagnostic image, false positives are avoided in management and acquisition processes of said information. It is concluded that, for the diagnostic images services in telemedicine, the technique of decision trees guarantees the accessibility and robustness in the acquisition and manipulation of medical images, in improvement of the diagnoses and medical procedures in patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20storage" title="cloud storage">cloud storage</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=diagnostic%20image" title=" diagnostic image"> diagnostic image</a>, <a href="https://publications.waset.org/abstracts/search?q=search" title=" search"> search</a>, <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title=" telemedicine"> telemedicine</a> </p> <a href="https://publications.waset.org/abstracts/99706/performance-analysis-of-search-medical-imaging-service-on-cloud-storage-using-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99706.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">204</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">4464</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">4463</span> Improving University Operations with Data Mining: Predicting Student Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mladen%20Dragi%C4%8Devi%C4%87">Mladen Dragičević</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirjana%20Peji%C4%87%20Bach"> Mirjana Pejić Bach</a>, <a href="https://publications.waset.org/abstracts/search?q=Vanja%20%C5%A0imi%C4%8Devi%C4%87"> Vanja Šimičević</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems. <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=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20models" title=" prediction models"> prediction models</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20success" title=" student success"> student success</a> </p> <a href="https://publications.waset.org/abstracts/7653/improving-university-operations-with-data-mining-predicting-student-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7653.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">407</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">4462</span> Decision Trees Constructing Based on K-Means Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Loai%20Abdallah">Loai Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Yousef"> Malik Yousef</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ensemble%20clustering" title="ensemble clustering">ensemble clustering</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=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=K%20nearest%20neighbors" title=" K nearest neighbors"> K nearest neighbors</a> </p> <a href="https://publications.waset.org/abstracts/89656/decision-trees-constructing-based-on-k-means-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89656.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">4461</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">4460</span> Neural Network Based Decision Trees Using Machine Learning for Alzheimer's Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tracy%20Lin%20Huan"> Tracy Lin Huan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Meenakshi%20Sundaram"> S. Meenakshi Sundaram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%27s%20diagnosis" title="Alzheimer's diagnosis">Alzheimer's diagnosis</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=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <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=pattern%20classification" title=" pattern classification"> pattern classification</a> </p> <a href="https://publications.waset.org/abstracts/77725/neural-network-based-decision-trees-using-machine-learning-for-alzheimers-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77725.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">297</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">4459</span> Algorithm for Recognizing Trees along Power Grid Using Multispectral Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Hamamura">C. Hamamura</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Gialluca"> V. Gialluca</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much of the Eclectricity Distributors has about 70% of its electricity interruptions arising from cause "trees", alone or associated with wind and rain and with or without falling branch and / or trees. This contributes inexorably and significantly to outages, resulting in high costs as compensation in addition to the operation and maintenance costs. On the other hand, there is little data structure and solutions to better organize the trees pruning plan effectively, minimizing costs and environmentally friendly. This work describes the development of an algorithm to provide data of trees associated to power grid. The method is accomplished on several steps using satellite imagery and geographically vectorized grid. A sliding window like approach is performed to seek the area around the grid. The proposed method counted 764 trees on a patch of the grid, which was very close to the 738 trees counted manually. The trees data was used as a part of a larger project that implements a system to optimize tree pruning plan. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20pattern%20recognition" title="image pattern recognition">image pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=trees%20pruning" title=" trees pruning"> trees pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=trees%20recognition" title=" trees recognition"> trees recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/31298/algorithm-for-recognizing-trees-along-power-grid-using-multispectral-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31298.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">499</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">4458</span> Heritage Tree Expert Assessment and Classification: Malaysian Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.-Y.-S.%20Lau">B.-Y.-S. Lau</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.-C.-T.%20Jonathan"> Y.-C.-T. Jonathan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.-S.%20Alias"> M.-S. Alias</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heritage trees are natural large, individual trees with exceptionally value due to association with age or event or distinguished people. In Malaysia, there is an abundance of tropical heritage trees throughout the country. It is essential to set up a repository of heritage trees to prevent valuable trees from being cut down. In this cross domain study, a web-based online expert system namely the Heritage Tree Expert Assessment and Classification (HTEAC) is developed and deployed for public to nominate potential heritage trees. Based on the nomination, tree care experts or arborists would evaluate and verify the nominated trees as heritage trees. The expert system automatically rates the approved heritage trees according to pre-defined grades via Delphi technique. Features and usability test of the expert system are presented. Preliminary result is promising for the system to be used as a full scale public system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arboriculture" title="arboriculture">arboriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=Delphi" title=" Delphi"> Delphi</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a>, <a href="https://publications.waset.org/abstracts/search?q=heritage%20tree" title=" heritage tree"> heritage tree</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20forestry" title=" urban forestry"> urban forestry</a> </p> <a href="https://publications.waset.org/abstracts/73407/heritage-tree-expert-assessment-and-classification-malaysian-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73407.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">312</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">4457</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">4456</span> Development of the Academic Model to Predict Student Success at VUT-FSASEC Using Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Langa%20Hendrick%20Musawenkosi">Langa Hendrick Musawenkosi</a>, <a href="https://publications.waset.org/abstracts/search?q=Twala%20Bhekisipho"> Twala Bhekisipho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FSASEC" title="FSASEC">FSASEC</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20environment%20model" title=" academic environment model"> academic environment model</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=k-nearest%20neighbor" title=" k-nearest neighbor"> k-nearest neighbor</a>, <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=popularity%20index" title=" popularity index"> popularity index</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/77040/development-of-the-academic-model-to-predict-student-success-at-vut-fsasec-using-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77040.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">200</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">4455</span> Determination of Water Pollution and Water Quality with Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C3%87i%C4%9Fdem%20Bak%C4%B1r">Çiğdem Bakır</a>, <a href="https://publications.waset.org/abstracts/search?q=Mecit%20Y%C3%BCzkat"> Mecit Yüzkat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the 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=water%20quality" title=" water quality"> water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20pollution" title=" water pollution"> water pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/162995/determination-of-water-pollution-and-water-quality-with-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162995.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">81</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">4454</span> Discerning Divergent Nodes in Social Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehran%20Asadi">Mehran Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Afrand%20Agah"> Afrand Agah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20social%20networks" title="online social networks">online social networks</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=social%20cloud%20computing" title=" social cloud computing"> social cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction%20and%20collaboration" title=" interaction and collaboration"> interaction and collaboration</a> </p> <a href="https://publications.waset.org/abstracts/129011/discerning-divergent-nodes-in-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129011.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">157</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">4453</span> Estimation of Carbon Uptake of Seoul City Street Trees in Seoul and Plans for Increase Carbon Uptake by Improving Species</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Woo%20Park">Min Woo Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin%20Do%20Chung"> Jin Do Chung</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyu%20Yeol%20Kim"> Kyu Yeol Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Byoung%20Uk%20Im"> Byoung Uk Im</a>, <a href="https://publications.waset.org/abstracts/search?q=Jang%20Woo%20Kim"> Jang Woo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hae%20Yeul%20Ryu"> Hae Yeul Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nine representative species of trees among all the street trees were selected to estimate the absorption amount of carbon dioxide emitted from street trees in Seoul calculating the biomass, amount of carbon saved, and annual absorption amount of carbon dioxide in each of the species. Planting distance of street trees in Seoul was 1,851,180 m, the number of planting lines was 1,287, the number of planted trees was 284,498 and 46 species of trees were planted as of 2013. According to the result of plugging the quantity of species of street trees in Seoul on the absorption amount of each of the species, 120,097 ton of biomass, 60,049.8 ton of amount of carbon saved, and 11,294 t CO2/year of annual absorption amount of carbon dioxide were calculated. Street ratio mentioned on the road statistics in Seoul in 2022 is 23.13%. If the street trees are assumed to be increased in the same rate, the number of street trees in Seoul was calculated to be 294,823. The planting distance was estimated to be 1,918,360 m, and the annual absorption amount of carbon dioxide was measured to be 11,704 t CO2/year. Plans for improving the annual absorption amount of carbon dioxide from street trees were established based on the expected amount of absorption. First of all, it is to improve the annual absorption amount of carbon dioxide by increasing the number of planted street trees after adjusting the planting distance of street trees. If adjusting the current planting distance to 6 m, it was turned out that 12,692.7 t CO2/year was absorbed on an annual basis. Secondly, it is to change the species of trees to tulip trees that represent high absorption rate. If increasing the proportion of tulip trees to 30% up to 2022, the annual absorption rate of carbon dioxide was calculated to be 17804.4 t CO2/year. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=absorption%20of%20carbon%20dioxide" title="absorption of carbon dioxide">absorption of carbon dioxide</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20of%20absorbing%20carbon%20dioxide" title=" source of absorbing carbon dioxide"> source of absorbing carbon dioxide</a>, <a href="https://publications.waset.org/abstracts/search?q=trees%20in%20city" title=" trees in city"> trees in city</a>, <a href="https://publications.waset.org/abstracts/search?q=improving%20species" title=" improving species"> improving species</a> </p> <a href="https://publications.waset.org/abstracts/24639/estimation-of-carbon-uptake-of-seoul-city-street-trees-in-seoul-and-plans-for-increase-carbon-uptake-by-improving-species" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24639.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">362</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">4452</span> Advanced Combinatorial Method for Solving Complex Fault Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20de%20Jes%C3%BAs%20Rivero%20Oliva">José de Jesús Rivero Oliva</a>, <a href="https://publications.waset.org/abstracts/search?q=Jes%C3%BAs%20Salom%C3%B3n%20Llanes"> Jesús Salomón Llanes</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Perdomo%20Ojeda"> Manuel Perdomo Ojeda</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Torres%20Valle"> Antonio Torres Valle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Combinatorial explosion is a common problem to both predominant methods for solving fault trees: Minimal Cut Set (MCS) approach and Binary Decision Diagram (BDD). High memory consumption impedes the complete solution of very complex fault trees. Only approximated non-conservative solutions are possible in these cases using truncation or other simplification techniques. The paper proposes a method (CSolv+) for solving complex fault trees, without any possibility of combinatorial explosion. Each individual MCS is immediately discarded after its contribution to the basic events importance measures and the Top gate Upper Bound Probability (TUBP) has been accounted. An estimation of the Top gate Exact Probability (TEP) is also provided. Therefore, running in a computer cluster, CSolv+ will guarantee the complete solution of complex fault trees. It was successfully applied to 40 fault trees from the Aralia fault trees database, performing the evaluation of the top gate probability, the 1000 Significant MCSs (SMCS), and the Fussell-Vesely, RRW and RAW importance measures for all basic events. The high complexity fault tree nus9601 was solved with truncation probabilities from 10-²¹ to 10-²⁷ just to limit the execution time. The solution corresponding to 10-²⁷ evaluated 3.530.592.796 MCSs in 3 hours and 15 minutes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=system%20reliability%20analysis" title="system reliability analysis">system reliability analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20risk%20assessment" title=" probabilistic risk assessment"> probabilistic risk assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20tree%20analysis" title=" fault tree analysis"> fault tree analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=basic%20events%20importance%20measures" title=" basic events importance measures"> basic events importance measures</a> </p> <a href="https://publications.waset.org/abstracts/186780/advanced-combinatorial-method-for-solving-complex-fault-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186780.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">45</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">4451</span> Valuing Public Urban Street Trees and Their Environmental Spillover Benefits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20F.%20Franco">Sofia F. Franco</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacob%20Macdonald"> Jacob Macdonald</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper estimates the value of urban public street trees and their complementary and substitution value with other broader urban amenities and dis-amenities via the residential housing market. We estimate a lower bound value on a city’s tree amenities under instrumental variable and geographic regression discontinuity approaches with an application to Lisbon, Portugal. For completeness, we also explore how urban trees and in particular public street trees impact house prices across the city. Finally, we jointly analyze the planting and maintenance costs and benefits of urban street trees. The estimated value of all public trees in Lisbon is €8.84M. When considering specifically trees planted alongside roads and in public squares, the value is €6.06M or €126.64 per tree. This value is conditional on the distribution of trees in terms of their broader density, with higher effects coming from the overall greening of larger areas of the city compared to the greening of the direct neighborhood. Detrimental impacts are found when the number of trees is higher near street canyons, where they may exacerbate the stagnation of air pollution from traffic. Urban street trees also have important spillover benefits due to pollution mitigation around €6.21 million, or an additional €129.93 per tree. There are added benefits of €26.32 and €28.58 per tree in terms of flooding and heat mitigation, respectively. With significant resources and policies aimed at urban greening, the value obtained is shown to be important for discussions on the benefits of urban trees as compared to mitigation and abatement costs undertaken by a municipality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urban%20public%20goods" title="urban public goods">urban public goods</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20street%20trees" title=" urban street trees"> urban street trees</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20boundary%20discontinuities" title=" spatial boundary discontinuities"> spatial boundary discontinuities</a>, <a href="https://publications.waset.org/abstracts/search?q=geospatial%20and%20remote%20sensing%20methods" title=" geospatial and remote sensing methods"> geospatial and remote sensing methods</a> </p> <a href="https://publications.waset.org/abstracts/145452/valuing-public-urban-street-trees-and-their-environmental-spillover-benefits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145452.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">177</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">4450</span> Historical Landscape Affects Present Tree Density in Paddy Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ha%20T.%20Pham">Ha T. Pham</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuichi%20Miyagawa"> Shuichi Miyagawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ongoing landscape transformation is one of the major causes behind disappearance of traditional landscapes, and lead to species and resource loss. Tree in paddy fields in the northeast of Thailand is one of those traditional landscapes. Using three different historical time layers, we acknowledged the severe deforestation and rapid urbanization happened in the region. Despite the general thinking of decline in tree density as consequences, the heterogeneous trend of changes in total tree density in three studied landscapes denied the hypothesis that number of trees in paddy field depend on the length of land use practice. On the other hand, due to selection of planting new trees on levees, existence of trees in paddy field are now rely on their values for human use. Besides, changes in land use and landscape structure had a significant impact on decision of which tree density level is considered as suitable for the landscape. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20photographs" title="aerial photographs">aerial photographs</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%20change" title=" land use change"> land use change</a>, <a href="https://publications.waset.org/abstracts/search?q=traditional%20landscape" title=" traditional landscape"> traditional landscape</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20in%20paddy%20fields" title=" tree in paddy fields"> tree in paddy fields</a> </p> <a href="https://publications.waset.org/abstracts/15536/historical-landscape-affects-present-tree-density-in-paddy-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15536.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">419</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">4449</span> Evaluation of Monumental Trees in Bursa City in Terms of Cultural Landscape</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Murat%20Zencirkiran">Murat Zencirkiran</a>, <a href="https://publications.waset.org/abstracts/search?q=Nilufer%20Seyidoglu%20Akdeniz"> Nilufer Seyidoglu Akdeniz</a>, <a href="https://publications.waset.org/abstracts/search?q=Elvan%20Ender%20Altay"> Elvan Ender Altay</a>, <a href="https://publications.waset.org/abstracts/search?q=Zeynep%20Pirselimoglu%20Batman"> Zeynep Pirselimoglu Batman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Monumental trees make an important contribution to the cultural interaction between societies. At the same time, monument trees, which are considered as symbols of some beliefs, are living beings that are transmitted from generation to generation. Mystical, folkloric and dimensional aspects of our cultural heritage and the link between the past and present, the memorial trees of the generations of the stories conveyed the story of the legends at the same time with the aesthetic features of the objects attract attention. There are many monumental trees that witness historical processes in Bursa, which is a land of very different cultures from the Prusias (BC 232-192). Within this scope, monumental trees located within the boundaries of Bursa province and their contribution to urban culture were evaluated. Monument plane trees recorded in Bursa and its districts were determined by the Ministry of Environment and Urbanization, the Governorship of Bursa, the Provincial Directorate of Environment and Urbanism, the Directorate of Protection of Natural Assets, and these trees were examined in situ. As a result of the inspections made, the monument trees living today are classified according to their species. Within the scope of the study, it was determined that there were 1001 monumental tree species in different species within the boundaries of Bursa province. 71.83% of the recorded species were Platanus species and 11.79% were Pinus species. On the other hand, the stories about the contribution of cultural landscapes to the examples of living or now-disappearing examples of Bursa history from these monumental trees have been compiled and presented in the study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bursa" title="Bursa">Bursa</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20landscape" title=" cultural landscape"> cultural landscape</a>, <a href="https://publications.waset.org/abstracts/search?q=landscape" title=" landscape"> landscape</a>, <a href="https://publications.waset.org/abstracts/search?q=monumental%20trees" title=" monumental trees"> monumental trees</a> </p> <a href="https://publications.waset.org/abstracts/97855/evaluation-of-monumental-trees-in-bursa-city-in-terms-of-cultural-landscape" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97855.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">426</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">4448</span> Feature Extraction and Impact Analysis for Solid Mechanics Using Supervised Finite Element Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edward%20Schwalb">Edward Schwalb</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20Dehmer"> Matthias Dehmer</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Schlenkrich"> Michael Schlenkrich</a>, <a href="https://publications.waset.org/abstracts/search?q=Farzaneh%20Taslimi"> Farzaneh Taslimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ketron%20Mitchell-Wynne"> Ketron Mitchell-Wynne</a>, <a href="https://publications.waset.org/abstracts/search?q=Horen%20Kuecuekyan"> Horen Kuecuekyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mechanical%20design%20validation" title="mechanical design validation">mechanical design validation</a>, <a href="https://publications.waset.org/abstracts/search?q=FEA" title=" FEA"> FEA</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20decision%20tree" title=" supervised decision tree"> supervised decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network." title=" convolutional neural network."> convolutional neural network.</a> </p> <a href="https://publications.waset.org/abstracts/108185/feature-extraction-and-impact-analysis-for-solid-mechanics-using-supervised-finite-element-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108185.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">139</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">4447</span> Empirical and Indian Automotive Equity Portfolio Decision Support</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Sankar">P. Sankar</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20James%20Daniel%20Paul"> P. James Daniel Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=Siddhant%20Sahu"> Siddhant Sahu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Indian%20automotive%20sector" title="Indian automotive sector">Indian automotive sector</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20decisions" title=" stock market decisions"> stock market decisions</a>, <a href="https://publications.waset.org/abstracts/search?q=equity%20portfolio%20analysis" title=" equity portfolio analysis"> equity portfolio analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifiers" title=" decision tree classifiers"> decision tree classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20data%20analysis" title=" statistical data analysis"> statistical data analysis</a> </p> <a href="https://publications.waset.org/abstracts/6454/empirical-and-indian-automotive-equity-portfolio-decision-support" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6454.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">485</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">4446</span> The Use of Boosted Multivariate Trees in Medical Decision-Making for Repeated Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebru%20Turgal">Ebru Turgal</a>, <a href="https://publications.waset.org/abstracts/search?q=Beyza%20Doganay%20Erdogan"> Beyza Doganay Erdogan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boosted%20multivariate%20trees" title="boosted multivariate trees">boosted multivariate trees</a>, <a href="https://publications.waset.org/abstracts/search?q=longitudinal%20data" title=" longitudinal data"> longitudinal data</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20regression%20tree" title=" multivariate regression tree"> multivariate regression tree</a>, <a href="https://publications.waset.org/abstracts/search?q=panel%20data" title=" panel data"> panel data</a> </p> <a href="https://publications.waset.org/abstracts/87009/the-use-of-boosted-multivariate-trees-in-medical-decision-making-for-repeated-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87009.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">203</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">4445</span> WSN System Warns Atta Cephalotes Climbing in Mango Fruit Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Federico%20Hahn%20Schlam">Federico Hahn Schlam</a>, <a href="https://publications.waset.org/abstracts/search?q=Ferm%C3%ADn%20Mart%C3%ADnez%20Sol%C3%ADs"> Fermín Martínez Solís</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Leaf-cutting ants (Atta cephalotes) forage from mango tree leaves and flowers to feed their colony. Farmers find it difficult to control ants due to the great quantity of trees grown in commercial orchards. In this article, IoT can support farmers for ant detection in real time, as production losses can be considered of 324 US per tree.A wireless sensor network, WSN, was developed to warn the farmer from ant presence in trees during a night. Mango trees were gathered into groups of 9 trees, where the central tree holds the master microcontroller, and the other eight trees presented slave microcontrollers (nodes). At each node, anemitter diode-photodiode unitdetects ants climbing up. A capacitor is chargedand discharged after being sampled every ten minutes. The system usesBLE (Bluetooth Low Energy) to communicate between the master microcontroller by BLE.When ants were detected the number of the tree was transmitted via LoRa from the masterto the producer smartphone to warn him. In this paper, BLE, LoRa, and energy consumption were studied under variable vegetation in the orchard. During 2018, 19 trees were attacked by ants, and ants fed 26.3% of flowers and 73.7% of leaves. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BLE" title="BLE">BLE</a>, <a href="https://publications.waset.org/abstracts/search?q=atta%20cephalotes" title=" atta cephalotes"> atta cephalotes</a>, <a href="https://publications.waset.org/abstracts/search?q=LoRa" title=" LoRa"> LoRa</a>, <a href="https://publications.waset.org/abstracts/search?q=WSN-smartphone" title=" WSN-smartphone"> WSN-smartphone</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title=" energy consumption"> energy consumption</a> </p> <a href="https://publications.waset.org/abstracts/148229/wsn-system-warns-atta-cephalotes-climbing-in-mango-fruit-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148229.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">158</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">4444</span> A Data-Mining Model for Protection of FACTS-Based Transmission Line</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kalagura">Ashok Kalagura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a data-mining model for fault-zone identification of ﬂexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-ﬂow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distance%20relaying" title="distance relaying">distance relaying</a>, <a href="https://publications.waset.org/abstracts/search?q=fault-zone%20identification" title=" fault-zone identification"> fault-zone identification</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forests" title=" random forests"> random forests</a>, <a href="https://publications.waset.org/abstracts/search?q=RFs" title=" RFs"> RFs</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=thyristor-controlled%20series%20compensator" title=" thyristor-controlled series compensator"> thyristor-controlled series compensator</a>, <a href="https://publications.waset.org/abstracts/search?q=TCSC" title=" TCSC"> TCSC</a>, <a href="https://publications.waset.org/abstracts/search?q=unified%20power-%EF%AC%82ow%20controller" title=" unified power-ﬂow controller"> unified power-ﬂow controller</a>, <a href="https://publications.waset.org/abstracts/search?q=UPFC" title=" UPFC "> UPFC </a> </p> <a href="https://publications.waset.org/abstracts/32579/a-data-mining-model-for-protection-of-facts-based-transmission-line" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32579.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">423</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">4443</span> Application of Machine Learning Techniques in Forest Cover-Type Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saba%20Ebrahimi">Saba Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hedieh%20Ashrafi"> Hedieh Ashrafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20methods" title="classification methods">classification methods</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</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=forest%20cover-type%20dataset" title=" forest cover-type dataset"> forest cover-type dataset</a> </p> <a href="https://publications.waset.org/abstracts/137985/application-of-machine-learning-techniques-in-forest-cover-type-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137985.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">217</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">4442</span> Using Neural Networks for Click Prediction of Sponsored Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afroze%20Ibrahim%20Baqapuri">Afroze Ibrahim Baqapuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilya%20Trofimov"> Ilya Trofimov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models. <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=sponsored%20search" title=" sponsored search"> sponsored search</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20advertisement" title=" web advertisement"> web advertisement</a>, <a href="https://publications.waset.org/abstracts/search?q=click%20prediction" title=" click prediction"> click prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=click-through%20rate" title=" click-through rate"> click-through rate</a> </p> <a href="https://publications.waset.org/abstracts/22874/using-neural-networks-for-click-prediction-of-sponsored-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22874.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">572</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">4441</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">4440</span> Trees in Different Vegetation Types of Mt. Hamiguitan Range, Davao Oriental, Mindanao Island, Philippines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janece%20Jean%20A.%20Polizon">Janece Jean A. Polizon</a>, <a href="https://publications.waset.org/abstracts/search?q=Victor%20B.%20Amoroso"> Victor B. Amoroso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mt. Hamiguitan Range in Davao Oriental, Mindanao Island, Philippines is the only protected area with pygmy forest and a priority site for protection and conservation. This range harbors different vegetation types such as agroecosystem, dipterocarp forest, montane forest and mossy forest. This study was conducted to determine the diversity of trees and shrubs in different vegetation types of Mt. Hamiguitan Range. Transect walk and 16 sampling plots of 20 x 20 m were established in the different vegetation types. Specimens collected were classified and identified using the Flora Malesiana and type images. Assessment of status was determined based on International Union for the Conservation of Nature (IUCN). There were 223 species of trees, 141 genera and 71 families. Of the vegetation types, the pygmy forest obtained a comparatively high diversity value of H=1.348 followed by montane forest with H=1.284. The high species importance value (SIV) of Diospyros philippinensis for trees indicates that these species have an important role in regulating the stability of the ecosystem. The tree profile of the pygmy forest is different due to the ultramafic substrate causing the dwarfness of the trees. These forest types should be given high priority for protection and conservation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diversity" title="diversity">diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=Mt%20Hamiguitan" title=" Mt Hamiguitan"> Mt Hamiguitan</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation" title=" vegetation"> vegetation</a>, <a href="https://publications.waset.org/abstracts/search?q=trees" title=" trees"> trees</a>, <a href="https://publications.waset.org/abstracts/search?q=shrubs" title=" shrubs"> shrubs</a> </p> <a href="https://publications.waset.org/abstracts/17818/trees-in-different-vegetation-types-of-mt-hamiguitan-range-davao-oriental-mindanao-island-philippines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17818.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">409</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">4439</span> Effect of Chilling Accumulation on Fruit Yield of Olive Trees in Egypt</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20H.%20El-Sheikh">Mohamed H. El-Sheikh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20F.%20Zahran"> Hoda F. Zahran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Olive tree (Olea europaea L.) is considered as a Mediterranean tree which belongs to genus Olea that may comprise about 35 species. In fact, the crop requires mild to cool winters with a chilling accumulation from November to February with average temperatures varying between two groups of accumulated chilling hours (h1) of less than 7.2 °C (C1) and other group (h2) of less than 10 °C (C2) for flower bud differentiation. This work aims at studying the impact of chilling accumulation hours on the fruit yield of olive trees in Borg El Arab City, Alexandria Governorate, Egypt as a case study. Trees were aged around 7 years in 2010 and were exposed to chilling accumulation hours of h1, which was average of 280 hours under C1, and average h2 was around 150 hours under C2 the resulted fruit yield was around 0.5 kg/tree. On the hand, trees were aged around 7 years at 2016 showed that when average of h1 was around 390 hours under C1 and average h2 was around 220 hours under C2 then fruit yield was around 10 kg/tree. Increasing of fruit yield proved chilling accumulation effect on olive trees. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chilling%20accumulation" title="chilling accumulation">chilling accumulation</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20yield" title=" fruit yield"> fruit yield</a>, <a href="https://publications.waset.org/abstracts/search?q=Olea%20europaea" title=" Olea europaea"> Olea europaea</a>, <a href="https://publications.waset.org/abstracts/search?q=olive" title=" olive"> olive</a> </p> <a href="https://publications.waset.org/abstracts/63113/effect-of-chilling-accumulation-on-fruit-yield-of-olive-trees-in-egypt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63113.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">291</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">4438</span> Segregation Patterns of Trees and Grass Based on a Modified Age-Structured Continuous-Space Forest Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jian%20Yang">Jian Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Atsushi%20Yagi"> Atsushi Yagi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tree-grass coexistence system is of great importance for forest ecology. Mathematical models are being proposed to study the dynamics of tree-grass coexistence and the stability of the systems. However, few of the models concentrates on spatial dynamics of the tree-grass coexistence. In this study, we modified an age-structured continuous-space population model for forests, obtaining an age-structured continuous-space population model for the tree-grass competition model. In the model, for thermal competitions, adult trees can out-compete grass, and grass can out-compete seedlings. We mathematically studied the model to make sure tree-grass coexistence solutions exist. Numerical experiments demonstrated that a fraction of area that trees or grass occupies can affect whether the coexistence is stable or not. We also tried regulating the mortality of adult trees with other parameters and the fraction of area trees and grass occupies were fixed; results show that the mortality of adult trees is also a factor affecting the stability of the tree-grass coexistence in this model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=population-structured%20models" title="population-structured models">population-structured models</a>, <a href="https://publications.waset.org/abstracts/search?q=stabilities%20of%20ecosystems" title=" stabilities of ecosystems"> stabilities of ecosystems</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20competitions" title=" thermal competitions"> thermal competitions</a>, <a href="https://publications.waset.org/abstracts/search?q=tree-grass%20coexistence%20systems" title=" tree-grass coexistence systems"> tree-grass coexistence systems</a> </p> <a href="https://publications.waset.org/abstracts/102872/segregation-patterns-of-trees-and-grass-based-on-a-modified-age-structured-continuous-space-forest-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102872.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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20trees&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=decision%20trees&page=3">3</a></li> <li class="page-item"><a class="page-link" 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