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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="extra trees classifier"> <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> 1501</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: extra trees classifier</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1501</span> Enhanced Extra Trees Classifier for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maurice%20Ntahobari">Maurice Ntahobari</a>, <a href="https://publications.waset.org/abstracts/search?q=Levin%20Kuhlmann"> Levin Kuhlmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Boley"> Mario Boley</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhinoos%20Razavi%20Hesabi"> Zhinoos Razavi Hesabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection. <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=seizure%20prediction" title=" seizure prediction"> seizure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=extra%20tree%20classifier" title=" extra tree classifier"> extra tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SHAP" title=" SHAP"> SHAP</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a> </p> <a href="https://publications.waset.org/abstracts/155126/enhanced-extra-trees-classifier-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155126.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">113</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">1500</span> Walmart Sales Forecasting using Machine Learning in Python</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niyati%20%20Sharma">Niyati Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Om%20%20Anand"> Om Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Kumar%20Prasad"> Sanjeev Kumar Prasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20algorithm" title="random forest algorithm">random forest algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression%20algorithm" title=" linear regression algorithm"> linear regression algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=extra%20trees%20classifier" title=" extra trees classifier"> extra trees classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error" title=" mean absolute error"> mean absolute error</a> </p> <a href="https://publications.waset.org/abstracts/138978/walmart-sales-forecasting-using-machine-learning-in-python" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138978.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">149</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">1499</span> A Dynamic Round Robin Routing for Z-Fat Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20O.%20Adda">M. O. Adda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a topology called Zoned fat tree (Z-Fat tree) which is a further extension to the classical fat trees. The extension relates to the provision of extra degree of connectivity to maximize the number of deployed ports per routing nodes, and hence increases the bisection bandwidth especially for slimmed fat trees. The extra links, when classical routing is used, tend, in deterministic environment, to be under-utilized for some traffic patterns, hence achieving poor performance. We suggest two versions of a dynamic round robin scheme that outperforms the classical D-mod-k and S-mod-K routing and show by simulation that our proposal utilize all the extra added links to the classical fat tree, and achieve better performance for general applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deterministic%20routing" title="deterministic routing">deterministic routing</a>, <a href="https://publications.waset.org/abstracts/search?q=fat%20tree" title=" fat tree"> fat tree</a>, <a href="https://publications.waset.org/abstracts/search?q=interconnection" title=" interconnection"> interconnection</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20pattern" title=" traffic pattern"> traffic pattern</a> </p> <a href="https://publications.waset.org/abstracts/40045/a-dynamic-round-robin-routing-for-z-fat-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40045.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">484</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">1498</span> Classification of Red, Green and Blue Values from Face Images Using k-NN Classifier to Predict the Skin or Non-Skin</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 study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=k-NN%20classifier" title="k-NN classifier">k-NN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=skin%20or%20non-skin%20classification" title=" skin or non-skin classification"> skin or non-skin classification</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20values" title=" RGB values"> RGB values</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/86538/classification-of-red-green-and-blue-values-from-face-images-using-k-nn-classifier-to-predict-the-skin-or-non-skin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86538.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">248</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">1497</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">1496</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">313</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">1495</span> Segmentation of Liver Using Random Forest Classifier </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gajendra%20Kumar%20%20Mourya">Gajendra Kumar Mourya</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinesh%20%20Bhatia"> Dinesh Bhatia</a>, <a href="https://publications.waset.org/abstracts/search?q=Akash%20%20Handique"> Akash Handique</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunita%20Warjri"> Sunita Warjri</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Achaab%20Amir"> Syed Achaab Amir </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, Medical imaging has become an integral part of modern healthcare. Abdominal CT images are an invaluable mean for abdominal organ investigation and have been widely studied in the recent years. Diagnosis of liver pathologies is one of the major areas of current interests in the field of medical image processing and is still an open problem. To deeply study and diagnose the liver, segmentation of liver is done to identify which part of the liver is mostly affected. Manual segmentation of the liver in CT images is time-consuming and suﬀers from inter- and intra-observer diﬀerences. However, automatic or semi-automatic computer aided segmentation of the Liver is a challenging task due to inter-patient Liver shape and size variability. In this paper, we present a technique for automatic segmenting the liver from CT images using Random Forest Classifier. Random forests or random decision forests are an ensemble learning method for classification that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. After comparing with various other techniques, it was found that Random Forest Classifier provide a better segmentation results with respect to accuracy and speed. We have done the validation of our results using various techniques and it shows above 89% accuracy in all the cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20validation" title=" image validation"> image validation</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=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/77535/segmentation-of-liver-using-random-forest-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77535.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">313</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">1494</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">1493</span> Parkinson’s Disease Detection Analysis through Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhtasim%20Shafi%20Kader">Muhtasim Shafi Kader</a>, <a href="https://publications.waset.org/abstracts/search?q=Fizar%20Ahmed"> Fizar Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Annesha%20Acharjee"> Annesha Acharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used. <p class="card-text"><strong>Keywords:</strong> <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=adaptive%20boosting" title=" adaptive boosting"> adaptive boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=bagging%20classifier" title=" bagging classifier"> bagging classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20classifier" title=" random forest classifier"> random forest classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=XBG%20classifier" title=" XBG classifier"> XBG classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor%20classifier" title=" k nearest neighbor classifier"> k nearest neighbor classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20classifier" title=" support vector classifier"> support vector classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20boosting%20classifier" title=" gradient boosting classifier"> gradient boosting classifier</a> </p> <a href="https://publications.waset.org/abstracts/148163/parkinsons-disease-detection-analysis-through-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148163.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">129</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">1492</span> Use of Fractal Geometry in Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20M.%20Alkoot">Fuad M. Alkoot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20geometry" title="fractal geometry">fractal geometry</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=classifier" title=" classifier"> classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a> </p> <a href="https://publications.waset.org/abstracts/141274/use-of-fractal-geometry-in-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141274.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">1491</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">1490</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">1489</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">1488</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">1487</span> Speaker Recognition Using LIRA Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nestor%20A.%20Garcia%20Fragoso">Nestor A. Garcia Fragoso</a>, <a href="https://publications.waset.org/abstracts/search?q=Tetyana%20Baydyk"> Tetyana Baydyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernst%20Kussul"> Ernst Kussul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20learning" title="extreme learning">extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LIRA%20neural%20classifier" title=" LIRA neural classifier"> LIRA neural classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20identification" title=" speaker identification"> speaker identification</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20recognition" title=" voice recognition"> voice recognition</a> </p> <a href="https://publications.waset.org/abstracts/112384/speaker-recognition-using-lira-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112384.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">1486</span> Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Sopharak">A. Sopharak</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Uyyanonvara"> B. Uyyanonvara</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Barman"> S. Barman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diabetic%20retinopathy" title="diabetic retinopathy">diabetic retinopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=microaneurysm" title=" microaneurysm"> microaneurysm</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20Bayes%20classifier" title=" naive Bayes classifier"> naive Bayes classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title=" SVM classifier"> SVM classifier</a> </p> <a href="https://publications.waset.org/abstracts/3939/comparing-svm-and-naive-bayes-classifier-for-automatic-microaneurysm-detections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3939.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">329</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">1485</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">1484</span> Measuring Multi-Class Linear Classifier for Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Susilawati%20Mohamad">Fatma Susilawati Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Abdul%20Manaf"> Azizah Abdul Manaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadhillah%20Ahmad"> Fadhillah Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Zarina%20Mohamad"> Zarina Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Suryani%20Wan%20Awang"> Wan Suryani Wan Awang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-class" title="multi-class">multi-class</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20classifier" title=" linear classifier"> linear classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor" title=" nearest neighbor"> nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/51310/measuring-multi-class-linear-classifier-for-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51310.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">538</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">1483</span> Comparison of the Effectiveness of Tree Algorithms in Classification of Spongy Tissue Texture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roza%20Dzierzak">Roza Dzierzak</a>, <a href="https://publications.waset.org/abstracts/search?q=Waldemar%20Wojcik"> Waldemar Wojcik</a>, <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Kacejko"> Piotr Kacejko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20analysis" title=" texture analysis"> texture analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20algorithms" title=" tree algorithms"> tree algorithms</a> </p> <a href="https://publications.waset.org/abstracts/107923/comparison-of-the-effectiveness-of-tree-algorithms-in-classification-of-spongy-tissue-texture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107923.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">178</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">1482</span> Propylene Self-Metathesis to Ethylene and Butene over WOx/SiO2, Effect of Nano-Sized Extra Supports (SiO2 and TiO2)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adisak%20Guntida">Adisak Guntida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propylene self-metathesis to ethylene and butene was studied over WOx/SiO2 catalysts at 450 °C and atmospheric pressure. The WOx/SiO2 catalysts were prepared by incipient wetness impregnation of ammonium metatungstate aqueous solution. It was found that, adding nano-sized extra supports (SiO2 and TiO2) by physical mixing with the WOx/SiO2 enhanced propylene conversion. The UV-Vis and FT-Raman results revealed that WOx could migrate from the original silica support to the extra support, leading to a better dispersion of WOx. The ICP-OES results also indicate that WOx existed on the extra support. Coke formation was investigated on the catalysts after 10 h time-on-stream by TPO. However, adding nano-sized extra supports led to higher coke formation which may be related to acidity as characterized by NH3-TPD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extra%20support" title="extra support">extra support</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomaterial" title=" nanomaterial"> nanomaterial</a>, <a href="https://publications.waset.org/abstracts/search?q=propylene%20self-metathesis" title=" propylene self-metathesis"> propylene self-metathesis</a>, <a href="https://publications.waset.org/abstracts/search?q=tungsten%20oxide" title=" tungsten oxide"> tungsten oxide</a> </p> <a href="https://publications.waset.org/abstracts/25494/propylene-self-metathesis-to-ethylene-and-butene-over-woxsio2-effect-of-nano-sized-extra-supports-sio2-and-tio2" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25494.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">245</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">1481</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">429</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">1480</span> Random Forest Classification for Population Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Regina%20Chua">Regina Chua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments. <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=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</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=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a> </p> <a href="https://publications.waset.org/abstracts/154919/random-forest-classification-for-population-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154919.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">94</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">1479</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">1478</span> Classification of Forest Types Using Remote Sensing and Self-Organizing Maps</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanderson%20Goncalves%20e%20Goncalves">Wanderson Goncalves e Goncalves</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Alberto%20Silva%20de%20S%C3%A1"> José Alberto Silva de Sá</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon. <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=computational%20intelligence" title=" computational intelligence"> computational intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/57742/classification-of-forest-types-using-remote-sensing-and-self-organizing-maps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57742.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">361</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">1477</span> Neuronal Networks for the Study of the Effects of Cosmic Rays on Climate Variations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jossitt%20Williams%20Vargas%20Cruz">Jossitt Williams Vargas Cruz</a>, <a href="https://publications.waset.org/abstracts/search?q=Aura%20Jazm%C3%ADn%20P%C3%A9rez%20R%C3%ADos"> Aura Jazmín Pérez Ríos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlations" title="correlations">correlations</a>, <a href="https://publications.waset.org/abstracts/search?q=cosmic%20rays" title=" cosmic rays"> cosmic rays</a>, <a href="https://publications.waset.org/abstracts/search?q=sun" title=" sun"> sun</a>, <a href="https://publications.waset.org/abstracts/search?q=sunspots%20and%20variations." title=" sunspots and variations."> sunspots and variations.</a> </p> <a href="https://publications.waset.org/abstracts/163231/neuronal-networks-for-the-study-of-the-effects-of-cosmic-rays-on-climate-variations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163231.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">74</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">1476</span> A Comparative Study of k-NN and MLP-NN Classifiers Using GA-kNN Based Feature Selection Method for Wood Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Uswah%20Khairuddin">Uswah Khairuddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubiyah%20Yusof"> Rubiyah Yusof</a>, <a href="https://publications.waset.org/abstracts/search?q=Nenny%20Ruthfalydia%20Rosli"> Nenny Ruthfalydia Rosli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=wood%20recognition%20system" title=" wood recognition system "> wood recognition system </a> </p> <a href="https://publications.waset.org/abstracts/25573/a-comparative-study-of-k-nn-and-mlp-nn-classifiers-using-ga-knn-based-feature-selection-method-for-wood-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25573.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">545</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">1475</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">1474</span> Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data Towards Mapping Fruit Plantations in Highly Heterogenous Landscapes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yingisani%20Chabalala">Yingisani Chabalala</a>, <a href="https://publications.waset.org/abstracts/search?q=Elhadi%20Adam"> Elhadi Adam</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Adem%20Ali"> Khalid Adem Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smallholder%20agriculture" title="smallholder agriculture">smallholder agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20trees" title=" fruit trees"> fruit trees</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title=" data fusion"> data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a> </p> <a href="https://publications.waset.org/abstracts/183366/machine-learning-classification-of-fused-sentinel-1-and-sentinel-2-image-data-towards-mapping-fruit-plantations-in-highly-heterogenous-landscapes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183366.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">54</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">1473</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">292</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">1472</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=extra%20trees%20classifier&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extra%20trees%20classifier&page=3">3</a></li> <li class="page-item"><a class="page-link" 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