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neighbors</title> <meta name="description" content="Search results for: K nearest neighbors"> <meta name="keywords" content="K nearest neighbors"> <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 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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="K nearest neighbors"> <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> 366</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: K nearest neighbors</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">366</span> Application of Fuzzy Approach to the Vibration Fault Diagnosis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Khelil">Jalel Khelil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title="fault diagnosis">fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20classification%20k-nearest%20neighbor" title=" fuzzy classification k-nearest neighbor"> fuzzy classification k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration" title=" vibration "> vibration </a> </p> <a href="https://publications.waset.org/abstracts/3115/application-of-fuzzy-approach-to-the-vibration-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3115.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">466</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">365</span> Searching k-Nearest Neighbors to be Appropriate under Gaming Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae%20Moon%20Lee">Jae Moon Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flocking%20behavior" title="flocking behavior">flocking behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20agents" title=" heterogeneous agents"> heterogeneous agents</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/8228/searching-k-nearest-neighbors-to-be-appropriate-under-gaming-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8228.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">302</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">364</span> The Classification Performance in Parametric and Nonparametric Discriminant Analysis for a Class- Unbalanced Data of Diabetes Risk Groups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lily%20Ingsrisawang">Lily Ingsrisawang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tasanee%20Nacharoen"> Tasanee Nacharoen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=error%20rate" title="error rate">error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap" title=" bootstrap"> bootstrap</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20risk%20groups" title=" diabetes risk groups"> diabetes risk groups</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors" title=" k-nearest neighbors "> k-nearest neighbors </a> </p> <a href="https://publications.waset.org/abstracts/23799/the-classification-performance-in-parametric-and-nonparametric-discriminant-analysis-for-a-class-unbalanced-data-of-diabetes-risk-groups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23799.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">434</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">363</span> Comparative Study Using WEKA for Red Blood Cells Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jameela%20Ali">Jameela Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20A.%20Jalab"> Hamid A. Jalab</a>, <a href="https://publications.waset.org/abstracts/search?q=Loay%20E.%20George"> Loay E. George</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahim%20Ahmad"> Abdul Rahim Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Suliman"> Azizah Suliman</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Al-Jashamy"> Karim Al-Jashamy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=K-nearest%20neighbors%20algorithm" title="K-nearest neighbors algorithm">K-nearest neighbors algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=red%20blood%20cells" title=" red blood cells"> red blood cells</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/11462/comparative-study-using-weka-for-red-blood-cells-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11462.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">362</span> A Comparative Study for Various Techniques Using WEKA for Red Blood Cells Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jameela%20Ali">Jameela Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20A.%20Jalab"> Hamid A. Jalab</a>, <a href="https://publications.waset.org/abstracts/search?q=Loay%20E.%20George"> Loay E. George</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahim%20Ahmad"> Abdul Rahim Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Suliman"> Azizah Suliman</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Al-Jashamy"> Karim Al-Jashamy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=red%20blood%20cells" title="red blood cells">red blood cells</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20networks" title=" radial basis function neural networks"> radial basis function neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=suport%20vector%20machine" title=" suport vector machine"> suport vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20algorithm" title=" k-nearest neighbors algorithm"> k-nearest neighbors algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15631/a-comparative-study-for-various-techniques-using-weka-for-red-blood-cells-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15631.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">480</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">361</span> An Efficient Machine Learning Model to Detect Metastatic Cancer in Pathology Scans Using Principal Component Analysis Algorithm, Genetic Algorithm, and Classification Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bliss%20Singhal">Bliss Singhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</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=k-nearest%20neighbors" title=" k-nearest neighbors"> k-nearest neighbors</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=logistic%20regression" title=" logistic regression"> logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/164933/an-efficient-machine-learning-model-to-detect-metastatic-cancer-in-pathology-scans-using-principal-component-analysis-algorithm-genetic-algorithm-and-classification-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164933.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">360</span> The Optimum Mel-Frequency Cepstral Coefficients (MFCCs) Contribution to Iranian Traditional Music Genre Classification by Instrumental Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abbasi%20Layegh">M. Abbasi Layegh</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Haghipour"> S. Haghipour</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Athari"> K. Athari</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Khosravi"> R. Khosravi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tafkikialamdari"> M. Tafkikialamdari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach to find the optimum mel-frequency cepstral coefficients (MFCCs) for the Radif of Mirzâ Ábdollâh, which is the principal emblem and the heart of Persian music, performed by most famous Iranian masters on two Iranian stringed instruments ‘Tar’ and ‘Setar’ is proposed. While investigating the variance of MFCC for each record in themusic database of 1500 gushe of the repertoire belonging to 12 modal systems (dastgâh and âvâz), we have applied the Fuzzy C-Mean clustering algorithm on each of the 12 coefficient and different combinations of those coefficients. We have applied the same experiment while increasing the number of coefficients but the clustering accuracy remained the same. Therefore, we can conclude that the first 7 MFCCs (V-7MFCC) are enough for classification of The Radif of Mirzâ Ábdollâh. Classical machine learning algorithms such as MLP neural networks, K-Nearest Neighbors (KNN), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and Support Vector Machine (SVM) have been employed. Finally, it can be realized that SVM shows a better performance in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radif%20of%20Mirz%C3%A2%20%C3%81bdoll%C3%A2h" title="radif of Mirzâ Ábdollâh">radif of Mirzâ Ábdollâh</a>, <a href="https://publications.waset.org/abstracts/search?q=Gushe" title=" Gushe"> Gushe</a>, <a href="https://publications.waset.org/abstracts/search?q=mel%20frequency%20cepstral%20coefficients" title=" mel frequency cepstral coefficients"> mel frequency cepstral coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c-mean%20clustering%20algorithm" title=" fuzzy c-mean clustering algorithm"> fuzzy c-mean clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20%28KNN%29" title=" k-nearest neighbors (KNN)"> k-nearest neighbors (KNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20model%20%28GMM%29" title=" gaussian mixture model (GMM)"> gaussian mixture model (GMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20model%20%28HMM%29" title=" hidden markov model (HMM)"> hidden markov model (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/37296/the-optimum-mel-frequency-cepstral-coefficients-mfccs-contribution-to-iranian-traditional-music-genre-classification-by-instrumental-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37296.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">446</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">359</span> Algebras over an Integral Domain and Immediate Neighbors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shai%20Sarussi">Shai Sarussi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Let S be an integral domain with field of fractions F and let A be an F-algebra. An S-subalgebra R of A is called S-nice if R∩F = S and the localization of R with respect to S \{0} is A. Denoting by W the set of all S-nice subalgebras of A, and defining a notion of open sets on W, one can view W as a T0-Alexandroff space. A characterization of the property of immediate neighbors in an Alexandroff topological space is given, in terms of closed and open subsets of appropriate subspaces. Moreover, two special subspaces of W are introduced, and a way in which their closed and open subsets induce W is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=integral%20domains" title="integral domains">integral domains</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandroff%20topology" title=" Alexandroff topology"> Alexandroff topology</a>, <a href="https://publications.waset.org/abstracts/search?q=immediate%20neighbors" title=" immediate neighbors"> immediate neighbors</a>, <a href="https://publications.waset.org/abstracts/search?q=valuation%20domains" title=" valuation domains"> valuation domains</a> </p> <a href="https://publications.waset.org/abstracts/131023/algebras-over-an-integral-domain-and-immediate-neighbors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131023.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">176</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">358</span> Identification of Breast Anomalies Based on Deep Convolutional Neural Networks and K-Nearest Neighbors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayyaz%20Hussain">Ayyaz Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Tariq%20Sadad"> Tariq Sadad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=DCNN" title=" DCNN"> DCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN" title=" KNN"> KNN</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/118200/identification-of-breast-anomalies-based-on-deep-convolutional-neural-networks-and-k-nearest-neighbors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118200.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">357</span> Benchmarking Machine Learning Approaches for Forecasting Hotel Revenue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rachel%20Y.%20Zhang">Rachel Y. Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20K.%20Anderson"> Christopher K. Anderson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hotel%20revenue" title="hotel revenue">hotel revenue</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest-neighbors" title=" k-nearest-neighbors"> k-nearest-neighbors</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=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20model" title=" prediction model"> prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20tree" title=" regression tree"> regression tree</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/99179/benchmarking-machine-learning-approaches-for-forecasting-hotel-revenue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99179.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">132</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">356</span> Union-Primes and Immediate Neighbors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shai%20Sarussi">Shai Sarussi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The union of a nonempty chain of prime ideals in a noncommutative ring is not necessarily a prime ideal. An ideal is called union-prime if it is a union of a nonempty chain of prime ideals but is not a prime. In this paper, some relations between chains of prime ideals and the induced chains of union-prime ideals are shown; in particular, the cardinality of such chains and the cardinality of the sets of cuts of such chains are discussed. For a ring R and a nonempty full chain of prime ideals C of R, several characterizations for the property of immediate neighbors in C are given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prime%20ideals" title="prime ideals">prime ideals</a>, <a href="https://publications.waset.org/abstracts/search?q=union-prime%20ideals" title=" union-prime ideals"> union-prime ideals</a>, <a href="https://publications.waset.org/abstracts/search?q=immediate%20neighbors" title=" immediate neighbors"> immediate neighbors</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaplansky%27s%20conjecture" title=" Kaplansky's conjecture"> Kaplansky's conjecture</a> </p> <a href="https://publications.waset.org/abstracts/131024/union-primes-and-immediate-neighbors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131024.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">130</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">355</span> Indian Premier League (IPL) Score Prediction: Comparative Analysis of Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rohini%20Hariharan">Rohini Hariharan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yazhini%20R"> Yazhini R</a>, <a href="https://publications.waset.org/abstracts/search?q=Bhamidipati%20Naga%20Shrikarti"> Bhamidipati Naga Shrikarti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indian%20premier%20league%20%28IPL%29" title="indian premier league (IPL)">indian premier league (IPL)</a>, <a href="https://publications.waset.org/abstracts/search?q=cricket" title=" cricket"> cricket</a>, <a href="https://publications.waset.org/abstracts/search?q=score%20prediction" title=" score prediction"> score prediction</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=support%20vector%20machines%20%28SVM%29" title=" support vector machines (SVM)"> support vector machines (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=xgboost" title=" xgboost"> xgboost</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20regression" title=" multiple regression"> multiple regression</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20%28KNN%29" title=" k-nearest neighbors (KNN)"> k-nearest neighbors (KNN)</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=sports%20analytics" title=" sports analytics"> sports analytics</a> </p> <a href="https://publications.waset.org/abstracts/185364/indian-premier-league-ipl-score-prediction-comparative-analysis-of-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185364.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">53</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">354</span> Dealing with Neighbors: River Water Sharing between India, Pakistan and Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Pujari">Ashutosh Pujari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The sharing of natural resources is one of the most important aspects of relations between two neighboring countries, especially when it is a resource that has a presence in both the countries in question. River water is an important resource that is shared between India and its neighbors, namely Pakistan and Bangladesh. India shares Indus, Chenab, and Jhelum rivers with Pakistan, while with Bangladesh, it shares Ganges and Brahmaputra. However, it is interesting to note how does India deals with her sharing of water with these two countries. Although water sharing with both the countries has been dotted by irritants over the years, relations with Bangladesh is undoubtedly better in this respect. Given the common history of the region, this paper analyses the reasons behind this difference in the relationship between India and her neighbors and its implications for the present times. Through critical analysis of literature and the official policy of all the governments involved and the narratives present, this paper tries to present understand how India’s relations with its neighbors are a function of geopolitics, culture, and perceptions on both sides. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geopolitics" title="geopolitics">geopolitics</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20water%20sharing" title=" river water sharing"> river water sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=India-Pakistan%20relations" title=" India-Pakistan relations"> India-Pakistan relations</a>, <a href="https://publications.waset.org/abstracts/search?q=India-Bangladesh%20relations" title=" India-Bangladesh relations"> India-Bangladesh relations</a> </p> <a href="https://publications.waset.org/abstracts/126988/dealing-with-neighbors-river-water-sharing-between-india-pakistan-and-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126988.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">198</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">353</span> Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soha%20A.%20Bahanshal">Soha A. Bahanshal</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20G.%20Kim"> Byung G. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor. <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=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fuzzy%20weighted%20k-nearest%20neighbor" title=" hybrid fuzzy weighted k-nearest neighbor"> hybrid fuzzy weighted k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetic%20hospital%20readmission" title=" diabetic hospital readmission"> diabetic hospital readmission</a> </p> <a href="https://publications.waset.org/abstracts/129397/hybrid-fuzzy-weighted-k-nearest-neighbor-to-predict-hospital-readmission-for-diabetic-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129397.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">186</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">352</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN" title=" k-NN"> k-NN</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=traffic%20speed%20prediction" title=" traffic speed prediction"> traffic speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/43415/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43415.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">363</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">351</span> A Risk Assessment Tool for the Contamination of Aflatoxins on Dried Figs Based on Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kottaridi%20Klimentia">Kottaridi Klimentia</a>, <a href="https://publications.waset.org/abstracts/search?q=Demopoulos%20Vasilis"> Demopoulos Vasilis</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidiropoulos%20Anastasios"> Sidiropoulos Anastasios</a>, <a href="https://publications.waset.org/abstracts/search?q=Ihara%20Diego"> Ihara Diego</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolaidis%20Vasileios"> Nikolaidis Vasileios</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonopoulos%20Dimitrios"> Antonopoulos Dimitrios</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aflatoxins" title="aflatoxins">aflatoxins</a>, <a href="https://publications.waset.org/abstracts/search?q=Aspergillus%20spp." title=" Aspergillus spp."> Aspergillus spp.</a>, <a href="https://publications.waset.org/abstracts/search?q=dried%20figs" title=" dried figs"> dried figs</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors" title=" k-nearest neighbors"> k-nearest neighbors</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=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/141738/a-risk-assessment-tool-for-the-contamination-of-aflatoxins-on-dried-figs-based-on-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141738.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">184</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">350</span> Urban Land Cover from GF-2 Satellite Images Using Object Based and Neural Network Classifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lamyaa%20Gamal%20El-Deen%20Taha">Lamyaa Gamal El-Deen Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20Sharawi"> Ashraf Sharawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GF-2%20images" title="GF-2 images">GF-2 images</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction-rectification" title=" feature extraction-rectification"> feature extraction-rectification</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbour%20object%20based%20classification" title=" nearest neighbour object based classification"> nearest neighbour object based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20algorithms" title=" segmentation algorithms"> segmentation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20classification" title=" neural network classification"> neural network classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/84243/urban-land-cover-from-gf-2-satellite-images-using-object-based-and-neural-network-classifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84243.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">389</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">349</span> Nearest Neighbor Investigate Using R+ Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rutuja%20Desai">Rutuja Desai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Search engine is fundamentally a framework used to search the data which is pertinent to the client via WWW. Looking close-by spot identified with the keywords is an imperative concept in developing web advances. For such kind of searching, extent pursuit or closest neighbor is utilized. In range search the forecast is made whether the objects meet to query object. Nearest neighbor is the forecast of the focuses close to the query set by the client. Here, the nearest neighbor methodology is utilized where Data recovery R+ tree is utilized rather than IR2 tree. The disadvantages of IR2 tree is: The false hit number can surpass the limit and the mark in Information Retrieval R-tree must have Voice over IP bit for each one of a kind word in W set is recouped by Data recovery R+ tree. The inquiry is fundamentally subordinate upon the key words and the geometric directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title="information retrieval">information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor%20search" title=" nearest neighbor search"> nearest neighbor search</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20search" title=" keyword search"> keyword search</a>, <a href="https://publications.waset.org/abstracts/search?q=R%2B%20tree" title=" R+ tree"> R+ tree</a> </p> <a href="https://publications.waset.org/abstracts/33680/nearest-neighbor-investigate-using-r-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33680.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">289</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">348</span> Detection of Powdery Mildew Disease in Strawberry Using Image Texture and Supervised Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Mahmud">Sultan Mahmud</a>, <a href="https://publications.waset.org/abstracts/search?q=Qamar%20Zaman"> Qamar Zaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Travis%20Esau"> Travis Esau</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20Chang"> Young Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=powdery%20mildew" title="powdery mildew">powdery mildew</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=textural%20analysis" title=" textural analysis"> textural analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20co-occurrence%20matrix" title=" color co-occurrence matrix"> color co-occurrence matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors" title=" k-nearest neighbors"> k-nearest neighbors</a> </p> <a href="https://publications.waset.org/abstracts/96613/detection-of-powdery-mildew-disease-in-strawberry-using-image-texture-and-supervised-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96613.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">120</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">347</span> Analysis of Structural Phase Stability of Strontium Sulphide under High Pressure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shilpa%20Kapoor">Shilpa Kapoor</a>, <a href="https://publications.waset.org/abstracts/search?q=Namrata%20Yaduvanshi"> Namrata Yaduvanshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Pawar"> Pooja Pawar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadhna%20Singh"> Sadhna Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Three Body Interaction Potential (TBIP) model is developed to study the high pressure phase transition of SrS having NaCl (B1) structure at room temperature. This model includes the long range Columbic, three body interaction forces, short range overlap forces operative up to next nearest neighbors and zero point energy effects. We have investigated the phase transition with pressure, volume collapse and second order elastic constants and found results well suited with available experimental data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phase%20transition" title="phase transition">phase transition</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20order%20elastic%20constants" title=" second order elastic constants"> second order elastic constants</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20body%20interaction%20forces" title=" three body interaction forces"> three body interaction forces</a>, <a href="https://publications.waset.org/abstracts/search?q=volume%20collapses" title=" volume collapses"> volume collapses</a> </p> <a href="https://publications.waset.org/abstracts/30522/analysis-of-structural-phase-stability-of-strontium-sulphide-under-high-pressure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30522.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">527</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">346</span> Kernel-Based Double Nearest Proportion Feature Extraction for Hyperspectral Image Classiﬁcation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hung-Sheng%20Lin">Hung-Sheng Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Hsuan%20Li"> Cheng-Hsuan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20method" title=" kernel method"> kernel method</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20nearest%20proportion%20feature%20extraction" title=" double nearest proportion feature extraction"> double nearest proportion feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20double%20nearest%20feature%20extraction" title=" kernel double nearest feature extraction"> kernel double nearest feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/54639/kernel-based-double-nearest-proportion-feature-extraction-for-hyperspectral-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54639.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">344</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">345</span> First-Principles Calculations of Hydrogen Adsorbed in Multi-Layer Graphene</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shafiul%20Alam">Mohammad Shafiul Alam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mineo%20Saito"> Mineo Saito</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Graphene-based materials have attracted much attention because they are candidates for post silicon materials. Since controlling of impurities is necessary to achieve nano device, we study hydrogen impurity in multi-layer graphene. We perform local spin Density approximation (LSDA) in which the plane wave basis set and pseudopotential are used. Previously hydrogen monomer and dimer in graphene is well theoretically studied. However, hydrogen on multilayer graphene is still not clear. By using ﬁrst-principles electronic structure calculations based on the LSDA within the density functional theory method, we studied hydrogen monomers and dimers in two-layer graphene. We found that the monomers are spin-polarized and have magnetic moment 1 µB. We also found that most stable dimer is much more stable than monomer. In the most stable structures of the dimers in two-layer graphene, the two hydrogen atoms are bonded to the host carbon atoms which are nearest-neighbors. In this case two hydrogen atoms are located on the opposite sides. Whereas, when the two hydrogen atoms are bonded to the same sublattice of the host materials, magnetic moments of 2 µB appear in two-layer graphene. We found that when the two hydrogen atoms are bonded to third-nearest-neighbor carbon atoms, the electronic structure is nonmagnetic. We also studied hydrogen monomers and dimers in three-layer graphene. The result is same as that of two-layer graphene. These results are very important in the field of carbon nanomaterials as it is experimentally difficult to show the magnetic state of those materials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=first-principles%20calculations" title="first-principles calculations">first-principles calculations</a>, <a href="https://publications.waset.org/abstracts/search?q=LSDA" title=" LSDA"> LSDA</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20gra-phene" title=" multi-layer gra-phene"> multi-layer gra-phene</a>, <a href="https://publications.waset.org/abstracts/search?q=nanomaterials" title=" nanomaterials "> nanomaterials </a> </p> <a href="https://publications.waset.org/abstracts/40081/first-principles-calculations-of-hydrogen-adsorbed-in-multi-layer-graphene" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40081.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">331</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">344</span> A Modified NSGA-II Algorithm for Solving Multi-Objective Flexible Job Shop Scheduling Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Teymourifar">Aydin Teymourifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurkan%20Ozturk"> Gurkan Ozturk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ozan%20Bahadir"> Ozan Bahadir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> NSGA-II is one of the most well-known and most widely used evolutionary algorithms. In addition to its new versions, such as NSGA-III, there are several modified types of this algorithm in the literature. In this paper, a hybrid NSGA-II algorithm has been suggested for solving the multi-objective flexible job shop scheduling problem. For a better search, new neighborhood-based crossover and mutation operators are defined. To create new generations, the neighbors of the selected individuals by the tournament selection are constructed. Also, at the end of each iteration, before sorting, neighbors of a certain number of good solutions are derived, except for solutions protected by elitism. The neighbors are generated using a constraint-based neural network that uses various constructs. The non-dominated sorting and crowding distance operators are same as the classic NSGA-II. A comparison based on some multi-objective benchmarks from the literature shows the efficiency of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flexible%20job%20shop%20scheduling%20problem" title="flexible job shop scheduling problem">flexible job shop scheduling problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=NSGA-II%20algorithm" title=" NSGA-II algorithm"> NSGA-II algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=neighborhood%20structures" title=" neighborhood structures"> neighborhood structures</a> </p> <a href="https://publications.waset.org/abstracts/96824/a-modified-nsga-ii-algorithm-for-solving-multi-objective-flexible-job-shop-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96824.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">229</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">343</span> The Use of AI to Measure Gross National Happiness</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riona%20Dighe">Riona Dighe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=gross%20national%20happiness" title=" gross national happiness"> gross national happiness</a> </p> <a href="https://publications.waset.org/abstracts/160021/the-use-of-ai-to-measure-gross-national-happiness" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160021.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">118</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">342</span> 2D Monte Carlo Simulation of Grain Growth under Transient Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Phaneesh">K. R. Phaneesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Anirudh%20Bhat"> Anirudh Bhat</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Mukherjee"> G. Mukherjee</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20T.%20Kashyap"> K. T. Kashyap</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extensive Monte Carlo Potts model simulations were performed on 2D square lattice to investigate the effects of simulated higher temperatures effects on grain growth kinetics. A range of simulation temperatures (KTs) were applied on a matrix of size 10002 with Q-state 64, dispersed with a wide range of second phase particles, ranging from 0.001 to 0.1, and then run to 100,000 Monte Carlo steps. The average grain size, the largest grain size and the grain growth exponent were evaluated for all particle fractions and simulated temperatures. After evaluating several growth parameters, the critical temperature for a square lattice, with eight nearest neighbors, was found to be KTs = 0.4. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20grain%20size" title="average grain size">average grain size</a>, <a href="https://publications.waset.org/abstracts/search?q=critical%20temperature" title=" critical temperature"> critical temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=grain%20growth%20exponent" title=" grain growth exponent"> grain growth exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20steps" title=" Monte Carlo steps"> Monte Carlo steps</a> </p> <a href="https://publications.waset.org/abstracts/26332/2d-monte-carlo-simulation-of-grain-growth-under-transient-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26332.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">523</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">341</span> Using Historical Data for Stock Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Stoica">Sofia Stoica</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</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=opening%20price" title=" opening price"> opening price</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a> </p> <a href="https://publications.waset.org/abstracts/159522/using-historical-data-for-stock-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159522.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">189</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">340</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">339</span> Identity Verification Using k-NN Classifiers and Autistic Genetic Data</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> DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN).  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20data" title=" genetic data"> genetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=identity%20verification" title=" identity verification"> identity verification</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor" title=" k nearest neighbor"> k nearest neighbor</a> </p> <a href="https://publications.waset.org/abstracts/75552/identity-verification-using-k-nn-classifiers-and-autistic-genetic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75552.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">257</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">338</span> A Combination of Independent Component Analysis, Relative Wavelet Energy and Support Vector Machine for Mental State Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20The%20Hoang%20Anh">Nguyen The Hoang Anh</a>, <a href="https://publications.waset.org/abstracts/search?q=Tran%20Huy%20Hoang"> Tran Huy Hoang</a>, <a href="https://publications.waset.org/abstracts/search?q=Vu%20Tat%20Thang"> Vu Tat Thang</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20T.%20Quyen%20Bui"> T. T. Quyen Bui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a> </p> <a href="https://publications.waset.org/abstracts/46144/a-combination-of-independent-component-analysis-relative-wavelet-energy-and-support-vector-machine-for-mental-state-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46144.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">384</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">337</span> An Analysis of Classification of Imbalanced Datasets by Using Synthetic Minority Over-Sampling Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20A.%20Alfattni">Ghada A. Alfattni </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates.  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20datasets" title="imbalanced datasets">imbalanced datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=SMOTE" title=" SMOTE"> SMOTE</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=logistic%20regression" title=" logistic regression"> logistic regression</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=nearest%20neighbour" title=" nearest neighbour"> nearest neighbour</a> </p> <a href="https://publications.waset.org/abstracts/50056/an-analysis-of-classification-of-imbalanced-datasets-by-using-synthetic-minority-over-sampling-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50056.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">350</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=K%20nearest%20neighbors&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=K%20nearest%20neighbors&page=3">3</a></li> <li class="page-item"><a class="page-link" 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