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learning</title> <meta name="description" content="Search results for: imbalanced learning"> <meta name="keywords" content="imbalanced learning"> <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="imbalanced learning"> <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> 7229</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: imbalanced learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7229</span> A Survey in Techniques for Imbalanced Intrusion Detection System Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Abedzadeh">Najmeh Abedzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthew%20Jacobs"> Matthew Jacobs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IDS" title="IDS">IDS</a>, <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=sampling%20algorithms" title=" sampling algorithms"> sampling algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/149498/a-survey-in-techniques-for-imbalanced-intrusion-detection-system-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149498.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">327</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">7228</span> An Empirical Evaluation of Performance of Machine Learning Techniques on Imbalanced Software Quality Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruchika%20Malhotra">Ruchika Malhotra</a>, <a href="https://publications.waset.org/abstracts/search?q=Megha%20Khanna"> Megha Khanna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20proneness" title="change proneness">change proneness</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20validation" title=" empirical validation"> empirical validation</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20learning" title=" imbalanced learning"> imbalanced learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20techniques" title=" machine learning techniques"> machine learning techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=object-oriented%20metrics" title=" object-oriented metrics"> object-oriented metrics</a> </p> <a href="https://publications.waset.org/abstracts/41990/an-empirical-evaluation-of-performance-of-machine-learning-techniques-on-imbalanced-software-quality-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41990.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">418</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">7227</span> Towards a Balancing Medical Database by Using the Least Mean Square Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Belammi">Kamel Belammi</a>, <a href="https://publications.waset.org/abstracts/search?q=Houria%20Fatrim"> Houria Fatrim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilayer%20neural%20networks" title="multilayer neural networks">multilayer neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=k-%20nearest%20neighbor" title=" k- nearest neighbor"> k- nearest neighbor</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=imbalanced%20medical%20data" title=" imbalanced medical data"> imbalanced medical data</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20mean%20square%20algorithm" title=" least mean square algorithm"> least mean square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes" title=" diabetes"> diabetes</a> </p> <a href="https://publications.waset.org/abstracts/33277/towards-a-balancing-medical-database-by-using-the-least-mean-square-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33277.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">532</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">7226</span> An Ensemble Deep Learning Architecture for Imbalanced Classification of Thoracic Surgery Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saba%20%20Ebrahimi">Saba Ebrahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Ahmadian"> Saeed Ahmadian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hedie%20%20Ashrafi"> Hedie Ashrafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20models" title=" ensemble models"> ensemble models</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20classification" title=" imbalanced classification"> imbalanced classification</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer" title=" lung cancer"> lung cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=TS%20patient%20selection" title=" TS patient selection"> TS patient selection</a> </p> <a href="https://publications.waset.org/abstracts/128394/an-ensemble-deep-learning-architecture-for-imbalanced-classification-of-thoracic-surgery-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128394.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">145</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">7225</span> Machine Learning Facing Behavioral Noise Problem in an Imbalanced Data Using One Side Behavioral Noise Reduction: Application to a Fraud Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salma%20El%20Hajjami">Salma El Hajjami</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamal%20Malki"> Jamal Malki</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20Bouju"> Alain Bouju</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Berrada"> Mohammed Berrada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise. <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=imbalanced%20data" title=" imbalanced data"> imbalanced data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/127869/machine-learning-facing-behavioral-noise-problem-in-an-imbalanced-data-using-one-side-behavioral-noise-reduction-application-to-a-fraud-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127869.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">7224</span> Supervised/Unsupervised Mahalanobis Algorithm for Improving Performance for Cyberattack Detection over Communications Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhika%20Ranjan%20Roy">Radhika Ranjan Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classiﬁcation tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title="Mahalanobis distance">Mahalanobis distance</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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=NS-KDD" title=" NS-KDD"> NS-KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20intrinsic%20dimensionality" title=" local intrinsic dimensionality"> local intrinsic dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=chi-square" title=" chi-square"> chi-square</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20semi-definite" title=" positive semi-definite"> positive semi-definite</a>, <a href="https://publications.waset.org/abstracts/search?q=area%20under%20the%20curve" title=" area under the curve"> area under the curve</a> </p> <a href="https://publications.waset.org/abstracts/161865/supervisedunsupervised-mahalanobis-algorithm-for-improving-performance-for-cyberattack-detection-over-communications-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161865.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">78</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">7223</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7222</span> A Priority Based Imbalanced Time Minimization Assignment Problem: An Iterative Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ekta%20Jain">Ekta Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalpana%20Dahiya"> Kalpana Dahiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Vanita%20Verma"> Vanita Verma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a priority based imbalanced time minimization assignment problem dealing with the allocation of n jobs to m < n persons in which the project is carried out in two stages, viz. Stage-I and Stage-II. Stage-I consists of n1 ( < m) primary jobs and Stage-II consists of remaining (n-n1) secondary jobs which are commenced only after primary jobs are finished. Each job is to be allocated to exactly one person, and each person has to do at least one job. It is assumed that nature of the Stage-I jobs is such that one person can do exactly one primary job whereas a person can do more than one secondary job in Stage-II. In a particular stage, all persons start doing the jobs simultaneously, but if a person is doing more than one job, he does them one after the other in any order. The aim of the proposed study is to find the feasible assignment which minimizes the total time for the two stage execution of the project. For this, an iterative algorithm is proposed, which at each iteration, solves a constrained imbalanced time minimization assignment problem to generate a pair of Stage-I and Stage-II times. For solving this constrained problem, an algorithm is developed in the current paper. Later, alternate combinations based method to solve the priority based imbalanced problem is also discussed and a comparative study is carried out. Numerical illustrations are provided in support of the theory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=assignment" title="assignment">assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced" title=" imbalanced"> imbalanced</a>, <a href="https://publications.waset.org/abstracts/search?q=priority" title=" priority"> priority</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20minimization" title=" time minimization"> time minimization</a> </p> <a href="https://publications.waset.org/abstracts/75198/a-priority-based-imbalanced-time-minimization-assignment-problem-an-iterative-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75198.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">234</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">7221</span> Improved Classification Procedure for Imbalanced and Overlapped Situations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hankyu%20Lee">Hankyu Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data. <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=imbalanced%20data%20with%20class%20overlap" title=" imbalanced data with class overlap"> imbalanced data with class overlap</a>, <a href="https://publications.waset.org/abstracts/search?q=split%20data%20space" title=" split data space"> split data space</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/52079/improved-classification-procedure-for-imbalanced-and-overlapped-situations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52079.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">308</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">7220</span> One vs. Rest and Error Correcting Output Codes Principled Rebalancing Schemes for Solving Imbalanced Multiclass Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alvaro%20Callejas-Ramos">Alvaro Callejas-Ramos</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorena%20Alvarez-Perez"> Lorena Alvarez-Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Benitez-Buenache"> Alexander Benitez-Buenache</a>, <a href="https://publications.waset.org/abstracts/search?q=Anibal%20R.%20Figueiras-Vidal"> Anibal R. Figueiras-Vidal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This contribution presents a promising formulation which allows to extend the principled binary rebalancing procedures, also known as neutral re-balancing mechanisms in the sense that they do not alter the likelihood ratio <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bregman%20divergences" title="Bregman divergences">Bregman divergences</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20multiclass%20classifi-cation" title=" imbalanced multiclass classifi-cation"> imbalanced multiclass classifi-cation</a>, <a href="https://publications.waset.org/abstracts/search?q=informed%20re-balancing" title=" informed re-balancing"> informed re-balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=invariant%20likelihood%20ratio" title=" invariant likelihood ratio"> invariant likelihood ratio</a> </p> <a href="https://publications.waset.org/abstracts/135039/one-vs-rest-and-error-correcting-output-codes-principled-rebalancing-schemes-for-solving-imbalanced-multiclass-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135039.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">215</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">7219</span> Adaptive Swarm Balancing Algorithms for Rare-Event Prediction in Imbalanced Healthcare Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinyan%20Li">Jinyan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20Fong"> Simon Fong</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Wong"> Raymond Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Sabah"> Mohammed Sabah</a>, <a href="https://publications.waset.org/abstracts/search?q=Fiaidhi%20Jinan"> Fiaidhi Jinan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imbalanced%20dataset" title="Imbalanced dataset">Imbalanced dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic%20algorithm" title=" meta-heuristic algorithm"> meta-heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=SMOTE" title=" SMOTE"> SMOTE</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data "> big data </a> </p> <a href="https://publications.waset.org/abstracts/41481/adaptive-swarm-balancing-algorithms-for-rare-event-prediction-in-imbalanced-healthcare-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41481.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7218</span> Fair Federated Learning in Wireless Communications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shayan%20Mohajer%20Hamidi">Shayan Mohajer Hamidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=federated%20learning" title="federated learning">federated learning</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20communications" title=" wireless communications"> wireless communications</a>, <a href="https://publications.waset.org/abstracts/search?q=fairness" title=" fairness"> fairness</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20data" title=" imbalanced data"> imbalanced data</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20preservation" title=" privacy preservation"> privacy preservation</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20privacy" title=" differential privacy"> differential privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/167868/fair-federated-learning-in-wireless-communications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167868.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">75</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">7217</span> The Influence of English Learning on Ethnic Kazakh Minority Students’ Identity (Re)Construction at Chinese Universities </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharapat%20Sharapat">Sharapat Sharapat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> English language is perceived as cultural capital in many non-native English-speaking countries, and minority groups in these social contexts seem to invest in the language to be empowered and reposition themselves from the imbalanced power relation with the dominant group. This study is devoted to explore how English learning influence minority Kazakh students’ identity (re)construction at Chinese universities from the scope of ‘imagined community, investment, and identity’ theory of Norton (2013). To this end the three research questions were designed as follows: 1) Kazakh minority students’ English learning experiences at Chinese universities; 2) Kazakh minority students’ views about benefits and opportunities of English learning; 3) the influence of English learning on Kazakh minority students’ identity (re)construction. The study employs an interview-based qualitative research method by interviewing nine Kazakh minority students in universities in Xinjiang and other inland cities in China. The findings suggest that through English learning, some students have reconstructed multiple identities as multicultural and global identities, which created ‘a third space’ to break limits of their ethnic and national identities and confused identity as someone in-between. Meanwhile, most minority students were empowered by the English language to resist inferior or marginalized positions and reconstruct imagined elite identity. However, English learning disempowered students who have little previous English education in school and placed them on unequal footing with other students, which further escalated the educational inequities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=minority%20in%20China" title="minority in China">minority in China</a>, <a href="https://publications.waset.org/abstracts/search?q=identity%20construction" title=" identity construction"> identity construction</a>, <a href="https://publications.waset.org/abstracts/search?q=multilingual%20education" title=" multilingual education"> multilingual education</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20empowerment" title=" language empowerment"> language empowerment</a> </p> <a href="https://publications.waset.org/abstracts/129160/the-influence-of-english-learning-on-ethnic-kazakh-minority-students-identity-reconstruction-at-chinese-universities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129160.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">231</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">7216</span> Selection of Soil Quality Indicators of Rice Cropping Systems Using Minimum Data Set Influenced by Imbalanced Fertilization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theresa%20K.">Theresa K.</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanmugasundaram%20R."> Shanmugasundaram R.</a>, <a href="https://publications.waset.org/abstracts/search?q=Kennedy%20J.%20S."> Kennedy J. S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nutrient supplements are indispensable for raising crops and to reap determining productivity. The nutrient imbalance between replenishment and crop uptake is attempted through the input of inorganic fertilizers. Excessive dumping of inorganic nutrients in soil cause stagnant and decline in yield. Imbalanced N-P-K ratio in the soil exacerbates and agitates the soil ecosystems. The study evaluated the fertilization practices of conventional (CFs), organic and Integrated Nutrient Management system (INM) on soil quality using key indicators and soil quality indices. Twelve rice farming fields of which, ten fields were having conventional cultivation practices, one field each was organic farming based and INM based cultivated under monocropping sequence in the Thondamuthur block of Coimbatore district were fixed and properties viz., physical, chemical and biological were studied for four cropping seasons to determine soil quality index (SQI). SQI was computed for conventional, organic and INM fields. Comparing conventional farming (CF) with organic and INM, CF was recorded with a lower soil quality index. While in organic and INM fields, the higher SQI value of 0.99 and 0.88 respectively were registered. CF₄ received with a super-optimal dose of N (250%) showed a lesser SQI value (0.573) as well as the yield (3.20 t ha⁻¹) and the CF6 which received 125 % N recorded the highest SQI (0.715) and yield (6.20 t ha⁻¹). Likewise, most of the CFs received higher N beyond the level of 125 % except CF₃ and CF₉, which recorded lower yields. CFs which received super-optimal P in the order of CF₆&CF₇>CF₁&CF₁₀ recorded lesser yields except for CF₆. Super-optimal K application also recorded lesser yield in CF₄, CF₇ and CF₉. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rice%20cropping%20system" title="rice cropping system">rice cropping system</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20quality%20indicators" title=" soil quality indicators"> soil quality indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20fertilization" title=" imbalanced fertilization"> imbalanced fertilization</a>, <a href="https://publications.waset.org/abstracts/search?q=yield" title=" yield"> yield</a> </p> <a href="https://publications.waset.org/abstracts/144949/selection-of-soil-quality-indicators-of-rice-cropping-systems-using-minimum-data-set-influenced-by-imbalanced-fertilization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144949.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7215</span> A Hybrid Feature Selection and Deep Learning Algorithm for Cancer Disease Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niousha%20Bagheri%20Khulenjani">Niousha Bagheri Khulenjani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Saniee%20Abadeh"> Mohammad Saniee Abadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title="cancer classification">cancer 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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/113624/a-hybrid-feature-selection-and-deep-learning-algorithm-for-cancer-disease-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113624.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">111</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">7214</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7213</span> Predictive Modelling of Aircraft Component Replacement Using Imbalanced Learning and Ensemble Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dangut%20Maren%20David">Dangut Maren David</a>, <a href="https://publications.waset.org/abstracts/search?q=Skaf%20Zakwan"> Skaf Zakwan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prognostics" title="prognostics">prognostics</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven" title=" data-driven"> data-driven</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20classification" title=" imbalance classification"> imbalance classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/100049/predictive-modelling-of-aircraft-component-replacement-using-imbalanced-learning-and-ensemble-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7212</span> Enhancing Fault Detection in Rotating Machinery Using Wiener-CNN Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20R.%20Moshtagh">Mohamad R. Moshtagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Bagheri"> Ahmad Bagheri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=gearbox" title=" gearbox"> gearbox</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=wiener%20method" title=" wiener method"> wiener method</a> </p> <a href="https://publications.waset.org/abstracts/169701/enhancing-fault-detection-in-rotating-machinery-using-wiener-cnn-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169701.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">80</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">7211</span> Artificial Reproduction System and Imbalanced Dataset: A Mendelian Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anita%20Kushwaha">Anita Kushwaha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20computation" title="bio-inspired computation">bio-inspired computation</a>, <a href="https://publications.waset.org/abstracts/search?q=nature-%20inspired%20computation" title=" nature- inspired computation"> nature- inspired computation</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20computing" title=" natural computing"> natural computing</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/54679/artificial-reproduction-system-and-imbalanced-dataset-a-mendelian-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54679.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">272</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">7210</span> A Review of Machine Learning for Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aravindraj%20D."> Aravindraj D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadathulla%20A."> Sadathulla A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/72161/a-review-of-machine-learning-for-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72161.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">445</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">7209</span> Prediction of All-Beta Protein Secondary Structure Using Garnier-Osguthorpe-Robson Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Tejasri">K. Tejasri</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Suvarna%20Vani"> K. Suvarna Vani</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Prathyusha"> S. Prathyusha</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ramya"> S. Ramya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proteins" title="proteins">proteins</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20structure%20elements" title=" secondary structure elements"> secondary structure elements</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-sheets" title=" beta-sheets"> beta-sheets</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-strands" title=" beta-strands"> beta-strands</a>, <a href="https://publications.waset.org/abstracts/search?q=alpha-helices" title=" alpha-helices"> alpha-helices</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a> </p> <a href="https://publications.waset.org/abstracts/158938/prediction-of-all-beta-protein-secondary-structure-using-garnier-osguthorpe-robson-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158938.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">7208</span> Leveraging Learning Analytics to Inform Learning Design in Higher Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mingming%20Jiang">Mingming Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This literature review aims to offer an overview of existing research on learning analytics and learning design, the alignment between the two, and how learning analytics has been leveraged to inform learning design in higher education. Current research suggests a need to create more alignment and integration between learning analytics and learning design in order to not only ground learning analytics on learning sciences but also enable data-driven decisions in learning design to improve learning outcomes. In addition, multiple conceptual frameworks have been proposed to enhance the synergy and alignment between learning analytics and learning design. Future research should explore this synergy further in the unique context of higher education, identifying learning analytics metrics in higher education that can offer insight into learning processes, evaluating the effect of learning analytics outcomes on learning design decision-making in higher education, and designing learning environments in higher education that make the capturing and deployment of learning analytics outcomes more efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title="learning analytics">learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20design" title=" learning design"> learning design</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20higher%20education" title=" big data in higher education"> big data in higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning%20environments" title=" online learning environments"> online learning environments</a> </p> <a href="https://publications.waset.org/abstracts/149822/leveraging-learning-analytics-to-inform-learning-design-in-higher-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149822.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">170</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">7207</span> EDM for Prediction of Academic Trends and Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Trupti%20Diwan">Trupti Diwan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute. <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=educational%20data%20mining" title=" educational data mining"> educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20failure" title=" student failure"> student failure</a>, <a href="https://publications.waset.org/abstracts/search?q=grammar-based%20genetic%20programming" title=" grammar-based genetic programming"> grammar-based genetic programming</a> </p> <a href="https://publications.waset.org/abstracts/20702/edm-for-prediction-of-academic-trends-and-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20702.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">422</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">7206</span> An Adaptive Oversampling Technique for Imbalanced Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaukat%20Ali%20Shahee">Shaukat Ali Shahee</a>, <a href="https://publications.waset.org/abstracts/search?q=Usha%20Ananthakumar"> Usha Ananthakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets. <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=imbalanced%20dataset" title=" imbalanced dataset"> imbalanced dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=Lowner-John%20ellipsoid" title=" Lowner-John ellipsoid"> Lowner-John ellipsoid</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20based%20clustering" title=" model based clustering"> model based clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=oversampling" title=" oversampling"> oversampling</a> </p> <a href="https://publications.waset.org/abstracts/83833/an-adaptive-oversampling-technique-for-imbalanced-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83833.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">418</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">7205</span> A Monte Carlo Fuzzy Logistic Regression Framework against Imbalance and Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgios%20Charizanos">Georgios Charizanos</a>, <a href="https://publications.waset.org/abstracts/search?q=Haydar%20Demirhan"> Haydar Demirhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Duygu%20Icen"> Duygu Icen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logistic%20regression" title="fuzzy logistic regression">fuzzy logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy" title=" fuzzy"> fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic" title=" logistic"> logistic</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/175302/a-monte-carlo-fuzzy-logistic-regression-framework-against-imbalance-and-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175302.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">7204</span> A Machine Learning Approach for Detecting and Locating Hardware Trojans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaiwen%20Zheng">Kaiwen Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Wanting%20Zhou"> Wanting Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Nan%20Tang"> Nan Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Lei%20Li"> Lei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuanhang%20He"> Yuanhang He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hardware%20trojans" title="hardware trojans">hardware trojans</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20properties" title=" physical properties"> physical properties</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=hardware%20security" title=" hardware security"> hardware security</a> </p> <a href="https://publications.waset.org/abstracts/164285/a-machine-learning-approach-for-detecting-and-locating-hardware-trojans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164285.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">146</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">7203</span> OSEME: A Smart Learning Environment for Music Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Sofianos">Konstantinos Sofianos</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Stefanidakis"> Michael Stefanidakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, advances in information and communication technologies offer a range of opportunities for new approaches, methods, and tools in the field of education and training. Teacher-centered learning has changed to student-centered learning. E-learning has now matured and enables the design and construction of intelligent learning systems. A smart learning system fully adapts to a student's needs and provides them with an education based on their preferences, learning styles, and learning backgrounds. It is a wise friend and available at any time, in any place, and with any digital device. In this paper, we propose an intelligent learning system, which includes an ontology with all elements of the learning process (learning objects, learning activities) and a massive open online course (MOOC) system. This intelligent learning system can be used in music education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20learning%20systems" title="intelligent learning systems">intelligent learning systems</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20education" title=" music education"> music education</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a> </p> <a href="https://publications.waset.org/abstracts/168933/oseme-a-smart-learning-environment-for-music-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168933.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">311</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">7202</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">7201</span> How to Guide Students from Surface to Deep Learning: Applied Philosophy in Management Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lihong%20Wu">Lihong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Young"> Raymond Young</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge" title="knowledge">knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20learning" title=" surface learning"> surface learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/143479/how-to-guide-students-from-surface-to-deep-learning-applied-philosophy-in-management-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143479.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">501</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">7200</span> Blended Learning through Google Classroom</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lee%20Bih%20Ni">Lee Bih Ni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses that good learning involves all academic groups in the school. Blended learning is learning outside the classroom. Google Classroom is a free service learning app for schools, non-profit organizations and anyone with a personal Google account. Facilities accessed through computers and mobile phones are very useful for school teachers and students. Blended learning classrooms using both traditional and technology-based methods for teaching have become the norm for many educators. Using Google Classroom gives students access to online learning. Even if the teacher is not in the classroom, the teacher can provide learning. This is the supervision of the form of the teacher when the student is outside the school. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blended%20learning" title="blended learning">blended learning</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20app" title=" learning app"> learning app</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20classroom" title=" google classroom"> google classroom</a>, <a href="https://publications.waset.org/abstracts/search?q=schools" title=" schools"> schools</a> </p> <a href="https://publications.waset.org/abstracts/108493/blended-learning-through-google-classroom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108493.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">146</span> </span> 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