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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms by Al-Rawashdeh, Mohammad, Al Nawaiseh, Moh’d, Yousef, Isam, Bisharah, Majdi, Alkhadrawi, Sajeda, Al-Bdour, Hamza

“…This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. …”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification by Mohammed, Alhassan Afnan

“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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Predicting the classification of heart failure patients using optimized machine learning algorithms by Ahmed, Marzia, Mohd Herwan, Sulaiman, Hassan, Md Maruf, Bhuiyan, Touhid

“…This study proposes an optimized machine learning approach using Gradient Boosting Machine (GBM) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) to predict heart failure survival. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves by Lia, Kamelia

“…In the next phase, the datasets are optimized using the Salp Swarm Algorithm (SSA), which improves classification accuracy. …”
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Classification of Diabetes Mellitus using Ensemble Algorithms by Noor, N.A.B.S., Elamvazuthi, I., Yahya, N.

“…The objective of this study is to perform DM classification using various machine learning algorithms. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Detection of corneal arcus using rubber sheet and machine learning methods by Ramlee, Ridza Azri

“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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Integration of GWO-LSSVM for time series predictive analysis by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Bariah, Yusob, Ernawan, Ferda

“…The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. …”
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Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification by Al-Sharhan, Salah, Bimba, Andrew

“…The GA-based approach is used to optimize the various features obtained. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…However, the learning complexity of classification is increased due to the expansion number of learning model. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
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Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying by Khaw , Hui Ying

“…To classify image noise type, the CNN trained with Backpropagation (BP) algorithm and Stochastic Gradient Descent (SGD) optimization technique are implemented. …”
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Feature selection optimization using hybrid relief-f with self-adaptive differential evolution by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Ahmad Nazri, Azree Shahrel, Mohamed, Raihani, Abd Manaf, Syaifulnizam

“…Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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An efficient anomaly intrusion detection method with evolutionary neural network by Sarvari, Samira

“…The second anomaly detection method is the Evolutionary Kernel Neural Network Random Weights (EKNNRW) in order to increase the accuracy of classification. The third proposed method is a new Evolutionary Neural Network (ENN) algorithm with a combination of Genetic Algorithm and Multiverse Optimizer (GAMVO) as a training part of ANN to create efficient anomaly-based detection with low false alarm rate. …”
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Mental Stress Classification Among Higher Education Students In Malaysia From Electroencephalogram (Eeg) Using Convolutional Neural Network With Modified Stochastic Gradient D... by Rashid, Nur Ramizah Ramino

“…This study investigates the classification of mental stress among Malaysian university students using Electroencephalogram (EEG) data and a 1D-Convolutional Neural Network (1D-CNN) optimized with Modified Stochastic Gradient Descent (SGD). …”
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Enhancement Of Static Code Analysis Malware Detection Framework For Android Category-Based Application by Aminordin, Azmi

“…The result shows that the detection of malware within application category achieves higher accuracy compared to application with non-category based. In increasing the reliability, the results obtained are then validated by using statistical analysis procedure which each machine learning classification algorithm are iterate 50 times. …”
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