An Empirical Study to Evaluate the Relationship of Object-Oriented Metrics and Change Proneness

Abstract: Software maintenance deals with changes or modifications which software goes through. Change prediction models

#Change proneness # empirical validation # machine learning # object-oriented and software quality

Using Deep Learning for Automatically Determining Correct Application of Basic Quranic

Jordan University of Science and Technology, Jordan

#Articulation rules (Ahkam Al-Tajweed) # Mel-Frequency Cepstral Coefficient (MFCC) # Linear predictive Code (LPC) # Wavelet Packet Decomposition (WPD) # Hidden Markov Model based Spectral Peak Location (HMM-SPL) # Convolutional Deep Belief Network (CDBN); k-Nearest Neighbors (KNN); Support Vector Machines (SVM); Artificial Neural Network (NN) # Random Forest (RF) # multiclass classifier # bagging; t-Test

Assessment of Ensemble Classifiers Using the Bagging

Hassan Mohamed1, Abdelazim Negm1, Mohamed Zahran2, and Oliver Saavedra3 1Department of Environmental Engineering, Egypt-Japan University of Science and Technology, Egypt 2Department of Geomatics Engineering, Benha University, Egypt 3Department of Civil Engineering, Tokyo Institute of Technology, Japan

#Bagging; classification; ensemble; landsat satellite magery

Enhanced Bagging (eBagging): A Novel Approach for Ensemble Learning

Goksu Tuysuzoglu1 and Derya Birant2 1Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Turkey 2Department of Computer Engineering, Dokuz Eylul University, Turkey

#Bagging # boosting # classification algorithms # machine learning # random forest # supervised learning

Enhancement of the Heuristic Optimization Based on Extended Space Forests using Classifier Ensembles

Zeynep Kilimci1,3 and Sevinç Omurca2

#Classifier ensembles # extended space forests # ant colony optimization # decision tree

Machine Learning-Based Model for Prediction of Power Consumption in Smart Grid

Shamik Tiwari,Anurag Jain,Kusum Yadav,Rabie Ramadan

#Smart grid #grid stability #machine learning #load balancing #prediction model