Modeling and Predicting of News Popularity in Social Media Sources

The popularity of news,which conveys newsworthy events which occur during day to people,is substantially important for the spectator or audience.People interact with news website and share news links or their opinions.This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources.These techniques consist of basically two phrases:a)the training data is sent as input to the classifier algorithm,b)the performance of prelearned algorithm is tested on the testing data.And so,a knowledge discovery from the data is performed.In this context,firstly,twelve datasets from a set of data are obtained within the frame of four categories:Economic,Microsoft,Obama and Palestine.Second,news popularity prediction in social network services is carried out by utilizing Gradient Boosted Trees,Multi-Layer Perceptron and Random Forest learning algorithms.The prediction performances of all algorithms are examined by considering Mean Absolute Error,Root Mean Squared Error and the R-squared evaluation metrics.The results show that most of the models designed by using these algorithms are proved to be applicable for this subject.Consequently,a comprehensive study for the news prediction is presented,using different techniques,drawing conclusions about the performances of algorithms in this study.

Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis

Parkinson’s disease is a serious disease that causes death.Recently,a new dataset has been introduced on this disease.The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis.By and large,original DNN models were designed by using specific or random number of neurons and layers.This study analyzed the effects of parameters,i.e.,neuron number and activation function on the model performance based on growing and pruning approach.In other words,this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order to find out the best Deep Neural Networks model.In this context of this study,several models were designed and evaluated.The overall results revealed that the Deep Neural Networks were significantly successful with 99.34%accuracy value on test data.Also,it presents the highest prediction performance reported so far.Therefore,this study presents a model promising with respect to more accurate Parkinson’s disease diagnosis.