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

Computer Modeling in Engineering & Sciences 2020年第2期122卷 619-632页

作者： kemal akyol Kastamonu University Kuzeykent YerleşkesiKastamonu37100Turkey.

Parkinson’s disease is a serious disease that causes ***,a new dataset has been introduced on this *** aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease *** and large,original DNN models were designed by using specific or random number of neurons and *** study analyzed the effects of parameters,i.e.,neuron number and activation function on the model performance based on growing and pruning *** 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 *** this context of this study,several models were designed and *** overall results revealed that the Deep Neural Networks were significantly successful with 99.34%accuracy value on test ***,it presents the highest prediction performance reported so ***,this study presents a model promising with respect to more accurate Parkinson’s disease diagnosis.

关键词： Parkinson’s disease machine learning growing and pruning deep neural networks.

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Modeling and Predicting of News Popularity in Social Media Sources

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Computers, Materials & Continua 2019年第7期61卷 69-80页

作者： kemal akyol BahaŞen Faculty of Engineering and Architecture Kastamonu University37100KastamonuTurkey Faculty of Engineering Yıldırım Beyazıt University06500AnkaraTurkey

The popularity of news,which conveys newsworthy events which occur during day to people,is substantially important for the spectator or *** interact with news website and share news links or their *** study uses supervised learning based machine learning techniques in order to predict news popularity in social media *** 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 *** so,a knowledge discovery from the data is *** this context,firstly,twelve datasets from a set of data are obtained within the frame of four categories:Economic,Microsoft,Obama and ***,news popularity prediction in social network services is carried out by utilizing Gradient Boosted Trees,Multi-Layer Perceptron and Random Forest learning *** prediction performances of all algorithms are examined by considering Mean Absolute Error,Root Mean Squared Error and the R-squared evaluation *** results show that most of the models designed by using these algorithms are proved to be applicable for this ***,a comprehensive study for the news prediction is presented,using different techniques,drawing conclusions about the performances of algorithms in this study.

关键词： News popularity sentiment scores social network services Gradient Boosted Machines Multi-Layer Perceptron Random Forest

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