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MURATLI, ÖMER CAN

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ARAŞT GÖR ADAYI

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MURATLI

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ÖMER CAN

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2017 - 20192
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    Virtual reality based rehabilitation system for Parkinson and multiple sclerosis patients
    (IEEE, 345 E 47Th St, New York, Ny 10017 USA, 2017) Kihe, Maria Merve; Çatal, Çağatay; MURATLI, ÖMER CAN; 108363
    The aim of this study is to present a virtual reality based rehabilitation system for Parkinson and Multiple Sclerosis (MS) patients. In this study, physical rehabilitation software has been developed using Virtual Reality (VR) technology. The VR environment was used to find a unique solution to the problems of balance, tremor and movement coordination that MS and Parkinson patients suffer from. Virtual reality environment was designed and implemented using UNITY 3D game engine. The generated virtual environment was supported by Kinect, delivered to the virtual reality viewer of Google Cardboard with third party software, and this whole system provided the virtual reality environment. The interaction of the patients with the virtual environment helped these patients to tackle with their problems. The movements in the joint area of the patient were detected using the Microsoft Kinect human-machine interface. Kinect transferred user movements to the computer based on the serial communication. This prototype system will be deployed into a rehabilitation center in Turkey for in-depth analysis and experiments.
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    PublicationOpen Access
    Performance tuning for machine learning-based software development effort prediction models
    (2019) Ertuğrul, Egemen; Baytar, Zakir; Çatal, Çağatay; MURATLI, ÖMER CAN
    Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer perceptron as its underlying algorithm, applies binning of the features to transform continuous features and one-hot-encoding technique to transform categorical data into numerical values as feature transformation techniques, does feature selection based on the principal component analysis method, and performs parameter tuning based on the GridSearch algorithm. We demonstrate that our effort prediction model mostly outperforms the other existing models in terms of prediction accuracy based on the mean absolute residual parameter.