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In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed...
We proposed a three-dimensional quantitative analysis tool for posterior shapes of eyeballs deformed by such as high myopia diseases, using 3D-MRI. In order to analyze temporal progressions of this disease, we have also proposed developing an integrated vessel analysis system for retinal fundus images. In this system, an extraction tool of retinal vessels, a quantitative analysis tool of retinal vessels...
Effort Estimation is a very important activity for planning and scheduling of software project life cycle in order to deliver the product on time and within budget. Machine learning techniques are proving very useful to accurately predict software effort values. This paper presents a review of various machine-learning techniques using in estimation of software project effort namely Artificial Neural...
The dexterity of active hand prosthetics is limited not only due to the limited availability of dexterous prosthetic hands, but mainly due to limitations in interfaces. How is an amputee supposed to command the prosthesis what to do (i.e., how to grasp an object) and with what force (i.e., holding a hammer or grasping an egg)? So far, in literature, the most interesting results have been achieved...
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression,...
A procedure for designing non-linear models for predicting time series is proposed. It is based on a set of rules emerging from a previously fitted ARIMA model. These rules are extracted from the set of coefficients in the ARIMA model, so they consider the autocorrelation structure of the time series, but a nonlinear approach is adopted. The proposed procedure is intended to help the user in the task...
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