Sürdürülebilir Kalkınma Amaçları

• 1
• 1
• 1

• MakaleAçık Erişim2024/r/bayburt/16390
  Parkinson’s Disease Detection Via Machine Learning Using Data Splitting and Validation Methods

  Engin, Mustafa Alptekin

  Parkinson’s disease (PD), a neurological disorder, negatively affects the lives of patients and their caregivers. PD, which is very difficult to diagnose early by examining the clinical characteristics of the person, can be diagnosed using voice recordings. However, the inconsistent performance results of the models obtained from the evaluation of voice recordings through machine learning techniques limit the usability of these models to aid in diagnosing physicians. This study used a database of 195 voice data obtained from 31 individuals, 23 of whom have PD. The classification of the voices as healthy or patient was based on the 22 features in the database. The split ratios 90/10, 80/20, 70/30, 50/50 and 30/70 were used to select the training and test phase data, respectively. In addition, each split ratio was evaluated using 10-fold cross-validation, 5-fold cross-validation, holdout validation and resubstitution validation methods in the training phase, which is the initial process that will directly affect the other classification procedures. In addition, the classification process was performed using quadratic discriminant analysis, support vector machine, ensemble bagged tree, k-nearest neighbours and neural network classifiers. All procedures were repeated 10 times to ensure consistency of results and randomisation of split ratios. As a result, the k-nearest neighbours classifier with 80/20 splitting ratio and 10-fold cross-validation was determined to be the most successful among the compared methods with 95.64±3.21% accuracy. Therefore, it can be seen that much more successful results can be obtained by analysing only the effects of the existing parameters of the classifiers.

  • 
  İncele
• MakaleAçık Erişim2017/r/bayburt/15317
  Exergetic Comparison of Single and Double Effect Absorption Cooling Cycles

  Karaali, Rabi

  Absorption cooling cycles are environmental and can use solar or waste heat for cooling with very small electric power. This workpresents exergy analysis of a double effect parallel flow and single effect absorption cooling systems for comparison. A computerprogram is developed for the thermodynamic properties of lithium bromide-water solutions by the author in FORTRAN codes forthe exergy analysis. The double effect parallel flow absorption systems have better advantages than the single effect absorption system.The coefficient performance (COP) and the exergetic coefficient performance (ECOP) of the double effect parallel flow absorptionsystems are higher than the single cycles. For the double effect cycle COP and ECOP are found as 1.195 and 0.28, and for the singleeffect cycle COP and ECOP are found as 0.68 and 0.23, respectively. For each component the exergy loss and exergy destruction iscalculated. Most of the irreversibilities are found in the evaporator and in the absorber which about 74 % for the double effect, and 72% for the single effect of the total irreversibility. It is concluded that the performance of the evaporator and the absorber is crucial forthe two cycles. Improving and better design of these two components will directly improve and affect positively the working conditionsand the performance of the cycles.

  • 
  • 
  İncele