Abdülkadir Gümüşçü

Genetic algorithm based feature selection on diagnosis of Parkinson disease via vocal analysis

Parkinsons disease is a neurological disorder that affects the quality of life of a patient adversely, especially in elder people. Parkinsons disease first manifests itself as slowness, imbalance and trembling in motor movements. There are many methods such as gait analysis and voice analysis for diagnosis of the disease. In this study, genetic algorithm based feature selection method is proposed for the voice analysis that one of the methods used in the diagnosis of Parkinsons disease. Classification success rates of selected attributes are calculated by SVM classification algorithm and validated by LOOCV method. The classification success rate of the proposed method was compared with previous studies using the same classification and validation method. As a result of the comparison, more successful results were obtained than those using SVM classification and LOOCV validation methods.

Vessel segmentation in angiography images with multi-scale filters

Analysis of coronary angiography is very important for the diagnosis and treatment of heart diseases. The quality of the image is very low due to lack of usage of the X-ray and injected material, which are harmful to human health. For accurate segmentation and analysis the images need to be enhancement. In this study, a Gabor and 2d comparison filter was used to enhance the images and to highlight the vessel structures. It calculates the responses of different size and directional filters in both filters used. The improved images were segmented by P-Tile and Otsu thresholding methods and their performance was compared. The segmentation was performed on 5 different angiograms. The Gabor filter and the P-Tile thresholding method were found to be more successful with accuracy rate of mean all experiment %90,86 and maxium 92.4%.

Comparing inertia weights of particle swarm optimization in multimodal functions

Particle swarm optimization (PSO) is a state-of-the-art algorithm in meta-heuristic optimization study area. It is a swarm based algorithm that mimic fish or birds behaviors in the nature. Success rate of convergence in an optimization algorithm depends on control balancing between exploration and exploitation. Inertia weight coefficient parameter controls convergence rate of PSO algorithm. In this paper, different inertia weight: constant, random, linear decreasing and global-local best methods are used in CEC 2017 multimodal benchmark functions. Multimodal functions have huge numbers of local optima. Seven multimodal functions are used with 10 and 30 variable dimensions. Obtained result and run time statistics are compared and shown in graphs.

HARRAN ÜNİVERSİTESİ SÜREKLİ EĞİTİM MERKEZİ İŞ SAĞLIĞI VE GÜVENLİĞİ KURSU UZAKTAN EĞİTİM PORTALI

Occupational health and security expertise has given people the opportunity to make money because of the rapidly spreading concept of occupational health and security nowadays. In order for private and public institutions to offer classes, they need to satisfy many criterions that are required by the Ministry of Labor and Social Security The institutions that offer classes for ISG, prefer to teach online as well as face to face so long as to increase the profitability. CSGB has set some criterions in online education. In this work, all the software that was used in the online education portal of occupational health and safety that was offered by the Harran University continuing education center, will be explained