Impact on Brain due to Alcoholism using Improved Fuzzy C-Regression based Alcohol Detection
1Gayathriselvaraj*, Department of Computer Applications, Bharathiar University , Coimbatore.
2Dr. M. Punithavalli*, Department of Computer Applications, Bharathiar University, Coimbatore.
Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 1317-1325 | Volume-9 Issue-2, December 2019. | Retrieval Number: A5309119119/2019©BEIESP | DOI: 10.35940/ijitee.A5309.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In general, two risk factors such as alcohol expectancy and impulsivity have been concerned with alcohol abuse Currently, many people have been addicted to alcoholism and have an Alcohol Use Disorder (AUD) that affects neurons behavior in the human brain. Still, how such risk factors interrelate to estimate the alcoholism. To solve this problem, Fuzzy C-Regression based Alcoholism Detection (FCRAD) method has been proposed that segments the Region-Of-Interests (ROIs) from the human brain image to predict the Gray Matter Volume (GMV) reduction in the right posterior insula in women and the left thalamus in both men and women efficiently. However, it requires the detection of GMV reduction in the other brain image regions. This multi-modality can decrease the fuzziness of the partition and the crisp membership degrees were not derived easily. Therefore in this article, the GMV reduction in other regions of the brain images including right posterior insula in women and left thalamus in both men and women has been detected, an Improved FCRAD (IFCRAD) method is proposed to simplify the segmentation of the brain images by considering the second regularization term in the objective function of the FCR to take into account the noisy data. Also, the Euclidean distance is replaced with the Voronoi distance for computing different fuzzy membership functions. Moreover, new error measure and reward function are used in the objective function of the FCR to reward nearly crisp membership functions and to obtain more crisp partition. So, the brain images are segmented into gray-matter images that derive the ROIs to analyze the GMV reduction with less complexity. Finally, the experimental results illustrate the proposed IFCRAD method achieves higher accuracy than the existing AD methods.
Keywords: Alcohol use Disorder, Gray Matter Volume, Fuzzy C-regression, ROI Segmentation, Voronoi Diagram
Scope of the Article: Fuzzy Logics