The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed system's performance. method, the classification accuracy has been compared using different types of classifiers. These classifiers are Naïve Bayesian, KNN, J48, and SVM. The range of the identification accuracy for all the processed databases using the proposed scenario is between (%93.8- %97.8). The system was executed using MATHLAB R2017, 2.10 GHz processor, and 4 GB RAM.
במחקר הזה ניתחנו מספר נאומים של שמעון פרס, אנחנו התמקדנו בהשפעה והשכנוע אצל שמעון פרס ואיך הוא יכול להעביר את המסרים של נאומיו בסגנון פרגמטי כדי להגיע ללבו של הציבור.
גם כן, התמקדנו בסגנון הפוליטי שהוא חושב כי התחום הזה צריך להיות ברור מול הציבור וגם כן מול דעת הקהל הבינלאומי מתוך השימוש במונחים בעלי השפעה ושכנוע להגיע למטרות המבוקשות.
במלים אחרות, שמעון פרס, לעתים, מתמקד בשפה מליצית ויעמוד הרב
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