CCNN: An Artificial Intelligent based Classifier to Credit Card Fraud Detection System with Optimized Cognitive Learning Model

Nowadays digital transactions play a vital role in money transaction processes. Last 5 years statistical report portrays the growth of internet money transaction especially credit card and unified payments interface. Mean time increasing numerous banking threats and digital transaction fraud rates also growing significantly. Data engineering techniques provide ultra supports to detect credit card forgery problems in online and offline mode transactions. This credit card fraud detection (CCFD) and prevention-based data processing issues raising because of two major reasons first, classification rate of legitimate and forgery uses is frequently changing, and next one is fraud detection dataset values are vastly asymmetric. Through this research work investigating performance of various existing classifier with our proposed cognitive convolutional neural network (CCNN) classifier. Existing classifiers like Logistic Regression (LR), K-nearest neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM). These models are facing various challenges of low performance rate and high complexity because of low hit rate and accuracy. Through this research work we introduce cognitive learning-based CCNN classifier methodology with artificial intelligence for achieve maximum accuracy rate and minimal complexity issues. For experimental data analysis uses dataset of credit card transactions attained from specific region cardholders containing 284500 transactions and its various features. Also, this dataset contains unstructured and non-dimensional data are converted into structured data with the help of over sample and under sample method. Performance analysis shows proposed CCNN classifier model provide significant improvement on accuracy, specificity, sensitivity and hit rate. The results are shown in comparison. After cross-validation, the accuracy of the CCNN classification algorithm model for transaction fraudulent detection archived 99% which using the over-sampling model

Enhancement Ear-based Biometric System Using a Modified AdaBoost Method

أن الهدف الرئيسي من هذا البحث هو تعزيز نموذج المصادقة البيومترية والتصنيف باستخدام الأذن كجزء مميز من الوجه لأنها لا تتغير بمرور الوقت ولا تتأثر بتعابير الوجه. النموذج المقترح هو سيناريو جديد لتعزيز دقة التعرف على الأذن من خلال تعديل خوارزمية تعزيز المصنف (AdaBoost) لتحسين التعلم التكيفي. للتغلب على قيود إضاءة الصورة والانسداد وسوء تسجيل الصورة نستخدم تقنية تحويل ميزة المقياس الثابت لاستخراج الميزات. تم استخدام مراحل متتالية مختلفة لتحسين دقة التصنيف. هذه المراحل هي الحصول على الصور والمعالجة المسبقة والتصفية والتنعيم واستخراج الميزات. لتقييم أداء الطريقة المقترحة، تمت مقارنة دقة التصنيف باستخدام أنواع مختلفة من المصنفات. هذه المصنفات هي Naïve Bayesian وKNN وJ48 وSVM.، خلصنا إلى أن مدى دقة التعريف لجميع قواعد البيانات التي تمت معالجتها باستخدام السيناريو المقترح يتراوح بين (٪ 93.8-٪ 97.8). تم تنفيذ النظام باستخدام MATHLAB R2017 بمعالج 2.10 جيجا هرتز و4 جيجا بايت رام.          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

Age and gender as cyber attribution features in keystroke dynamic-based user classification processes.

Keystroke dynamics are used to authenticate users, to reveal some of their inherent or acquired characteristics and to assess their mental and physical states. The most common features utilized are the time intervals that the keys remain pressed and the time intervals that are required to use two consecutive keys. This paper examines which of these features are the most important and how utilization of these features can lead to better classification results. To achieve this, an existing dataset consisting of 387 logfiles is used, five classifiers are exploited and users are classified by gender and age. The results, while demonstrating the application of these two characteristics jointly on classifiers with high accuracy, answer the question of which keystroke dynamics features are more appropriate for classification with common classifiers