Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep learning model was utilized to resize images and feature extraction. Finally, different ML classifiers have been tested for recognition based on the extracted features. The effectiveness of each classifier was assessed using various performance metrics. The results show that the proposed system works well, and all the methods achieved good results; however, the best results obtained were for the Support Vector Machine (SVM) with a linear kernel.
Personal intelligence is thinking about an other person , understanding him, have sympathy and differentiation between people, and to appreciate their own point of view, with the sensitivity to their motives, behavior, and goals, so this intelligence involves dealing with a person or group of persons effectively and in normal or logical manner.
Emotions management is to achieve emotional balance by controlling the emotions continuously, self disciplining, keeping away from excitement sources, and dealing with bad situations in constructive way to achieve the psychological stability .
- the study aims
Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory char
... Show MoreIn this work a fragile watermarking scheme is presented. This scheme is applied to digital color images in spatial domain. The image is divided into blocks, and each block has its authentication mark embedded in it, we would be able to insure which parts of the image are authentic and which parts have been modified. This authentication carries out without need to exist the original image. The results show the quality of the watermarked image is remaining very good and the watermark survived some type of unintended modification such as familiar compression software like WINRAR and ZIP
Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measu
... Show MoreCompressing an image and reconstructing it without degrading its original quality is one of the challenges that still exist now a day. A coding system that considers both quality and compression rate is implemented in this work. The implemented system applies a high synthetic entropy coding schema to store the compressed image at the smallest size as possible without affecting its original quality. This coding schema is applied with two transform-based techniques, one with Discrete Cosine Transform and the other with Discrete Wavelet Transform. The implemented system was tested with different standard color images and the obtained results with different evaluation metrics have been shown. A comparison was made with some previous rel
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