In this work, animal bones with different shapes and sizes were used to study the characteristics of the ground penetrating Radar system wares reflected by these bones. These bones were buried underground in different depths and surrounding media. The resulting data showed that the detection of buried bones with the GPR technology is highly dependent upon the surrounding media that the bones were buried in. Humidity is the main source of signal loss in such application because humidity results in low signal-to-noise ratio which leads to inability to distinguish between the signal reflected by bones from that reflected by the dopes  in the media such as rock .

    In this paper, we devoted to use circular shape sliding block, in image edge determination. The circular blocks have symmetrical properties in all directions for the mask points around the central mask point. Therefore, the introduced method is efficient to be use in detecting image edges, in all directions curved edges, and lines. The results exhibit a very good performance in detecting image edges, comparing with other edge detectors results.

Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time.  In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D

          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

    Crow Search Algorithm (CSA) can be defined as one of the new swarm intelligence algorithms that has been developed lately, simulating the behavior of a crow in a storage place and the retrieval of the additional food when required. In the theory of the optimization, a crow represents a searcher, the surrounding environment represents the search space, and the random storage of food location represents a feasible solution. Amongst all the food locations, the one where the maximum amount of the food is stored is considered as the global optimum solution, and objective function represents the food amount. Through the simulation of crows’ intelligent behavior, the CSA attempts to find the optimum solutions to a variety of the proble

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

Abstract: Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a col

Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto

The need for information web-searching is needed by many users nowadays. They use the search engines to input their query or question and wait for the answer or best search results. As results to user query the search engines many times may be return irrelevant pages or not related to information need. This paper presents   a proposed model to provide the user with efficient and effective result through search engine, based on modified chicken swarm algorithm  and cosine similarity    to eliminate and delete irrelevant pages(outliers) from the ranked list results, and to improve the results of the user's query   . The proposed model is applied to Arabic dataset and use the ZAD corpus dataset for 27