Intelligent spoken system is constructed to recognize numbers spoken in Arabic language by different people. Series of operations are performed on audio sound file as pre-processing stages. A novel approach is applied to extract features of audio files called Max Mean Log to reduce audio file dimensions in an efficient manner. Several stages of initial processing are used to prepare the file for the next step of the recognition process. The recognition process begins with the use of Antlion’s advanced intelligence algorithm to determine the type of the spoken number in Arabic and later convert it to a visual text that represents the value of the spoken number. The current proposal method is relatively fast and very effective. The percentage of recognizing numbers spoken by the proposed algorithm is 99%. For 1,800 different audio files, the error rate was 1%. Additional 40 audio files were used that are different from people’s original dataset. Due to an additional examination of the system and its ability to recognize the audio file, the rate of discrimination for such files was 72.5%.

<p>In this paper certain type of biometric measurements has been used to identify the cone beam computed tomography (CBCT) radiograph of the subject in a fast and reliable way. Where the CBCT radiograph of a person is used as a data and stored in database for later use in a person’s recognition process. The aim of this research is to use various stages of the preprocessing operations of the CBCT radiograph to obtain the clearest possible image that will help us in the identification process more easily and precisely. The contourlet transformation was used for feature extraction of each particular CBCT image and the results were processed by a new hybrid particle swarm optimization (PSO) named "contourlet PSO" algorithm (CPSO), which is faster and produce more precise (due to apply contourlet algorithm) than traditional PSO. The proposed algorithm (CPSO) gave a detection ratio of 98% after its application on 100 CBCT radiographs.</p>

In modern devices, many personal identification systems are used using various biometrics to confirm the identity of an individual and identify him for several purposes. Some of these essential biometrics are used in this paper to help identify a person while attaining social distance because of the widespread epidemics. The features of the face, eyes, nose, and finally, the features of the mouth are used in this article. The work begins by detecting the parts of multibiometric from the input images using Viola-Jones face detection algorithm with a modification to it then segment them. After that, various initial treatment processes begin which helps clarify them to facilitate subsequent operations. Also, a Histogram of oriented gradient method(HOG) is used to extract the significant features of those image segments. The extracted features from these segments are entered into the developed cuckoo search algorithm(DCSA), the best image segment within the used dataset is searched for similar in terms of characteristics to the entered image segment. The work has also been developed so that the system is executed on two cores using parallel processing technology to utilize the processor as much as possible and reduce the time it takes to implement the system and identify the person concerned. A high identification rate has been reached, reaching 99.25%, and overall speed up 1.40323 sec relative to serial execution.

Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (Developed Bat Algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.

<p>Face detecting and tracking in video clips is very important in many areas of<br />daily life. All institutions, public departments, streets, and large stores use<br />cameras from a security point of view, and detecting and tracking human<br />faces is necessary for indexing and preserving information concerning the<br />visual media. This paper presents a novel method for hybridizing the<br />Viola_Jones face detection algorithm to track and identify a human face in<br />video sequences. The method represents a combination of Viola Jones'<br />algorithm with a measured normalized cross-correlation (NCC) algorithm<br />with a template matching method using the Manhattan distance measure<br />equation in the video between successive sequences After that, the fuzzy<br />logic method is added in comparing the image of the face to be detected with<br />the images of templates taken in the proposed algorithm, which increased the<br />accuracy of the results, which reached 99.3%.</p>