Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima

Automatic document summarization technology is evolving and may offer a solution to the problem of information overload. Multi-document summarization is an optimization problem demanding optimizing more than one objective function concurrently. The proposed work considers a balance of two significant objectives: content coverage and diversity while generating a summary from a collection of text documents. Despite the large efforts introduced from several researchers for designing and evaluating performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary. The design of gener

To determine the important pathogenic role of celiac disease in triggering several autoimmune disease, thirty patients with Multiple Sclerosis of ages (22-55) years have been investigated and compared with 25 healthy individuals. All the studied groups were carried out to measure anti-tissue transglutaminase antibodies IgA IgG by ELISA test, anti-reticulin antibodies IgA and IgG, and anti-endomysial antibodies IgA and IgG by IFAT. There was a significant elevation in the concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to control groups (P≤0.05), there was 4(13.33%) positive results for anti-reticulin antibodies IgA and IgG , 3(10%) positive results for anti-endomysial antibodies IgA and IgG . There were 4 pos

To determine the important pathogenic role of celiac disease in triggering several
autoimmune disease, thirty patients with Multiple Sclerosis of ages (22-55) years
have been investigated and compared with 25 healthy individuals. All the studied
groups were carried out to measure anti-tissue transglutaminase antibodies IgA IgG
by ELISA test, anti-reticulin antibodies IgA and IgG, and anti-endomysial
antibodies IgA and IgG by IFAT. There was a significant elevation in the
concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to
control groups (P≤0.05), there was 4(13.33%) positive results for anti-reticulin
antibodies IgA and IgG , 3(10%) positive results for anti-endomysial antibodies

   A simple and smart algorithm was presented to recognize car plates in parking at the College of Science for Women, University of Baghdad, Iraq. The study consists of recording video clips of all cars parked in the selected area. The studied camera heights were1m and 2m, and the video clips were 19 and 30. Images were extracted from the video clip to be used for training data for the cascade method. Haar classification was used to detect license plates after the training step. Viola-jones algorithm was applied to the output of Haar’s data for both camera heights (1m and 2m). The accuracy was calculated for all data with different weather conditions and local time recoding. The accuracy is 100% for all data in this study.

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     Widespread COVID-19 infections have sparked global attempts to contain the virus and eradicate it. Most researchers utilize machine learning (ML) algorithms to predict this virus. However, researchers face challenges, such as selecting the appropriate parameters and the best algorithm to achieve an accurate prediction. Therefore, an expert data scientist is needed. To overcome the need for data scientists and because some researchers have limited professionalism in data analysis, this study concerns developing a COVID-19 detection system using automated ML (AutoML) tools to detect infected patients. A blood test dataset that has 111 variables and 5644 cases was used. The model is built with three experiments using Python's Auto-

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa