Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Recently, there has been an increase in the prevalence of ulcerative colitis (UC), and inflammatory bowel diseases (IBD) worldwide, especially in certain recently industrialized countries like China and In¬dia. Globally, the prevalence of UC, a chronic illness that affects the large intestine, is rising. Fifty adherent invasive Escherichia coli (AIEC) isolates were identified from ulcerative colitis biopsy samples originating from the Gastrointestinal tract (GIT) and Hepatology teaching hospitals/medical city in Baghdad City. The test’s results demonstrated that the AIEC isolates had a high level of resistance to the majority of the an-tibiotics under investigation. Enterobacterial Repetitive Intergenic Consensus (ERIC-PCR) and m

The elbow joint is one of the important and mobile joints in a way that allows it to perform its functions. The injury occurs when the joint tendon and arm muscles are subjected to repeated partial ruptures as a result of excessive and repetitive work, as well as the patient not being subjected to correct rehabilitation programs, and only rest. From here, the researchers decided to study this problem by preparing rehabilitation exercises accompanying ultrasound and assessing their impact on the rehabilitation of the elbow joint. The sample included male patients aged 30-40 years, and the tests were determined, which included testing the range of motion of the elbow joint from the flexion position and the rotation outward position, the mu

Background: Type 2 diabetes mellitusand chronic periodontitis hold a close relationship that has been the focus of many researches. Currently there is an appreciation to the role of adipose tissue-derived substances "the adipokines" in immune-inflammatory responses; also, there is an interest in using the simple non-invasive saliva in diagnosing and linking oral and general health problems. The current study aims to determine the periodontal health status in the chronic periodontitis patients with and without poorly or well controlled type 2 diabetes mellitus, measure the salivary levels of two adipokines "leptin and resistin", pH and flow rate and then correlate between these clinical periodontal, biochemical and physical parameters in eac

RA is a complex chronic disease, primarily affects the lining of the synovial joints and can cause progressive disability, premature death, and socioeconomic burdens. The clinical manifestations of symmetrical joint involvement include arthralgia, swelling, redness, and even limiting the range of motion. The pathogenesis is not known therefore the study included Rheumatoid arthritis (RA) is one of systemic inflammatory diseases that characterized by a progressive disabling course. The study included patients suffering from RA and as healthy controls. immunological and genetic factor were evaluated in each subject by using serum level of cytokine and genetic factor. The etiopathology of the disease is not well known for this reason the study