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.

Objective: This study goal was to screen participants from different settings in Baghdad for depression using Beck Depression Inventory (BDI) scale and identify factors influencing the levels of depression. Methods: This cross-sectional study included a convenience sample of 313 people from four settings (teaching hospital, college of medicine, college of pharmacy, and high school) in Baghdad, Iraq. The participants were screened using paper survey relying on the BDI scale during spring 2018. Using multiple linear regression analysis, we measured the association between depression scores and six participant factors. Results: The overall prevalence of depression in our sample was 57.2%. Female participants had higher BDI

Basrah is the richest town and the economic capital of Iraq. It suffers from lack of drinking water. This project is a dream to supply drinking water to Basrah citizens within WHO standards. Water should pass sedimentation and filtration stages before interring reverse osmosis unit. The design is carried out using lewaplus2 software. Several parameters should be selected in the design step membrane type, number of stages, number per element in each stage, and the recovery percentage. An optimization is carried out using Minitab ver. 18 for the acceptable limit of TDS and minimum cost and it was found that the optimum conditions were 52% for first stage, the numbers of vessels are 20 for both the first and second stage. In addition,

The Bartholin gland cyst is a condition that occurs in approximately 2% of women, most of whom are of reproductive age. Although benign pathology, it is associated with significant patient discomfort. This disorder is caused by the obstruction and consequent dilation of the cyst duct. Definitive treatment involves the surgical removal of the entire cyst. Other alternative treatments include Marsupialization, Word catheter, and the use of CO2 laser. CO2 laser can be used either to vaporize or to excise the Bartholin gland cyst. The Objectives: The purpose of the study was to evaluate the efficacy and safety of (10600nm) CO2 laser in the treatment of Bartholin gland cyst. Patients, Materials & Methods: This study was done in laser medi

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

A successful synthesis of SBA-15 with semi-spherical morphology and suitable texture properties was achieved using hydrothermal technique. Sodium silicate, derived from silica sand from Iraq, was used as a silica precursor, and Pluronic p123 triblock copolymer was used as a template with a strong acid media (pH < 1). The effect of adding (1–3) g of either polyvinyl alcohol (PVA), Polyvinyl pyrrolidone (PVP -k30), or Sodium dodecyl sulfate (SDS) dispersant agents on the texture properties of the synthesized SBA-15 was studied. The SBA-15 sample underwent various characterization techniques, including X-ray diffraction (XRD), Fourier transform infrared (FTIR), atomic force microscopy (AFM), nitrogen adsorption-desorption using Br