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.

 Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image

Water scarcity is one of the most important problems facing humanity in various fields such as economics, industry, agriculture, and tourism. This may push people to use low-quality water like industrial-wastewater. The application of some chemical compounds to get rid of heavy metals such as cadmium is an environmentally harmful approach. It is well-known that heavy metals as cadmium may induce harmful problems when present in water and invade to soil, plants and food chain of a human being. In this case, man will be forced to use the low quality water in irrigation. Application of natural materials instead of chemicals to remove cadmium from polluted water is an environmental friendly approach. Attention was drawn in this research wor

Abstract:

      In this research we discussed the parameter estimation and variable selection in Tobit quantile regression model in present of multicollinearity problem. We used elastic net technique as an important technique for dealing with both multicollinearity and variable selection. Depending on the data we proposed Bayesian Tobit hierarchical model with four level prior distributions . We assumed both tuning parameter are random variable and estimated them with the other unknown parameter in the model .Simulation study was used for explain the efficiency of the proposed method and then we compared our approach with (Alhamzwi 2014 & standard QR) .The result illustrated that our approach

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

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

Welcome to International Journal of Research in Social Sciences & Humanities (IJRSSH). It is an international refereed journal of Social Sciences, Humanities & Linguistics in English published quarterly, both print and online.

Background: Strangles is a highly contagious equine respiratory disease caused by Streptococcus equi subsp. equi. It is a globally significant pathogen and one of the most common infectious agents in horses. In Iraq, no sequencing data on this pathogen are available, and only two molecular studies have been published to date. This study provides preliminary insights into strain diversity and provides a foundation for future large-scale investigations. Aim: This study aimed to investigate the molecular characteristics, identify SeM gene alleles, and perform a phylogenetic analysis of S. equi isolates from horses in Baghdad, Iraq. Methods: We analyzed 59 Streptococcus spp. isolates previously obtained from equine clinical sample

It is found that hypersensitive teeth have a larger number and wider patent tubules than those of non-sensitive teeth. Objective: The aim of this study is to compare between the effects of diode laser at different power densities, with and without sodium fluoride on the sealing of exposed dentinal tubules and dentin permeability. Materials and methods: 118 teeth were used. Samples were divided into three major groups. The first consisted of 100 teeth used for permeability test. The second consisted of 16 teeth for measuring external surface temperature elevation while irradiation. The third, in turn, consisted of one pair of teeth observed under SEM for dentine surface morphology analysis. Results: For dentin permeability measurement, th