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.
An environmentally friendly technique was used to prepare titanium dioxide@ silver (core shell) (TiO₂@Ag NPs) using chard leaf extract, a natural stabilizer and reductant. A nanocomposite (NCs) of TiO₂@Ag supported by halloysite nanotubes (HNTs), TiO2@Ag/HNT NCs, was prepared under microwave irradiation. The microwave technique is used to accelerate the reaction and enhance the homogeneity of nanoparticle distribution. Spectroscopic and structural analyses were performed on the resulting nanocomposite. X-ray diffraction (XRD) revealed a clear crystalline structure with grain sizes ranging from 7 to 15 nm, with an average of ~11 nm, the transmission electron microscope (TEM) revealed that the size of nanoparticles in the TiO₂@Ag/HNT N
... Show MoreFurfural is a toxic aromatic aldehyde that can cause a severe environmental problem especially the wastewater drown from petroleum refinery units. In the present work, a useless by-product from local furniture manufacturing industry; sawdust was used as raw material for the preparation of activated carbon which is chemically activated with phosphoric acid. The effect of adsorption variables which include initial pH of solution (2-9), agitation speed (50-250) rpm, agitation time (15-120) min, initial concentration of furfural (50-250) ppm, and amount of adsorbent material (0.5-2.5) g for the three adsorbents used (prepared activated carbon, commercial activated carbon and raw sawdust) were investigated in a batch process
... Show MoreIn this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo
... Show MoreThe twelve samples of agricultural soils from four regions in Al-Najaf governorate with sampling plant with soil. Physical properties of the soil where studied, such as electrical conductivity ranged from (136.33-1070.00)μS/cm-3, and moisture which ranged between the values (0.39-36.48)%. The chemical analysis of the soil have included the proportion of calcium carbonate the ratio between (44.00-48.00%) has been observed increasing amounts of calcium carbonate in surface models. The pH where results indicate that pH values were close to study models ranged between (6.88-7.42) these values generally within the normal range for the measured pH values of the Iraqi soil. The amount of gypsum ranged betwe
... Show MoreDiabetic mellitus is one of the main risk factors of fungal infections because poor glycemic control is associated with a high level of glucose in blood and saliva which could be treated as nutrient to fungi. This study aimed to isolate and identification of pathogenic fungi from diabetic patient. 140 samples were taken from different places of human body from the national center of diabetic patients that related to Mustansiriyah University / college of medicine and Al-yarmuk Hospital in Baghdad. 84 sample (60%) tested positive to fungi and 56 sample (40%) tested negative to fungi. The most frequented fungi isolated have been chosen for molecular identification by PCR (Millerozyma farinosa and Candida orthopsilosis) using specific pri
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