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

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.

The [2-hydroxy-1, 2-diphynel-ethanone oxime] was reacted with 1, 2-dichloroethan to give the new ligand [H2L]. this ligand was reacted with some metal ions (Co (II), Ni (II), Cu (II), Zn (II) and Cd (II) in methanol as a solvent to give a series of new (1: 1) complexes of the general formula [M (HL)] Cl,(where: M= Co (II), Ni (II), Cu (II), Zn (II) and Cd (II)) are isolated All compounds have been characterized by spectroscopic methods [IR, UV-Vis] atomic absorption. Chloride content along with conductivity measurements. From the above data the proposed molecular structure for (Co, Cu, Ni, Zn and Cd) complexes adopting a tetrahedral structure

The [2-hydroxy -1,2-diphynel-ethanone oxime] was reacted with 1,2- dichloroethan to give the new ligand [H2L].this ligand was reacted with some metal ions (Co(II),Ni(II),Cu(II),Zn(II) and Cd(II) in methanol as a solvent to give a series of new (1:1)complexes of the general formula [ M(HL)]Cl ,( where : M= Co(II),Ni(II),Cu(II),Zn(II) and Cd(II)) are isolated All compounds have been characterized by spectroscopic methods [ I.R , U.V -Vis ] atomic absorption . Chloride content along with conductivity measurements. From the above data the proposed molecular structure for (Co, Cu, Ni, Zn and Cd) complexes adopting a tetrahedral structure.

A new Schiff base ligand Bis-1,4-di[N-3-(2-hydroxy-1-amino)- acetophenonylidene] benzylidene [L] and its complexes with (Mn(II) ,Co(II) ,Ni(II and Cu(II)) were synthesized . The ligand was prepared in two steps. In the first step a solution of (terphthalaldehyde) in methanol reacts under reflux with (p-aminoacetophenone) to give an intermediate compound [1-[3-({4-[(3-Acetyl-phenylimino)-methyl]-benzylidene}-amino)-phenyl]- ethanone which reacts in the second step with (2-Amino-phenol) giving the mentioned ligand. The complexes were synthesized by addition the corresponding metal salt solution to the solution of the ligand in methanol under reflux in (1:1) metal to ligand ratio. On the basis of, molar conductance, I.R., UV-Vis, HPLC, chlorid

The buildup factor was measured after irradiating Iraq carbon black powder using each of and sources respectively, using mixing ratios 40% & 50% for thickness range . The results showed that the buildup factor depends on energy and has limited dependence on the mixing ratio. The QIFT program succeeded accenting for the experimental results even for expected values more than 4 m.f.p outside the thickness range.