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Density Functional Theory (DFT) plays a vital role in the study of organic materials. Lawsonia inermis is identified among the promising organic candidates to replace toxic and expensive solar cell material. The present study reported step by step computational procedure used to exposed the beauty of the organic solar cell candidate using Gaussian09 software. Stability check, optimization at different basis set, Homo-Lumo, band gap and Infrared spectrum (IR) at different medium were reported. It was found that the ground state energy, Homo-Lumo at both vacuum and Medium, band gaps, were found to be -16607.3717574 eV, -7.431 eV -3.584 eV (Vacuum) -7.311 eV -3.515 eV (Medium) 3.847 eV (Vacuum) 3.79 eV (Medium) respectively. For the IR it was found that there is shift and increase in the intensity due to the solvent effect on the material. These results were compared with the literature and are in agreement. It can be concluded that the Lawsonia inermis at solvent medium can enhance smooth and easier electron hole transport by looking at the band gap and hence it can and will be a good candidate organic material for solar cell production.

With the Corona Virus Disease 2019 (COVID-19) global pandemic ravaging the world, all sectors of life were affected including education. This led to many schools taking distance learning through the use of computer as a safer option. Facial emotion means a lot to teacher’s assessment of his performance and relation to his students. Researchers has been working on improving the face monitoring and human machine interface. In this paper we presented different types of face recognition methods which include: Principal component analysis (PCA); Speeded Up Robust Features (SURF); Local binary pattern (LBP); Gray-Level Co-occurrence Matrix (GLCM) and also the group sparse coding (GSC) and come up with the fusion of LBP, PCA, SURF GLCM with GSC. Linear Kernel Support Vector Machine (LSVM) Classifier out-performed Polynomial, RBF and Sigmoid kernels SVM in the emotion classification. Results obtained from experiments indicated that, the new fusion method is capable of differentiating different types of face emotions with higher accuracy compare with the state-of-the-art methods currently available.Keywords— Face Recognition, Principal Component Analysis, Speeded Up Robust Features, Grey Level Co-Occurrence Matrix

Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resulting from deep learning denotes a promising avenue for assisting in the diagnosis of breast cancer. In this paper, an android software for breast cancer classification using deep learning approach based on a Convolutional Neural Network (CNN) was developed. The software aims to classify the breast tumors to benign or malignant. Experimental results on histopathological images using the BreakHis dataset shows that the DenseNet CNN model achieved high processing performances with 96% of accuracy in the breast cancer classification task when compared with state-of-the-art modelsKeywords— Breast cancer classification, Convolutional Neural Network (CNN), deep learning, DenseNet, histopathological images

A Schiff base of 3-[(2-Hydroxy-phenylimino)-methyl]-6-methoxy -quinolin-2- ol was synthesized by a 1:1 molar condensation of 2-Hydroxy-6-methoxy-3-quinolinecarboxaldehyde and 2-aminophenol. The metal (II) complexes were synthesized by refluxing the ethanolic solutions of the Schiff base and the chloride salts of the metals. Melting point, decomposition temperature, solubility, elemental analysis, fourier transform infrared spectroscopy, magnetic susceptibility and molar conductivity measurements were used to characterized the Schiff base and its metal complexes. The Schiff base is yellow and it has a melting point of 251 OC. The decomposition temperature of the Cd (II) and Cu (II) complexes were 282 and 270 OC respectively. The elemental analysis of the complexes established the formation of 1:1 metal - ligand ratio. The non-electrolytic natures of the complexes were revealed by the molar conductivity values. The behavior of the Schiff base and its coordination with the metal ions was suggested by the infrared spectral data via the azomethine nitrogen and hydroxyl oxygen after deprotonation. The solubility test of the Schiff base and its metal complexes were carried out by using different solvents. The antibacterial and antifungal activity were performed and discussed.