AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets

Twitter is one of the social media platforms that is extensively used to share public opinions. Arabic text detection system (ATDS) is a challenging computational task in the field of Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The detection of misogyny in Arabic text has received a lot of attention in recent years due to the racial and verbal violence against women on social media platforms. In this paper, an Arabic text recognition approach is presented for detecting misogyny from Arabic tweets. The proposed approach is evaluated using the Arabic Levantine Twitter Dataset for Misogynistic, and it gained recognition accuracies of 90.0% and 89.0% for binary and multi-class tasks, respectively. The proposed approach seems to be useful in providing practical smart solutions for detecting Arabic misogyny on social media

A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images

The COVID-19 pandemic has been a global health problem since December 2019. To date, the total number of confirmed cases, recoveries, and deaths has exponentially increased on a daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly classify the COVID-19 disease from both chest X-ray (CXR) and CT images. Two AI-based deep learning models, namely ResNet50 and EfficientNetB0, are adopted and trained using both chest X-ray and CT images. The public datasets, consisting of 7863 and 2613 chest X-ray and CT images, are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNetB0 consistently performed a better classification result, achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively. For the hybrid AI-based model, the overall classification accuracy of 99.58% is achieved. The proposed hybrid deep learning system seems to be trustworthy and reliable for assisting health care systems, patients, and physicians

Density functional theory calculations, vibrational spectral analysis and topological analysis of 1-acethyl-2(4-isopropoxy-3-methoxyphenyl) cyclopropane with docking studies

A systematic spectroscopic investigationof 1-acethyl-2(4-isopropoxy-3-methoxyphenyl) cyclopropanewasperformed by utilizing Density functional theory approaches at B3LYP level usingGaussian 09 W software package. The FT-IR and FT-Raman techniques were utilized to assign the spectral properties of the title compound. On the basis of Natural Bond Orbital (NBO) analysis,the transfer of second order perturbation energies and ElectronDensity (ED) from filled lone pairs of Lewis base to unfilled Lewis acid sites wereanalysed. The chemical stability,distribution of energy and energetic behaviour of the compound were calculated from the Highest Occupied and Lowest Unoccupied Molecular Orbital (HOMO-LUMO) Analysis. The nucleophilic and electrophilic locales of the moleculewas perceived by the Molecular electrostatic potential (MEP). NCI investigation gives data around the inter and intra non covalent interlinkages. By using the Multiwavefunction software the topological analysis of ELF and LOL were performed. The chemical reactivity sites were determined by means of the fukui function. The assignments of vibrational spectra were computed using thevibration energy distribution analysis (VEDA). Drug similarityfactors were intended to understand the biological aspects. Vadar software was used to generate the Ramachandran plot. The bioactivity of the title compound was confirmed fromthe molecular docking studies

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells