Regression models are one of the most important models used in modern studies, especially research and health studies because of the important results they achieve. Two regression models were used: Poisson Regression Model and Conway-Max Well-  Poisson), where this study aimed to make a comparison between the two models and choose the best one between them using the simulation method and at different sample sizes (n = 25,50,100) and with repetitions (r = 1000). The Matlab program was adopted.) to conduct a simulation experiment, where the results showed the superiority of the Poisson model through the mean square error criterion (MSE) and also through the Akaiki criterion (AIC) for the same distribution.

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This study aims to conduct an exhaustive comparison between the performance of human translators and artificial intelligence-powered machine translation systems, specifically examining the top three systems: Spider-AI, Metacate, and DeepL. A variety of texts from distinct categories were evaluated to gain a profound understanding of the qualitative differences, as well as the strengths and weaknesses, between human and machine translations. The results demonstrated that human translation significantly outperforms machine translation, with larger gaps in literary texts and texts characterized by high linguistic complexity. However, the performance of machine translation systems, particularly DeepL, has improved and in some contexts

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

Condensation of 1,2- dibromo ethane with para hydroxy benzoic acid gave 1,2-Ethane-bis- 4-oxybenzoic [1]. This Compound was converted with the thionyl chloride to give 1,2-Ethane-bis- 4-oxybenzoyl chloride [2]. Reaction of compound [2] with thiosemicarbizades gave 1,2-Ethanebis[4-oxybenzoyl-thiosemicarbazide] [3] and opteined 1,2-Ethane-bis[3-mercapto-5-phenoxy- 1,2,4-triazole] [4] from treatment compound [3] with NaOH (4%) .The new compounds 1,2- Ethane-bis[3-(substituted thioacyl)-4-(substituted acyl)-5 phenoxy-1,2,4-triazole] [5a-d] and 1,2- Ethane-bis[3-(substituted alkylthio)-5 phenoxy-1,2,4-trizole] [5e-f] derived from compound [4] were synthesized and characterized by physical and spectral data. All the compounds [4], [5a-d] and [5e-

4-amino-3-(4-(((4-hydroxy-3,5dimethoxybenzyl)oxy)methyl)phenyl)-1,2,4-triazole-5-thione was synthesized by to method the first one from melt reaction of 4-(((4-hydroxy-3,5-dimethoxybenzyl)oxy)methyl)benzoic acid with Thiocarbonyldihydrazide, the second method from convert the corresponded acid hydrazide to potassium 2-(4-(((4-hydroxy-3,5-dimethoxybenzyl)oxy)methyl)benzoyl)hydrazinecarbodithioate salt then react with hydrazine hydrate. Newly Schiff base (7a-7f) were synthesized from reaction the 4-amino-1,2,4-triazol with substituted hydroxybenzaldehyde. The resulting compounds were characterized by IR, 1H-NMR, 13C-NMR, and HRMS data. 2,2-Diphenyl-1-picrylhydrazide (DPPH) and ferric reducing antioxidant power (FRAP) assays were used to scree

Newly series of 6,6’-((2-(Aryl)dihydropyrimidine-1,3(2H,4H)-diyl)bis(methylene))bis(2-methoxy phenol) (3a-i) were synthesized from cyclization of 6,6’-((propane-1,3-diylbis (azanediyl)) bis(methylene)) bis(2-methoxyphenol) with several aryl aldehyde in the presence of acetic acid. The newly compounds characterized from their IR, NMR and EIMs spectra. The antioxidant capacity of these compounds screened by utilizing DPPH and FRAP assays. Compounds 3g and 3i exhibited significant antioxidant capability in both assays. Docking study for these compounds as a potential inhibitors of gyrase enzyme were carried out. Compound 3g exhibited significant inhibition with binding free energies (DG) higher than novobiocin. compounds 2, 3a, 3b, 3

This work includs synthesis of several Schiff bases by condensation of 6- methoxy – 2- amino benzothiazole with some aldehydes and ketones (2- hydroxyl benzaldehyde, 4- hydroxyl benzaldehyde, 4- N,N –dimethy amino acetophenone, benzophenone) to abtain schiff bases (1-5). These schiff bases were found to react with phthalate anhydride to give oxazepine derivatives (6-10) that were reacted with primary aromatic amines to give Diazepine derivatives (11-15). Besides, we prepared new tetrazole derivatives (16-20) from the reaction of the prepared Schiff bases with sodium azide in the prepared compounds that were characterized by physical properties, FT-IR and some of the 1H-NMR and 13C –NMR spectroscopy.