New Schiff base [3-(3-acetylthioureido)pyrazine-2-carboxylic acid][L] has been prepared through 2 stages, the chloro acetyl chloride has been reacting with the ammonium thiocyanate in the initial phase for producing precursor [A], after that [A] has been reacting with the 3-amino pyrazine-2-carboxilic acid to provide a novel bidentate ligand [L], such ligand [L] has been reacting with certain metal ions in the Mn(II), VO(II), Ni(II), Co(II), Zn(II), Cu(II), Hg(II), and Cd(II) for providing series of new metal complexes regarding general molecular formula [M(L)2XY], in which; VO(II); X=SO4,Y=0, Co(II), Mn(II), Cu(II), Ni(II), Cd(II), Zn(II), and Hg(II); Y=Cl, X=Cl. Also, all the compounds were characterized through spectroscopic techniques [

Schiff base (methyl 6-(2- (4-hydroxyphenyl) -2- (1-phenyl ethyl ideneamino) acetamido) -3, 3-dimethyl-7-oxo-4-thia-1-azabicyclo[3.2.0] heptane-2-carboxylate)Co(II), Ni(II), Cu (II), Zn (II), and Hg(II)] ions were employed to make certain complexes. Metal analysis M percent, elemental chemical analysis (C.H.N.S), and other standard physico-chemical methods were used. Magnetic susceptibility, conductometric measurements, FT-IR and UV-visible Spectra were used to identified. Theoretical treatment of the generated complexes in the gas phase was performed using the (hyperchem-8.07) program for molecular mechanics and semi-empirical computations. The (PM3) approach was used to determine the heat of formation (ΔH˚f), binding energy (ΔEb), an

Computational Thinking (CT) is very useful in the process of solving everyday problems for undergraduates. In terms of content, computational thinking involves solving problems, studying data patterns, deconstructing problems using algorithms and procedures, doing simulations, computer modeling, and reasoning about abstract things. However, there is a lack of studies dealing with it and its skills that can be developed and utilized in the field of information and technology used in learning and teaching. The descriptive research method was used, and a test research tool was prepared to measure the level of (CT) consisting of (24) items of the type of multiple-choice to measure the level of "CT". The research study group consists of

    Smishing is the delivery of phishing content to mobile users via a short message service (SMS). SMS allows cybercriminals to reach out to mobile end users in a new way, attempting to deliver phishing messages, mobile malware, and online scams that appear to be from a trusted brand. This paper proposes a new method for detecting smishing by combining two detection methods. The first method is uniform resource locators (URL) analysis, which employs a novel combination of the Google engine and VirusTotal. The second method involves examining SMS content to extract efficient features and classify messages as ham or smishing based on keywords contained within them using four well-known classifiers: support vector machine (SVM), random

     Intelligent systems can be used to build systems that simulate human behavior. One such system is lip reading. Hence, lip reading is considered one of the hardest problems in image analysis, and thus machine learning is used to solve this problem, which achieves remarkable results, especially when using a deep neural network, in which it dives deeply into the texture of any input. Microlearning is the new trend in E-learning. It is based on small pieces of information to make the learning process easier and more productive. In this paper, a proposed system for multi-layer lip reading is presented. The proposed system is based on micro content (letters) to achieve the lip reading process using deep learning and auto-correction mo

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness o

     Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method. 

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This

This research introduces a proposed hybrid Spam Filtering System (SFS) which consists of Ant Colony System (ACS), information gain (IG) and Naïve Bayesian (NB). The aim of the proposed hybrid spam filtering is to classify the e-mails with high accuracy. The hybrid spam filtering consists of three consequence stages. In the first stage, the information gain (IG) for each attributes (i.e. weight for each feature) is computed. Then, the Ant Colony System algorithm selects the best features that the most intrinsic correlated attributes in classification. Finally, the third stage is dedicated to classify the e-mail using Naïve Bayesian (NB) algorithm. The experiment is conducted on spambase dataset. The result shows that the accuracy of NB