The aim of the present study was to evaluate the variations in the phytochemical compositions and the antimicrobial activities of Syzygium grande and Oenanthe javanica leaf extracts against Bacillus sp. The antibacterial activity of the methanol, 70% methanol, ethyl acetate, and hexane extracts of both plant species were examined in vitro, against various Bacillus species. In addition, the 1H Nuclear Magnetic Resonance (1H NMR) spectroscopy-based metabolomics was employed to gain insights into the correlations between the chemical constituents of the bioactive extract to the biological activity. The hexane extract of S. grande leaves and methanolic extract of O. javanica leaves showed more potent anti Bacillus activity, among the test extracts. Principal component analysis (PCA) successfully differentiated the extracts on the basis of their metabolite profiles. The results of the Partial Least Square (PLS) analysis showed that the non-polar compounds of the hexane extract of S. grande (alanine, betulin, β-sitosterol, β-caryophyllene, acetic acid and 3-hydroxybutyric acid) were strongly correlated to its anti-Bacillus activity. On the other hand, choline, ellagic acid, and gallic acid, the metabolites present in the methanolic extract of O. javanica, were strongly correlated to its anti-Bacillus activity. On this basis, it was therefore concluded that these conmpounds could be the potential bioactive constituents, contributing to the anti-Bacillus activity of the individual plant species. Further in-depth investigations into the potential utilization of the two plants for useful applications in managing and control of Bacillus sp., will help pave the way towards their valorization.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Counting the number of people in many estimation systems, such as still images or video frames, is a buoyant research area that is challenging in the field of computer vision. It plays a considerable role in a variety of applications, such as security, management, education, and commerce. The purpose of this paper is to suggest a system to estimate the number of people in digital still images based on the Face Detection method. This system composed of two parts: face detection and counting of detected faces. In the detection step, the Skin Color Face Detection method was applied on the input of a digital still image. In the counting part, the obtained detected faces by the Skin Color Face Detection method have counted to estimate the number of people in an input color image with simple software and simple low-cost hardware. The skin color face detection algorithm was tested using 133 images from the People Image Groups dataset, which contains about 2573 color images of people, to test the proposed system. based on the obtained results, the best precision achieved of the proposed Skin Color face detection algorithm was 85%