One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

Autoría: Nuha Mohsin Dhahi. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 5, 2022. Artículo de Revista en Dialnet.

    This article reveals the first record of the parasitoid wasp, Monodontomerus obscurus Westwood (Hymenoptera, Torymidae) from Iraq.  A total of 27 specimens were emerged from mud nests of sphecoid wasp of Sceliphron sp. (Hymenoptera, Sphecidae), that collected from a wall at a residential garden in Dohuk province.  A short morphological description is presented.

This study was contacted on Orchidaceae, it is a second largest family in flowering plants, comes
after Asteraceae, and approximately has 25,000–30,000 species in 750–800 genera in the world.
This study included 10 genera, Anacamptis (five species), Androrchis (two species),
Cephalanthera (one species), Dactylorhiza (three species), Epipactis (two species),
Himantoglossum (two species), Limodorum (one species), Neotinea (one species), Ophrys (seven
species ), and Orchis (four species). The name abbreviation of authors of plant names follows the
International Plant Name Index (IPNI) (https://www.ipni.org/). Images of type collections of most
species were checked on virtual herbaria (https://he