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 resolutions. By considering features from multiple levels, the detection algorithm can better capture both global and local characteristics of the manipulated regions, enhancing the accuracy of forgery detection. To achieve a high accuracy rate, this paper presents a variety of scenarios based on a machine-learning approach. In Copy-Move detection, artifacts and their properties are used as image features and support Vector Machine (SVM) to determine whether an image is tampered with. The dataset is manipulated to train and test each classifier; the target is to learn the discriminative patterns that detect instances of copy-move forgery. Media Integration and Call Center Forgery (MICC-F2000) were utilized in this paper. Experimental evaluations demonstrate the effectiveness of the proposed methodology in detecting copy-move. The implementation phases in the proposed work have produced encouraging outcomes. In the case of the best-implemented scenario involving multiple trials, the detection stage achieved a copy-move accuracy of 97.8 %.
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
... Show MoreHerpes simplex virus (HSV) is a common human pathogen that causes severe infections in newborns and immunocompromised patients. Conjunctivitis or corneal epithelial keratitis is caused by HSV type 1 all over the world and at all times of the year. The present study was aimed at detecting HSV in patients suffering from conjunctivitis. One hundred and ten (110) clinical samples (90 patients and 20 controls, both males and females) of eye conjunctiva swabs were collected from patients of different ages. The samples were analyzed using qPCR and ELISA techniques. The qPCR results revealed that HSV was present in 47 (52.2%) of the 90 patients who were infected. Of these patients, 25 (48.0%) were males and 22 (57.8%) were females, indicati
... Show MoreCdO films were deposited on substrates from glass, Silicon and Porous silicon by thermal chemical spray pyrolysis technique with different thicknesses (130 and 438.46) nm. Measurements of X-ray diffraction of CdO thin film proved that the structure of the Polycrystalline is cubic lattice, and its crystallite size is located within nano scale range where the perfect orientation is (200). The results show that the surface’s roughness and the root mean square increased with increasing the thickness of prepared films. The UV-Visible measurements show that the CdO films with different thicknesses possess an allowed direct transition with band gap (4) eV. AFM measurement revealed that the silicon porosity located in nano range. Cadmium oxide f
... Show MoreCalendula officinalis L. (Asteraceae) known as marigold is known to have several pharmacological activities and used for the treatment of several diseases as measles, jaundice, constipation and several inflammations. Marigold flowers contain several chemical constituents mainly flavonoids, triterpenoids and essential oil. In this study marigold flowers cultivated in Iraq had been investigated for its flavonoids content. The study revealed the presence of quercetin and kaempferol glycosides and the absence of myricetin glycosides. The flowers were extracted with ethanol 70% fractionated with different solvent and the flavonoids were isolated by preparative HPLC. The isolated flavonoids were identified by measuring melting points, UV, IR,
... Show MoreLymphoma is a cancer arising from B or T lymphocytes that are central immune system components. It is one of the three most common cancers encountered in the canine; lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic conditions and to improve decision-making around treating and what treatment type to use. This study aimed to evaluate a potential novel biomarker related to iron metabolism,
... Show MoreOne 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 th
... Show MoreDetection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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