Deepfake is a type of artificial intelligence used to create convincing images, audio, and video hoaxes and it concerns celebrities and everyone because they are easy to manufacture. Deepfake are hard to recognize by people and current approaches, especially high-quality ones. As a defense against Deepfake techniques, various methods to detect Deepfake in images have been suggested. Most of them had limitations, like only working with one face in an image. The face has to be facing forward, with both eyes and the mouth open, depending on what part of the face they worked on. Other than that, a few focus on the impact of pre-processing steps on the detection accuracy of the models. This paper introduces a framework design focused on this aspect of the Deepfake detection task and proposes pre-processing steps to improve accuracy and close the gap between training and validation results with simple operations. Additionally, it differed from others by dealing with the positions of the face in various directions within the image, distinguishing the concerned face in an image containing multiple faces, and segmentation the face using facial landmarks points. All these were done using face detection, face box attributes, facial landmarks, and key points from the MediaPipe tool with the pre-trained model (DenseNet121). Lastly, the proposed model was evaluated using Deepfake Detection Challenge datasets, and after training for a few epochs, it achieved an accuracy of 97% in detecting the Deepfake
This study aimed to investigate the prevalence of intestinal helminth infections in humans and detect Toxocara spp. in cats, with a focus on assessing the impact of age and gender on infection rates. Traditional diagnostic methods have historically limited the accurate identification of helminth infections in humans. Analysis of 450 human stool samples revealed an overall helminth infection rate of 5.7% using conventional techniques. The specific infection rates were 0.4% for Strongyloides stercoralis, 0.6% for Schistosoma mansoni, 1.7% for Hymenolepis nana, and 2.8% for Ascaris lumbricoides. Notably, no infections were recorded in the 30–39 and ≥40-year age groups, while the highest infection rate (16.3%, P≤0.01) was observed in indi
... Show MoreProstate cancer is the commonest male cancer and the second leading cause of cancer-related death in men. Over many decades, prostate cancer detection represented a continuous challenge to urologists. Although all urologists and pathologists agree that tissue diagnosis is essential especially before commencing active surgical or radiation treatment, the best way to obtain the biopsy was always the big hurdle. The heterogenicity of the tumor pathology is very well seen in its radiological appearance. Ultrasound has been proven to be of limited sensitivity and specificity in detecting prostate cancer. However, it was the only available targeting technique for years and was used to guide biopsy needle passed transrectally or transperineally
... Show MoreThe Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats. This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat
... Show MoreThe research seeks to identify the impact of fraud detection skills in the settlement of compensatory claims for the fire and accident insurance portfolio and the reflection of these skills in preventing and reducing the payment of undue compensation to some who seek profit and enrichment at the expense of the insurance contract. And compensatory claims in the portfolio of fire and accident insurance in the two research companies, which show the effect and positive return of the detection skills and settlement of the compensation on the amount of actual compensation against the claims inflated by some of the insured, The research sample consisted of (70) respondents from a community size (85) individuals between the director and assistan
... Show MoreDetecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.
BACKGROUND: HLA-B27 can effect clinical presentation and course of ankylosing spondylitis. Different detection techniques of HLA-B27 are available with variable sensitivities and specificities. OBJECTIVE: To compare serologic and molecular diagnostic techniques of detecting HLA-B27 status and to correlate it with some clinical variables among ankylosing spondylitis patients. PATIENTS AND METHODS: A cross-sectional study was conducted on 83 Iraqi patients with ankylosing spondylitis. Clinical and laboratory evaluations were reported. HLA-B27 status was determined in all patients by real-time PCR using HLA-B27 RealFast™ kit; ELISA method was used as well to detect soluble serum HLA-B27 antigens using Human Leukocyte Antigen® kit. RESULTS:
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show MoreBackground: Hyperlipidemia is an elevated fat (lipids), mostly cholesterol and triglycerides, in the blood. These lipids usually bind to proteins to remain circulated so-called lipoprotein. Aims of the study: To determine taste detection threshold and estimate the trace elements (zinc) in serum and saliva of those patients and compare all of these with healthy control subjects. Methods: Eighty subjects were incorporated in this study, thy were divided into two groups: forty patients on simvastatin treatment age between (35-60) years, and forty healthy control of age range between (35-60) years. Saliva was collected by non-stimulated technique within 10 minutes. Serum was obtained from each subject. Zinc was estimated in serum and saliva
... Show MoreThis research focuses on detecting the financial corruption cases in Iraq in light of adoption the strategic audit, the paper deals with the problem of the proliferation corruption cases particularly financial in Iraq and dramatically in the presence of audit and control devices as well as inspection and integrity devices, which indicates the existence of deficiencies and weaknesses in those devices in the implementation of audit and control functions in order to detect the corruption cases in the economic units in Iraq.
Stems objective of this research through the provision of approach of strategic audit concepts and indicate the extent importance of adopting of strategic audit as a means to detect the f
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