The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimizing deep learning models to operate efficiently on mobile devices, (2) ensuring real-time inference without compromising accuracy, (3) maintaining user privacy when processing sensitive facial data, and (4) addressing the variability in mobile phone cameras, input resolution, and platform limitations across Android and iOS. Furthermore, the increasing sophistication of identity spoofing attacks—such as 3D masks and AI-generated faces—demands more sophisticated, robust, and multimodal detection technologies. The research findings provide a clear roadmap toward practical solutions. By evaluating the latest deep learning architectures, datasets, and anti-spoofing metrics, the study proposes a comprehensive React Native deployment path using TensorFlow Lite and TensorFlow.js to ensure cross-platform compatibility. The proposed system offers a unified classification of identity spoofing attacks and defense mechanisms, along with a structured evaluation framework that compares on-device processing with server-side detection. The results demonstrate that optimized models can achieve high accuracy, low false accept/rejection rates, and sub-second processing speeds on mobile devices. Ultimately, the study provides practical design guidelines for building robust, privacy-preserving, efficient, and real-world consumer-grade fake face detection systems.
Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai
... Show MoreText categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th
... Show MoreComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes
... Show MoreRetinopathy of prematurity (ROP) can cause blindness in premature neonates. It is diagnosed when new blood vessels form abnormally in the retina. However, people at high risk of ROP might benefit significantly from early detection and treatment. Therefore, early diagnosis of ROP is vital in averting visual impairment. However, due to a lack of medical experience in detecting this condition, many people refuse treatment; this is especially troublesome given the rising cases of ROP. To deal with this problem, we trained three transfer learning models (VGG-19, ResNet-50, and EfficientNetB5) and a convolutional neural network (CNN) to identify the zones of ROP in preterm newborns. The dataset to train th
The current research aimed to identify the tasks performed by the internal auditors when developing a business continuity plan to face the COVID-19 crisis. It also aims to identify the recovery and resuming plan to the business environment. The research followed the descriptive survey to find out the views of 34 internal auditors at various functional levels in the Kingdom of Saudi Arabia. Spreadsheets (Excel) were used to analyze the data collected by a questionnaire which composed of 43 statements, covering the tasks that the internal auditors can perform to face the COVID-19 crisis. Results revealed that the tasks performed by the internal auditors when developing a business continuity plan to face the COVID-19 crisis is to en
... Show MoreBackground: Liver metastasis significantly complicates cancer prognosis, yet easily accessible markers for its early detection and monitoring remain crucial. This study aimed to comprehensively evaluate key hematological parameters as potential indicators for liver metastasis in Iraqi patients. Methods: We conducted a cross-sectional study comparing hematological profiles between 90 patients (presumably with liver metastasis) and 30 healthy controls. White Blood Cell (WBC) count, Lymphocyte percentage, Neutrophil percentage, and Neutrophil-to-Lymphocyte Ratio (NLR) were analyzed. Given non-normal data distributions (confirmed by the Shapiro-Wilk test), group comparisons were performed using the non-parametric Mann-Whitney U test.
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