Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Computer-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

The physician's commitment to medical insight is affected by several factors that vary from patient to patient in terms of the nature of the disease, the severity of the disease, the age of the patient, and the purpose of undergoing medical intervention. There are circumstances surrounding patients that require the physician to reduce the insight towards them, by concealing medical information. The physician must firmly commit to expanding the scope of his vision to a wider extent than in normal medical work. Therefore, we will discuss in this regard the cases in which medical explanation is reduced and the cases that require confirmation in the following order.

Cilnidipine is a dihydropyridine calcium channel blocker used to improve the neurological outcome following subarachnoid hemorrhage. It belongs to BCS class II drugs that have a low oral bioavailability of 13%, thus preparation as nanoparticles would be expected to improve bioavailability. The aim of the study is to prepare Cilnidipine as nanoparticles using different carriers and co-carriers, concentrations, and types. Cilnidipine nanoparticles were prepared by a solvent anti-solvent method using different carriers (Soluplus®, Poloxamer 188, PVA cold) with co-stabilizers (PEG200, glycerol) at different ratios. Based on the obtained results, formula N4, which included Soloplus in a 5:5:1.19 weight ratio of drug to

That less duration takes larva mature into a virgin under field conditions is one day during Alguetrh from April to October and last longer to more than a day during Alguetrh from November to February and up to nine days in January when low minimum temperature to zero degreespercentage that these larvae can not be a high percentage in the field ranging from 90-100% reduced Hessian Alnsphaly 85% during the months of July and Cape