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 asp

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative

Background: Enterococcus faecalis is emerging as an important endodontic pathogen, which can persist in the environment for extended periods after treatment and may cause endodontic failure. It is known to produce biofilms, a community of bacteria enclosed within a protective polymeric matrix. This study aimed to establish whether the biofilm formation by Enterococcus faecalis can be inhibited with steralium, co+steralium, and 5% sodium hypochlorite in the root surface environment. Materials and Methods: Extracted human teeth were biomechanically prepared, vertically sectioned, placed in the tissue culture wells exposing the root canal surface to E. faecalis to form a biofilm. At the end of the 3rd and 6th weeks, all groups were treated fo

The first molecular research on Iraqi centipede fauna is presented in this article. Between October 2022 and May 2023, during various climatic circumstances, centipedes were collected from several locations in four provinces of Iraq. Three families, represented by four genera, underwent molecular identification, and five species were found. From the order Scolopendromorpha family Scolopendridae, two species were recorded, Scolopendra morsitans Linnaeus, 1758, and S. cingulata Latreille, 1829, Cormocephalus sp.; while from the order Lithobiomorpha, family Lithobiidae, one species was recorded for first time in Iraq; Lithobius crassipes L. Koch, 1862 from the order Geophilomorpha family Himantariidae, one species Bothriogaster Signata