Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

Scams remain among top cybercrime incidents happening around the world. Individuals with high susceptibility to persuasion are considered as risk-takers and prone to be scam victims. Unfortunately, limited number of research is done to investigate the relationship between appeal techniques and individuals' personality thus hindering a proper and effective campaigns that could help to raise awareness against scam. In this study, the impact of fear and rational appeal were examined as well as to identify suitable approach for individuals with high susceptibility to persuasion. To evaluate the approach, pretest and posttest surveys with 3 separate controlled laboratory experiments were conducted. This study found that rational appeal treatm

While many educators are highly focused on state test, it is important to consider that
over the course of a year, instructors can build in many opportunities to assess how learners
are learning. Therefore, assessment techniques are considered a good method to get benefit
for both instructors and learners in the process of teaching and learning. The sample consists
of 27 learners who participated in TOEFL training course in the Development and Continuous
Education Centre. Validity and reliability were verified.
To fulfill the aims and verify the hypothesis which reads as follows” It is hypothesized
that the TOEFL learners' scores will not be increased after TOEFL course training.” T-test
for two dependent samp

Background: Healing of a tooth extraction socket is a complex process involving tissue repair and regeneration. It involves chemotaxis of appropriate cells into the wound, Transformation of undifferentiated mesenchymal cells to osteoprogenitor cells, proliferation and differentiation of committed bone forming cells, extracellular matrix synthesis, mineralization of osteoid, maturation and remodeling of bone. These cellular events are precisely controlled and regulated by specific signaling molecules. Some of these like transforming growth factor beta (TGF-?), vascular endothelial growth factor (VEGF), bone morphogenetic proteins (BMP) and insulin like growth factors (IGF) are well conserved proteins involved in the initial response to injur

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

Because of the experience of the mixture problem of high correlation and the existence of linear MultiCollinearity between the explanatory variables, because of the constraint of the unit and the interactions between them in the model, which increases the existence of links between the explanatory variables and this is illustrated by the variance inflation vector (VIF), L-Pseudo component to reduce the bond between the components of the mixture.

    To estimate the parameters of the mixture model, we used in our research the use of methods that increase bias and reduce variance, such as the Ridge Regression Method and the Least Absolute Shrinkage and Selection Operator (LASSO) method a

Data centric techniques, like data aggregation via modified algorithm based on fuzzy clustering algorithm with voronoi diagram which is called modified Voronoi Fuzzy Clustering Algorithm (VFCA) is presented in this paper. In the modified algorithm, the sensed area divided into number of voronoi cells by applying voronoi diagram, these cells are clustered by a fuzzy C-means method (FCM) to reduce the transmission distance. Then an appropriate cluster head (CH) for each cluster is elected. Three parameters are used for this election process, the energy, distance between CH and its neighbor sensors and packet loss values. Furthermore, data aggregation is employed in each CH to reduce the amount of data transmission which le

The interplay of predation, competition between species and harvesting is one of the most critical aspects of the environment. This paper involves exploring the dynamics of four species' interactions. The system includes two competitive prey and two predators; the first prey is preyed on by the first predator, with the former representing an additional food source for the latter. While the second prey is not exposed to predation but rather is exposed to the harvest. The existence of possible equilibria is found. Conditions of local and global stability for the equilibria are derived. To corroborate our findings, we constructed time series to illustrate the existence and the stability of equilibria numerically by varying the different values

During 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

During 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