The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

This study aimed to identify the role of digital transformation in improving the quality of the internal audit process. The researchers used the descriptive analytical approach, and the questionnaire was relied on as a study tool that was applied to the study population consisting of internal auditors working in banks listed on the Palestine Stock Exchange, and their number was (85). A checker and a comprehensive inventory method was used due to the small size of the community, where the response data of the questionnaire was analyzed and interpreted using the Statistical Packages Program (SPSS).

The results of the study showed that the use of digital transformation contributes to ensuring the quality of

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

The search (floors and transparent role in the design studios satellite channels) and presented as a study-oriented, and the research aims to identify the role of flooring transparent spaces studios satellite channels and side performative and aesthetic, and formulas design to highlight the role floors transparent spaces studios satellite channels. And highlight the importance of research, particularly in its contribution to the clarification of the concept of the relationship between transparency and performing aesthetic treatments for floors by clarifying its role in the designs of the internal spaces of the studios, as well as his contribution to the founding of the theory of looking at the base of such concepts. To achieve the object

The long-term monitoring of land movements represents the most successful application of the Global Navigation Satellite System (GNSS), particularly the Global Positioning System. However, the application of long term monitoring of land movements depends on the availability of homogenous and consistent daily position time series of stations over a period of time. Such time series can be produced very efficiently by using Precise Point Positioning and Double Difference techniques based on particular sophisticated GNSS processing softwares. Nonetheless, these rely on the availability of GNSS products which are precise satellite orbit and clock, and Earth orientation parameters. Unfortunately, several changes and modifications have been mad

Communication has seen a big advancement through ages; concepts, procedures and technologies, it has also seen a similar advancement of language. What unites language and media is the fact that each one of them guides and contributes to the other; media exists and results from language and from the other sign systems, and what strengthens this connection is the symbolic language system, as media helps it by providing knowledge and information. The change that occurred through time must leave a significant trace in the media, for example Diction, which has changed concerning development and growth, also the ways and mediums of media have become manifold and widespread. This change affected the recipient whether it was a reader, listener o

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete