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.

Palm vein recognition technology is a one of the most effective biometric technologies for personal identification. Palm acquisition techniques are either contact-based or contactless-based. The contactless-based palm vein system is considered more accurate and efficient when used in modern applications, but it may suffer from problems like pose variations and the delay in the matching process. This paper proposes a contactless-based identification system for palm vein that involves two main steps; First, the central region of the palm is cropped using fast extract region of interest algorithm, then the features are extracted and classified using altered structure of Residual Attention Network, which is a developed version of convolution

     The main task of creating new digital images of different skin diseases is to increase the resolution of the specific textures and colors of each skin disease. In this paper, the performance of generative adversarial networks has been optimized to generate multicolor and histological color digital images of a variety of skin diseases (melanoma, birthmarks, and basal cell carcinomas). Two architectures for generative adversarial networks were built using two models: the first is a model for generating new images of dermatology through training processes, and the second is a discrimination model whose main task is to identify the generated digital images as either real or fake. The gray wolf swarm algorithm and the whale swarm alg

Due to the difficulties that Iraqi students face when writing in the English language, this preliminary study aimed to improve students' writing skills by using online platforms remotely. Sixty first-year students from Al-Furat Al–Awsat Technical University participated in this study. Through these platforms, the researchers relied on stimuli, such as images, icons, and short titles to allow for deeper and more accurate participations. Data were collected through corrections, observations, and feedback from the researchers and peers. In addition, two pre and post-tests were conducted. The quantitative data were analysed by SPSS statistical Editor, whereas the qualitative data were analyzed using the Piot table, an Excel sheet. The resu

      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

     Automated medical diagnosis is an important topic, especially in detection and classification of diseases. Malaria is one of the most widespread diseases, with more than 200 million cases, according to the 2016 WHO report. Malaria is usually diagnosed using thin and thick blood smears under a microscope. However, proper diagnosis is difficult, especially in poor countries where the disease is most widespread. Therefore, automatic diagnostics helps in identifying the disease through images of red blood cells, with the use of machine learning techniques and digital image processing. This paper presents an accurate model using a Deep Convolutional Neural Network build from scratch. The paper also proposed three CNN

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

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

     A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets