Lately, the use of the Internet has led to an increase in social networking sites. The world has become an open environment, and social networking sites have been increasingly used to exchange medical experiences, and they have been adopted in many cases as basic references in obtaining medical advice, which has led to the misuse of medicines. A growing problem, abuse of prescription medications can have a negative impact on all age groups and come with adverse health consequences, as individuals in societies become susceptible to many drug interactions and serious side effects and reduced drug efficacy, which makes a simple health problem turn into a complex health problem; our study aims to classify drug use in Arabic content in social media (use, abuse) by using both Machine Learning (ML) algorithms and AraBERT model. Many studies detect the drug abuse in the English language. There are no studies on Arabic language. Arabic social media dataset was created from Facebook with nearly 7,000 posts. We used different ML classifiers; the most famous of them are Support Vector Machine (SVM), Decision Tree (J48), Naive Bayes (NB), K-Nearest Neighbor (KNN) and Random Forest (RF). We also applied AraBERT based model; CNN-AraBERT, RNN-AraBERT, and LSTM-AraBERT. The classifier's accuracy was evaluated by calculating the F1-Measure, Recall, and Precision measurements. The results indicated that CNN-AraBERT classifier is given the highest value of F1-Measure for Facebook dataset for both classification tasks with (98.3%) for binary classification and (90.99%) for multi-classification.

Abstract Security management is the process of identifying a company's assets (such as people, buildings, equipment, systems, and information assets) and then developing, documenting, and implementing policies and procedures to secure those assets. Meanwhile, artificial intelligence (AI) applications are flourishing thanks to advances in deep learning and numerous hardware architecture improvements based on cloud edge computing (CEC) issues are associated with the Internet of Things (IoT), including inadequate security measures, user ignorance, and the dreaded active monitoring. Therefore, a 6G wireless communication‐assisted security management using artificial intelligence (WC‐SM‐AI) has been introduced to enhance security. The energy‐efficient 6G real‐time communication framework and the enhanced deep neural network security module are key components of this architecture. The first module optimizes network lifespan and spectral efficiency while reducing energy consumption and latency. Another module offers a more secure network connection while increasing privacy, data integrity, and access. For this reason, this article discusses how AI can strengthen the security of 6G networks while promoting strategy problems and solutions.

Abstract Technology‐driven control measures could be an important tool to control the COVID‐19 pandemic crisis. This study evaluates the potentiality of emerging technologies such as 5G and 6G communication, Deep Learning (DL), big data, Internet of Things (IoT) etc. for controlling the COVID‐19 transmission and ensuring health safety. The healthcare sector is able to provide a unified, rapid, and incessant service to people by applying modern wireless connectivity tools like 5G or 6G during the COVID‐19 pandemic. This study has identified eight key areas of applications for the COVID‐19 management like infection detection; travel history analysis; identification of infection symptoms; early detection; transmission identification; access to information in lockdown; movement of people; and development of medical treatments and vaccines. Data have been collected from the respondents living in Sakaka city, KSA during pandemic. This study reveals that most people receive information from social networking sites, health professionals, and television without facing any challenges. The analysis shows that, during the COVID‐19 pandemic, about 42% of respondents felt tense always or most of the time in a day. Only 28.6% of respondents felt tense sometimes, whereas the remainder (about 30%) did not feel tense in relation to the COVID‐19 crisis. Satisfaction with COVID‐19‐related information is also positively correlated with COVID‐19‐related information literacy ( r = 0.53, p < 0.01) that is also positively correlated with depression or emotion, anxiety, and stress ( r = ‐0.15, p < 0.05). The long‐term pandemic is creating several psychological symptoms including anxiety, stress, and depression, irrespective of age.

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Basketball is a sport that requires high athletes’ skills and physical fitness and is deeply loved by the people in our country. This paper studies the application of neural network‐based motion sensors in basketball technology and physical fitness evaluation system. The ideal effect of the system is to scientifically analyze relevant data through intelligent algorithms and provide more accurate diagnosis suggestions. Recognizing human movements requires collecting various data of the human body through motion sensors. The data acquisition components of this system are based on considerations of portability and power consumption and are equipped with equipment with strong computing power to realize the functions of data preprocessing, training, and recognition of the recognition model. The system only needs to send the data in the data collector to the computing device; it can effectively realize the action recognition and judge whether the athlete’s technical action and physical fitness level meet the standard. From the experimental data, the pass rate of the subjects in the 1000‐meter run was 83.3%, and the excellent rate was 10%; the pass rate in the 1‐mile run was 90%, and the excellent rate was 6.7%; and the pass rate in the 20‐meter round trip was only at 56.67%; it can be seen that there is still room for improvement in the reaction speed and agility of most subjects. According to intelligent data analysis, athletes can better understand where they have shortcomings and improve their physical fitness and basketball skills through targeted training.

Sign language encompasses the movement of the arms and hands as a means of communication for people with hearing disabilities. An automated sign recognition system requires two main courses of action: the detection of particular features and the categorization of particular input data. In the past, many approaches for classifying and detecting sign languages have been put forward for improving system performance. However, the recent progress in the computer vision field has geared us towards the further exploration of hand signs/gestures’ recognition with the aid of deep neural networks. The Arabic sign language has witnessed unprecedented research activities to recognize hand signs and gestures using the deep learning model. A vision-based system by applying CNN for the recognition of Arabic hand sign-based letters and translating them into Arabic speech is proposed in this paper. The proposed system will automatically detect hand sign letters and speaks out the result with the Arabic language with a deep learning model. This system gives 90% accuracy to recognize the Arabic hand sign-based letters which assures it as a highly dependable system. The accuracy can be further improved by using more advanced hand gestures recognizing devices such as Leap Motion or Xbox Kinect. After recognizing the Arabic hand sign-based letters, the outcome will be fed to the text into the speech engine which produces the audio of the Arabic language as an output.