Smile Mask Development of Cryptography Performance of MOLAZ Method (MOLAZ-SM)

Concealment of information is the most important things of interest to scientists and users alike. The work of many researchers to find new ways and methods for building specialized systems to protect the information from hackers. The method of those techniques AES and an adopted by the U.S. Department of Defense and launched in the eighties to the world. Even so, it parallels the evolution of these methods to penetrate systems. Researchers were developed this method for the protection of this algorithm. In the end of 2010 the researcher Engineer Moceheb Lazam during his studies at the Masters in the Universiti Utara Malaysia, develop this algorithm in order to keep the encryption and decoding. It was called MOLAZ. It used two algorithms AES 128 and AES 256 bits, and switching between them using special key (K,). In addition, it uses two keys to encryption and decryption. However, this method needs to be develops and supports the protection of information. Therefore, in 2011 appeared MOLAZ-SM. It presents a study is the development of this system by adding the mask technique to prevent the use of the style of repeated attempts to enter the key. The system depends on the base "If you enter a true key, you obtain to the truth information, but if you enter the false key; you obtains to the false information.

Empowering Communication: A Deep Learning Framework for Arabic Sign Language Recognition with an Attention Mechanism

This article emphasises the urgent need for appropriate communication tools for communities of people who are deaf or hard-of-hearing, with a specific emphasis on Arabic Sign Language (ArSL). In this study, we use long short-term memory (LSTM) models in conjunction with MediaPipe to reduce the barriers to effective communication and social integration for deaf communities. The model design incorporates LSTM units and an attention mechanism to handle the input sequences of extracted keypoints from recorded gestures. The attention layer selectively directs its focus toward relevant segments of the input sequence, whereas the LSTM layer handles temporal relationships and encodes the sequential data. A comprehensive dataset comprised of fifty frequently used words and numbers in ArSL was collected for developing the recognition model. This dataset comprises many instances of gestures recorded by five volunteers. The results of the experiment support the effectiveness of the proposed approach, as the model achieved accuracies of more than 85% (individual volunteers) and 83% (combined data). The high level of precision emphasises the potential of artificial intelligence-powered translation software to improve effective communication for people with hearing impairments and to enable them to interact with the larger community more easily

Systematic review of training environments with motor imagery brain–computer interface: Coherent taxonomy, open issues and recommendation pathway solution

The brain–computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current topic; however, to the best of our knowledge, this is the first attempt to highlight these challenges and open issues in signal processing methods, techniques and pattern recognition in smart BCI training environments. This work illustrates the effect of the theoretical perspectives associated with BCI works for research development in smart training environments. Consequently, this research copes with these issues via a systematic review protocol to help the large community of BCI users, especially people with disabilities. Fundamentally, four substantial databases, namely, IEEE, ScienceDirect, Scopus and PubMed contain a considerable amount of technical and scientific articles relevant to smart BCI training systems. A set of 375 articles is collected from 2010 to 2020 to reveal a clear picture and a better understanding of all the academic literature through a final set of 25 articles. In addition, this research provides the state of the art for signal processing, feature extraction, classification techniques and smart training environment characteristics for MI-based BCI applications. This study also reports the challenges and issues identified by the researchers as well as recommended solutions to solve the persistent problems. This study introduces the state-of-the art virtual and augmented reality environments as a smart platform and the neurofeedback schemes used for MI-based smart BCI training systems. Moreover, this study highlights for the first time 10 concepts of smart training in a virtual environment applied in MI and BCI, and investigates the evaluation of these concepts against the literature to gain only 45.55%. Collectively, the implication of this study will offer the opportunity of deploying an efficient smart BCI training system in terms of data acquisition and recording, pattern recognition and smart environment for BCI users and rehabilitation programmes

Telehealth utilization during the Covid-19 pandemic: A systematic review

During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors’ implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond