Sign language (SL) is a vital mode of communication, bridging the gap between the deaf and hearing communities. However, SL, despite its paramount importance, has received relatively limited attention from researchers. Its unique structural characteristics, distinct from those of natural languages, present novel challenges that require innovative solutions. Remarkable technological advancements, notably in Artificial Intelligence (AI) and machine learning, offer promising avenues for automated Sign Language translation Systems (SLTS). This review study addresses the crucial need for a comprehensive synthesis of existing research by systematically examining and evaluating the progress made in SLTS. By analyzing 58 research papers, with a particular emphasis on the most frequently cited papers from each year up to 2023, we shed light on the field’s current state, identifying key advancements and challenges. This review followed a systematic approach based on clear guidelines. The methodology involved defining research questions, formulating queries, selecting studies based on clear criteria, and extracting pertinent information to address the research objectives. This review found that deep learning techniques, such as convolutional and recurrent neural networks, have shown high accuracy in sign language recognition, and their performance in recognizing the variety of signs has steadily improved over time. Additionally, integrating non-manual features has proven pivotal in enhancing recognition accuracy. Future research should refine advanced deep learning models and integrate non-manual features to improve system accuracy and applicability. These ongoing advancements hold the potential to revolutionize communication and break down barriers for individuals who rely on sign language as their primary mode of communication.

In today’s world, news outlets have changed dramatically; newspapers are obsolete, and radio is no longer in the picture. People look for news online and on social media, such as Twitter and Facebook. Social media contributors share information and trending stories before verifying their truthfulness, thus, spreading rumors. Early identification of rumors from social media has attracted many researchers. However, a relatively smaller number of studies focused on other languages, such as Arabic. In this study, an Arabic rumor detection model is proposed. The model was built using transformer-based deep learning architecture. According to the literature, transformers are neural networks with outstanding performance in natural language processing tasks. Two transformers-based models, AraBERT and MARBERT, were employed, tested, and evaluated using three recently developed Arabic datasets. These models are extensions to the BERT, Bidirectional Encoder Representations from Transformers, a deep learning model that uses transformer architecture to learn the text representations and leverages the attention mechanism. We have also mitigated the challenges introduced by the imbalanced training datasets by employing two sampling techniques. The experimental results of our proposed approaches achieved 0.97 accuracy. This result demonstrated the effectiveness of the proposed method and outperformed other existing Arabic rumor detection methods.

Consumer electronics have transformed the way we interact with technology, improving convenience and connectivity in day-to-day lives. In the healthcare sector, recent technologies have resulted in enhanced diagnosis, treatment, and patient care. Wearables, artificial intelligence-based data analytics, and telemedicine transform the way of monitoring and managing health, fostering a proactive approach to well-being. The popularity of ChatGPT is proven great potential for AI-generated content (AIGC) that has formed a major impact on the artificial intelligence (AI) community and accelerates the reconsidering of the prospects of general AI. The AIGC is also exposed as a considerable scope to impulse healthcare electronics (HE). Although generative AI has achieved popularity like the formation of images, it could be employed for producing synthetic tabular information. The production of synthetic electronic health records (EHR) undertakes to increase the utilization of machine learning (ML) methods that commonly function with massive quantities of data. ML will identify non-intuitive classifier patterns that permit a new integration of patient feature predictive ability. Currently, deep learning (DL) techniques are effectively utilized in EHR data from medical domains. DL methods excellently captured the significant and beneficial features and patterns from the comprehensive medical information in EHR data. This study presents AI-generated content for Synthetic Electronic Health Record Generation with a Deep Learning-based Diagnosis (SEHRG-DLD) Model. The focus of the SEHRG-DLD technique is to initially generate the synthetic EHR data and then analyze the medical data for disease diagnosis using the DL model. The SEHRG-DLD technique comprises a two-stage process: synthetic data generation and disease diagnosis. At the initial stage, the SEHRG-DLD technique uses the ChatGPT tool to generate synthetic EHR data. Then, the SEHRG-DLD technique undergoes the disease diagnosis process using three sub-processes namely Harris Hawks Optimization (HHO) based feature selection, deep belief network (DBN) based classification, and Golden Jackal Optimization (GJO) based hyperparameter tuning. The application of the HHO and GJO algorithms helps in accomplishing enhanced diagnostic performance of the SEHRG-DLD technique. The performance analysis of the SEHRG-DLD technique is examined by employing the ChatGPT-generated dataset. The experimental results clearly stated the supremacy of the SEHRG-DLD technique over other recent methods for different measures.