People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

The problem of malicious activities in online social networks, such as Sybil attacks and malevolent use of fake identities, can severely affect the social activities in which users engage while online. For example, this problem can affect content publishing, creation of friendships, messaging, profile browsing, and commenting. Moreover, fake identities are often created to disseminate spam, use the private information of other users, commit fraud, and so on. A malicious person can generate numerous fake accounts for these purposes to reach a large number of trustworthy users. Thus, these types of malicious accounts must be detected and deactivated as quickly as possible. However, this objective is challenging, because a fake account can exhibit trustworthy behaviors and have a type of name that will prevent it from being detected by the security system. In this paper, we provide a comprehensive survey of literature from 2006 to 2016 on Sybil attacks in online social networks and use of social networks as a tool to analyze and prevent these attack types. We first review existing Sybil attack definitions, including those in the context of online social networks. We then discuss a new taxonomy of Sybil attack defense schemes and methodologies. Finally, we compare the literature and identify areas for further research in Sybil attacks in online social networks.

The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for decision-making processes. Information credibility defines a measure of the fitness of information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. Additionally, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human-computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006–2017 as applied to the context of the microblogging platform, Twitter. Known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in social media context.

In this study, the fusion of cortical and muscular activities based on discriminant correlation analysis DCA) is developed to recognize bilateral lower limb movements. Electromyography (EMG) and electroencephalography (EEG) signals were concurrently recorded from 28 healthy subjects while performing various ankle joint movements. The two types of biosignals were fused at feature level, and five different classifiers were used for the purpose of movement recognition. The performance of the classifiers with multimodal and single modality data were assessed with five different sampling window sizes. The results demonstrated that the use of a multimodal approach results in an improvement of the classification accuracy with a linear discriminator analysis classifier (LDA). The highest recognition accuracy was 96.64 ± 4.48% with a window size of 250 sample points, in contrast with 89.99 ± 7.94% for EEG data alone. Furthermore, the multimodal fusion based on DCA was validated with fatigued EMG signal to investigate the robustness of the fusion technique against the muscular fatigue. In addition, the statistical analysis result demonstrates that the proposed fusion approach provides a substantial improvement in motion recognition accuracy 96.64 ± 4.48% (p < 0.0001) compared to method based on a single modality.

Driver fatigue or drowsiness detection techniques can significantly enhance road safety measures and reduce traffic accidents. These approaches used different sensor technologies to acquire the human physiological and behavioral characteristics to investigate the driver's vigilance state. Although the driver's vigilance detection technique has attracted significant interest recently, few studies have been conducted to review it systematically. These studies provide a thorough overview of the most advanced driver vigilance detection method available today in terms of sensor technology for scholars and specialists. This research is geared towards achieving three main objectives. Firstly, it aims to systematically gather, evaluate, and synthesize information from previous research published between 2014 and May 2024 on driver's state and driving sensors and their implementation on detection algorithms. It aims to provide a thorough review of the present state of research on wearable and unwearable sensor technology for driver fatigue detection, focusing on reporting experimental results in this field. This information will be necessary for experts and scientists seeking to advance their knowledge in this field. Lastly, the research aims to identify gaps in knowledge that require further investigation and recommend future research directions to help address these gaps. This way, it will contribute to the advancement of the field and provide beneficial insights for future researchers.