No abstract

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.INDEX TERMS Low power wide area network, long range wide area network, forward error correction, energy efficiency, internet of things, adaptive scheduling algorithm, Gaussian mixture model, spreading factor, adaptive data rate, end device, quality of service, chirp spread spectrum, packet success ratio.

Sign language is still the best communication mean between the deaf and hearing impaired citizens. Due to the advancements in technology, we are able to find various research attempts and efforts on Automatic Sign Language Recognition (ASLR) technology for many languages including the Arabic language. Such attempts have simplified and assisted the interpretation between spoken and sign languages. In fact, the technologies that translate between spoken and sign languages have become popular today. Being the first comprehensive and up-to-date review that studies the state-of-the-art ASLR in perspective to Arabic Sign Language Recognition (ArSLR), this review is a contribution to ArSLR research community. In this paper, the research background and fundamentals of ArSLR are provided. ArSLR research taxonomies, databases, open challenges, future research trends, and directions, and a roadmap to ArSLR research are presented. This review investigates two major taxonomies. The primary taxonomy that is related to the capturing mechanism of the gestures for ArSLR, which can be either a Vision-Based Recognition (VBR) approach or Sensor-Based Recognition (SBR) approach. The secondary taxonomy that is related to the type and task of the gestures for ArSLR, which can be either the Arabic alphabet, isolated words, or continuous sign language recognition. In addition, less research attempts have been directed towards Arabic continuous sign language recognition task compared to other tasks, which marks a research gap that can be considered by the research community. To the best of our knowledge, all previous research attempts and reviews on sign language recognition for ArSL used forehand signs. This shows that the backhand signs have not been considered for ArSL tasks, which creates another important research gap to be filled up. Therefore, we recommend more research initiatives to contribute to these gaps by using an SBR approach for signers' dependent and independent approaches.

The exponentially growing concern of cyber-attacks on extremely dense underwater sensor networks (UWSNs) and the evolution of UWSNs digital threat landscape has brought novel research challenges and issues. Primarily, varied protocol evaluation under advanced persistent threats is now becoming indispensable yet very challenging. This research implements an active attack in the Adaptive Mobility of Courier Nodes in Threshold-optimized Depth-based Routing (AMCTD) protocol. A variety of attacker nodes were employed in diverse scenarios to thoroughly assess the performance of AMCTD protocol. The protocol was exhaustively evaluated both with and without active attacks with benchmark evaluation metrics such as end-to-end delay, throughput, transmission loss, number of active nodes and energy tax. The preliminary research findings show that active attack drastically lowers the AMCTD protocol’s performance (i.e., active attack reduces the number of active nodes by up to 10%, reduces throughput by up to 6%, increases transmission loss by 7%, raises energy tax by 25%, and increases end-to-end delay by 20%).

The epidemic is a doorway. It has created a new portal that involves re-defining of living styles, working habits, health care, and socialization among people and nonhuman species. The World health organization has announced publically that novel coronavirus as a pandemic. The problem is to overcome the situation of this pandemic which involves the social distancing issues, by transforming the conventional medical health care systems to digital health care system. Post pandemic demands a fundamental change of healthcare systems that have the abilities to handle those issues. Meanwhile, as the pandemic becomes more severe, the most important objective is the paradigm shift in conventional health systems to combat the pandemic by purposing the taxonomy on the basis of digital technologies. Thus, an Artificial intelligence (AI) and Internet of things (IoT) based digital healthcare system model is proposed. Moreover, a thorough investigation is conducted, ensuring in a timely guide to the possibilities of digital health transformation as a current and future response to pandemic issues.

Face recognition is one of the most approachable and accessible authentication methods. It is also accepted by users, as it is non-invasive. However, aging results in changes in the texture and shape of a face. Hence, age is one of the factors that decreases the accuracy of face recognition. Face aging, or age progression, is thus a significant challenge in face recognition methods. This paper presents the use of artificial neural network with model-agnostic meta-learning (ANN-MAML) for face-aging recognition. Model-agnostic meta-learning (MAML) is a meta-learning method used to train a model using parameters obtained from identical tasks with certain updates. This study aims to design and model a framework to recognize face aging based on artificial neural network. In addition, the face-aging recognition framework is evaluated against previous frameworks. Furthermore, the performance and the accuracy of ANN-MAML was evaluated using the CALFW (Cross-Age LFW) dataset. A comparison with other methods showed superior performance by ANN-MAML.

No abstract

Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the "support vector machine" (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel. When tested on publicly available image dataset "CASIA" v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.