Concurrency and Computation

Wang, Heda; Zhang, Shuyan

doi: 10.1002/cpe.8338pmid: N/A

This paper introduces an enhanced BERT‐DPCNN model for the task of Chinese news text classification. The model addresses the common challenge of balancing accuracy and computational efficiency in existing models, especially when dealing with large‐scale, high‐dimensional text data. To tackle this issue, the paper proposes an improved BERT‐DPCNN model that integrates BERT's pre‐trained language model with DPCNN's efficient convolutional structure to capture deep semantic information and key features from the text. Additionally, the paper incorporates the zebra optimization algorithm (ZOA) to dynamically optimize the model's hyperparameters, overcoming the limitations of manual tuning in traditional models. By automatically optimizing hyperparameters such as batch size, learning rate, and the number of filters through ZOA, the model's classification performance is significantly enhanced. Experimental results demonstrate that the improved ZOA‐BERT‐DPCNN model outperforms traditional methods on the THUCNEWS Chinese news dataset, not only verifying its effectiveness in news text classification tasks but also showcasing its potential to enhance classification performance.

Pratap, Bhanu; Singh, Ayush; Mehra, Pawan Singh

doi: 10.1002/cpe.8333pmid: N/A

Internet of Drones (IoD) is one of the most beneficial and has many versatile applications like Surveillance and Security, Delivery and Logistics, Environmental Monitoring, Agriculture, and so forth. The IoD network is crucial for collecting sensitive data like geo‐coordinates, vehicle traffic data, and property details while surveying the various deployment locations in smart cities. The communication between users and drones can be compromised over insecure wireless channels by multiple attacks such as Man‐in‐the‐middle‐attack, Denial of Service, and so forth. Many schemes have already been propounded in the field of IoD. Still, many of them cannot address the resource constraints problem of drones, and existing protocols have higher computation and communication costs. Therefore, this paper has proposed a robust and efficient Hyper‐Elliptic Curve‐based authentication scheme (REHAS), which provides a session key for secure communication. Artificial Identities are generated using a hash function and random numbers. Fuzzy Extractor is used for user biometric authentication, which makes the smart device secure when lost. HECC is used with a smaller bit size of 80 bits rather than ECC of 160 bits. The security of the REHAS has been ensured using Scyther simulation. Furthermore, the resilience, safety, and robustness of REHAS are ensured by Informal security analysis. Lastly, a comparative study of the REHAS has been performed with other related Authentication and key agreement (AKA) protocols regarding communication cost, Computation cost, and security features, demonstrating that REHAS incurred less computation cost (6.7171 ms), communication overhead (1696 bits), and energy consumption (22.5 mJ) than other existing AKA schemes.

Chen, Xiaowen; Lv, Jian; Xie, Qingsheng

doi: 10.1002/cpe.8339pmid: N/A

Fatigue detection holds paramount importance in the timely identification of safety hazards. Nonetheless, prevailing fatigue detection methodologies often overlook the diverse spectrum of fatigue features or temporal cues. To address this lacuna, we introduce fatigue detection based on blood volume pulse signal and multi‐physical features (FDBVPS‐MF). Initially, a non‐invasive technique is employed to extract the blood volume pulse signal (BVPS) from the forehead region, which is subsequently fed into a one‐dimensional convolutional neural network (1D CNN) to formulate a fatigue detection model based on BVPS. Concurrently, features such as percentage of eyelid closure (PERCLOS), blink frequency (BF), and maximum closing time (MCT) are computed from eye images, and amalgamated with yawning frequency (YF) derived from mouth images to generate multi‐physical features (MF). MF is then input into the 1D CNN network to construct a fatigue detection model based on MF. Subsequently, employing weights, derived through Adaboosting, a fusion approach is executed to integrate the outputs of the two fatigue detection models, thus facilitating multi‐modal fatigue detection. On the UTA‐RLDD dataset, the proposed FDBVPS‐MF exhibits an accuracy and precision of 88.9% and 88.2%, respectively. Experimental findings substantiate the superior efficacy of FDBVPS‐MF over conventional methodologies.