The rapid outbreak of COVID-19 has caused serious harm and infected tens of millions of people worldwide. Since there is no specific treatment, wearing masks has become an effective method to prevent the transmission of COVID-19 and is required in most public areas, which has also led to a growing demand for automatic real-time mask detection services to replace manual reminding. However, few studies on face mask detection are being conducted. It is urgent to improve the performance of mask detectors. In this paper, we proposed the Properly Wearing Masked Face Detection Dataset (PWMFD), which included 9205 images of mask wearing samples with three categories. Moreover, we proposed Squeeze and Excitation (SE)-YOLOv3, a mask detector with relatively balanced effectiveness and efficiency. We integrated the attention mechanism by introducing the SE block into Darknet53 to obtain the relationships among channels so that the network can focus more on the important feature. We adopted GIoUloss, which can better describe the spatial difference between predicted and ground truth boxes to improve the stability of bounding box regression. Focal loss was utilized for solving the extreme foreground-background class imbalance. Besides, we performed corresponding image augmentation techniques to further improve the robustness of the model on the specific task. Experimental results showed that SE-YOLOv3 outperformed YOLOv3 and other state-of-the-art detectors on PWMFD and achieved a higher 8.6% mAP compared to YOLOv3 while having a comparable detection speed.

Industry 5.0 is a futuristic transformative model that aims to develop a hyperconnected, automated, and data-driven industrial ecosystem. This digital transformation will boost productivity and efficiency throughout the production process but will be more prone to new sophisticated cyber-attacks. Deep learning-based Intrusion Detection Systems (IDS) have the potential to recognize intrusions with high accuracy. However, these models are complex and are treated as a black box by developers and security analysts due to the inability to interpret the decisions made by these models. Motivated by the challenges, this paper presents an explainable and resilient IDS for Industry 5.0. The proposed IDS is designed by combining bidirectional long short-term memory networks (BiLSTM), a bidirectional-gated recurrent unit (Bi-GRU), fully connected layers and a softmax classifier to enhance the intrusion detection process in Industry 5.0. Then, we employ the SHapley Additive exPlanations (SHAP) mechanism to interpret and understand the features that contributed the most in the decision of the proposed cyber-resilient IDS. The evaluation of the proposed model using the explainability can ensure that the model is working as expected. The experimental results based on the CICIDDoS2019 dataset confirms the superiority of the proposed IDS over some recent approaches.

This paper presents a comprehensive review of multimodal emotion recognition (MER), a process that integrates multiple data modalities such as speech, visual, and text to identify human emotions. Grounded in biomimetics, the survey frames MER as a bio-inspired sensing paradigm that emulates the way humans seamlessly fuse multisensory cues to communicate affect, thereby transferring principles from living systems to engineered solutions. By leveraging various modalities, MER systems offer a richer and more robust analysis of emotional states compared to unimodal approaches. The review covers the general structure of MER systems, feature extraction techniques, and multimodal information fusion strategies, highlighting key advancements and milestones. Additionally, it addresses the research challenges and open issues in MER, including lightweight models, cross-corpus generalizability, and the incorporation of additional modalities. The paper concludes by discussing future directions aimed at improving the accuracy, explainability, and practicality of MER systems for real-world applications.

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

The Internet of Things (IoT) has proven to be a billion-dollar industry. Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks. The diversity of the IoT, on the one hand, leads to the benefits of the integration of devices into a smart ecosystem, but the heterogeneous nature of the IoT makes it difficult to come up with a single security solution. However, the centralized intelligence and programmability of software-defined networks (SDNs) have made it possible to compose a single and effective security solution to cope with cyber threats and attacks. We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices so that no burden is placed on them. We use a state-of-the-art dataset, CICDDoS 2019, to train our algorithm. The results evaluated by this algorithm achieve high accuracy with a minimal false positive rate (FPR) and testing time. We also perform 10-fold cross-validation, proving our results to be unbiased, and compare our results with current benchmark algorithms.

The deployment of Internet of Things (IoT) systems in Smart Agriculture (SA) operates in extreme environments including wind, snowfall, flooding, landscape, and so on for collecting and processing real-time data. The increased connectivity and broad adoption of IoT devices with low-power communications on farmland support farmers in making data-driven decisions using various Artificial Intelligence (AI) techniques. Furthermore, in such an environment, edge computing is also utilized to provide computationally intensive, latency-sensitive, and bandwidth-demanding services at the edge of the network. However, protecting edge-to-Things in the extreme environment of SA is challenging, due to the volume of data, and also attackers exploit network gateways to perform Distributed Denial of Service (DDoS) attacks. Motivated by the aforementioned challenges, we develop a novel deep learning-based Intrusion Detection System (IDS) for edge-envisioned SA in extreme environments. Specifically, a hybrid approach is developed by combining bidirectional gated recurrent unit, long-short term memory with softmax classifier to detect attacks at the edge of the network. To allow faster learning, the proposed IDS employs the Truncated Backpropagation through Time (TBPTT) approach to handle lengthy sequences of network data. Furthermore, we suggest an attack scenario with deployment architecture for the proposed IDS in the extreme environment of SA. Extensive experiments using three publicly available datasets namely, CIC-IDS2018, ToN-IoT, and Edge-IIoTset prove the effectiveness of the proposed IDS over some traditional and contemporary state-of-the-art techniques.