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Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-16页

作者： Amin, Muhammad Ullah, Khalil Asif, Muhammad Shah, Habib Waheed, Abdul Din, Irfanud Department of Electronics University of Peshawar Peshawar25120 Pakistan Department of Physical & amp Numerical Sciences Qurtuba University of Science & ampIT Peshawar25100 Pakistan Department of Software Engineering University of Malakand Malakand23050 Pakistan Department of Computer Science King Khalid University Abha62529 Saudi Arabia Department of Computer Science Women University Swabi Swabi23430 Pakistan Department of Computer Science The Superior University Lahore Lahore54000 Pakistan

Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solutio

关键词： Electrocardiograms

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Principal component self-attention mechanism for melanoma hyperspectral image recognition 11

Principal component self-attention mechanism for melanoma hy...

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Liang, Hong Li, Nanying Xue, Jiaqi Long, Yaqian Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450397056

Early detection of melanoma and prompt treatment are key approaches to reducing melanoma-related deaths. In order to improve the ability of early detection of melanoma, this paper introduces a set of hyperspectral images (HSIs) data captured by dermoscopy using hyperspectral technology, and based on this data, proposes a principal component self-attention mechanism (PCSAM) method for the classification of dysplastic nevus and melanoma. The proposed method uses principal component analysis technology to amplify the differences in spectral features of the lesions and extract some new features that are convenient for classification. In addition, under the action of the attention mechanism, the spectral features of melanoma are fully paid attention to, and the contextual spatial information between each HSI block can also be utilized. Finally, a comparison experiment is carried out using RGB images and HSIs. Experimental results demonstrate that the spectral features of melanoma can significantly improve the classification accuracy, and it also shows that the participation of hyperspectral technology can effectively improve the recognition accuracy of dysplastic nevus and melanoma, which reflects the advantages of HSI compared with the traditional image. © 2022 ACM.

关键词： Principal component analysis

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Spectral Context-aware Transformer for Cholangiocarcinoma Hyperspectral Image Segmentation 2022

Spectral Context-aware Transformer for Cholangiocarcinoma Hy...

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Li, Nanying Xue, Jiaqi Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450395465

In the medical field, medical images generally do not have corresponding accurate annotations. The reason is that doctors do not have enough time to finely annotate a large number of medical images. Therefore, this paper proposes a spectral context-aware Transformer (SCAT) segmentation method for cholangiocarcinoma hyperspectral image under the condition of a small sample. In the SCAT method, each band is input as each patch into the Transformer model, which mines the spatial geometric structure information, and obtains the relevant features of the region of interest. In addition, class token is introduced to better characterize contextual information. Experimental results on cholangiocarcinoma hyperspectral dataset prove that the proposed method can effectively improve the segmentation effect and provide diagnostic assistance for doctors. © 2022 ACM.

关键词： Image segmentation

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Network Traffic Classifiers for Internet of Things Intrusion Detection using Machine Learning Classifiers 3

Network Traffic Classifiers for Internet of Things Intrusion...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Uniyal, Anita Mishra, Amit Kumar Manushi Pant, Yogesh Computer Science & Engineering GRD IMT Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India GRD IMT Department of Computer Science & Engineering Dehradun India Software Business Systems Consultanting India Private Limited India

ISBN: (纸本)9798350303360

In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and interact with soundings via smart devices, this is possible because of the technological advancement such as cloud computing and Internet of things. With the latest technologies we are using advance algorithms such as machine learning to make our decision more effective. The importance and need of smart devices in life have more vulnerability attacks so it needs concrete security mechanism and privacy policies to safeguard from cyber security and threats via intrusion detection. The existing IoT security literature is thoroughly analysed in this paper, outlining the many vulnerabilities and possible defences. The paper also looks into the use of machine learning techniques for traffic inflow classification, highlighting comparisons of different classifiers for problems that are special to different domains and different machine learning techniques for early identification of intrusion detection. The paper is concluded with a review of the current problems and potential future directions for IoT security research. © 2023 IEEE.

关键词： Intrusion detection

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Prior Information Guided Coarse-to-fine Dual-branch Encoding Network for Fovea Localization and Optic Disc/Cup Segmentation

Prior Information Guided Coarse-to-fine Dual-branch Encoding...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Xie, Hai Zhao, Jia Zhao, Danrui Huang, Limin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Southwest University School of Computer Information & Science Shenzhen China

ISBN: (纸本)9798350337488

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets. © 2023 IEEE.

关键词： coarse-to-fine dual-branch fovea localization optic disc and cup segmentation Prior information

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Navigating IoT Security: Architectures, Emerging Threats, and Adaptive Countermeasures

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IEEE Access 2025年 13卷 98888-98908页

作者： Kandah, Farah Mendis, Thilina Medury, Lalith Sherawat, Hemant Wang, Haofan Auburn University Department of Computer Science and Software Engineering AL United States

The Internet of Things (IoT) has revolutionized modern life, yet its interconnected nature poses significant security challenges. This survey investigates threats and countermeasures associated with IoT ecosystems, categorizes threats targeting network infrastructure, devices, and data security, and provides a taxonomy to understand vulnerabilities. By analyzing IoT architectures—including Cloud, Fog, and Edge—the study highlights how each architecture presents distinct vulnerabilities when integrated with IoT systems. It explores research on several countermeasures, including intrusion detection systems, secure authentication protocols, blockchain technology, and machine learning integration, evaluating both their advantages and limitations. Through a systematic review of recent literature, the survey identifies articles that contribute to IoT security understanding. The study advocates for lightweight, adaptable security measures that can be applied across diverse architectures and emphasizes the importance of standardized protocols through collaborative efforts among stakeholders. By mapping the current IoT security landscape, this survey guides future research and development toward a more secure and resilient IoT ecosystem, emphasizing the need for both innovation and standardization. Additionally, it critically evaluates the effectiveness of existing solutions and provides actionable insights into addressing the evolving security challenges within IoT environments. © 2013 IEEE.

关键词： Architecture Cloud Countermeasures Edge Fog IoT Security

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LigNet: Lightweight Hand Tracking for Edge Intelligence 15

LigNet: Lightweight Hand Tracking for Edge Intelligence

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Huang, Yijing Luo, Chengwen Feng, Xingyu Yang, Zhongru Zhang, Jin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

In recent years, as the rise of edge intelligence hand tracking applications have emerged in various IoT systems and applications, such as human-computer interaction, sign language translation, and motion rehabilitation, etc. Due to the hardware and energy limitation of embedded IoT devices, hand tracking is difficult to achieve real-time continuous operation. To achieve this goal, in this paper we propose a set of optimization methods such as space-based error loss and Gaussian process constraints to design a lightweight hand tracking model with low storage overhead and high recognition efficiency. We design and implement the system, and the effectiveness of our proposed methods is verified in the evaluation comparing with the state-of-the-art. © 2022 IEEE.

关键词： Human computer interaction

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Integrating Real-Time and Non-Real-Time Collaborative Programming: Workflow, Techniques, and Prototypes

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Proceedings of the ACM on Human-computer Interaction 2023年第GROUP期7卷 1-19页

作者： Ma, Yifan Qi, Batu Xu, Wenhua Wang, Mingjie Du, Bowen Fan, Hongfei School of Software Engineering Tongji University Shanghai China College of Design and Innovation Tongji University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom

Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one critical issue with this emerging technique is the lack of integration with non-real-time collaboration. Specifically, contributions from multiple programmers in a real-time collaboration session cannot be distinguished and accurately recorded in the version control system. In this study, we propose a scheme that integrates real-time and non-real-time collaborative programming with a novel workflow, and contribute enabling techniques to realize such integration. As a proof-of-concept, we have successfully implemented two prototype systems named CoEclipse and CoIDEA, which allow programmers to closely collaborate in a real-time fashion while preserving the work's compatibility with traditional non-real-time collaboration. User evaluation and performance experiments have confirmed the feasibility of the approach and techniques, demonstrated the good system performance, and presented the satisfactory usability of the prototypes. © 2023 ACM.

关键词： Control systems

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Tissue specific prediction of N^(6)-methyladenine sites based on an ensemble of multi-input hybrid neural network

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BIOCELL 2022年第4期46卷 1105-1121页

作者： CANGZHI JIA DONG JIN XIN WANG QI ZHAO School of Science Dalian Maritime UniversityDalian116026China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

N^(6)-Methyladenine is a dynamic and reversible post translational modification,which plays an essential role in various biological *** of the current inability to identify m6A-containing mRNAs,computational approaches have been developed to identify m6A sites in DNA *** to improve prediction performance,we introduced a novel ensemble computational approach based on three hybrid deep neural networks,including a convolutional neural network,a capsule network,and a bidirectional gated recurrent unit(BiGRU)with the self-attention mechanism,to identify m6A sites in four tissues of three *** a total of 11 datasets,we selected different feature subsets,after optimized from 4933 dimensional features,as input for the deep hybrid neural *** addition,to solve the deviation caused by the relatively small number of experimentally verified samples,we constructed an ensemble model through integrating five sub-classifiers based on different training *** compared through 5-fold cross-validation and independent tests,our model showed its superiority to previous methods,im6A-TS-CNN and iRNA-m6A.

关键词： M6A sites Deep hybrid neural networks Ensemble model Feature selection

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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