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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Extended Reality as an Assistive technology for Patients Suffering from Neurodegenerative Diseases 5

Extended Reality as an Assistive Technology for Patients Suf...

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5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Akilesh, S. Tulasi Raman, R. John, Sharon Pai, Tarun Vinod Sakthivel, V. Prakash, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331529437

Neurodegenerative diseases with memory impairment and difficulties in recognizing people pose significant challenges to patients' well-being and independence. This research proposes an approach leveraging extended reality (XR) headsets and computer Vision techniques to enhance person identification for such patients. The system utilizes Unity AR Foundation and OpenCV for real-time face detection and recognition, enabling accurate identification of familiar individuals within the wearer's environment with an immersive AR experience, overlaying digital information next to recognized faces in the wearer's field of view. This research aims to improve social interactions, reduce panic/confusion, and improve overall quality of life for patients with neurodegenerative diseases and represents an advancement in the intersection of healthcare and augmented reality technologies. © 2024 IEEE.

关键词： Assistive technology

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Deep Learning-Based Predictive Model of Molecular Properties 6

Deep Learning-Based Predictive Model of Molecular Properties

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Yang, Mingyu Chen, Xiangyang Lv, Weijie School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

With the rapid development of computer hardware and machine learning algorithms in recent decades, artificial intelligence has created a boom in various industries. computer technology has been widely used in all stages of drug discovery, from machine learning to deep learning, and scientists have conducted numerous studies on the physicochemical properties of drugs. With the advancement of computer technology and deep learning algorithms, it has become possible to obtain large-scale molecular characterization data, extract different types of molecular representations, and construct more subtle models. Among them, molecular graph structure data is increasingly used in deep learning to predict molecular properties. It directly extracts the two-dimensional structural information of molecules, avoiding the defects of indirect structural information extraction in traditional methods. However, the performance of different algorithms based on molecular fingerprints, descriptors, molecular pictures and molecular graph structures is currently controversial. Some studies have found that descriptor-based models outperform graph-based models, while others do the opposite. This shows the difference in the impact of different tasks and datasets on the models. In this experiment, molecular graphs and molecular fingerprints are combined, and molecular graphs are fed into graph neural networks to extract features after the attention mechanism, molecular fingerprints are fed into a fully connected layer to extract features, and the features extracted from the two molecular representations are fused together and a specific activation function is adopted to see if the accuracy and stability of the model can be effectively improved. © 2024 IEEE.

关键词： Adversarial machine learning

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Privacy-Preserving Employee Attrition Prediction using Deep Learning and Fully Homomorphic Encryption

Privacy-Preserving Employee Attrition Prediction using Deep ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Rajesh, R. Harshavardhan, S. Kirthana, B. Shreya, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Employee attrition presents a significant challenge for organizations, impacting both finances and operations. Mean-while, the automation of human resource processes, including recruitment and performance monitoring, heavily relies on vast amounts of personal data. Traditional predictive models for employee attrition have increasingly turned to deep learning techniques due to their superior accuracy in deciphering complex data patterns. However, these methods raise serious privacy concerns due to their reliance on sensitive employee information. This paper introduces a novel solution by integrating Fully Homomorphic Encryption (FHE) with deep learning, ensuring data privacy without compromising predictive performance. This integration not only enhances attrition prediction accuracy but also maintains employee data confidentiality, potentially improving organizational efficiency and trust in HR processes. © 2024 IEEE.

关键词： Differential privacy

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Visible-Infrared Person Re-Identification Based on Dual Branch Structure and Attention 6

Visible-Infrared Person Re-Identification Based on Dual Bran...

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Zan, Guo Chen, Xiangyang Jiang, Zheng Lv, Weijie Yang, Mingyu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Visible-infrared person re-identification (VI-ReID) is a difficult cross-modal pedestrian retrieval *** to the modality gaps between visible (VIS) and infrared (IR) images. In the process of visible-infrared person re-identification, it is difficult to extract effective information from modality-shared features due to the additional cross-modality discrepancy. To address this problem, we propose a visible-infrared person re-identification method based on dual-branch structure and attention. Firstly, a dual-branch convolution module is added to the ResNet-50 backbone network to extract more plentiful modality-shared features of different scales by using the dual-branch structure and dilated convolution with different expansion rates. Moreover, we design a Non- Local attention module to obtain the correlation between different pixels in spatial and channel dimensions by introducing and improving the Non-Local blocks, thereby enhancing the ability of the network to extract structural information. Finally, we design an efficient joint loss strategy to optimize the network to further reduce the modality discrepancy. Our method shows excellent performances on both SYSU-MM01 and RegDB datasets. © 2024 IEEE.

关键词： attention mechanism dual-branch structure Non-Local block ResNet VI-ReID

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Enhancing Cyberbullying Detection Through Keyword Filtering: A Comparative Study of ML and DL Approaches

Enhancing Cyberbullying Detection Through Keyword Filtering:...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kaarthika, R. Hemamalini, R. Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

Cyberbullying represents the use of online platforms like social media, gaming platforms, and similar platforms to harass a person, hurt a person, or threaten a person. In this study, keyword filtering is introduced to enhance cyberbullying detection. Various machine learning models, including Logistic Regression, Random Forest, and Support Vector Machine, as well as a deep learning model Convolutional Neural Network, are implemented on original and keyword-labelled datasets. The performance of these models is compared using various evaluation metrics. Our findings demonstrate that the performance of models on the keyword-labeled dataset is significantly higher, with Logistic Regression achieving 98.24%, Random Forest at 99.77%, SVM at 99.67%, and CNN at 99.95% accuracy. This study highlights the effectiveness of keyword filtering features in enhancing the detection of cyberbullying, providing a robust approach to creating safer online environments. © 2024 IEEE.

关键词： Support vector regression

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Deep Learning-Based Robust Hybrid Approaches for Brain Tumor Classification in Magnetic Resonance Images

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Journal of The Institution of Engineers (India): Series B 2023年 1-15页

作者： Krishna, S. Rama Ravela, Chittibabu Mantena, Srihari Varma Sirajuddin, Mohammad Deol, G. Joel Sunny Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Vaddeswaram India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Information Technology Kallam Haranadhareddy Institute of Technology Dasarapalem India Department of Computer Science and Engineering Kallam Haranadhareddy Institute of Technology Andhra Pradesh Dasarapalem India

Classification of brain images is a very challenging problem among the most helpful and commonly employed procedures in the medical system. Deep learning, a subset of artificial intelligence, has pioneered new techniques for automatic medical image investigation. The goal of this study was to create two deep learning-based hybrid models to segment and classify brain MRI data. The first hybrid model is a combination of Fully Convolutional Networks and Residual Networks for segmentation and classification. The second is a hybrid of SegNet for segmentation and MobileNet for classification. The analysis is based on a labelled dataset containing images of glioma, meningioma, pituitary, and no tumor. The hybrid models proposed above are built using Python. The suggested models exhibit a high accuracy of 93.9% and 91.3% for a brain MRI dataset, respectively. Furthermore, the proposed models were compared and evaluated against other existing hybrid models in terms of dice score, Positive predictive value, False predictive value, precision, recall, specificity, and F1 score. As a result, the proposed deep-learning technique can assist doctors as well as radiologists in detecting brain tumors early. © 2023, The Institution of Engineers (India).

关键词： Magnetic resonance imaging

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PPaxos: An Adaptive Pull-Based Group Consensus Protocol for Edge Networks 30

PPaxos: An Adaptive Pull-Based Group Consensus Protocol for ...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Hao, Wei Xu, Guangping Wang, Jianshe Wen, Yanyan Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798331515966

Distributed applications are increasingly deployed at the edge to provide low-latency user access. The hierarchical and localized distribution of edge nodes presents challenges for traditional consensus protocols. The push-based replication method, where the leader pushes updates to followers, can become a performance bottleneck in bandwidth-constrained edge environments. Furthermore, the variation in resources and processing capabilities among edge nodes makes it difficult for this replication method to adapt to the specific conditions of each node, thereby affecting system performance. This paper proposes a new consensus protocol, PPaxos, which employs a pull-based replication method. In this approach, follower nodes within a group proactively pull data from relay nodes, and the relay nodes pull data from the leader node. We have designed an adaptive load-aware pull strategy that allows each node to dynamically adjust its pull frequency based on its own state and to dynamically select relay nodes to reduce performance fluctuations and instability factors. Additionally, a cross-group multi-link communication mechanism ensures that the system remains stable even during relay node failures. Experimental results indicate that PPaxos outperforms other protocols in terms of throughput and latency. Specifically, under conditions of limited bandwidth in edge networks, PPaxos achieves approximately a 2 9. 6% improvement in throughput and a 3 4. 8 8% reduction in latency. © 2024 IEEE.

关键词： Bandwidth

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Robust Object Tracking with Gradient Activation Siamese Network 16

Robust Object Tracking with Gradient Activation Siamese Netw...

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Yan, Zixuan Lu, Xiaofeng Pang, Tiantian Xu, Jingbo Xi'an University of Technology School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798331506896

Deep learning technology has driven continuous advancements in the visual tracking field. In order to overcome various challenges, Siamese-based trackers and Attention-based trackers improve tracking performance by adding or deepening the network structure. However, they also increase training costs and model complexity. Lightweight trackers focus on achieving efficient and real-time tracking with limited computational resources, but these methods often face issues with lower accuracy and robustness in complex scenarios. Our approach - Gradient Activation Siamese Network(Grad-Siam), adds fully connected layers to the backbone of SiamFC to obtain gradient activation information. By using the gradient activation strategy, we dynamically update the feature extraction process of the template and search branches. We trained on GOT-10k and validated on multiple standard test datasets. Experiments show that Grad-Siam balances accuracy and efficiency well, while also enhancing robustness in complex scenarios. © 2024 IEEE.

关键词： Object detection

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