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An Enhanced Feature Fusion Network Model for Cervical Cancer Prediction

An Enhanced Feature Fusion Network Model for Cervical Cancer...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Pragatheeswari, B. Mary, Jameela Priya, S. Roshni Dhivyaa, C.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540821

Healthcare providers and researchers that work with patients who have cervical cancer face a significant challenge because it is one of the world's most common causes of death. Worldwide, cervical cancer is one of the major causes of death among women according to the World Health Organization (WHO). Pap smear, also called the Pap Test, is used to examine the cells present in the cervix for abnormal cells. However, manual examination of these pap smears is prone to human error. This work aims to explore the use of deep learning models to detect cancer from pap smear images and develop an ensemble model to improve the accuracy. Our proposed model uses pretrained CNN models through transfer learning to extract features, perform feature concatenation followed by dimensionality reduction and hyperparameter tuning, which are then fed into a neural network for multiclass classification. Hyperparameters are fine-tuned with the help of Bayesian optimization to improve the model's performance. Our proposed model has achieved an accuracy of 96.4%, sensitivity of 96.41% and specificity of 96.66%. © 2024 IEEE.

关键词： Dimensionality reduction

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RAllo: Region Attention-based Edge Resource Allocation in Mobile Internet of Things

RAllo: Region Attention-based Edge Resource Allocation in Mo...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Jiaxuan Yu, Genghua Chen, Zhigang Central South University School of Computer Science and Engineering Changsha410075 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China

ISBN: (纸本)9798350351255

With the advancement of autonomous driving and intelligent transportation systems, there has been a notable increase in the demand for multi-modal task processing by mobile edge terminals. However, intelligent vehicles need to offload complex tasks to servers due to their constrained processing capabilities and limited storage. In this context, the multi-access edge servers which are closer to vehicle terminals, emerge as a superior alternative. Nonetheless, the heterogeneous distribution of traffic flows across time and space can lead to significant communication delays due to potential overloading of some edge servers when offloading schemes employ random resource allocation. To address these challenges, this paper proposes a region attention-based edge resource allocation (RAllo) model to allocate the computing resource by predict regional demand. A regional attention-based traffic prediction model (RATra) extracts crucial spatio-temporal information using a temporal module and a graph attention network to forecast the regional demand. As for the resource allocation, after getting the predicted demand of tasks based on RATra, the radiating-breadth-first-allocation (RBFA) algorithm is designed for task scheduling within and across regions based on task demand. Finally, the results of experiments demonstrate the superiority of RAllo in reducing time consumption and enhancing MEC server utilization compared to existing methods. © 2024 IEEE.

关键词： Mobile edge computing

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Pose Prediction for Six-axis Industrial Robots Based on Spatio - Temporal Dense Graph Convolutional Networks 5

Pose Prediction for Six-axis Industrial Robots Based on Spat...

引用

5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Fu, Honggang Chen, Deng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331518677

Pose prediction can timely determine whether the posture of industrial robots is abnormal, thereby reducing the harm caused by abnormal actions of industrial robots. The pose prediction task requires modeling the complex dynamics of industrial robots, so it is necessary to effectively understand the complex spatio-temporal joint dynamics of industrial robot postures. This process requires considering the nonlinear relationships and coupling effects between joints, making this task very challenging. This paper proposes a pose prediction method suitable for six-axis industrial robots, achieving the interaction between space and time. Finally, in the decoding layer, we use a temporal convolution (TCN) to decode the spatio-temporal features of historical frame poses to predict future frame poses. Through long-term prediction MPJPE (mean per joint position error) experiments and long-term pose prediction visualization experiments on a self-made six-axis industrial robot dataset, the average MPJPE for short-term predictions is 7.1 and the average MPJPE for more difficult long-term pose predictions is 11.4. In the long-term prediction visualization experiments, we can accurately predict the long-term future poses. © 2024 IEEE.

关键词： Industrial robots

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Handling and Research on Imbalanced Data Using VAE-SAGAN 24

Handling and Research on Imbalanced Data Using VAE-SAGAN

引用

9th International Conference on Cyber Security and Information engineering, ICCSIE 2024

作者： Ye, Chaoqian Wu, Shaofei Liu, Jun Hu, Zhuoya School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400718137

Fraud detection datasets typically contain a large number of normal transaction samples and very few fraudulent samples, resulting in a severely imbalanced dataset. This imbalance affects the model’s classification ability, making it prone to misjudgments, which can lead to significant financial losses. Therefore, addressing data imbalances is crucial for improving the accuracy and reliability of fraud detection models. In this study, we propose a fusion model based on Variational Autoencoder and Self-Attention Generative Adversarial Network (VAE-SAGAN) to tackle the data imbalance problem. First, we preprocess the majority of samples using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method to filter out overlapping and noisy samples and remove them. Then, we input the minority samples into the VAE-SAGAN model for training, enabling the model to generate more realistic minority samples. In this study, we compare the proposed sampling method with existing sampling algorithms across different datasets and classifiers. Experimental results show that the proposed method outperforms existing sampling algorithms in terms of F1, G-mean, AUC, and Accuracy, and exhibits relatively stable generalization ability across multiple datasets. This research not only enhances the performance of fraud detection models but also provides more reliable technical support for risk management in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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TruChit: A Blockchain-Based Trusted Chit Fund System with Creditworthiness Evaluation 2

TruChit: A Blockchain-Based Trusted Chit Fund System with Cr...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sharma, Akhilesh Chandrakar, Preeti Department of Computer Science & Engineering National Institute of Technology Raipur India

Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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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 ...

引用

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...

引用

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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Multiview and Decision Fusion in Stacking Ensemble to Predict Anti-Cancer Peptides

Multiview and Decision Fusion in Stacking Ensemble to Predic...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Aditya Singh, Deepak National Institute of Technology-Raipur Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

The spread of a life-threatening ailment such as cancer poses a substantial menace to the entirety of human beings. In 2020, this disease accounted for 10 million global fatalities, prompting us to seek novel approaches to cancer treatment. Despite the number of medicinal options available, they frequently come with a high cost and adverse consequences on our bodies. Anticancer peptides are the newly emerging domain of cancer diagnostics due to their minimal risk of adverse reactions. The present investigation proposed a multiview and decision fusion in stacking ensemble learning to predict anticancer peptides. Multiview and decision fusion is an emerging deep learning method that improves overall efficiency by combining information from diverse perspectives. Finally, this study utilizes three 1-D Conventional Neural Networks as a base classifier and logistic regression as a meta-classifier to form a stacking ensemble to predict anticancer peptides. The proposed ensemble archives a 95.52% classification accuracy, 0.835 MCC value, and 0.971 AUC score on a benchmark ENNAACT dataset and outperforms state-of-the-art techniques. © 2024 IEEE.

关键词： Lung cancer

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Disease Detection in Tomato Plant using Deep Residual Learning 3

Disease Detection in Tomato Plant using Deep Residual Learni...

引用

3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Sharaff, Aakanksha Dhale, Aman Gunda, Druva Kumar Agrawal, Nisheeth National Institute of Technology Raipur Computer Science and Engineering Raipur India

ISBN: (纸本)9798350374766

With the accelerated rise of human population, food is bound to be quintessential of our worries in the future, thus mitigating plant diseases, and errors in their detection would be extremely helpful. The traditional method of examining plant diseases involved an erudite of the field to be present, but various developments in neural networks have made this process trivial by requiring only the image of the crop for its disease detection. There has been a myriad of attempts to improve the scope of plant disease detection to try and make it a lot more computer-dependent and mitigate the involvement of the end-user by using deep learning. This is achieved by using convolutional neural networks which excel in the field of image detection. This paper is based on the fine-tuning aspect of deep learning, i.e., this paper tries to improve the performance of a pre-trained model and compare the accuracies of preexisting models which are popular in this aspect of plant disease detection. This model has been trained specifically for tomato plant leaf dataset, which was sourced from Kaggle, which includes 10 distinct classes including both diseased and healthy leaves. © 2024 IEEE.

关键词： Convolutional neural networks

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Key Management and Authentication Scheme Forward Trusted Pervasive Container Computing 2

Key Management and Authentication Scheme Forward Trusted Per...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Hei, XinHong Xue, YuZhan Wang, YiChuan Liu, YiFan Peng, Chen Sun, Xiang School of Computer Science and Engineering Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies. © 2024 IEEE.

关键词： Cloud computing architecture

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