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Key information extraction method of traditional Chinese medicine records based on TF-IDF and K-means 7

Key information extraction method of traditional Chinese med...

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7th international conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2022

作者： Xueqi, Shan Suohuai, Zhang Xuedong, Jing Yanlong, Hong Shanghai Institute of Technology School of Mechanical Engineering Shanghai China Shanghai University of Traditional Chinese Medicine Engineering Center Shanghai China

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

At present, computer technology is widely used in medicine, such as text mining and text clustering. Text clustering is a common application in the field of natural semantic processing, and can also be applied to the processing of Traditional Chinese Medicine medical records. the clustered medical records are convenient for doctors to conduct more targeted disease analysis. this paper adopts the method of building Term Frequency-Inverse Document Frequency matrix, combined with technical means such as word frequency statistics, to achieve clustering of limited medical case texts. the main work of this paper: 1. Extract key information from Traditional Chinese Medicine medical records, and compare the extraction results with artificial symptom analysis. 2. Use the k-means text clustering algorithm to perform cluster analysis on a small number of medical records, and generate subject words for each category. Results: the Jieba library can effectively extract the keywords of medical records, and the medical records clustering can achieve the expected results. © 2022 IEEE.

关键词： Cluster analysis

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Iterative partial computation offloading algorithm for LEO satellite edge computing served by MEO satellite 24

Iterative partial computation offloading algorithm for LEO s...

引用

2024 7th international conference on computer information Science and Artificial Intelligence, CISAI 2024

作者： Wang, Jin Zhang, Guoting Bai, Zhen Guo, Yinchun Yang, Yang Beijing Institute of Tracking and Telecommunications Technology Beijing China Shanghai Aerospace Electronic Technology Institute Shanghai China School of Electronics and Information Engineering Beihang University Beijing China

ISBN: (纸本)9798400707254

this paper studies the energy-efficient optimization problem for low earth orbit (LEO) satellite edge computing served by medium earth orbit (MEO) satellite when partial computation offloading strategy is adopted. An optimization model with the goal of minimizing the total energy consumption of LEO satellites is established, with constraints of task tolerable delay, transmit power, and CPU-cycle frequency, forming a challenging mixed-integer and nonlinear problem. Based on the non-convexity of the optimization problem and the coupling variables, the problem is decomposed into three manageable subproblems as local computing subproblem, task offloading subproblem, and resource allocation subproblem, which are solved using analytical method, convex optimization, and Lagrange dual decomposition method, respectively. Based on the solution of the subproblems, alternative iterations are performed to converge to the optimal value. the simulation results show that when using the partial computation offloading optimization algorithm, the total energy consumption required for LEO satellites is significantly lower than that of local computing and full offloading strategies. © 2024 Copyright held by the owner/author(s).

关键词： Computation offloading

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the Convergence of DevOps and Cloud Computing: A Redefining Software Development 7

The Convergence of DevOps and Cloud Computing: A Redefining ...

引用

7th international conference on Image information processing, ICIIP 2023

作者： Dhanveer Prakash, M.P. Sharma, Nidhi Lovely Professional University School of Computer Science Engineering Punjab Phagwara India

ISBN: (纸本)9798350371406

In the current digital world, the combination of DevOps methods with cloud computing provides a better approach to software development, deployment, and administration. the findings investigate the pros and cons of using DevOps concepts and methodologies in the cloud environment. DevOps helps to upscale the speed, durability, and efficiency of software development by emphasizing collaboration, automation, and continuous improvement in the cloud. With the ability to scale and resource versatility, the cloud is a great platform for applying DevOps approaches. the CI/CD pipelines, infrastructure as code (IaC), containerization, orchestration, and automated testing play a major role in integrating DevOps in a cloud environment. In the end, the author has provided an overview of how DevOps and cloud integration may increase innovation, and efficiency to build a culture of cooperation. the continuous improvement provides significant insights for enterprises aiming to enhance their software development, and deployment processes in an increasingly cloud-centric environment by assessing the present status, and future development of DevOps methods in cloud computing. © 2023 IEEE.

关键词： Efficiency

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Skin Disease Detection Based on Improved Conformer 24

Skin Disease Detection Based on Improved Conformer

引用

2024 7th international conference on computer information Science and Artificial Intelligence, CISAI 2024

作者： Zhou, Xuanxin Ge, Xianlei Jiang, Bing School of Electronic Engineering Huainan Normal University Anhui Huainan China College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9798400707254

Skin diseases are a global health problem, and the accuracy of skin disease diagnosis is undoubtedly considered an important factor in the treatment and recovery of patients. However, the original Conformer model has limitations in extracting multi-scale features from images. To address this problem, we introduce the wavelet transform convolution (WTConv2d) and Squeeze-and-Excitation Attention (SEAttention) modules. the former enhances the model’s ability to extract multi-scale features from images, and the latter improves the expression of key features by adaptively adjusting channel weights. Experiments on the DermNet dermatosis dataset show that the improved WHC model outperforms the traditional Conformer model in terms of accuracy, recall and F1 score after the same number of training iterations. this paper provides new ideas and methods for the automated classification of dermatosis. © 2024 Copyright held by the owner/author(s).

关键词： Wavelet transforms

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Implementation of a High-precision TIME-aware Shaper in TSN 7

Implementation of a High-precision TIME-aware Shaper in TSN

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7th international conference on computer and Communication Systems, ICCCS 2022

作者： Peng, Yunsong Ren, Jie Gong, Kai Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Time-sensitive networking (TSN) is a new industrial communication technology being actively promoted by the international industry. the deterministic transmission of time-triggered (TT) traffic in TSN is guaranteed by Time-aware shaper (TAS), which is defined in IEEE 802.1Qbv. However, if the TSN technology needs to be applied to the existing network environment, the deterministic transmission is difficult to achieve. this paper designs and implements a TAS to realizes deterministic transmission of TT streams in TSN. Experimental results show that the proposed TAS can achieve good shaping effects and can meet the transmission requirements of the TT streams. © 2022 IEEE.

关键词： IEEE Standards

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Brain Tumor Grade Detection Using Transfer Learning and Residual Multi-head Attention Network 1

引用

7th international conference on computer Vision and Image processing, CVIP 2022

作者： Kakarla, Jagadeesh Venkateswarlu, Isunuri Bala Indian Institute of Information Technology Design and Manufacturing Kancheepuram India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Brain tumor grade detection is one of the perpetual tasks in brain image classification. Deep learning models are the most successful for multi-class classification which are trained for non-medical image classification. thus, there is a need for re-training and feature enhancement for better performance in medical image classification. In this paper, we have proposed a residual multi-head attention network to uplift the retraining process with polished feature extraction. the proposed model consists of three parts including a pre-trained EfficientNetB4, a residual multi-head attention network, and a dense network. the residual multi-head attention network utilizes the attention block with three convolution layers for better tumor detection. the residual connection used in the network avoids the vanishing gradient problem. We have extracted a two-class (low-grade/high-grade) dataset from REMBRANDT repository. the proposed model has attained an accuracy of 96.39% and outperforms its competing models in vital metrics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Transfer learning

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Hybrid Deep Learning Model for Cyber-Attack Detection 7

Hybrid Deep Learning Model for Cyber-Attack Detection

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Premkumar, M. Lakshmi, R. Velrajkumar, P. Priya, S.Gayathri Tanguturi, Rama Chaithanya Murali, S. Sivaramkrishnan, M. Department of Information Technology Namakkal Mallasamudharam India RNS Institute of Technology Bengaluru Affiliated to VTU Department of Computer Science and Engineering Belgaum India CMR Institute of Technology Bengaluru India R. M. D. Engineering College Kavaraipettai Department of Electronics and Communication Engineering Chennai India PACE Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Karpagam College of Engineering Department of EEE Coimbatore India

ISBN: (纸本)9798350397253

Better detection and avoidance of intrusions solutions are in high demand because of the worldwide upsurge in hacking on computer networks. New technologies like fog computing, cloud computing, and the Internet of things have dramatically increased the potential for cyberattacks and other forms of cyber risk. these attacks can compromise computer network infrastructures, web services, and social media platforms, resulting in economic and reputation loss. Because of its usefulness in detecting and halting malicious actions, intrusion detection systems (IDS) play a crucial part in network defence mechanisms. In this study, a hybrid Deep Learning (DL) network was used to identify the cyberattack. this effort began with the collection and processing of cyber-attack data from the NSL-KDD. Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LS TMs), and hybrid CNN+Bi-LSTMs are the DL models trained with 80% of processed data. the remaining 20% of the data is used for testing the models after they have been trained. Both positive and negative metrics are used to assess the results of the testing phase. When compared to other networks, the recommended CNN+Bi-LSTM model achieves the highest score for positive metrics and the lowest score for negative metrics. © 2023 IEEE.

关键词： Feature extraction

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

引用

7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. the usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. the weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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7th IFIP TC 10 international Embedded Systems Symposium, IESS 2022

7th IFIP TC 10 International Embedded Systems Symposium, IES...

引用

7th IFIP TC 10 international Embedded Systems Symposium, IESS 2022

ISBN: (纸本)9783031342134

the proceedings contain 12 papers. the special focus in this conference is on international Embedded Systems. the topics include: Synthetic Data for Machine Learning on Embedded Systems in Precision Agriculture;using Network Architecture Search for Optimizing Tensor Compression;simulation of Timing Attacks and Challenges for Early Side-Channel Security Analysis;adaptation for Energy Saving in Time-Triggered Systems Using Meta-scheduling with Sample Points;communication Layer Architecture for a Production Line Digital Twin Using Hierarchical Colored Petri Nets;Exploiting Heterogeneity in PIM Architectures for Data-Intensive Applications;Demonstrating Scalability of the Checkerboard GPC with SystemC TLM-2.0;MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference;Memristor-only LSTM Acceleration with Non-linear Activation Functions;Minimizing Memory Contention in an APNG Encoder Using a Grid of processing Cells.

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Research on NTRU-based Anonymous Authentication and Key Negotiation Protocol for VANETs 7

Research on NTRU-based Anonymous Authentication and Key Nego...

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7th international conference on Cyber Security and information Engineering, ICCSIE 2022

作者： Yang, Jiayu Li, Fei Zhang, Zhuoran School of Cybersecurity Chengdu University of Information Technology Chengdu China College of Blockchain Industry Chengdu University of Information Technology Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9781665455749

To address the issues of privacy protection, information security, timeliness, and difficulty in resisting quantum attacks in Telematics, this paper proposes an NTRU-based authentication and key negotiation protocol for VANETs (Vehicular Adhoc Networks), which applies NTRU(Number theory Research Unit) to the process of certificate-free authentication in VANETs and uses the mechanism of mutual authentication to ensure message integrity and privacy;user privacy is protected by using pseudo-identity to achieve user anonymity. Security proofs and analysis show that the protocol is resistant to quantum attacks and satisfies security requirements such as integrity, anonymity, traceability, forward security and backward security, and has better performance in terms of computational overhead and communication overhead. © 2022 IEEE.

关键词： Authentication

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