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International Conference on Electronics Technology (ICET)

作者： Sijjad Ali Asad Ali Amir Hamza Maryam Jamil Muhammad Uzair Rana Zaki Abdul Bari M. Hunza Nabel Akram Hamid Sharif College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Media and Communication Shenzhen University China College of Physics and Optoelectronics Engineering Shenzhen University Shenzhen China School of Government Shenzhen University Shenzhen China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

Cardiovascular diseases have the highest death rate in the whole world, which stresses the need for taking more preventive steps. This paper proposes a novel neural network model with multimodal machine learning techniques, able to detect cardiac arrhythmias rapidly and accurately. By using massive amounts of data, the model exhibits great performance, specifically in distinguishing normal and ventricular extra beats, which demonstrates its ability in feature extraction and classification. It is such a great achievement that helps to carry out disease surveillance and early diagnosis through an instant interpretation of complex ECG patterns. The study relies upon algorithmic modifications and real-time data processing, proposing a framework that combines wearable technology with AI diagnostics for reliable cardiac monitoring. The results contribute to the design of cardiac healthcare technology, which can be used to develop further advances in cardiac health monitoring and prevention of diseases.

关键词： Analytical models Visualization Heart beat Surveillance Prevention and mitigation Electrocardiography Robustness

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Analyzing and Comparing Results for Multiple Machine Learning Models 4

Analyzing and Comparing Results for Multiple Machine Learnin...

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2023 4th International Conference on computer engineering and Intelligent Control, ICCEIC 2023

作者： Ng, Jefferson Li, Yijian Ji, Xuxiao Yang, Can Wan, Jiaqiao Lu, Xingyu University of California Department of Mechanical Engineering Irvine92697 United States Courant Institute of Mathematical Sciences New York University NY10012 United States University of Waterloo School of Computer Science WaterlooCAN2L 3G1 United States Oregon State University Department of Electrical Engineering and Computer Science Corvallis97331 United States Zhengzhou University Software College Zhengzhou450000 China United World College Changshu China Jiangsu215500 China

ISBN: (纸本)9798350308877

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. This paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep Feature Rotation for Multimodal Image Style Transfer

arXiv

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arXiv 2022年

作者： Nguyen, Son Truong Tuyen, Nguyen Quang Phuc, Nguyen Hong School of Mechanical Engineering Hanoi University of Science and Technology Viet Nam School of Computer Science and Engineering University of Aizu Japan Department of Software Engineering Eastern International University Viet Nam

Recently, style transfer is a research area that attracts a lot of attention, which transfers the style of an image onto a content target. Extensive research on style transfer has aimed at speeding up processing or generating high-quality stylized images. Most approaches only produce an output from a content and style image pair, while a few others use complex architectures and can only produce a certain number of outputs. In this paper, we propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while not only producing diverse outputs but also still achieving effective stylization compared to more complex methods. Our approach is representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. We also analyze our method by visualizing output in different rotation weights. © 2022, CC BY.

关键词： Rotation

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Authentication Scheme Based on Non-Interactive Zero-Knowledge Proof for Mobile Health

Authentication Scheme Based on Non-Interactive Zero-Knowledg...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Edward Mensah Acheampong Shijie Zhou Yongjian Liao Emmanuel Antwi-Boasiako Isaac Amankona Obiri School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Electronic Science and Technology of China Chengdu China

Digital technologies, such as wireless body area networks (WBANs) for mobile health (mHealth) applications, are expected to enhance the quality of the public health care system. Although mHealth can improve patients' quality of health by offering outpatient real-time health monitoring systems instead of being stuck in the hospital all the time to monitor chronic diseases. The major challenge in adopting mHealth is data security and privacy. The health data routed on the internet from the patient monitoring device to the health center for remote monitoring is vulnerable to confidentiality attacks. To handle this issue, we present an authentication scheme based on non-interactive zero-knowledge proof (NIZKP), which issues certificates and authenticates monitoring devices each time performing transactions without revealing sensitive information. Our authentication scheme provides a high level of security with a low computational cost, which is lightweight for WBANs.

关键词： Performance evaluation Wireless communication Wireless sensor networks Patient monitoring Hospitals Authentication Real-time systems

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Analysis of Web Monitoring Servers and Tools for Cloud Computing Services 1st

Analysis of Web Monitoring Servers and Tools for Cloud Compu...

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1st International Conference on engineering software for Modern Challenges, ESMoC 2021

作者： Sarwar, Nadeem Qureshi, Junaid Nasir Iqbal, Tahir Waheed, Haroon Abdul Yasin, Muhammad Hammad Irshad, Asma Computer Science Department Bahria University Lahore Campus Lahore Pakistan Department of Software Engineering University of Central Punjab Lahore Pakistan Faculty of Computing Riphah International University Islamabad Pakistan School of Biochemistry & Biotechnology University of the Punjab Lahore Pakistan

ISBN: (纸本)9783031199677

This research aims to find best cloud service monitoring tools and techniques according to trending parameters. The services of cloud computing are most popular services now a days. Many organizations are migrating to cloud technology in order to reduce the management’s complications of resources. The effective monitoring methodologies should be implemented, to certify the Quality of Experience (QoE) and Quality of Services (QoS) of cloud computing services for end users. For this purpose, cloud services should be monitor and control using different tools and techniques. There are so many server-based or web-based monitoring tools are available. In this paper, analysis of different tools is based on their pricing, working efficiency, key features, platforms and environments in which they could be used. This paper also analyzes the cloud computing structure and investigates that which type of monitoring services are required at which level. This research paper shows that no monitoring tool is mature enough to fulfil all requirements and desires of clients. This research also interprets that which tool is recommended for which kind of platform and environment. © 2022, Springer Nature Switzerland AG.

关键词： Quality of service

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Leveraging Lexical Features for Chinese Named Entity Recognition via Static and Dynamic Weighting

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第1期48卷

作者： Zhang, Dong Chi, Chengying Zhan, Xuegang School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan China

Named entity recognition (NER) is an essential research assignment in natural language processing. Entity boundaries and word segmentation information are crucial determinants that affect the accuracy of Chinese named entity recognition. Researchers generally utilize two methods: word-based method and character-based method. Word-based methods rely heavily on segmentation tools. These methods can cause error propagation problems from word segmentation incorrectly. Character-based methods lack word-level information and word boundaries information, which will cause the problem of missing information. A novel neural entity recognition model fused with self-matched lexical word features is proposed for Chinese NER tasks in response to these problems. In the embedding layer, they were using the attention mechanism to extract the semantic features of word sequences, using the statistical character frequency to extract the syntactic features. Our proposed model is validated in many datasets. The F1 score of 73.9% and 57.65% was obtained on the OntoNotes 4 and Weibo datasets. The F1 score of 90.76% and 95.58% was obtained on the MSRA and Resume datasets, and the stateof-the-art experimental results are obtained on the Resume benchmark dataset. © 2021, IAENG International Journal of computer science. All Rigths Reserved.

关键词： Semantics

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Hybrid Segmentation and 3d Imaging: Comprehensive Framework for Breast Cancer Patient Segmentation and Classification Based on Digital Breast Tomosynthesis

SSRN

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SSRN 2024年

作者： Idress, Wail M. Abouda, Khalid A. Javed, Rawal Aoun, Muhammad Ghadi, Yazeed Yasin Shahzad, Tariq Mazhar, Tehseen Ibrahim, Ali M.A. School of Automation Central South University Changsha410000 China Department of CS and IT Ghazi University Dera Ghazi Khan Punjab Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg2006 South Africa Department of Computer Science International Islamic University Islamabad Pakistan Lahore51000 Pakistan School of Computer Science and Engineering Central South University Changsha410083 China

Breast cancer is among the deadliest conditions and the second-leading reason for cancer mortality in women. Breast cells begin to develop into malignant, cancerous tumors, which is how breast cancer develops. Self-tests and routine professional examinations aid in early diagnosis, which increases the likelihood of surviving. A major difficulty for researchers is the categorization of breast cancer as a medical procedure. The prevailing aim of this research is the earlier recognition of breast cancer using 3D images and AI monitors. Initially, the mammogram images are collected and preprocessed to enhance the image quality. Noise removal is done using a Multistage Selective Convolution Filter (MSCF), contrast enhancement is done using an Enhanced Contrast Limited Adaptive Histogram (ECLAH), and an Improved Canny Operator (ICO) is employed for edge detection. Segmentation is done using the DENSE Squeeze Excitation Network (DENSE SE- Net). The preprocessed images are converted to 3D images using Digital Breast Tomosynthesis (DBT). From the hybrid segmented and 3D images, the objects are identified utilizing the You Only Look Once v7 (YOLO v7), and the images are classified into mild, medium, severe, and unaffected women using semi-supervised CNN (SS CNN). Finally, an AI monitor is used to monitor the patients. The simulation findings reveal that the suggested technique yields higher accuracy, precision, recall, and F1 score and better ROC characteristics than the existing approaches. © 2024, The Authors. All rights reserved.

关键词： Diseases

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An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud-edge computing

An incentive mechanism for crowdsourcing markets with social...

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作者： Xu, Xiaolong Cai, Qing Zhang, Guoming Zhang, Jie Tian, Wei Zhang, Xiaorui Liu, Alex X. School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China Department of Computer Science and Engineering Michigan State University MI United States

Crowdsourcing is emerging as a powerful paradigm that utilizes the distributed devices to sense, collect, and upload data to satisfy the requirements of the users. Currently, with the popularity of edge computing, edge device users can act as recruiters or participants to publish or perform crowdsourcing tasks and share feedback. However, due to the individual selfishness, it is still a challenge to maximize the social welfare distribution of all the participants and the recruiters for the crowdsourcing market. In view of this challenge, an incentive mechanism for the crowdsourcing market with social welfare maximization in the cloud-edge computing is designed in this paper. Technically, a double action model under the cloud-edge computing framework is proposed first, which aims to maximize the social welfare maximization and meanwhile meet the demands of incentive compatibility, individual rationality, market clearing, and budget constraint. Secondly, a corresponding incentive mechanism is designed based on the double auction model to achieve the market fairness. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of the mechanism. © 2018 John Wiley & Sons, Ltd.

关键词： Crowdsourcing

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Semantic Preserving Learning for Task-Oriented Point Cloud Downsampling

Semantic Preserving Learning for Task-Oriented Point Cloud D...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianyu Xiong Tao Dai Yaohua Zha Xin Wang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Research Center of Artificial Intelligence Peng Cheng Laboratory

Recent years have witnessed a tremendous growth in the scale and resolution of point clouds. To facilitate the applications of point cloud in downsampling tasks (e.g., point cloud classification), several task-oriented downsampling works have been developed by training with the task-specific loss with one-hot encoded label. However, these methods still suffer from performance degradation at high downsampling scales. In this paper, we propose a general semantic-preserved downsampling framework (SPDF) for point clouds by exploiting the rich knowledge inherent in the task network. Specifically, we firstly refine the previous pipeline to generate richer semantic supervised information. Then, the semantic feature learning is subdivided into label-level and feature-level to guide the training of downsampling network, which can better limit the semantic loss during downsampling. Extensive experiments on the benchmark dataset show that SPDF outperforms state-of-the-art downsampling methods.

关键词： Point cloud compression Training Representation learning Knowledge engineering Degradation Semantics Pipelines

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CMIVGSD: CircRNA-miRNA Interaction Prediction Based on Variational Graph Auto-Encoder and Singular Value Decomposition

CMIVGSD: CircRNA-miRNA Interaction Prediction Based on Varia...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Qian, Yurong Zheng, Jingjing Zhang, Zhe Jiang, Ying Zhang, Jiaxuan Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China University of California San Diego Department of Cognitive Science La Jolla United States

ISBN: (纸本)9781665401265

A large amount of evidence shows that circular RNAs(circRNAs) participate in transcription and translation regulation and function as 'micro RNA(miRNA)-sponges'. Recognizing circRNA-miRNA interaction is helpful to understand the function of circRNAs, especially its role in complex diseases. Obtaining interactive information based on traditional biological experiments is usually small-scale, time-consuming, and laborious. Considering that there are few calculation methods, it is urgent to develop efficient and accurate methods to extract the interaction between circRNA and miRNA. In this work, we proposed a computational framework called CMIVGSD, which uses singular value decomposition and graph variational auto-encoders to predict circRNA-miRNA interaction. To our best knowledge, CMIVGSD is the first calculation framework to predict circRNA-miRNA interaction. CMIVGSD uses the singular value decomposition (SVD) algorithm to obtain linear features from the circRNA-miRNA interaction matrix. We have constructed the similarity networks of circRNA and miRNA, respectively. The graph variational auto-encoder (VGAE) is employed to mine the non-linear features of circRNA-miRNA in similarity networks. Finally, we combine linear and non-linear features and use LightGBM to predict interaction scores. We performed five-fold cross-validation experiments. Experimental results show that our proposed method is better than other methods. The case study further proves the effectiveness of CMIVGSD in predicting circRNA-miRNA interaction. © 2021 IEEE.

关键词： Singular value decomposition

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