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2023 ieee International symposium on Medical Measurements and Applications, MeMeA 2023

作者： Bae, Wan D. Alfonso, Angelo Stanko, David Hao, Lili Le, Linh Horak, Matthew Seattle University Department of Computer Science Seattle United States Amazon Aws Lambda Seattle United States

ISBN: (纸本)9781665493840

Predicting rare events is a critical task in many medical applications. Recent improvements in sensor technologies and superior computing power have enabled machine learning techniques to provide valuable data insights pertaining to patients' symptoms and risk factors. However, for individual-level analysis, class imbalance and small sizes of training datasets for classification models are the main roadblocks to the full utilization of machine learning for prediction of rare events in data starved medical applications. While several techniques have been proposed in this field, little exists in the literature about their performance in data starved contexts. In this study, we propose a novel extension to synthetic minority oversampling techniques, called "Average Neighbor Vector Oversampling (ANVO)", which maintains the data distribution within the minority class but increases data diversity and keeps similar class boundaries. We systematically compare it with state-of-the-art data augmentation techniques using synthetic and real asthma patient datasets. Further, we present a transfer learning framework that integrates oversampling techniques and focuses on retraining a large data model with a small amount of specialized training data and is therefore well-suited to data starved contexts. Results from our experiments demonstrate that the proposed solutions improved the performance of classifiers with a significant increase, 23.7%-61.6%, in sensitivity compared with baseline models. © 2023 ieee.

关键词： Classification (of information)

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Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms 3

Multitemporal analysis in Google Earth Engine for detecting ...

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3rd ieee India Geoscience and Remote Sensing symposium, InGARSS 2023

作者： Iandolo, Mariapia Rita Razzano, Francesca Zarro, Chiara Yogesh, G.S. Ullo, Silvia Liberata University of Sannio Engineering Department Benevento Italy Intelligentia srl Aerospace Department Benevento Italy East Point College Engineering Department Bangalore India

ISBN: (纸本)9798350325591

The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021 to monitor urban growth. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data. The code has been made open source and the repository for the paper is available on our GitHub page. © 2023 ieee.

关键词： Landsat

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A Simulator Driven by Trajectory Big data for Network Feature Extraction and data Transmission 19

A Simulator Driven by Trajectory Big Data for Network Featur...

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19th ieee International Conference on Mobile Ad Hoc and Smart Systems (MASS)

作者： Wang, Xiaohui Jiang, Ruobing Zhang, He Liu, Chao Hong, Feng Ocean Univ China Dept Comp Sci & Technol Qingdao Peoples R China

ISBN: (数字)9781665471800

ISBN: (纸本)9781665471800

Due to the rapidly growing communication requirements among mobile real-world communication devices, e.g., smartphones, vehicles, and vessels, and the resulted exponentially increasing complexity of network structures, network simulators have attracted increasing attention for the design, analysis, and evaluation of network protocols in both academia and industry. However, existing simulators fail to integrate critical function modules of network feature extraction and visualization. While significant learning burden is inevitable but unbearable for users. In this paper, we propose a trajectory big data driven network simulator with interactive visualization for large-scale networking, feature extraction and data transmission. Our simulator consists of three key modules of trajectory data preprocessing, network feature extraction, and routing protocol evaluation. The integration of multiple function modules with intuitive performance visualization greatly enhances usage convenience. In particular, diverse social network features are available for users to achieve in-depth perception on practical networks and facilitate better design of realistic protocols. A novel social community discovering algorithm dedicated for ocean vessel network is proposed as a supplement to land-based mobile networks. The developed simulator is extensively evaluated with a real dataset of vessel trajectories over East China Sea. Experimental results illustrate the feasible functions, step-by-step visualization, and convenient interfaces.

关键词： network simulator trajectory big data network feature extraction interactive visualization

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Application of Neural Networks for Network Traffic Monitoring and analysis 8

Application of Neural Networks for Network Traffic Monitorin...

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8th International symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Kushnerov, Oleksandr Murr, Pierre Herasymov, Serhii Milevskyi, Stanislav Melnyk, Marharyta Golovashych, Sergii Sumy State University Department of Economic Cybernetics Sumy Ukraine International University of Science and Technology in Kuwait Computer Engineering Department Kuwait Kuwait 'Kharkiv polytechnic institute' Cybersecurity department National Technical University Kharkiv Ukraine Set University Department of Cyber Security and information protection Kyiv Ukraine National Technical University 'Kharkiv polytechnic institute' Department of Software Engineering and Management Intelligent Technologies Kharkiv Ukraine

ISBN: (纸本)9798350354423

Monitoring and analyzing network traffic are critically important tasks in the context of increasing cyber threats. Traditional data processing methods often struggle to handle the large volumes and complexity of modern network traffic, necessitating the use of advanced technologies such as neural networks and machine learning. This article demonstrates the application of neural networks for monitoring and analyzing network traffic, highlighting their ability to learn from large datasets to detect complex patterns and anomalies. Using TensorFlow, we developed a neural network model based on an autoencoder that effectively identifies and classifies anomalies in network traffic. The model's effectiveness was validated using a synthetic dataset from Kaggle, showing high accuracy on both training and testing data. Our approach significantly improves the efficiency and accuracy of threat detection compared to traditional methods, making it a valuable tool for enhancing cybersecurity. © 2024 ieee.

关键词： Cyber attacks

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Self-Reinforcing Unsupervised Matching

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2022年第8期44卷 4404-4418页

作者： Lu, Jiang Li, Lei Zhang, Changshui Tsinghua Univ THUAI Inst Artificial Intelligence Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligence Technol & Syst Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Automat Beijing 100084 Peoples R China

Remarkable gains in deep learning usually benefit from large-scale supervised data. Ensuring the intra-class modality diversity in training set is critical for generalization capability of cutting-edge deep models, but it burdens human with heavy manual labor on data collection and annotation. In addition, some rare or unexpected modalities are new for the current model, causing reduced performance under such emerging modalities. Inspired by the achievements in speech recognition, psychology and behavioristics, we present a practical solution, self-reinforcing unsupervised matching (SUM), to annotate the images with 2D structure-preserving property in an emerging modality by cross-modality matching. Specifically, we propose a dynamic programming algorithm, dynamic position warping (DPW), to reveal the underlying element correspondence relationship between two matrix-form data in an order-preserving fashion, and devise a local feature adapter (LoFA) to allow for cross-modality similarity measurement. On these bases, we develop a two-tier self-reinforcing learning mechanism on both feature level and image level to optimize the LoFA. The proposed SUM framework requires no any supervision in emerging modality and only one template in seen modality, providing a promising route towards incremental learning and continual learning. Extensive experimental evaluation on two proposed challenging one-template visual matching tasks demonstrate its efficiency and superiority.

关键词： visualization Task analysis Heuristic algorithms Dynamic programming Manuals Tensors Pattern analysis Continual learning self-learning system unsupervised learning image matching dynamic programming

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Performance and Comparative analysis of Elliptic Curve Cryptography and RSA

Performance and Comparative Analysis of Elliptic Curve Crypt...

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2023 ieee East-West Design and Test symposium, EWDTS 2023

作者： Jintcharadze, Elza Abashidze, Marina Batumi Shota Rustaveli State University Department of Computer Science Batumi Georgia Batumi Maritime Academy Maritime Faculty Batumi Georgia

ISBN: (纸本)9798350314847

Due to the development of computer technologies, it becomes necessary to create new or improve existing information security algorithms. data security is one of the important issues, especially for increasing amount of transactions via the internet. Today, there are several cryptosystems for information security that are recognized as standards, including the public key cryptosystem RSA. However, Elliptic Curve Cryptography (ECC) has also recently become relevant. Performing large-key encryption quickly requires high device performance, which creates a problem. Elliptic curve cryptography handles this problem well. This paper presents a comparison of the performance and efficiency of the elliptic curve cryptographic method and the asymmetric RSA algorithm. Within the framework of the thesis, a software code was created, which was used to compare the given algorithms. Comparative analysis showed that elliptic curve cryptography is faster than RSA algorithm. The main advantage of the ECC system is that it can provide the same security as RSA with a smaller key size. There is a big difference between ECC and RSA algorithms in terms of information security. Therefore, the ECC algorithm is better for working with embedded systems. © 2023 ieee.

关键词： Security of data

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Chest X-Ray Quality Assessment Method With Medical Domain Knowledge Fusion

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ieee ACCESS 2023年 11卷 22904-22916页

作者： Du, Shanshan Wang, Yingwen Huang, Xinyu Zhao, Rui-Wei Zhang, Xiaobo Feng, Rui Shen, Quanli Zhang, Jian Qiu Fudan Univ Sch Informat Sci & Technol Shanghai 200433 Peoples R China Fudan Univ Childrens Hosp Nursing Dept Shanghai 201102 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Fudan Univ Childrens Hosp Resp Dept Shanghai 201102 Peoples R China Fudan Univ Childrens Hosp Dept Radiol Shanghai 201102 Peoples R China

Digital X-ray radiography is widely used in clinical diagnosis. High quality chest X-ray is conducive to the accurate diagnosis of diseases by clinicians. However, the quality assessment of the chest X-ray images mainly depends on the subjective evaluation of doctors, and the results are influenced by the skill level and experience of the evaluators, involving many issues such as heavy workload and various uncertain factors in such subjective judgment. In this paper, we propose a chest X-ray quality assessment method that combines image-text contrastive learning with medical domain knowledge fusion. Based on pretraining the model from contrastive text-image pairs, large-scale real clinical chest X-ray and diagnostic report text information are fused, and the model is fine-tuned to achieve cross domain transfer learning. While improving the prediction accuracy of the algorithm, the cost of massive sample data annotation is avoided. The local visual patch features of the X-ray images are aligned with multiple text features to ensure that the visual features contain more fine-grained image information. Theoretical analysis and experimental results show that the contrastive learning algorithm based on the fusion of triplet information in medical knowledge graph and chest X-ray multi-modal data has achieved good performance in terms of accuracy. In addition, the method proposed in this paper can be easily extended to complete other tasks such as medical image multi-lesion segmentation and disease progression prediction.

关键词： X-ray imaging Medical diagnostic imaging Image quality Quality assessment Lungs visualization Quality control Biomedical imaging Chest X-ray INDEX TERMS image quality assessment no reference assessment image-text contrastive learning knowledge graph

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Heterogeneous and Customized Cost-Efficient Reversible Image Degradation for Green IoT

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ieee INTERNET OF THINGS JOURNAL 2023年第3期10卷 2630-2645页

作者： Zhao, Ruoyu Zhang, Yushu Lan, Rushi Hua, Zhongyun Xiang, Yong Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & G Intelligent Proc Guilin 541004 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia

With the large-scale deployment of the Internet of Things (IoT) in daily life, more and more privacy data are collected by IoT devices. These data are not directly physically controlled by users, which may cause privacy concerns. In fact, privacy has become one of the significant problems faced by IoT. In this article, we mainly study the protection of image privacy under the green IoT. We have conducted an in-depth analysis of the green IoT scenario and put forward the scope and corresponding goals that the scheme should have. Motivated by this, a novel image privacy protection scheme is proposed, i.e., heterogeneous and customized cost-efficient reversible image degradation for green IoT. This scheme fully considers the characteristics of privacy and the various users' diverse requirements to achieve a heterogeneous and customized privacy protection. Meanwhile, cost effectiveness cannot be confined to the efficiency of the direct image processing at the expense of greatly increasing costs in other aspects, such as transmission and reversion. It is mitigated by preserving some visual content in the privacy-protected image. It also improves the image compression efficiency and ensures that the user can select the desired image according to the visual content for reversion. Some experiments have been carried out to demonstrate that this work has achieved the proposed scope and corresponding goals.

关键词： Privacy Internet of Things Costs visualization Green products Degradation Cameras Cost efficiency green Internet of Things (IoT) privacy protection usability

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A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance data 14

A Visual Analytics Approach for the Diagnosis of Heterogeneo...

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14th ieee Pacific visualization symposium (ieee PacificVis)

作者： Zhang, Xiaoyu Fujiwara, Takanori Chandrasegaran, Senthil Brundage, Michael P. Sexton, Thurston Dima, Alden Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Delft Univ Technol Delft Netherlands NIST Gaithersburg MD 20899 USA

ISBN: (纸本)9781665439312

analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine repair and maintenance in an organization often need to be analyzed to diagnose errors and identify abnormal patterns. formalizi. root-cause analyses, and plan preventive maintenance. Such real-world datasets are also beset by issues such as inconsistent and/or missing entries. To conduct an effective diagnosis, it is important to extract and understand patterns from the data with support from analytic algorithms (e.g., finding that certain kinds of machine complaints occur more in the summer) while involving the human-in-the-loop. To address these challenges, we adopt existing techniques for dimensionality reduction (DR) and clustering of numerical, categorical, and text data dimensions, and introduce a visual analytics approach that uses multiple coordinated views to connect DR + clustering results across each kind of the data dimension stated. To help analysts label the clusters, each clustering view is supplemented with techniques and visualizations that contrast a cluster of interest with the rest of the dataset. Our approach assists analysts to make sense of machine maintenance logs and their errors. Then the gained insights help them carry out preventive maintenance. We illustrate and evaluate our approach through use cases and expert studies respectively, and discuss generalization of the approach to other heterogeneous data.

关键词： Visual analytics heterogeneous data high-dimensional data machine learning text analytics maintenance logs

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A Case Study on an End-to-End Safety Risk Management Process 70

A Case Study on an End-to-End Safety Risk Management Process

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70th Annual Reliability and Maintainability symposium (RAMS)

作者： Yellamati, David Deepak Goktas, Yavuz Hu, Yunwei Wayne State Univ Detroit MI USA Schneider Elect Rueil Malmaison France

ISBN: (纸本)9798350307696

Safety Risk Management plays a critical role in design and development of mission critical equipment such as Power Electronic Systems. These systems are used in data Centers, EV (Electric Vehicle) and EV Charging Infrastructure, Manufacturing Facilities, Hospitals and in many other industrial and commercial applications. Failures of these systems can potentially lead to Safety issues such as a) Human Injury due to Arc Flash, Shock Hazard, Explosion etc., or b) Property Damage, Regulatory non-compliance issues such as Unsuitable Voltage or Surge Protection during their useful life. These issues can have a detrimental effect on health of the organization and communities at large. analysis of the root causes for majority of these types of issues have been found to be due to lack of design robustness over the useful life because of ineffective Safety Risk Management process. In this paper, we describe an end-to-end streamlined process to manage Safety Risks during design using an example of a Capacitor Bank Sub-system that is part of Variable Frequency Drive (VFD) System (which is a Power Electronic System). This process consists of i) Identification of CTQs (Critical-to-Quality) characteristics from the Voice of Customer (VoC) & QFD (Quality Function Deployment) analysis, ii) Development of a Parameter (P) Diagram working backwards from the CTQs, iii) Development of Design Failure Modes & Effects analysis (DFMEA) from P-Diagram and identification of top safety risks via RPNs (Risk Priority Numbers) and Severity Ranking, iv) Deep dive and quantification of Safety Critical Risks i.e., End Effects of Severity of 9 (Property Damage, Regulatory non-compliance) & 10 (Human Injury) identified from DFMEA using FTA (Fault Tree analysis) and vii) Risk Mitigation & Quantification of Improvements. We have illustrated the application of end-to-end product risk management process on a Capacitor Bank Sub-system of the VFD system as an example and demonstrated that potential design ri

关键词： Variable Frequency Drive Voice of Customer Analytical Hierarchy Process Quality Function Deployment (QFD) Change Point analysis Parameter Diagram DFMEA Fault Tree analysis

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