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UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-Wide-Angle Transformation Multi-scale GAN 26th

UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra...

引用

26th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Fang, Zhaojie Chen, Zhanghao Wei, Pengxue Li, Wangting Zhang, Shaochong Elazab, Ahmed Jia, Gangyong Ge, Ruiquan Wang, Changmiao Hangzhou Dianzi Univ HDU ITMO Joint Inst Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Jinan Univ Shenzhen Eye Hosp Shenzhen 518040 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518060 Peoples R China Shenzhen Res Inst Big Data Med Big Data Lab Shenzhen 518172 Peoples R China

ISBN: (纸本)9783031439896;9783031439902

Fundus photography is an essential examination for clinical and differential diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF) techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have detrimental influences. To avoid negative impacts, cross-modality medical image generation algorithms have been proposed. Nevertheless, current methods in fundus imaging could not produce high-resolution images and are unable to capture tiny vascular lesion areas. This paper proposes a novel conditional generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO. Using multi-scale generators and a fusion module patch to better extract global and local information, our model can generate high-resolution images. Moreover, an attention transmit module is proposed to help the decoder learn effectively. Besides, a supervised approach is used to train the network using multiple new weighted losses on different scales of data. Experiments on an in-house UWF image dataset demonstrate the superiority of the UWAT-GAN over the state-of-the-art methods. The source code is available at: https://***/Tinysqua/UWAT-GAN.

关键词： Fluorescein Angiography Cross-modality Image Generation Ultra-Wide-angle Fundus Imaging Conditional Generative Adversarial Network

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A Design Scheme of data Security for Unmanned Aerial Vehicles 1

引用

6th European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid Information processing (ADHIP)

作者： Yang, Dongyu Zhao, Yue Yi, Zhongqiang Yang, Dandan He, Shanxiang Sci & Technol Commun Secur Lab Chengdu 610041 Peoples R China China Elect Technol Grp Corp Res Inst 30 Chengdu 610041 Peoples R China China Elect Technol Cyber Secur Co Ltd Chengdu 610041 Peoples R China Shanghai Tunnel Engn & Rail Transit Design & Res Shanghai Peoples R China Chengdu Luxingtong Informat Technol Co Ltd Chengdu Peoples R China

ISBN: (数字)9783031287879

ISBN: (纸本)9783031287862;9783031287879

Since the 21st century, informatization, modernization and intellectualization have become an important direction of science and technology development, especially in recent years, with the continuous improvement and perfection of artificial intelligence, 5G, edge computing and autonomous unmanned technology, the UAV industry has made unprecedented development and has been applied to many fields of social life. However, with the development and popularity of UAVs, the development of emerging technologies such as autonomous analysis, unmanned traffic control, UAV swarms, and artificial intelligence continue to increase the complexity of the unmanned systems domain, making cyber attacks against UAVs more and more frequent, and the chances of security threats and potential hazards are increasing. Currently, UAV cyber security has become a very important issue, especially UAV data security, but there are few papers that give systematic solutions for UAV data security. This paper first provides a systematic analysis of UAV security threats from a data security perspective. Next, the existing UAV security protection strategies are analyzed from three aspects: UAVplatform security, communication network security, and ground station security. Then, the proposed UAV data security design scheme is introduced in detail. Finally, the full paper is summarized and suggestions for the development of UAV security are given.

关键词： UAV data security Network security

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Comparing EMG Signal Classification with Raw and Time Domain Feature Extracted data

Comparing EMG Signal Classification with Raw and Time Domain...

引用

international conference on Advanced computing, ICAC

作者： Vaishaalee Trishaa.B V. Sathiesh Kumar Department of Electronics Engineering Madras Institute of Technology Anna University Chennai India

The Electromyogram (EMG) signal is a commonly used signal in biomedical engineering for studying muscle activity. This study investigates, the classification of EMG signals using raw data and Time Domain (TD)extracted data using popular Machine Learning(ML) algorithms such as Decision Tree(DT), Random Forest(RF), Naive Bayes(NB), Support Vector Machine(SVM),K-Nearest Neighbor(KNN), Logistic Regression(LR) and Linear Discriminant Analysis (LDA).Two online dataset one with 10 different finger movements, the other with 6 different wrist movements was analysed using the above mentioned algorithms. A custom made data set for 5 wrist actions was also classified. The EMG signals were collected from nine healthy individuals using a single channel with three electrodes from an EMG machine. On unprocessed data and TD data that was taken from three datasets, ML methods were used. The accuracy for the finger data, wrist movement data, and custom generated data showed that RF performed the best, with accuracy values of 0.97, 0.84, and 0.94 percent, respectively. Finally, Principal Component Analysis(PCA) was carried out, and outcomes were attained.

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Alzheimer's Disease Classification Approach Using MRI Imaging 3

Alzheimer's Disease Classification Approach Using MRI Imagin...

引用

3rd international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2023

作者： Khaled, Eman Amr, Farah Essam, Farah Nouh, Maryam Sherif, Youssef Yakan, Zyad Gayar, Ziad El Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Scinece Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped dataset with features extracted from the CNN model we provided an

关键词： Magnetic resonance imaging

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Research on bank customer churn model based on attention network

Research on bank customer churn model based on attention net...

引用

Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Yang Wang Shanhong Zheng Gang Liu Jiajin Li Changchun University of Technology Changchun China

In recent years, with the popularization of mobile devices and the rapid development of the mobile Internet, the growth of data has led to the obvious development of emerging industries such as Internet finance. In this context, the traditional financial industry has been severely impacted by the Internet finance industry. However, the growth of data is a "double-edged sword", which brings more management costs to enterprises and also contains more valuable information. User churn prediction is one of the most discussed issues in banking business. Exploring a user churn prediction model suitable for the existing data environment is of great significance to the development of banking business. In this paper, the attention weight is added to the three neural networks of LSTM and GRU after the data processing, and more accurate prediction results are obtained.

关键词： Electrical engineering Neural networks Finance Banking Predictive models Prediction algorithms data models

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Algorithm optimization of multi-source heterogeneous big data in the context of cloud computing

Algorithm optimization of multi-source heterogeneous big dat...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Yajie Liu Tianjin University of Technology Tianjin China

Nowadays, Internet technology has rapidly spread to all over the world, which brings a large amount of information data, and how to achieve efficient processing in these rapidly growing heterogeneous data has become a research hotspot in the field of big data nowadays. For this hotspot, this paper firstly introduces multi-source heterogeneous and cloud computing, and then proposes a multisource heterogeneous big data optimization method, which obtains big data information sources for data scheduling priority calculation by threshold comparison in the context of cloud computing, determines data by priority value, and selects suitable processors for execution, and proves that this algorithm has the advantages of higher acceleration ratio and throughput by comparing this algorithm with traditional big data scheduling methods.

关键词： Cloud computing Program processors Processor scheduling Optimization methods Computer applications Big data Throughput

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Smart Grid Privacy Via Differential Privacy Of Smart Metering data

Smart Grid Privacy Via Differential Privacy Of Smart Meterin...

引用

computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Abdulaziz Almaleh Shatha Lahiq Fatima Al-Shehri College of Computer Science King Khalid University Abha Saudi Arabia Information Security King Khalid University Abha Saudi Arabia

The information collected by smart meters may be used in several ways. The collection and analysis of this data needed a substantial amount of time and money for the development of the smart grid. Despite the potential advantages of smart meters, the dissemination of such data could have a considerable effect on the lack of privacy. Implementing algorithms for power analysis to gather and disclose a user’s personal information and interests based on the amount of power the user consumes has shown this. To protect the privacy of customers, we recommend using a differential privacy model for data aggregation after the collection of data by smart meters. To demonstrate this, we have used the Laplace mechanism to the row records of the smart meters, which protects the confidentiality of customer information.

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More Speaking or More Speakers?

More Speaking or More Speakers?

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dan Berrebbi Ronan Collobert Navdeep Jaitly Tatiana Likhomanenko Carnegie Mellon University Apple

Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best of our knowledge, there is no analysis of how the composition of the labelled and unlabelled datasets used in these methods affects the results. In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0), and a recent ST algorithm (slimIPL). We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa. Our findings suggest that SSL requires a large amount of unlabeled data to produce high accuracy results, while ST requires a sufficient number of speakers in the labelled data, especially in the low-regime setting. In this manner these two approaches improve supervised learning in different regimes of data composition.

关键词： Systematics Supervised learning Signal processing algorithms Training data Self-supervised learning Signal processing Acoustics

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The data and Images of Natural Disaster News Report Based on Artificial Intelligence Technology processing

The Data and Images of Natural Disaster News Report Based on...

引用

international conference on Computer Research and Development (ICCRD)

作者： Chunxiao Wang Ruiqi Peng Computer Science City University of Hong Kong Beijing China Data Journalism Communication University of China Beijing China

In recent years, the technology of writing data news report by using artificial intelligence has been rapidly developed. The news that is generated automatically has strong timeliness. However, due to lack of humanistic care, it can't be widely used in long reports. Based on the collection and analysis ability on data of artificial intelligence, the data news can be completed by program algorithms and machine learning. By taking use of natural disaster data extracted by crawler, and achieving recognition and classification of disaster weather warning images by convoluted networks, this paper comes up with the model of data and images in the natural disaster news report based on artificial intelligence technology processing.

关键词： Machine learning algorithms Image recognition Computational modeling Crawlers Machine learning Writing data models

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Prediction of obesity among school going children using Machine learning algorithms

Prediction of obesity among school going children using Mach...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： Seeta Devi Ranjana Chavan Harshita Gupta Harikrishnan R Symbiosis College of Nursing (SCON) Symbiosis International Deemed University (SIDU) Pune India Symbiosis Institute of Technology (SIT) Pune Campus Symbiosis International Deemed University (SIDU) India

Obesity has developed as a major global concern in the modern era due to the continuing trend towards unhealthy lifestyles characterized by excessive junk food consumption, insufficient sleep patterns, and prolonged periods of sedentary behavior. This research aims to predict the possibility of obesity using machine learning algorithms. We collected data from a total of 250 samples, which included moms and school-aged children of varied ages, as well as those affected by obesity and those who were not. Six well-ML learning techniques were used to study this. We used supervised machine learning techniques including RF, DT, GB, adaboost, catboost, and MLP to evaluate the suggested model. The results showed that combining the AdaBoost and MLP algorithms produced the best results, with 96% accuracy, 92% precision, 96% recall, and 94% F1. Using machine learning to analyze public health data can improve forecasts, find complicated patterns, and improve our understanding of issues such as identifying obesity risk factors.

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