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6th international conference on Biomedical Imaging, signal processing, ICBSP 2021

作者： Mittal, Bhuvan Oh, Junghwan Department of Computer Science and Engineering University of North Texas United States

ISBN: (纸本)9781450385817

the Covid-19 pandemic has caused more then 193 million cases and 4.1 million deaths worldwide as of July 2021. the Fleischner Society reported that Computerized Tomography (CT) is a useful tool for the early identification of Covid-19. Covid-19 disease induces lung changes which can be observed in lung CT predominantly as ground-glass opacification (GGO) and occasional consolidation in the peripheries. Moreover, it was reported that the percentage of lung showing disease correlates with the severity of the disease. therefore, segmentation of the disease areas in CT images is a logical first step to quantify disease severity. In this paper, we propose g CoviSegNet Enhanced' based on a U-Net with an 813-layer EfficientNetB7 encoder having an attention mechanism to segment the Covid-19 disease area observed in CT images of Covid-19 patients. CoviSegNet Enhanced is an improvement of our previous work g CoviSegNet'. the experiments performed on three public CT datasets and a detailed comparison with recently published work confirms that the proposed CoviNet Enhanced using deep learning approaches is highly effective for Covid-19 segmentation. © 2021 ACM.

关键词： Computerized tomography

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Fault Diagnosis of Electrical Equipment through thermal Imaging and Interpretable machine learning Applied on a Newly-introduced Dataset 6

Fault Diagnosis of Electrical Equipment through Thermal Imag...

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6th international conference on signal processing and Intelligent Systems (ICSPIS)

作者： Najafi, Mohamad Baleghi, Yasser Gholamian, Sayyed Asghar Mirimani, Seyyed Mehdi Noshivani Univ Technol Elect & Comp Engn Babol Iran Noshivani Univ Technol Power Syst Babol Iran

ISBN: (纸本)9781728186290

In this study, an interpretable, fully automated pipeline for condition monitoring of electrical equipment using thermal imaging is proposed. A wider array of defects in comparison with other thermography surveys is investigated. While many fault conditions led to significant heat dissipation, a number of fault conditions result in even less heat dissipation than that of healthy equipment, implying a challenging segmentation. To overcome this problem, a pre-processing step is applied which divides data into two distinct categories according to the equipment's thermal state, namely 'cold' and 'hot' states. Afterwards, Random Forest and AdaBoost classifiers are utilized for segmentation using a sliding window approach, with regard to Interpretable machine learning. Moreover, a new dataset of infrared images of transformer and 3-phase induction motors is created. the proposed method has been evaluated on the very same dataset, achieving state-of-the-art results.

关键词： Infrared thermography Interpretable machine learning Image processing Condition Monitoring

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Application process of machine learning in cyberspace security

Application process of machine learning in cyberspace securi...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Han Bing Sun Hao Zhong Fangwei Zou Shuai Qian Tao Yu Changjiang Xinjiang Information Industry Co. Ltd XinJiang China

ISBN: (纸本)9781665447300

Under the influence of the rapid development of computer Internet technology, computers and computer technology have been widely spread and applied in various fields of society. Hundreds of millions of network access points, networked devices and network applications have produced massive amounts of Data has brought huge difficulties and challenges to cyberspace security, and the emergence of machine learning has provided a series of effective analysis and decision-making tools, especially its advocacy for the effective use of computer information. Have human thinking, become smarter and smarter, in order to help people solve various computer network security problems. Guided by the first-level discipline of cyberspace security, this article focuses on the latest research results of the application of machine learning technology in the field of cyberspace security, and elaborates the application process of machine learning technology in cyberspace security research. Finally, it summarizes the problems in existing solutions, as well as the future development direction and challenges of machine learning technology in cyberspace security research.

关键词： Decision making Cyberspace machine learning Tools signal processing Internet Computer security

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EMERGING thREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT 22

EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT

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Proceedings of the 6th international conference on Future Networks & Distributed Systems

作者： Murooj Amer Taha Wijdan Mahood Khudhair Ahmed Mahmood Khudhur Omar Abdulkareem Mahmood Yousif I. Hammadi Riyam Shihab Ahmed Al-husseinawi Ahmed Aziz Department of Computer Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of International Business Management Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. these procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. this study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting.

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Research on the Key Technology of Network Security Based on machine learning

Research on the Key Technology of Network Security Based on ...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yong He School of Preschool Education Jiangxi Teachers College

ISBN: (纸本)9781665447300

With the continuous development of information technology, the huge number of network devices, applications, and the explosive expansion of network data have made the network environment increasingly complex, posing huge potential risks to network security. the targets of cyber attackers are no longer limited to cyber attacks on ordinary users, but they have shifted their targets to network environments with related backgrounds such as enterprises, governments, and countries. the diversification of network services has generated massive amounts of Internet data, and traditional network security technologies have been difficult to meet the current needs of network security in terms of performance and self-adaptability. Research on network security based on machine learning has achieved many results, showing strong capabilities in processing massive data, automatic learning, detection and identification, and broadening the development of ideas in the field of network security. In this paper, we combine machine learning-related technologies to improve intrusion detection performance and alarm correlation automation, and investigate key technologies such as machine learning-based network security situational awareness methods and dynamic data stream classification methods based on judgment feedback, in order to improve the detection performance, adaptive and generalization capabilities of machine learning-based network security technologies.

关键词： Knowledge engineering Correlation Network intrusion detection machine learning signal processing Internet Security

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Detection of Marine Chemical Pollution Based on Image processing and machine learning 22

Detection of Marine Chemical Pollution Based on Image Proces...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Lingyun Chen Mingjie Gao Langyu Wang Chuhan Xue Department of Information Beijing University of Technology China Department of Electrical and Computer Engineering Queen's University Canada School of Rail Transportation Soochow University China School of Electric Power South China University of Technology China

ISBN: (纸本)9781450397148

the problem of oil spills is lethal to the ocean ecosystem. To solve the problem, one of the most important key steps is to detect the ocean surface and judge whether there are or not oil spills. Remote sensing provides the advantage of controlling and observing events remotely, and it can cover the areas that people cannot access, so we use it to build a database. Next, we choose to use Matlab for the pre-image processing and then use the neural network by Python to realize and there are five pre-processing methods: expanding the dynamic histogram range in the ‘Y’ channel (method 1), expanding the dynamic histogram range in three channels (method 2), contrast enhancement (method 3), expanding the dynamic histogram range and then contrast enhancement (method 4), and contrast enhancement, and then expanding the dynamic histogram range (method 5). Finally, we use a neural network to test accuracy, in comparison, method 1 is the best and we improve the accuracy from 72% to 82%.

关键词： Contrast Histogram Image processing Neural network

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Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition 47th

Uncertainty Estimation in the Real World: A Study on Musi...

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47th European conference on Information Retrieval, ECIR 2025

作者： Watcharasupat, Karn N. Ding, Yiwei Ma, T. Aleksandra Seshadri, Pavan Lerch, Alexander Music Informatics Group Georgia Institute of Technology AtlantaGA United States University of Würzburg Würzburg Germany

ISBN: (纸本)9783031887109

Any data annotation for subjective tasks shows potential variations between individuals. this is particularly true for annotations of emotional responses to musical stimuli. While older approaches to music emotion recognition systems frequently addressed this uncertainty problem through probabilistic modeling, modern systems based on neural networks tend to ignore the variability and focus only on predicting central tendencies of human subjective responses. In this work, we explore several methods for estimating not only the central tendencies of the subjective responses to a musical stimulus, but also for estimating the uncertainty associated with these responses. In particular, we investigate probabilistic loss functions and inference-time random sampling. Experimental results indicate that while the modeling of the central tendencies is achievable, modeling of the uncertainty in subjective responses proves significantly more challenging with currently available approaches even when empirical estimates of variations in the responses are available. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Emotion recognition Psychometric machine learning Uncertainty quantification

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Augmented Pre-Segmentation Method for Medical Image Dataset Based on machine Vision 22

Augmented Pre-Segmentation Method for Medical Image Dataset ...

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Proceedings of the 2022 6th international conference on Computational Biology and Bioinformatics

作者： Xuan Huang Zhijun Yang Jiawei Yang Dapeng Zou Han Sun State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment China and Guangdong University of Technology China Foshan HDUP Technology Co. Ltd China and Guangdong University of Technology China Guangdong University of Technology China Chinese Academy of Sciences China and Guangdong University of Technology China

ISBN: (纸本)9781450397636

At present, the preparation of data is a costly and time-intensive process in in the deep learning tasks of medical images. At the same time, there is more noise in labeling, and the time cost of labeling is relatively high. We propose a method based on machine vision to reproduce multiple valid samples from original samples. In the process, the morphological feature information of the image is first identified and exacted from the medical image. Secondly, a priori features are added to divide the pictures while improving the sample availability rate. third, the Roberts quality evaluation score is calculated to exclude low-quality samples. the example presented in the experiment shows that the sample dataset was increased up to 50-100 times the original through image processing on laparoscopic vascular images. the samples reproduced by our method can also be marked with the thick label of the original image.

关键词： Data preprocessing machine vision Medical image

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Search for supersymmetric sleptons and charginos with the ATLAS detector

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JOURNAL OF INSTRUMENTATION 2023年第8期18卷 C08009页

作者： Ballabene, Eric Univ Bologna Dept Phys & Astron Augusto Righi Viale Berti Pichat 6-2 Bologna Italy Ist Nazl Fis Nucl Sez Bologna Viale Berti Pichat 6-2 Bologna Italy

A new search for the electroweak production of supersymmetric particles decaying into two leptons with missing transverse momentum is presented. Two simplified models are considered: direct pair production of sleptons decaying into the lightest neutralinos through leptons of the Standard Model (SM) and direct pair production of the lightest charginos decaying into the lightest neutralinos through bosons of the SM. the analysis targets phase space regions where the difference in mass between the slepton or the lightest chargino and the lightest neutralino is close to or below the mass of the boson. Such regions with compressed mass spectra have not been covered by any searches conducted so far due to the low cross-section of the supersymmetric signal. therefore, improved analysis strategies are crucial to separate the supersymmetric signal from the SM backgrounds. A search for an excess of same-flavour lepton pairs in opposite-sign lepton events is made in the direct slepton pair production analysis while a machine learning approach using boosted decision trees is exploited in the chargino pair production analysis and considering both the same-flavour and different-flavour channels. No significant excesses over the expected background are observed using proton-proton collisions data collected by the ATLAS experiment at root = 13 TeV and exclusion limits at 95% confidence level are set for each considered model. Exclusion limits are also set for selectrons and smuons separately and portions of the region excluded by the search of smuons pair production are expected to be compatible with the - 2 anomaly for small tan values.

关键词： Analysis and statistical methods Data processing methods

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Golub Kahan Based Methods for Partial Multidimensional Singular Value Decomposition

Golub Kahan Based Methods for Partial Multidimensional Singu...

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IEEE international conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Anas El Hachimi Mustapha Hached Khalide Jbilou Ahmed Ratnani UM6P Vanguard Center Mohammed VI Polytechnic University Green City Morocco Université du Littoral ULCO Calais France Laboratoire P. Painlevé Université de Lille Villeneuve d’Ascq France Laboratoire LMPA Université du Littoral ULCO Calais France Mohammed VI Polytechnic University Green City Morocco

Many applications of machine learning, data, image and signal processing require the design of algorithms capable of dealing with very large datasets. Extracting the most meaningful features out of these large datasets is often a computationally challenging task. In the matrix case, the Singular Value Decomposition (SVD) has shown to be a very efficient tool to do so. In recent years there has been a very significant increase in the amount of available data and the datasets are often formatted as tensors, for which some generalisations of the SVD have already been proposed for some specific choices of tensor-tensor products, well adapted to tensors of order 3. In this work, we propose a new generalisation, associated with the well-known Einstein product, regardless of the order of the tensors. As in the matrix case, only a few singular elements need to be computed and we address this problem by proposing an iterative approach based on the tensor Lanczos bidiagonalization applied to the Einstein product. We also propose a refinement to this method in order to increase its accuracy. We give numerical results for both synthetic and real data to illustrate the efficiency of these methods.

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