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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1866 条记录，以下是181-190 订阅

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machine learning based Offensive Internet Traffic Detection 3

Machine Learning based Offensive Internet Traffic Detection

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Gohel, Amit M. Vanjara, Pratik A. Marwadi University Faculty of Computer Applications Rajkot India Shree M. & N. Virani Science College Computer Science Rajkot India

ISBN: (纸本)9781665494991

Internet Traffic Classification is a vital focus in the domain of computer science. This paper emphasize on essential to study and recognize types of applications curving within network by that ISP or network operators can handle overall performance of a network. In this paper different strategy conventional method to order web traffic like Port and Pay-Load related along with machine learning Based strategy. The most well-known procedure utilized these days is machine learning (ML) strategy. In this paper, Un-supervised ML approach is applied to compare it with previously applied supervised ML approach with different datasets like textual, multi-media and cyber bullying on social media platforms as well. Expectation Maximization based clustering algorithm for unsupervised approach with more than 90% accuracy which is less than 10% more accurate than Naïve Bayes classifier for supervised approach. The earlier unknown applications and tools traffic is also determine using un-supervised technique. © 2022 IEEE.

关键词： Clustering algorithms

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Exploring the Potential of Transfer learning for Chatter Detection 3

Exploring the Potential of Transfer Learning for Chatter Det...

引用

3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Unver, Hakki Ozgur Sener, Batihan TOBB Univ Econ & Technol Dept Mech Engn Ankara Turkey

Chatter detection and avoidance are indispensable for many industries that rely on the machining process. The physics-based analytical models and recently successful machine learning methods can provide solutions using data from a unique setting. When the primary conditions of machining alter, new data needs to be collected, and analysis/training should be revised. Unfortunately, data collection is time-consuming and expensive for all machine learning applications. Therefore, broader applications of these methods are usually hindered at high production rate machining shops. Transfer learning aims to attenuate this critical barrier of machine learning implementations by transferring knowledge generated from a source domain to a different but related domain. As the concept has immense potential as an accelerator for machine learning applications, it has many prospects in Industry 4.0 framework. This article provides an introduction to transfer learning and briefly overviews its categorizations. Afterward, its potential for chatter detection is explored, and potential strategies are exemplified. Recent studies in the literature within the strategies are briefly presented as well. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Transfer learning Chatter Machining machine learning

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Research on machine Translation (MT) System Based on Deep Reinforcement learning 3

Research on Machine Translation (MT) System Based on Deep Re...

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3rd international conference on computer Science and Management Technology, ICCSMT 2022

作者： He, Junchen Fuzhou University of International Studies and Trade School of Foreign Languages Fujian Fuzhou350202 China University of Perpetual Help System DALTA Graduate School Las Pinas Philippines

ISBN: (纸本)9798350320404

With the deepening of globalization and the rapid development of human society and economic society in the world, the demand for machine translation in human society is also increasing rapidly, and the progress of artificial intelligence technology has put forward new requirements for the quality of machine translation. At the same time, the development of machine translation research has set a benchmark for other fields of natural language processing. Therefore, the research on machine translation not only has high practical value, but also can promote the progress of the theoretical research on natural language processing. Based on the idea of deep learning, this paper selects English language features as feature parameters, uses fine-tuning Python model training technology and feature-based deep learning method, and conducts deep reinforcement learning on Anglo-German machine translation model. The comparative experiment of Indian English-German neural machine translation model shows that, The machine translation system based on deep learning is obviously better than traditional machine translation, and the translation effect is obviously improved. © 2022 IEEE.

关键词： Reinforcement learning

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Use of transfer learning for detection of structural alterations 3

Use of transfer learning for detection of structural alterat...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Reyes-Carmenaty, Guillermo Perez, Marco A. Univ Ramon Llull IQS Sch Engn Via Augusta 390 Barcelona 08017 Spain

Structural health monitoring is a discipline dedicated to the detection, identification, location and quantification of damage in structures based on performance indicators. Given the aim of the discipline, non-destructive method for data acquisition are always preferred. One such method is vibration-based testing, with which this work concerns itself There has been widespread use of both machine learning applications when dealing with vibration data and use of computer vision-oriented machine learning models with pictures of the studied structure in order to address the concerns of structural health monitoring applications;this work propose a combination of the two. Since there are many pre-trained models for computer vision-oriented applications, this work successfully proposes a method for harnessing such models for processing of vibrational data through the use of transfer learning methodologies and finite element models. This can be achieved thanks to the visual nature of the Complex Frequency Domain Assurance Criterion (CFDAC) matrix, which can be obtained from vibrational data. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： transfer learning structural health monitoring machine learning convolutional neural networks frequency response function

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Remote Monitoring and Early Warning System of Intelligent Fault Alarm in Instrumentation with Extreme machine learning 3

Remote Monitoring and Early Warning System of Intelligent Fa...

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Zhu, Yidan Zhang, Xiaowei The 723 Institute of China State Shipbuilding Corporation Jiangsu Yangzhou225101 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Remote monitoring and early warning system of intelligent fault alarm in instrumentation with extreme machine learning is studied in the paper. The most notable feature of the application of intelligent instrumentation technology is the versatility of the functions. Although the volume of smart instrumentation is smaller than that of general traditional instrumentation, it has many functions, even more than before. Hence, the efficient analysis will be essential for the analysis. Traditional intelligent instruments mainly use computer technology in the measurement process, while virtual instruments absorb instrument technology on the basis of new technology, and take the software system as the key of virtual instrument. It has the characteristics of visibility, versatility, scalability and so on, and can bring great convenience to users. Hence, this paper studies the and early warning system of intelligent fault alarm in instrumentation with extreme machine learning. The novel models are designed for the efficient analysis. © 2022 IEEE.

关键词： Alarm systems

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Proceedings - 3rd international conference on Information Systems and Software Technologies, ICI2ST 2022

Proceedings - 3rd International Conference on Information Sy...

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3rd international conference on Information Systems and Software Technologies, ICI2ST 2022

ISBN: (纸本)9781665455176

The proceedings contain 25 papers. The topics discussed include: a convolutional neural network-based web prototype to support melanoma skin cancer detection;person re-identification system in a controlled environment based on soft biometric features: clothing color and body silhouette collected on short video sequences using computer vision and machine learning algorithms;identifying defective fruits and vegetables with hyper-spectral images: a brief tutorial;development of a video surveillance web application for the use of biosafety equipment with image recognition for workspaces;person re-identification using soft-biometric features: body silhouette and clothing texture in a multi-camera video surveillance environment;systematic mapping: translator language from sign language to Colombian formal language;evaluating extended reality application for a virtual museum. case study: Remigio Crespo Museum;and development of a system to detect stress using electrocardiographic signals and machine learning models.

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Proceedings of 3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

Proceedings of 3rd International Conference on Latest Trends...

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3rd international conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022

ISBN: (纸本)9781665487061

The proceedings contain 23 papers. The topics discussed include: an intelligent QR code scanning system for visually-impaired users;a computer aided technique for classification of patients with diabetes;an applied artificial intelligence technique for early prediction of diabetes disease;polarization-insensitive metalens with high efficiency for optical fiber communication;electrocardiograph based obstructive sleep apnea diagnoses;an application of artificial intelligence for an early and effective prediction of heart failure;an integrated machine learning framework for classification of cirrhosis, fibrosis, and hepatitis;heart attack prediction using machine learning approach;design of unit cells for groove gap waveguide at millimeter wave spectrum;future position estimation in case of GPS outages;design of customized high-performance on-chip and off-chip inductor to transmit power in millimeter distance compared with contrasting electromagnetic simulators;and application of digital twin technology in the field of autonomous driving test.

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A Novel Strategy for Gender Identification from Hand Dorsal Images using computer Vision 3

A Novel Strategy for Gender Identification from Hand Dorsal ...

引用

3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Chakrabarty, Navoneel Jalpaiguri Govt Engn Coll Dept CSE Jalpaiguri 735121 W Bengal India

ISBN: (纸本)9781538678084

Criminal Investigations and Archaeological Studies often involve Gender Determination from different body parts when dismembered human remains are encountered. The body parts may include hair or hand in criminal investigations and hand bone length or hand to index finger length ratio in archaeological research. Often in crimes or even in accidents, Forensic Science comes into play. Forensic Science, by definition, is the scientific application of criminal and civil laws for collection of scientific evidences for prosecution or defense or both. So, in Forensic Research, Gender Determination is a very integral part especially from hair or hand, which becomes difficult for anyone other than Forensic Scientists, to conclude. This study aims at classification of the gender of an individual into male and female from the image of his or her dorsal side of hand. A Deep learning-machine learning Hybrid Model is developed, yielding a Validation Accuracy of 100% and Recall of 1.0. Such an Automated System is highly essential in making Forensic Research, easy and hence swift analysis of scientific evidences.

关键词： Forensic Science classification dorsal Deep learning machine learning

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Stress Prediction in Indian School Students Using machine learning 3

Stress Prediction in Indian School Students Using Machine Le...

引用

3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Bisht, Aanchal Vashisth, Shreya Gupta, Muskan Jain, Ena DIT University School of Computing Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Today, kids from all over the world are under a tremendous amount of stress, which can have serious effects if not addressed promptly. Anxiety, frustration, anger, or uneasiness can all cause stress. computer science has advanced significantly as the digital world has progressed, particularly in the field of health care with the application of machine learning. We have presented a methodology that will assist schools, parents, and students in predicting the level of stress a student is experiencing. We developed a real-time database in which we surveyed over 190 school children aged 14 to 18 years old with a variety of 26 questions to analyze their stress and cure them early using machine learning Algorithms. Different types of machine learning algorithms are used, including Decision Tree, Logistic Regression, K Nearest Neighbors, and Random Forest, with a maximum accuracy of 88 per cent using K Nearest Neighbors. © 2022 IEEE.

关键词： Students

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Risk-Aware Linear Bandits with application in Smart Order Routing 22

Risk-Aware Linear Bandits with Application in Smart Order Ro...

引用

3rd ACM international conference on AI in Finance (ICAIF)

作者： Ji, Jingwei Xu, Renyuan Zhu, Ruihao Univ Southern Calif ISE Dept Los Angeles CA 90007 USA Cornell Univ SC Johnson Coll Business Ithaca NY USA

ISBN: (纸本)9781450393768

Motivated by practical considerations in machine learning for financial decision-making, such as risk-aversion and large action space, we initiate the study of risk-aware linear bandits. Specifically, we consider regret minimization under the mean-variance measure when facing a set of actions whose reward can be expressed as linear functions of (initially) unknown parameters. We first propose the Risk-Aware Explore-then-Commit (RISE) algorithm driven by the variance-minimizing G-optimal design. Then, we rigorously analyze its regret upper bound to show that, by leveraging the linear structure, the algorithm can dramatically reduce the regret when compared to existing methods. Finally, we demonstrate the performance of the RISE algorithm by conducting extensive numerical experiments in a synthetic smart order routing setup. Our results show that the RISE algorithm can outperform the competing methods, especially when the decision-making scenario becomes more complex.

关键词： online learning bandit regret analysis machine learning theory smart order routing algorithmic trading mean-variance risk-aware decision-making

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