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A robust classifier for Noise-corruption learning

A robust classifier for Noise-corruption Learning

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Linchang Zhao Hao Wei Mu Zhang Ruiping Li Qianbo Li Hongming Cai School of Computer Science Guiyang University Guiyang China School of Building Equipment Guizhou Polytechnic of Construction Guiyang China School of software Shanghai Jiao Tong University Shanghai China

Deep neural networks (DNNs) have achieved great success in various applications across many disciplines. However, their superior performance is susceptible to training set bias and noise label corruption. In order to overcome the overfitting of deep network models to corrupted labels, in addition to various regularization techniques and example re-weighting algorithms, teacher-student models based on the knowledge distillation paradigm are also a popular approach to solve these problems. However, these methods require careful tuning of additional hyperparameters, such as example mining plans and regularization hyperparameters. Unlike previous re-weighting methods, this paper proposes a novel meta-learning algorithm based on deep transfer hybrid model for training example weight allocation. the method combines the RN50STN model designed based on the spatial transformer network (STN) and pre-trained ResNet50, resulting in good performance in a wide range of noisy label corruption.

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machine learning-as-a-Service Performance Evaluation on Multi-class datasets 5

Machine Learning-as-a-Service Performance Evaluation on Mult...

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5th IEEE international conference on Smart Internet of things, SmartIoT 2021

作者： Noshiri, Nooshin Khorramfar, Mohammadreza Halabi, Talal University of Winnipeg Manitoba Canada

ISBN: (纸本)9781665445115

machine learning technologies have invaded our daily lives with a wide range of applications such as fraud detection, product recommendations, data mining, and image recognition. Businesses that need to process and analyze huge amounts of data are competing to become the first to adopt these solutions. Nonetheless, developing classification algorithms using machine learning frameworks is time-consuming, costly, and requires a team with technical capabilities. To reduce these expenses, market leader cloud providers have started to offer the machine learning-as-a-service (MLaaS) cloud delivery model. However, businesses and users are still faced with the challenge of deciding on which platform to adopt. In this paper, we evaluate the machine learning classifiers and performance of BigML, Microsoft Azure ML Studio, IBM Watson ML Studio, and Google AutoML Table platforms on the classification of multi-class datasets based on the average-micro F-score, training time, and cost to enable users to make a more informed decision. Since the choice of classifiers can have a crucial impact on the average-micro F-score, we trained all pre-built algorithms offered by each platform on given multi-class datasets to conduct a comprehensive investigation. the results show that Google AutoML provides the user with the highest average-micro F-score, but it is costly and requires more training time. this research will enable the developers of intelligent edge computing services that rely on MLaaS to select the most optimal platform for their applications needs. © 2021 IEEE.

关键词： Classification (of information)

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ICISDM 2021 - 5th international conference on Information System and data mining

ICISDM 2021 - 5th International Conference on Information Sy...

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5th international conference on Information System and data mining, ICISDM 2021

ISBN: (纸本)9781450389549

the proceedings contain 24 papers. the topics discussed include: Nepal stock market movement prediction with machine learning;development of virtual skill trainers and their validation study analysis using machine learning;use of efficient machine learning techniques in the identification of patients with heart diseases;the application of machine learning in bitcoin ransomware family prediction;predicting water quality parameters in lake pontchartrain using machine learning: a comparison on K-nearest neighbors, decision trees, and neural networks to predict water quality;actor-critic neural network based finite-time control for uncertain robotic systems;a survey on mainstream dimensions of edge computing;and automated dataset amplification and its application to small dataset object detection transfer learning.

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A Classification Method for Microseisms and Blasts in Mine Based on Time and Frequency Domains Analysis

A Classification Method for Microseisms and Blasts in Mine B...

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international conference on Applied machine learning (ICAML)

作者： Hu Ji Kai Zhan Yaqian Shi Beijing General Research Institute of Mining and Metallurgy Technology Group Key Laboratory of Nonferrous Intelligent Mining Technology National Center for International Joint Research on Green Metal Mining China-South Africa Joint Research Center for Mineral Resources Development China-South Africa “Belt and Road” Joint Laboratory for Sustainable Development and Utilization of Mineral Resources Beijing China

Microseismic monitoring is one of the important means for monitoring and early warning of mine ground pressure disasters. Due to the complex geological conditions of the mine and the numerous goaf areas, rock fracture signals undergo deformation, making it difficult to distinguish them from signals generated by artificial explosions. In this case, we conducted time-domain and frequency-domain analysis of the microseismic signals and artificial blasting signals collected by the BSN microseismic monitoring system, and obtain signal informations, including P-wave arrival, first peak, maximum peak, frequency spectrum of signal and P-wave. 8 typical features are extracted from the informations to build a classification model of microseismic and blast signals based on SVM with Gaussian function as the kernel function. the field data are collected by BSN microseism monitoring system of a metal mine in Guangxi, which are used to train and test the classification model. the results show that the accuracy rate of microseismic signals is larger than 80 % , which can help signals classification automatically in a certain extent.

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Feature-aware unsupervised learning with joint variational attention and automatic clustering 25

Feature-aware unsupervised learning with joint variational a...

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25th international conference on pattern recognition (ICPR)

作者： Wang, Ru Li, Lin Wang, Peipei Tao, Xiaohui Liu, Peiyu Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Univ Southern Queensland Sch Sci Toowoomba Qld Australia

ISBN: (纸本)9781728188089

Deep clustering aims to cluster unlabeled real-world samples by mining deep feature representation. Most of existing methods remain challenging when handling high-dimensional data and simultaneously exploring the complementarity of deep feature representation and clustering. In this paper, we propose a novel Deep Variational Attention Encoder-decoder for Clustering (DVAEC). Our DVAEC improves the representation learning ability by fusing variational attention. Specifically, we design a feature-aware automatic clustering module to mitigate the unreliability of similarity calculation and guide network learning. Besides, to further boost the performance of deep clustering from a global perspective, we define a joint optimization objective to promote feature representation learning and automatic clustering synergistically. Extensive experimental results show the promising performance achieved by our DVAEC on six datasets comparing with several popular baseline clustering methods.

关键词： Clustering algorithms

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Deep learning Based Automatic Detection Algorithm of Atrial Fibrillation Implemented on FPGA

Deep Learning Based Automatic Detection Algorithm of Atrial ...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Huang Tianyi Jiang Yuchen Wang Yijing Lyu Qinghui Lai Dakun University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu China University of Electronic Science and Technology of China Glasgow College Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Paroxysmal atrial fibrillation (PAF) is one of the most common arrhythmic diseases in clinical practice. the traditional method of PAF diagnosis is achieved by manual analysis of the patient's electrocardiogram by the doctor, which leads to significant manpower consumption and potential misdiagnosis and missed conditions. To cope with such limitations, this paper proposes an automatic diagnosis algorithm for atrial fibrillation (AF) based on ECG signals using convolution neural networks (CNNs). the algorithm utilized a one-dimensional convolution neural network based on the classical CNN Lenet-5 network structure, to automatically identify the RR interval characteristics of AF. the RR interval sequences were employed from a public database of 23 patients with AF (high-risk group) and 18 healthy subjects (normal group). the ten-fold cross- validation method was used to evaluate the model's performance through three metrics: specialty, sensitivity, and accuracy. Conclusively, the experimental results revealed that the average specialty (Spe), sensitivity (Sen), and accuracy (Acc) for automatic recognition of AF ECG signals achieved 98.61%, 98.72%, and 98.64%. the model was implemented on FPGA with optimization strategies including pipeline architecture and fixed-point number representation instead of floating-point number, resulting in the greatly reduced FPGA hardware resource of the model after optimization on the premise of maintaining accuracy.

关键词： Training Accuracy Sensitivity Pipelines Neural networks Atrial fibrillation Hardware Convolutional neural networks Field programmable gate arrays Optimization

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Elevated learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance

Elevated Learning based Secured Phishing Website Identificat...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： G. Ramkumar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Accuracy Phishing Scalability Transfer learning Robustness Natural language processing Real-time systems pattern recognition Security Protection

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Coral Detection with machine learning

Coral Detection with Machine Learning

引用

IEEE international conference on Computer Communication and the Internet (ICCCI)

作者： Fan-Hua Nan Yi-Zeng Hsieh Hao-Ching Chen Department of Aquaculture National Taiwan Ocean University Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

Ocean exploration has always been a costly, high-risk, and slow process, but it is an essential part of the environment. In order to reduce dependence on human labor and increase the speed of data processing, we will introduce a method of using machine learning to assist in coral area surveys in this paper. We will first provide an introduction to the principles of coral image recognition and the structure of the SOLOv2 model, followed by a presentation of the method used to train the model to identify corals, the results of further optimizing the original SOLOv2 model, and an algorithm used to estimate area.

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Web Application System of Handwritten Text recognition 5

Web Application System of Handwritten Text Recognition

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bodnia, Yevhen Kozulia, Mariia Natl Tech Univ Kharkiv Polytech Inst Kirpichova Str 2 Kharkiv Ukraine

the problem of text recognition is becoming increasingly important due to the active introduction of digital computing and the widespread use of word processors. pattern recognition is one of the most difficult from a mathematical point of view and one of the most popular areas of artificial intelligence programming. In the work is researched approaches and methods of solving text recognition problem, improved the performance of the available algorithms for text recognition and created algorithmic software. According to the analysis, neural networks were selected for handwriting recognition. the main advantage of using neural networks is a good generalization ability, the ability to use context analysis and recognize a symbol based on the surrounding symbols. the software implementation features of Hopfield and convolutional neural network, genetic algorithm, which were chosen as effective methods for recognizing handwritten text, were considered. Algorithmic software and web application that uses these methods for the task of handwritten text recognition is developed.

关键词： Handwritten text recognition pattern recognition recognition methods neural network genetic algorithm convolutional neural network Hopfield neural network machine learning data models

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Multifaceted Discrete Emotion recognition from EEG Physiological Signals via machine learning Techniques

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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Emerging Technology (INCET), international conference for

作者： Amriti Gupta Kunal Nayyar Harshit Chopra Neha Gahlan Divyashikha Sethia Dept. of Software Engineering Delhi Technological University Dept. of Electronics & Communication Engineering Delhi Technological University Dept. of Computer Engineering Delhi Technological University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Emotions, intricate mental states shaped by neu-rophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. the Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. this paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. this research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. the exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested.

关键词： Emotion recognition Accuracy Sleep Cognitive processes machine learning data collection Brain modeling

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