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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是1381-1390 订阅

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Devanagari Character recognition using Deep Convolution Neural Networks

Devanagari Character Recognition using Deep Convolution Neur...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Akshat Gupta Aman Jain Jubair Saifi Ajay Kumar Singh Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

ISBN: (纸本)9781665438124

In the presented paper, a Deep learning architecture for Devanagari handwritten character recognition is introduced. The dataset comprises of 94742 images of 58 distinct classes containing numerals, vowels and consonants from Devanagari script. Deep Convolution Neural Network (DCNN) have indicated better outcomes than traditional machine learning algorithms and Shallow networks in recognition tasks. The proposed algorithm achieved an accuracy of 99.20% on testing data with 98.31% accuracy on training data.

关键词： Deep learning machine learning algorithms Convolution Neural networks Training data Computer architecture Character recognition

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3rd international conference on Advanced Computing and Intelligent Engineering, ICACIE 2018

3rd International Conference on Advanced Computing and Intel...

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3rd international conference on Advanced Computing and Intelligent Engineering, ICACIE 2018

ISBN: (纸本)9789811510809

The proceedings contain 54 papers. The special focus in this conference is on Advanced Computing and Intelligent Engineering. The topics include: Wavelet Transform Domain Methods for Resolution Enhancement of Satellite Images;improving Query Results in Ontology-Based Case-Based Reasoning by Dynamic Assignment of Feature Weights;a Survey on Representation for Itemsets in Association Rule mining;efficient Clustering Using Nonnegative Matrix Factorization for Gene Expression dataset;design of Random Forest Algorithm Based Model for Tachycardia Detection;detection of Spam in YouTube Comments Using Different Classifiers;deep learning Architectures for Named Entity recognition: A Survey;effect of Familiarity on recognition of Pleasant and Unpleasant Emotional States Induced by Hindi Music Videos;early Detection of Breast Cancer Using Support Vector machine With Sequential Minimal Optimization;the Case Study of Brain Tumor data Analysis Using Stata and R;predictive data Analytics for Breast Cancer Prognosis;a Semantic Approach of Building Dynamic Learner Profile Model Using WordNet;prioritizing Public Grievance Redressal Using Text mining and Sentimental Analysis;an Automatic Summarizer for a Low-Resourced Language;printed Odia Symbols for Character recognition: A database Study;importance of data Standardization Methods on Stock Indices Prediction Accuracy;Feature Relevance Analysis and Feature Reduction of UNSW NB-15 Using Neural Networks on MAMLS;video Summarization Based on Optical Flow;Decision Support System for Black Classification of Dental Images Using GIST Descriptors;clustering Performance Analysis;pattern Analysis of Brain Functional Connectivity Parameters After Removal of Artifactual Motifs from EEG During Meditation;Hyper-heuristic Image Enhancement (HHIE): A Reinforcement learning Method for Image Contrast Enhancement.

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CycleGAN Based data Augmentation For Melanoma images Classification 20

CycleGAN Based Data Augmentation For Melanoma images Classif...

引用

3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Chen, Yixin Zhu, Yifan Chang, Yanfeng Kings Coll London London England London Sch Econ & Polit Sci London England

ISBN: (纸本)9781450375511

It is widely-known that melanoma is one of the deadliest skin cancers with a very high mortality rate, while it is curable with early identification. Therefore, early detection of melanoma is extremely necessary for the treatment of this disease. In recent decades, Convolutional Neural Networks (CNN) have achieved state-of-the-art performance in many different visual classification tasks, so they have also been employed in melanoma recognition tasks. Due to the complexity of the deep learning model and huge numbers of parameters, a large amount of labelled data is required to achieve a better training performance. However, in practical settings, it is difficult for many applications to obtain enough labelled sample data. This paper explore to solve this problems based on data augmentation strategy. In the experiment conducted in our paper, the training data is augmented through CycleGAN-based approaches to generate more training samples with detailed information, and then the CNN model can be trained using the artificially enlarged dataset. The experimental results show that the combination of CycleGAN data augmentation method and EfficientNet B1 can effectively saves the cost of manual annotation, while dramatically improves classification accuracy.

关键词： CycleGAN data augmentation Melanoma images Classification deep convolutional neural network

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Cyber Threat Analysis And Prediction Using machine learning

Cyber Threat Analysis And Prediction Using Machine Learning

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Kulvinder Singh Sudan Jha Chandigarh University Punjab

ISBN: (纸本)9781665438124

With the increase in cyber data attacks, the manual method of investigating cyber-attacks is more prone to errors and is time consuming. With the increase in advanced cyber threat attacks with the same patterns, timely investigation is not possible. There are many systems proposed which analyse and predict threats using various machine learning methods. In this model we applied machine learning algorithms to analyse and predict cyber-attacks.

关键词： Analytical models machine learning algorithms Intrusion detection machine learning Manuals Predictive models data mining

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Emotional Analysis of Clothing Product Reviews Based on machine learning

Emotional Analysis of Clothing Product Reviews Based on Mach...

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international conference on Applied machine learning (ICAML)

作者： Ying Hong Xiwen Shao Beijing Institute of Fashion Technology Beijing China

ISBN: (纸本)9781665421263

Emotional analysis of product reviews is a hot spot in current data mining research. Whether it is in academic or economic fields, text emotional analysis of e-commerce product reviews has great research value. This article used machine learning to conduct sentiment analysis for clothing e-commerce product reviews. Here, we used the data collection program to collect comment data. Then we performed preprocessing, word segmentation and sentiment labeling on the data. By setting different parameters to train the data, we build a sentiment classifier to automatically classify unknown data. We show experimentally that using machine learning methods to perform sentiment classification on product reviews can achieve good results.

关键词： Sentiment analysis machine learning algorithms Clothing machine learning data collection data models Internet

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Prediction of Malaria Fever Using Long-Short-Term Memory and Big data 3rd

Prediction of Malaria Fever Using Long-Short-Term Memory and...

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3rd international conference on Information and Communication Technology and Applications, ICTA 2020

作者： Awotunde, Joseph Bamidele Jimoh, Rasheed Gbenga Oladipo, Idowu Dauda Abdulraheem, Muyideen Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (纸本)9783030691424

Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos’ parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria. © 2020, Springer Nature Switzerland AG.

关键词： Diagnosis

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Application of Deep learning in Imaging diagnosis of Brain diseases

Application of Deep Learning in Imaging diagnosis of Brain d...

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

作者： Junchen Liang Zhaoze Wang Xiangyang Ye School of mathematics and statistics Xi'an Jiaotong University Xi'an China Wenzhou Kean University Zhejiang China College of Automotive Engineering Jilin University Chang Chun China

ISBN: (纸本)9781665417914

The brain is one of the most important parts of the human body, and the diagnosis of its diseases is of great significance to the treatment of diseases. With the rapid development of deep learning in recent years, its automatically extracted image features have significant advantages compared to traditional artificially extracted features. Therefore, more and more recognition methods based on deep learning are widely used in medical image recognition tasks (such as CT, MRI, PET-CT.). This paper will introduce the application of traditional methods and deep learning methods in various brain diseases. These methods are compared, analyzed, and summarized, then we explored their development status and future development trends.

关键词： Deep learning Image recognition machine learning algorithms Imaging Manuals Feature extraction Medical diagnosis

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Big data and machine learning in Healthcare: Tools & Challenges

Big Data and Machine Learning in Healthcare: Tools & Challen...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Kanika Pahwa Sharad Chauhan Chandigarh University

ISBN: (纸本)9781665438124

Normally healthcare is said to be information rich and to extract hidden data from such information-rich industry is difficult. It becomes necessary for healthcare informatics to deal with the advancement in technology and big data. This can be done by changing three core areas namely, to manage record electronically, integrate big data and computer-aided diagnosis. To resolve the mentioned challenges machine learning provides a range of techniques, algorithm, and different frameworks. This paper focuses on handling big data of health care to predict disease using machine leaning approaches.

关键词： Industries machine learning algorithms Medical services machine learning Big data Prediction algorithms data mining

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recognition of American Sign Language Gestures Based On Electromyogram (EMG) Signal With XGBoost machine learning

Recognition of American Sign Language Gestures Based On Elec...

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

作者： Hanrui Chen Tianyu Qin Yuehan Zhang Biqiang Guan Rose-Hulman Institute of Technology Terre Haute Indiana The State University of New Jersey New Jersey Electrical Engineering Information Technology and Computer Engineering RWTH Aachen University Aachen Germany School of International Education Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781665417914

This paper discusses a potential way to recognize different American Sign Language(ASL) with the help of machine learning. In order to recognize different ASL, we decided to collect the data by multi-channel surface electromyogram(EMG). All the signals are processed during the time domain. After that, we extract the features in the data. The feature extractions this paper uses are mean absolute value, standard deviation, variance, and skewness. The extracted features are going to be analyzed by a machine learning model. The machine learning model this paper uses is the XGBClassifier, and the overall accuracy is about 85%. The samples this method chooses are from different college students whose ages are between 19 to 25 years old. This method also included the muscle fatigue situation. There are two muscle tired statuses that can be distinguished in the measurement to increase the gesture recognition accuracy.

关键词： Electrodes machine learning Gesture recognition Muscles Assistive technologies Feature extraction Fatigue

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Integration of Complex System Simulation and data mining

Integration of Complex System Simulation and Data Mining

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

作者： Fa Zhang Beijing Institute of Technology Zhuhai School Zhuhai China

ISBN: (纸本)9781665417914

Simulation is a common method for studying the behavior of complex systems and revealing the mechanism of the system. However, complex systems have many parameters, non-linear interactions, and complex evolutionary dynamics. It is difficult to reveal the mechanism of complex systems. Especially complex system simulation experiments may produce a large amount of data. How to summarize the macroscopic mode of the system, identify key factors, and discover the relationship between input and output variables, still lacks an effective method. This paper proposes an integrated framework for simulation modeling and data mining, which combines data mining and simulation modeling to conduct iterative experimental exploration and analysis of complex systems. data mining techniques were used in multiple stages of modeling and simulation, including: ETL on raw data, text mining and process mining to build conceptual models, uniform experimental design to generate simulation data, and clustering of simulation data to identify system macro patterns, use stepwise regression, neural network, etc. to build a meta-model of a complex system. The introduction of data mining can improve the ability and efficiency of complex system modeling and simulation.

关键词： Text mining Analytical models Soft sensors Neural networks machine learning Big data data models

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