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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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Tensorized Feature Spaces for Feature Explosion

Tensorized Feature Spaces for Feature Explosion

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international conference on pattern recognition

作者： Ravdeep S. Pasricha Pravallika Devineni Evangelos E. Papalexakis Ramakrishnan Kannan University of California Riverside Riverside CA USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

In this paper11This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the ar... 详细信息

In this paper¹¹This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://***/downloads/doe-public-access-plan) This research used resources of the Oak Ridge Leadership computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-000R22725., we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose Orion, which takes as input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in R^K to high dimensional space in R^R, where R ≫ K, say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three dimensional tensor. We demonstrate the effectiveness of our meth

关键词： Training Support vector machines Tensors Government Supervised learning Explosions pattern recognition

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

1st International Conference on Computational Intelligence i...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

ISBN: (纸本)9789811390418

The proceedings contain 88 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Classification of Speech and Song Using Co-occurrence-Based Approach;Intelligent Analysis for Personality Detection on Various Indicators by Clinical Reliable Psychological TTH and Stress Surveys;automatic Multilingual System from Speech;an Improved Data Hiding Scheme Through Image Interpolation;generalization of Multi-bit Encoding Function Based Data Hiding Scheme;a Machine Learning Approach to Comment Toxicity Classification;a Minutia Detection Approach from Direct Gray-Scale Fingerprint Image Using Hit-or-Miss Transformation;face Identification via Strategic Combination of Local Features;a Study on Reversible Image Watermarking Using Xilinx System Generator;a Novel Enhancement and Segmentation of Color Retinal Image Based on Fuzzy Measure and Fuzzy Integral;SVM and MLP Based Segmentation and recognition of Text from Scene Images Through an Effective Binarization Scheme;user Identification and Authentication Through Voice Samples;template Matching for Kinship Verification in the Wild;Attack Prevention Scheme for Privacy Preservation (APSP) Using K Anonymity in Location Based Services for IoT;Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection;Singer Identification Using MFCC and CRP Features with Support Vector Machines;object Proposals Based on Variance Measure;deep Learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen;Robust Non-blind Video Watermarking Using DWT and QR Decomposition;detection and Classification of Earthquake Images from Online Social Media;On the Cryptanalysis of S-DES Using Binary Cuckoo Search Algorithm;numerical Integration Based Contrast Enhancement Using Simpson’s Method;self Driving Car.

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Changes in marketing brought by AI

Changes in marketing brought by AI

引用

software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Jin-Hee Lee Korea Soongsil Cyber University Seoul Korea

After the fourth industrial revolution, a business that utilizes Artificial Intelligence (AI) is expanding centered around IT industries and it is expected that the quality of AI services will improve. This study aims to examine changes in marketing through the advance and development of AI and to establish and apply marketing strategies to respond to future market changes. Based on existing data, the development of AI technology was examined and looked into changes in marketing and counterstrategies through cases overseas and South Korea. Artificial Intelligence technology has a close impact on our lives, changing our lives, and thus changing consumer patterns, perceptions, and consumer culture. In the future, innovative changes in AI technologies will require government policies, the vision of the corporation, and it is necessary to establish longer- term success strategies. Collaboration between companies and industries is also important.

关键词： Industries Government policies Collaboration Companies pattern recognition Artificial intelligence software engineering

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Complex pattern recognition using Feedback Algebraic Activation Function

Complex Pattern Recognition using Feedback Algebraic Activat...

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Communication, computing and Industry 4.0 (C2I4), international conference on

作者： P. Dhivya A. Bazilabanu Bannari Amman Institute of TechnologyErode Tamilnadu India Bannari Amman Institute of Technology Erode Tamilnadu India

ISBN: (纸本)9781665420143

Various researches on the Wisconsin breast cancer dataset in the prediction of benign and malignant were proposed in this work. The various classification models were used in the literature survey in which logistic regression give the better accuracy of 95.41%. But in machine learning model, the linear relationship between the features may lead to over fitting in that model. The neural network is a fully connected layer to find and understand the nonlinear combinations of the features in the dataset. To reduce the over fitting and improves the accuracy of the model, the Neural Network with Feedback Algebraic Activation (FAA) function is introduced and applied to the hidden layer in the network . In this, the error will be added as a feedback to the network and uses the keras callbacks and keras tuner for selecting the optimal parameters and produces the better results with the accuracy of 95.91% compared with all other activation functions in the neural network.

关键词： Tuners Error analysis Neural networks Fitting FAA Machine learning Propagation losses

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Traffic Prediction for Intelligent Transportation System using Machine Learning 3

Traffic Prediction for Intelligent Transportation System usi...

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3rd international conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020

作者： Meena, Gaurav Sharma, Deepanjali Mahrishi, Mehul Central University of Rajasthan Department of Computer Science Ajmer Rajasthan India Swami Keshvanand Institute of Technology Department of Information Technology Jaipur Rajasthan India

ISBN: (纸本)9781728116839

This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it's traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about all the above and many more daily life situations which can affect traffic then, a driver or rider can make an informed decision. Also, it helps in the future of autonomous vehicles. In the current decades, traffic data have been generating exponentially, and we have moved towards the big data concepts for transportation. Available prediction methods for traffic flow use some traffic prediction models and are still unsatisfactory to handle real-world applications. This fact inspired us to work on the traffic flow forecast problem build on the traffic data and *** is cumbersome to forecast the traffic flow accurately because the data available for the transportation system is insanely huge. In this work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyse the big-data for the transportation system with much-reduced complexity. Also, Image Processing algorithms are involved in traffic sign recognition, which eventually helps for the right training of autonomous vehicles. © 2020 IEEE.

关键词： soft computing

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19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

19th International Conference on Artificial Intelligence and...

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19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

ISBN: (纸本)9783030614003

The proceedings contain 110 papers. The special focus in this conference is on Artificial Intelligence and soft computing. The topics include: Challenging Human Supremacy: Evaluating Monte Carlo Tree Search and Deep Learning for the Trick Taking Card Game Jass;how Motivated Are You? A Mental Network Model for Dynamic Goal Driven Emotion Regulation;multi-agent Architecture for Internet of Medical Things;automatic Visual Quality Assessment of Biscuits Using Machine Learning;classifying Image Series with a Reoccurring Concept Drift Using a Markov Chain Predictor as a Feedback;explainable Cluster-Based Rules Generation for Image Retrieval and Classification;SURF Algorithm with Convolutional Neural Network as Face recognition Technique;preface;a New Approach to Detection of Abrupt Changes in Black-and-White Images;grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree;a Density-Based Prototype Selection Approach;flexTrustRank: A New Approach to Link Spam Combating;a Comparative Analysis of Similarity Measures in Memory-Based Collaborative Filtering;constructing Interpretable Decision Trees Using Parallel Coordinates;a Framework for e-Recruitment Recommender Systems;the Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System;n-ary Isolation Forest: An Experimental Comparative Analysis;in-The-Limit Clustering Axioms;hybrid Features for Twitter Sentiment Analysis;active Region-Based Full-Disc Solar Image Hashing;computer Based Stylometric Analysis of Texts in Ukrainian Language;newsminer: Enriched Multidimensional Corpus for Text-Based Applications;detecting Causalities in Production Environments Using Time Lag Identification with Cross-Correlation in Production State Time Series;Identification of Delays in AMUSE Algorithm for Blind Signal Separation of Financial Data;pre-training Polish Transformer-Based Language Models at Scale.

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19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

19th International Conference on Artificial Intelligence and...

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19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

ISBN: (纸本)9783030615338

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Proceedings - IEEE Congress on Cybermatics: 2020 IEEE international conferences on Internet of Things, iThings 2020, IEEE Green computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020

Proceedings - IEEE Congress on Cybermatics: 2020 IEEE Intern...

引用

2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on Smart Data, SmartData 2020

ISBN: (纸本)9781728176475

The proceedings contain 135 papers. The topics discussed include: real-time operating systems for cyber-physical systems: current status and future research;a semi-supervised dynamic ensemble algorithm for IoT anomaly detection;an efficient hybrid approach for brain tumor detection in MR images using Hadoop-MapReduce;drug-drug interaction extraction using pre-training model of enhanced entity information;breast cancer image classification based on CNN classifier;an assessment of the usability of machine learning based tools for the security operations center;machine learning recognition of gait identity via shoe embedded accelerometer;leveraging walking inertial pattern for terrain classification;multicast traffic throughput maximization through dynamic modulation and coding scheme assignment in wireless sensor networks;and X-DOG: an intelligent x-ray-based dangerous goods detection and automatic alarm system.

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Deep Stable Representation Learning on Electronic Health Records

Deep Stable Representation Learning on Electronic Health Rec...

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IEEE international conference on Data Mining (ICDM)

作者： Yingtao Luo Zhaocheng Liu Qiang Liu Carnegie Mellon University Pittsburgh USA Kuaishou Technology Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypothesis fail to consider the agnostic distribution shifts, diminishing the generalization ability of deep learning models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical correlations between procedures and diagnoses that may change in different environments will be exploited, which can cause sub-optimal performances of deep learning models and spurious correlation between historical EHR and future diagnosis. To address this problem, we propose to use a causal representation learning method called Causal Healthcare Embedding (CHE). CHE aims at eliminating the spurious statistical relationship by removing the dependencies between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of independence between the embedded diagnosis and procedure features. Based on causal view analyses, we perform the sample weighting technique to get rid of such spurious relationship for the stable learning of EHR across different environments. Moreover, our proposed CHE method can be used as a flexible plug-and-play module to enhance existing deep learning models on EHR. Extensive experiments on two public datasets and five state-of-the-art baselines unequivocally show that CHE can improve the prediction accuracy of deep learning models on out-of-distribution data by a large margin. In addition, the interpretability study shows that CHE could successfully leverage causal structures to reflect a more reasonable contribution of historical records for predictions.

关键词： Deep learning Representation learning Correlation Channel hot electron injection Medical services Predictive models Data models

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Detection and recognition of Skin Cancer in Dermatoscopy Images 2020

Detection and Recognition of Skin Cancer in Dermatoscopy Ima...

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2020 international conference on pattern recognition and Intelligent Systems, PRIS 2020

作者： Qian, Ying Zhao, Shuo Soft Engineering Institute Chong Qing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450387699

Melanoma and basal-cell carcinoma (BCC) are the two most common skin cancers, the death rate of melanoma is very high. If melanoma can be diagnosed early, the survival rate of patients will be greatly improved. But nevus and melanoma have similar appearances and symptoms. In order to reduce the cost for doctors to diagnose skin cancer, we proposed a computer-aided diagnostic system (CAD) that detects and identifies melanoma, nevus, and BCC in dermoscopy images. Firstly, use the hair removal algorithm, Gaussian filter and Wiener filter to remove the noise;Secondly, use the otsu to obtain the lesion area;then extract the texture and color features from the lesion area and use multiset discriminant correlation analysis (MDCA) to fuse the extracted features;finally, skin cancer is classified into melanoma, nevus, and BCC by KNN classification. Our aim is to select suitable features, test the effectiveness of MDCA, and compare the classification results with the methods in recent years. The improved algorithm was tested on the ISIC dataset, which included 469 images of melanoma, 127 images of basal cell carcinoma and 412 images of nevus. Compared with the methods in recent years, the selected features in this study combine with the MDCA method can improve the accuracy rate by 10.34%. © 2020 ACM.

关键词： Oncology

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