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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

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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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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Proceedings of 3rd international conference on Computer Vision and Image Processing 1

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丛书名： Advances in Intelligent Systems and computing

2020年

作者： Bidyut B. Chaudhuri Masaki Nakagawa Pritee Khanna Sanjeev Kumar

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Real-Time Surface EMG pattern recognition for Shoulder Motions Based on Support Vector Machine 2020

Real-Time Surface EMG Pattern Recognition for Shoulder Motio...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Yang Zhou Chaoyang Chen Mark Cheng Sreten Franovic Stephanie Muh Stephen Lemos Wayne State University Detroit MI USA Department of Biomedical Engineering Wayne State University Detroit Michigan USA Wayne State University Detroit Michigan USA Department of Orthopaedic Surgery Henry Ford Health System Detroit Michigan USA Orthopedic Surgery and Sports Medicine DMC Detroit MI USA

ISBN: (纸本)9781450387835

Surface electromyography (sEMG) signals contain humans' motion intentions and can be used for intuitive control of prostheses or exoskeleton. Although recent research proposes several pattern recognition methods based on sEMG and reported high accuracy, the real-time applications are still limited due to the relatively low accuracy and long time consumption. In this paper, we propose a real-time shoulder motion pattern recognition model based on surface electromyography (sEMG). The Delsys Trigno wireless EMG system with customized LabVIEW program is applied to acquire surface EMG generated by shoulder-related muscles during different shoulder motions. Surface EMG features were extracted and used to create motion recognition. Support Vector Machine (SVM) model was selected and trained for real-time motion recognition. Motion recognition results were given every 135 milliseconds. In order to evaluate the model, an experiment with four shoulder related motions was set up and conducted on five subjects. The experiment result shows that the average accuracy can reach 87.6% in offline training and 85.3% in real-time validation.

关键词： Shoulder motion recognition real-time support vector machine surface electromyography

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Keystroke pattern Authentication of Computer Systems Users as One of the Steps of Multifactor Authentication 2nd

Keystroke Pattern Authentication of Computer Systems Users a...

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2nd international conference on Computer Science, Engineering and Education Applications, ICCSEEA 2019

作者： Vysotska, Olena Davydenko, Anatolii National Aviation University Kiev Ukraine Pukhov Institute for Modeling in Energy Engineering of NAS of Ukraine Kiev Ukraine

ISBN: (纸本)9783030166205

In this paper, an authentication program was created for Ukrainian-speaking users of computer systems based on their keyboard style. To develop the algorithm of this program, a series of experiments were conducted. Based on the results of the experiments, the optimal handwriting characteristics were selected, which were subsequently analyzed for the implementation of recognition, also the requirements for educational samples and the stages of their selection and preliminary processing are determined. Besides considered the most critical parameters, setting which significantly increases the likelihood of correct recognition. This system is proposed to use as one of the stages of multifactor authentication. © 2020, Springer Nature Switzerland AG.

关键词： Authentication

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Depth Analysis of Different Medical Image Segmentation Techniques for Brain Tumor Detection

Depth Analysis of Different Medical Image Segmentation Techn...

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international conference on computing, Power and Communication Technologies (GUCON)

作者： Gupta, Kapil Kumar Dhanda, Namrata Kumar, Upendra Amity Univ Dept CSE Lucknow Uttar Pradesh India Shri Ramswaroop Mem Univ Inst Technol Dept CSE Barabanki India Amity Univ Lucknow Uttar Pradesh India IET Lucknow Uttar Pradesh India

ISBN: (纸本)9789811503399;9789811503382

Segmentation is the most significant step in studying and assimilating medical CT and MR images. To identify the feature areas in the medical images and to clip them, segmentation is used. Owing to the continued growth in technology and research areas, it becomes more challenging to take out the best method of image reconstruction. Therefore, many technologies have been made for the identification, detection and classification of brain tumor parts in MR images. Different radiology tools are used for identification and detection of the inner components of the human anatomy by physicians and radiologists without any surgery. In this paper, the authors paid attention on the segmentation analysis of brain tumor MR images. For the comparison purpose, the authors consider the parameters like run-time complexity, sensitivity and segmentation accuracy.

关键词： pattern recognition Medical imaging MRI Brain tumor diagnosis Image segmentation

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Image forgery detection using deep textural features from local binary pattern map

Image forgery detection using deep textural features from lo...

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5th international Symposium on Intelligent Systems Technologies and Applications (ISTA) / international conference on Applied soft computing and Communication Networks (ACN)

作者： Revi, K. Remya Wilscy, M. APJ Abdul Kalam Technol Univ Dept Comp Sci & Engn SAINTGITS Coll Engn Thiruvananthapuram Kerala India

Nowadays the manipulations of digital images are common due to easy access of many online photo editing applications and image editing softwares. Forged images are widely used in social media for creating deceitful propaganda of an individual or a particular event and for cooking up fake evidences even in court proceedings. Hence ensuring the integrity of digital images is of prime significance and it has become a hot research area. In this paper, a novel technique for image forgery detection is proposed. The method utilizes the layer activation of inception-ResNet-v2, a pretrained Convolutional Neural Network(CNN)to extract the deep textural features from Rotation Invariant - Local Binary pattern (RI-LBP) map of the chrominance image. Non-negative Matrix Factorization (NMF) technique is used to reduce the dimensionality of the extracted features. The dimensionality reduced features are used to train a quadratic Support Vector Machine(SVM) classifier to classify images into forged or authentic. The method is assessed on four benchmark datasets (CASIA ITDE v1.0, CASIA ITDE v2.0, CUISDE and IFS-TC). Extensive experimental analysis is done and the results show an improved detection accuracy compared to the state-of-the-art methods.

关键词： Deep learning rotation invariant-local binary pattern pretrained convolutional neural etworks deep textual features image forgery detection

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An Integrated Fuzzy-Based Methodology for Selection of Casting pattern Material 3rd

An Integrated Fuzzy-Based Methodology for Selection of Casti...

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3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Zindani, Divya Maity, Saikat Ranjan Bhowmik, Sumit Department of Mechanical Engineering National Institute of Technology Silchar SilcharAssam788010 India

ISBN: (纸本)9789811507502

Casting is a manufacturing process that is used for mass production of complex metal shapes. Sand casting is one of the variations of casting process which is also known as sand molded casting. The success of the final product depends to a great extent on the patterns used in the process which is a replica of the final product to be produced. pattern is used to prepare cavity into which the molten material is poured. Material of the pattern used in its construction and its design are the important primary considerations for achieving the best possible shapes from the sand casting process. The present work proposes a novel fuzzy-based multi-criteria decision making (MCDM) approach for selecting the best material for the pattern. The integrated framework integrates fuzzy decision making with the least explored Tomada de Decisão Interativa Multicritério (TODIM) MCDM method. Material selection has been done considering five material alternatives, i.e., wood, aluminum, steel, plastic, and cast iron;and seven criteria, i.e., machinability, wear resistance, strength, weight, reparability, resistance to corrosion, and resistance to swelling. It was established that aluminum is the best-ranked material alternative. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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Filter Pruning Based on Connection Sensitivity 2020

Filter Pruning Based on Connection Sensitivity

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

作者： Xu, Yinong Liao, Yunsen Zhao, Ying Soft Engineering Institute Chong Qing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450387699

For the goal of reducing the remarkable redundancy in deep convolutional neural networks (CNNs), we propose an efficient framework to compress and accelerate CNN models. This work focus on pruning at filter level, mainly removing those less important filters. Firstly, we measure the importance of the filter by introducing a saliency criterion based on its corresponding connection sensitivity. In addition, we apply an algorithm, which transform a vanilla CNN module, to provide a quantitative ranking. Next, we prune the redundancy by discarding unimportant filters. Finally, we fine-tune the network to improve its accuracy. We verify the effectiveness of our method with VGGNet and ResNet on multiple datasets, such as CIFAR-10 and ImageNet ILSVRC-12. For instance, we achieve more than 50% FLOPs reduction on ResNet-56 with virtually the same accuracy as the reference network. © 2020 ACM.

关键词： Neural network models

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