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4th international conference on Advances in Biomedical Engineering, ICABME 2017

作者： Al-Barazanchi, Kadhim Kamal Al-Neami, Auns Qusay Al-Timemy, Ali Hussein Department of Biomedical Engineering College of Engineering Al-Nahrain University Baghdad Iraq Department of Biomedical Engineering Al-Khwarizmi Engineering College University of Baghdad Baghdad Iraq

ISBN: (纸本)9781538616420

Intramuscular Electromyography (EMG) signal provides a significant source of information that plays an inevitable role in the diagnosis of neuromuscular disorders. the ensemble method represents a supervised machine learning algorithm that constructs a combination of classifiers to achieve accurate classification decision. In this respect, the aim of this study is to propose classification method for diagnosis of neuromuscular disorders including Amyotrophic lateral sclerosis (ALS) and myopathy diseases, through using publicly available EMG data, recorded by intramuscular single-channel EMG electrode. the ensemble of bagged tree algorithm generates and averages multiple versions of decision tree predicatore that are formed by producing a bootstrap replicate of the EMG learning data set. A set of ten time-domain features extracted from the intramuscular EMG recordings are classified by the ensemble of bagged tree algorithm. A classification accuracy rate of 92.8% was obtained with the proposed bagged tree classifier, which reports high classification accuracy. the outcome of this study can assist the clinicians to decide the correct diagnosis of neuromuscular disorders. © 2017 ieee.

关键词： pattern recognition

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Design Methodology for Single-Channel CNN-Based FER Systems 6

Design Methodology for Single-Channel CNN-Based FER Systems

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Parra, Dorfell Camargo, Carlos Universidad Nacional Department of Electric and Electronic Engineering Bogota Colombia

ISBN: (纸本)9798350300956

Facial Expression recognition (FER) systems classify emotions by using geometrical approaches or machine learning (ML) algorithms such as Convolutional Neural Networks (CNNs). Due to their complexity, these FER systems need to be implemented on high-performance hardware, which makes them unsuitable for embedded devices. To address this challenge, we propose a methodology for the design of low-complexity, CNN-based FER systems. Our methodology includes data preprocessing, Local Binary pattern (LBP) implementation, Data Augmentation (DA), and CNN design. Here, we also introduce the Model M6, a single-channel CNN that reaches an accuracy of 94% in less than 30 epochs. M6 has 306,182 parameters that correspond to 1.17 MB of memory. therefore, our methodology and M6 model are feasible for implementation onto embedded systems capable of computing floating point operations. We validated our methodology and M6 model using 66 tests with 6 CNN models and 4 training parameters (batch size, learning rate, number of epochs, optimizer). this validation was performed using the Japanese Female Facial Expression (JAFFE) dataset and TensorFlow. In each test, the relationship between parameters, layers, overfitting, and underfitting was studied. Moreover, we present a step-by-step guideline on how to design the single-channel CNN and provide open-source code for readers interested in reproducing our work. © 2023 ieee.

关键词： Local binary pattern

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Kernel based intrusion detection system 05

Kernel based intrusion detection system

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4th Annual ACIS international conference on Computer and Information Science, ICIS 2005

作者： Kim, Byung-Joo Kim, Il-Kon Dept. of Info. and Comm. Youngsan University Yangsan Korea Republic of Dept. of Computer Science Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)0769522963

Recently applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. Selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not proper method for realtime intrusion detection system. In this paper, we develop the realtime intrusion detection system which combining on-line feature extraction method with Least Squares Support Vector machine classifier. Applying proposed system to KDD CUP 99 data, experimental results show that it have remarkable performance compared to off-line intrusion detection system. © 2005 ieee.

关键词： pattern recognition systems

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (Soft Computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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Soft Computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

the author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th ieee int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) the extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 ieee comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: ieee int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) the Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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Probability-Induced Distance-Based Gesture Matching for Health care Using Microsoft's Kinect Sensor 4th

Probability-Induced Distance-Based Gesture Matching for Heal...

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4th international conference on Advanced Computing, Networking and Informatics (ICACNI)

作者： Pal, Monalisa Saha, Sriparna Konar, Amit Jadavpur Univ Elect & Tele Commun Engn Dept Kolkata W Bengal India

ISBN: (纸本)9789811033735;9789811033728

Detection of 14 healthcare-related gestures due to pain at different body parts is the target area of this work using Kinect sensor. the novelty of our work lies in suppressing the problem of compensation by the use of probability while using similarity matching technique for gesture recognition. the adopted method enhances the matching accuracy for all the similarity measures. A shared probability and similarity measure-based metric has been defined as the matching index. this unique technique contributes to field of health care under static gesture recognition as an application of machine learning with a high accuracy of 99.1071% in 0.0126 s using probability-induced city-block distance.

关键词： Distance matching Health care Kinect sensor Probability

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Research on Tibetan Archaeological Knowledge Search System Based on Knowledge Graph

Research on Tibetan Archaeological Knowledge Search System B...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Nuo Qun Tianhao Shan Gadeng Lobsang Xiaohong Yu Tashi Nyima Collaborative Innovation Center Tibet informatization by MOE and Tibet Autonomous Region School of Information Science and Technology Tibet University Lhasa China School of Information Science and Technology Tibet University Lhasa China Xizang Minzu University Xianyang Shaanxi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

As an important ethnic area with a long history and rich cultural heritage, the inheritance and protection of archaeological culture in Tibet has always been highly concerned at home and abroad. this study provides a systematic and comprehensive knowledge system for Tibetan archaeology by constructing knowledge graph of Tibetan cultural relics, combining named entity recognition algorithm and developing knowledge retrieval system. A TB-CRM data model suitable for Tibetan characteristics was created, and Python and Neo4j were used for data processing and storage. through the comparison and optimization of named entity recognition models, high precision information retrieval is realized. In the end, a full-featured and user-friendly retrieval system was developed to serve the research needs of both heritage lovers and professional scholars. the results of this study provide important support for the protection and inheritance of Tibetan cultural heritage and the digitization of archaeology, and look forward to the broad prospects brought by technological progress for future archaeological research.

关键词： Archeology Systematics Knowledge based systems Named entity recognition Knowledge graphs machine learning pattern recognition Cultural differences Protection Optimization

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international conference on Neural Computing for Advanced Applications 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Haijun Zhang Yinggen Ke Yuanyuan Mu Zhou Wu Tianyong Hao Zhao Zhang Weizhi Meng

ISBN: (数字)9789819958474

ISBN: (纸本)9789819958467

the two-volume set CCIS 1869 and 1870 constitutes the refereed proceedings of the 4th international conference on Neural Computing for Advanced Applications, NCAA 2023, held in Hefei, China, in July 2023.;the 83 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 211 submissions. the papers have been organized in the following topical sections: Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications; machine learning and deep learning for data mining and data-driven applications; Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vision and its industrial applications; Natural language processing, knowledge graphs, recommender systems, and their applications; Neural computing-based fault diagnosis and forecasting, prognostic management, and cyber-physical system security; Sequence learning for spreading dynamics, forecasting, and intelligent techniques against epidemic spreading (2); Applications of Data Mining, machine learning and Neural Computing in Language Studies; Computational intelligent Fault Diagnosis and Fault-Tolerant Control, and their Engineering Applications; and Other Neural computing-related topics.

关键词： Artificial Intelligence Computer Systems Organization and Communication Networks Computing Milieux Image Processing and Computer Vision Mathematics of Computing

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international conference on Neural Computing for Advanced Applications 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Haijun Zhang Yinggen Ke Yuanyuan Mu Zhou Wu Tianyong Hao Zhao Zhang Weizhi Meng

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

关键词： Artificial Intelligence Computer Systems Organization and Communication Networks Computing Milieux Image Processing and Computer Vision Mathematics of Computing

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Deep learning-Based Pipeline for the Detection of Multiple Ocular Diseases 4th

Deep Learning-Based Pipeline for the Detection of Multipl...

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4th international conference on machine Intelligence and Signal Processing, MISP 2022

作者： Angadi, Ananya Bhat, Aneesh N. Ankitha, P. Kumar, Parul S. Srinivasa, Gowri PES Center for Pattern Recognition Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9789819900466

Computer-aided diagnostic systems provide decision support for the early and accessible detection of ocular diseases. While predictive models exist for certain common diseases in isolation, there is a need to detect multiple diseases at the same time to ensure that rarer conditions do not go undiagnosed. In the present work, we propose a workflow to detect the presence of seven ocular diseases. We first determine the disease risk using EfficientNetB3 pre-trained on ImageNet. Next, for common diseases with a relatively larger representation of images in the dataset, we utilize a Vanilla CNN to classify each disease. For relatively less common diseases, characterized by fewer representative samples in the dataset, we employ transfer learning using EfficientNetB3 pre-trained on ImageNet. To deal with the class imbalance in the original data, we create representative datasets with respect to each disease. the resulting models were found to have high sensitivity (0.8–1.0) and promising accuracy (0.7–0.96). © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Video matting based on local-global features fusion 4

Video matting based on local-global features fusion

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Dong, Niuniu Liang, Yihui Zou, Kun Li, Wensheng Feng, Fujian School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, these methods suffer from performance degradation when dealing with complex scenes or natural background videos. To address this challenge, we propose a video matting method called Video Matting Based on Local-Global Features Fusion (VMBLGFF) which can extract both comprehensive global correlation features and local subtle features. VMBLGFF contains two closely connected networks: a transformer network that utilizes window and global attention mechanisms to obtain global correlation features within and cross windows, and a fusion network that integrates local subtle features into the global correlation features to supplement the local detail information which may be overlooked by the attention mechanisms. VMBLGFF alleviates the issue of limiting global correlation features and has been benchmarked on both synthetic and real datasets, and the results demonstrate that VMBLGFF improves the quality of video matting and exhibits good generalization performance. © 2024 SPIE.

关键词： Computer vision

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