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Facial Expression recognition Using Improved Adaptive Local Ternary pattern 3rd

Facial Expression Recognition Using Improved Adaptive Local ...

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3rd international conference on Computer Vision and Image Processing (CVIP)

作者： Saurav, Sumeet Singh, Sanjay Saini, Ravi Yadav, Madhulika Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vidyapith Rajasthan India

ISBN: (纸本)9789813292918;9789813292901

Recently, there has been a huge demand for assistive technology for industrial, commercial, automobile, and societal applications. In some of these applications, there is a requirement of an efficient and accurate system for automatic facial expression recognition (FER). Therefore, FER has gained enormous interest among computer vision researchers. Although there has been a plethora of work available in the literature, an automatic FER system has not yet reached the desired level of robustness and performance. In most of these works, there has been the dominance of appearance-based methods primarily consisting of local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP). Keeping in view the popularity of appearance-based methods, in this paper, we have proposed an appearance-based descriptor called Improved Adaptive Local Ternary pattern (IALTP) for automatic FER. This new descriptor is an improved version of ALTP, which has been proved to be effective in face recognition. We have investigated ALTP in more details and have proposed some improvements like the use of uniform patterns and dimensionality reduction via principal component analysis (PCA). The reduced features are then classified using kernel extreme learning machine (K-ELM) classifier. In order to validate the performance of the proposed method, experiments have been conducted on three different FER datasets using well-known evaluation measures such as accuracy, precision, recall, and F1-Score. The proposed approach has also been compared with some of the state-of-the-art works in literature and found to be more accurate and efficient.

关键词： Facial expression recognition (FER) Adaptive local ternary pattern (ALTP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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Wearable Sensors for Activity Analysis using SMO-based Random Forest over Smart home and Sports Datasets 3

Wearable Sensors for Activity Analysis using SMO-based Rando...

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3rd international conference on Advancements in Computational Sciences (ICACS)

作者： Tahir, Sheikh Badar Ud Din Jalal, Ahmad Batool, Mouazma Air Univ Dept Comp Sci Islamabad Pakistan

ISBN: (数字)9781728142357

ISBN: (纸本)9781728142357

Human activity recognition using MotionNode sensors is getting prominence effect in our daily life logs. Providing accurate information on human's activities and behaviors is one of the most challenging tasks in ubiquitous computing and human-Computer interaction. In this paper, we proposed an efficient model for having statistical features along SMO-based random forest. Initially, we processed a 1-D Hadamard transform wavelet and 1-D LBP based extraction algorithm to extract valuable features. For activity classification, we used sequential minimal optimization along with Random Forest over two benchmarks USC-HAD dataset and IMSB datasets. Experimental results show that our proposed model can compete with other state-of-the-art methods and can be effectively used to recognize robust human activities in terms of efficiency and accuracy.

关键词： Gait Analysis 1D Local Binary pattern (LBP) MotionNode Statistical features Wearable sensors

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Application of Artificial Intelligence in Libraries

Application of Artificial Intelligence in Libraries

引用

international conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Jingjing Li Hao Wang Heilongjiang Bayi Agricultural University Library Daqing China Heilongjiang Bayi Agricultural University College of Engineering Daqing China

ISBN: (纸本)9781665417334

Artificial intelligence (AI) has shown great promise for applications with large breakthroughs in pattern recognition, knowledge mapping, computer vision, and human-machine interaction. In this paper, firstly, the key technical features of AI in library application are studied, and its application in library stack building, stack management and information retrieval service is analyzed; then the existing problems of artificial intelligence in library application are analyzed; and finally the corresponding application service countermeasures are put forward, which can provide reference for related researchers and staff.

关键词： Industries Technological innovation Production Grasping Information retrieval Libraries pattern recognition

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Home Health Care Network Management Under Fuzzy Environment Using Meta-heuristic Algorithms 12th

Home Health Care Network Management Under Fuzzy Environment ...

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12th international conference on Soft Computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Goodarzian, Fariba Goodarzian, Aida Abraham, Ajith Dastgoshade, Sohaib Scientific Network for Innovation and Research Excellence 11 3rd Street NW P.O. Box 2259 AuburnWA98071 United States Department of Industrial Engineering Yazd University Yazd Iran

ISBN: (纸本)9783030736880

Home Health Care (HHC) Services is an alternative to hospitalization, which plays an important role in reducing hospital costs for patient care costs. Also, resource planning and routing problems are among the most important problems that need to be addressed when planning home health care resources. Home health care scheduling problem is a combination of the generalization of vehicle routing problem with the time window and the employee planning problem, which ultimately aims to identify and allocation tasks to therapists, taking into account time windows for patients and employee preferences while the best routes are identified. In this paper, we presented a bi-objective model in the planning horizon of more than one day in order to reduce system costs and increase employee satisfaction simultaneously. In addition, the routing-allocation-scheduling-planning of home health care problem is of NP-hard problem. Meanwhile, we use a fuzzy approach to cope with this uncertainty due to the uncertain nature of transportation and travel cost parameters in the routing-allocation-planning of HHC problem. To solve this model in different sizes, we used meta-heuristic algorithms. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Uncertainty analysis

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2020 international conference on Multimedia Analysis and pattern recognition, MAPR 2020

2020 International Conference on Multimedia Analysis and Pat...

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3rd international conference on Multimedia Analysis and pattern recognition, MAPR 2020

ISBN: (纸本)9781728165554

The proceedings contain 25 papers. The topics discussed include: a multi-scale context encoder for high quality restoration of facial images;automatic data generation for deep learning model training of image classification used for augmented reality on pre-school books;cleaning assessment in endoscopic esophageal images using u-net and a classification model;a camera-based solution for customer behavior identification;a novel generative model to synthesize face images for pose-invariant face recognition;interpolation based anime face style transfer;and how feature fusion can help to improve multi-shot person re-identification performance?.

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Prediction of parkinson's disease using local ternary pattern for gait signals 3

Prediction of parkinson's disease using local ternary patter...

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3rd international conference on Smart Systems and Inventive Technology, ICSSIT 2020

作者： Priya, S. Jeba Ebenezer, Shamila Mathu, T. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781728158211

Parkinson's disease (PD) is one of the common neurodegenerative diseases. The severity of the disease increases with age. The primary reason for Parkinson's disease is the deterioration of dopamine-producing brain cells in substantia. The cardinal symptoms of PD comprise of bradykinesia, tremor and rigidness which can be detected by the gait movements. In this study, the pattern recognition technique was utilized for feature extraction and classification using ANN showed virtuous accurateness when LTP technique was used. Eight statistical features were taken for the classification. The performance parameters such as accuracy, sensitivity, specificity and F1 score achieved for the lower bound are 97.02%, 96.55%, 99.57%, 97.09 % respectively. © 2020 IEEE.

关键词： Neurodegenerative diseases

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SCHEMATA: 3D Classification and Categorization of Ancient Terracotta Figurines 12

SCHEMATA: 3D Classification and Categorization of Ancient Te...

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12th international conference on Agents and Artificial Intelligence (ICAART)

作者： Zeckey, Alexander Langner, Martin Georg August Univ Gottingen Inst Digital Humanities Heinrich Duker Weg 12 Gottingen Germany

ISBN: (纸本)9789897583957

The goal of the starting case-study is not only to develop procedures for automatically generating corpora using 3D pattern recognition, but also to reflect on the associated schematizations and how they can be applied in computer science and visual sciences. For this purpose, methods of object mining in 3D data are to be developed. We chose an object group which is defined by its complexity in shape and the similarity between the objects: In 4th and 3rd century BC ancient Greece small terracotta figurines used to be an art form that was quite common. Based on 200 of those terracottas, a classification system will be elaborated with digital methods, which is able to meet the complexity of the artefacts. In close cooperation between computer science and archaeology, this experimental process leads to a fundamental examination of the concept of pattern recognition as a humanities category. The discussion of the various concepts and methods will be carried out in two complementary dissertations.

关键词： Object Mining Image Exploration Greek Terracotta Figurines Machine Learning Typology

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Probabilistic Neuro-Fuzzy System in Medical Diagnostic Task and its Lazy Learning-Selflearning 3

Probabilistic Neuro-Fuzzy System in Medical Diagnostic Task ...

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3rd international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Bodyanskiy, Yevgeniy Deineko, Anastasiia Pliss, Iryna Chala, Olha Kharkiv Natl Univ Radio Elect Control Syst Res Lab Kharkiv Ukraine

The computational intelligence system that is a hybrid of probabilistic neural network and the neuro-fuzzy system is proposed for solving the medical diagnostic tasks. The distinctive feature of the proposed system is the ability to process data that are given in different scales: numerical, ordinal, nominal, and binary. Also, the tuning process of the system is a hybrid of lazy learning and selflearning, according to T. Kohonen. Moreover, it is characterized by high processing speed, comparing to deep neural networks which are learning with error backpropagation procedures. The diagnostic system that is under consideration is characterized by uncomplicated computational implementation. It is intended to work with both short and long datasets in conditions of overlapping classes of diagnosis, which is typical for medical applications.

关键词： Medical Data Mining Probabilistic Neural Network Neuro-Fuzzy System Membership Function Lazy Learning pattern recognition

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The Fast $k-\text{NN}$ Algorithm Based on a Fixed Grid Method

The Fast $k-\text{NN}$ Algorithm Based on a Fixed Grid Metho...

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Artificial Intelligence, Robotics and Control (AIRC), international conference on

作者： Gene Eu Jan Kuan-Lin Su Hui-Ching Hsieh Chaomin Luo President&#x0027 s Office Tainan National University of the Arts Tainan City Taiwan Department of Electrical Engineering National Taipei University New Taipei City Taiwan Tainan National University of the Arts Tainan City Taiwan Department of Electrical and Computer Engineering Mississippi State University Mississippi State U. S. A.

ISBN: (数字)9781665459471

ISBN: (纸本)9781665459488

$k-\text{Nearest}$ Neighbor $(k-\text{NN})$ is a well-known instance-based learning algorithm; widely used in pattern recognition. A $k-\text{NN}$ classifier can generate highly accurate predictions if provided with sufficient training instances. Thus, it plays a pivotal role in many fields. However, though its accuracy can improve with more data, the need for computational resources increases as well. In this paper, we propose a novel approach which pre-partitions instance space into smaller cells in order to reduce computational cost and greatly reduce the time complexity. Assume every instance is mapped to a point in the $d-\text{dimensional}$ Euclidean space and a training set $D$ contains $n$ training instances. Given a query instance, the brute force $k-\text{NN}$ algorithm has the $O(nd)$ time complexity for predicting the class of a query instance. This algorithm can improve the time complexity.

关键词： Training Force Aerospace electronics Prediction algorithms Extraterrestrial measurements Partitioning algorithms pattern recognition

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

9th IAPR TC3 International Workshop on Artificial Neural Net...

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9th iapr TC3 international Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2020

ISBN: (纸本)9783030583088

The proceedings contain 24 papers. The special focus in this conference is on Artificial Neural Networks in pattern recognition. The topics include: Named entity disambiguation at scale;geometric attention for prediction of differential properties in 3d point clouds;preface;deep learning methods for image guidance in radiation therapy;how (not) to measure bias in face recognition networks;feature extraction: A time window analysis based on the x-ite pain database;pain intensity recognition - an analysis of short-time sequences in a real-world scenario;a deep learning approach for efficient registration of dual view mammography;deep transfer learning for texture classification in colorectal cancer histology;applications of generative adversarial networks to dermatologic imaging;typing plasmids with distributed sequence representation;kp-yolo: A modification of yolo algorithm for the keypoint-based detection of qr codes;using mask r-cnn for image-based wear classification of solid carbide milling and drilling tools;a hybrid deep learning approach for forecasting air temperature;intentional image similarity search;using cnns to optimize numerical simulations in geotechnical engineering;going for 2d or 3d? investigating various machine learning approaches for peach variety identification;a transfer learning end-to-end arabic text-to-speech (tts) deep architecture;ml-based trading models: An investigation during covid-19 pandemic crisis;innvestigate-gui - explaining neural networks through an interactive visualization tool;structured (de)composable representations trained with neural networks;long distance relationships without time travel: Boosting the performance of a sparse predictive autoencoder in sequence modeling.

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