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Character Recognition from Handwritten Image Using Convolutional Neural Networks 1

2nd International Symposium on Signal and Image Processing, ISSIP 2018

作者： Jana, Ranjan Bhattacharyya, Siddhartha Department of Information Technology RCC Institute of Information Technology KolkataWest Bengal India Department of Computer Application RCC Institute of Information Technology KolkataWest Bengal India Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Czech Republic

ISBN: (数字)9789811367830

ISBN: (纸本)9789811367823

Character recognition from handwritten images is of great interest in the pattern recognition research community for their good application in many areas. To implement the system, it requires two steps, viz., feature extraction followed by character recognition based on any classification algorithm. Convolutional neural network (CNN) is an excellent feature extractor and classifier. The performance of a CNN for a particular application depends on the parameters used in the network. In this article, a CNN is implemented for the MNIST dataset with appropriate parameters for training and testing the system. The system provides accuracy up to 98.85%, which is better with respect to others. It also takes very low amount of time for training the system. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Ordinal Preferences Driven Reputation Measurement for Online Services with User Incentive

Ordinal Preferences Driven Reputation Measurement for Online...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaodong Fu Li Liu Lijun Liu Yong Feng Kun Yue Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

A core source of raw information used as inputs to the reputation systems of online services is the feedback ratings provided by users. However, it is impossible that all users rate services with the same criteria and so ratings of different users are incommensurable. Meanwhile, users are not necessarily willing to provide honest feedbacks. Thus, aggregating dishonest cardinal ratings into reputation will potentially lead to unreliable and misleading reputation. In this paper, we propose a reputation model that aggregates ordinal user preferences rather than cardinal ratings for online services with user incentive. A distance metric is defined to measure the discrepancy between ordinal preferences. Then an optimal reputation model with the attributes of incentive compatible and individually rational is proposed. We design a B&B algorithm to solve the optimization problem so that a reputation vector that maximizes the total value of all users can be found efficiently. A comprehensive experimental study and performance analysis are conducted to evaluate the effectiveness and efficiency of the proposed method.

关键词： Measurement Web services Conferences Aggregates Performance analysis Optimization

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International Conference on Innovative Computing and Communications 1

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丛书名： Lecture Notes in Networks and Systems

2019年

作者： Siddhartha Bhattacharyya Aboul Ella Hassanien Deepak Gupta Ashish Khanna Indrajit Pan

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3D object recognition based on data fusion at feature level via principal component analysis 1st

3D object recognition based on data fusion at feature level ...

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1st International Symposium on Signal and Image Processing, ISSIP 2017

作者： Ibrahim, Abdelhameed Ella Hassanien, Aboul Bhattacharyya, Siddhartha Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Computers and Information Cairo University Cairo Egypt Department of Computer Application RCC Institute of Information Technology Kolkata 700 015 India Giza Egypt

ISBN: (纸本)9789811088629

In computer vision applications, recognizing a 3D object is considered an effective research area. The recognition of 3D objects can be accomplished by building accurate 3D models of these objects. The 3D object reconstruction is the creation of 3D models from a set of images. Different reconstruction techniques can be used to obtain 3D models using calibrated and uncalibrated images of the required object. In this work, a method for evaluating the effectiveness of using 3D models produced from different 3D modeling techniques is presented. Different feature extraction, matching, and fusion techniques of average, addition, principal component analysis, and discrete wavelet transform are used to get multiple models. The targeted object models are then used as input to an artificial neural network which is used to compute the recognition error value. Experiments use real-world images for modeling to evaluate the proposed method. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Object recognition

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Breast Cancer Classification in Mammograms Using Support Vector Machines (SVMs) and Histogram of Oriented Gradients (HOG)

Breast Cancer Classification in Mammograms Using Support Vec...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Rashmi Choudhry Sivadharshini B Madhav Dua V. Nirmala Saif O. Husain R. S. Arunkumar Department of Information Technology IES University Bhopal M.P. India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamilnadu Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This work focuses on breast cancer classification in mammograms using SVMs classifier and histogram of oriented gradients features. The envisioned system is designed in such a way to enhance the diagnostic precision through integration of the virtues of both SVMs and HOG in identifying discriminative characteristics of breast tissue. Special emphasis was given on comparing and testing the performance of the intended model with other models on a public mammogram dataset containing both malignant and benign cases; the optimized SVM model provided an accuracy of 92%. 5%, sensitivity of 91. 0%, specificity of 93. 74 (fig 5) and 8%, and an AUC-ROC of 0. 95. This study shows that the method is efficient in discriminating between the malignant and benign first axillary lymph node tissue. The developed CAD system was found to possess the prospect of becoming a valuable support system for radiologists in the early detection of breast cancer. Keywords: Breast cancer; Mammograms; Support Vector Machines; Histogram of Oriented Gradient Descent; computer-aided diagnosis.

关键词： Support vector machines Solid modeling Histograms Accuracy Sensitivity Mammography Breast cancer Vectors Telecommunication computing Testing

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CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 182-193页

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guodong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Macau University of Science and Technology 999078 China Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing100085 China Óptica y Electrónica Instituto Nacional de Astrofísica Puebla08007 Mexico Computer Science Department CINVESTAV-Zacatenco Mexico City07360 Mexico University of Chinese Academy of Sciences Beijing100049 China Westlake University Hangzhou310024 China

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://***/***/face-anti-spoofing/welcome/challengecvpr2019?authuser=0. © 2019 IEEE.

关键词： Face recognition

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BeeCup: A bio-inspired energy-efficient clustering protocol for mobile learning

arXiv

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arXiv 2020年

作者： Xia, Feng Zhao, Xuhai Zhang, Jianhui Ma, Jianhua Kong, Xiangjie School of Software Dalian University of Technology Dalian116620 China Institute of Computer Application Technology Hangzhou Dianzi University Hangzhou310018 China Faculty of Computer & Information Sciences Hosei University Tokyo184-8584 Japan

Mobile devices have become a popular tool for ubiquitous learning in recent years. Multiple mobile users can be connected via ad hoc networks for the purpose of learning. In this context, due to limited battery capacity, energy efficiency of mobile devices becomes a very important factor that remarkably affects the user experience of mobile learning. Based on the artificial bee colony (ABC) algorithm, we propose a new clustering protocol, namely BeeCup, to save the energy of mobile devices while guaranteeing the quality of learning. The BeeCup protocol takes advantage of biologically-inspired computation, with focus on improving the energy efficiency of mobile devices. It first estimates the number of cluster heads (CHs) adaptively according to the network scale, and then selects the CHs by employing the ABC algorithm. In case some CHs consume energy excessively, clusters will be dynamically updated to keep energy consumption balanced within the whole network. Simulation results demonstrate the effectiveness and superiority of the proposed protocol. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Retraction Note: Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 255-255页

作者： Lin, Qiang Li, Tongtong Shakeel, P. Mohamed Samuel, R. Dinesh Jackson School of Mathematics and Computer Science Northwest Minzu University LanzhouGansu China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University LanzhouGansu China Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University LanzhouGansu China Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Malaysia Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK

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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

arXiv

引用

arXiv 2020年

作者： Wei, Shoulin Luo, Kaida Wang, Feng Deng, Hui Mei, Ying Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China Center for Astrophysics Guangzhou University Guangzhou510006 China

The Square Kilometre Array (SKA) would be the worlds largest radio telescope with eventually over a square kilometre of collecting area. However, there are enormous challenges in its data processing. The using of modern distributed computing techniques to solve the problem of massive data processing in SKA is one of the most important challenges. In this study, basing on the Dask distribution computational framework, and taking the visibility function integral processing as an example, we adopt a multi-level parallelism method to implement distributed averaging over time and channel. Dask Array was used to implement super large matrix or arrays with supported parallelism. To maximize the usage of memory, we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance mechanism. The validity of the proposed pattern was also verified by using the Common Astronomy Software application (CASA), wherein we analyze the smearing effects on images reconstructed from different resolution visibilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Visibility

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Sentiment classification method for Chinese and Vietnamese bilingual news sentence based on convolution neural network

Sentiment classification method for Chinese and Vietnamese b...

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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2018

作者： He, Xiaoxu Gao, Shengxiang Yu, Zhengtao Liu, Shulong Hong, Xudong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Key Lab of Computer Technologies Application of Yunnan Province Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9783030002138

Sentiment classification of the Chinese and Vietnamese news is important to analysis public opinion of Chinese and Vietnamese. For cross-lingual sentiment analysis, this paper studies the method of sentiment analysis for Chinese and Vietnamese bilingual news sentence based on convolution neural network (CNN). Firstly, collect Chinese and Vietnamese news data from Internet, use the data collected to train word vector. Secondly, employ the pre-trained word vector and bilingual lexicon to project the Chinese news labeled data to bilingual word vector space, the problem of the lack of annotated corpus in Vietnamese is solved in a certain level. Finally, using convolution neural network to make cross-lingual sentiment classification. Experiment result shows that the F score of Vietnamese news classification has reached 86%. Compared with the traditional methods, the proposed method does not need to construct sentiment lexicon and perform feature extraction, and the performance of cross-lingual sentiment classification is also remarkable. © Springer Nature Switzerland AG 2019.

关键词： Sentiment analysis

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