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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Comparative analysis of histograms of oriented gradients and local binary pattern coefficients for facial emotion recognition 8

Comparative analysis of histograms of oriented gradients and...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Subudhiray, Swapna Palo, Hemanta Kumar Das, Niva Mohanty, Sachi Nandan Bhubaneswar India College of Engineering Pune Dept. of Computer Engineering Pune India

ISBN: (纸本)9789380544434

this paper examines the human expressive states dependent on facial pictures utilizing a few viable component extraction methods. It reproduces the K-Nearest Neighbor (k-NN) classifier to approve the adequacy of successful capabilities separated from the Local Binary pattern (LBP) and Histograms of Oriented Gradients (HOG) for the said task. An examination of the strategies has been made dependent on the normal acknowledgment precision of the classifiers utilizing the calculation unpredictability as a compromise. the component extraction methods have been approved for their discriminative force under various preparations for testing information division proportions, Kappa Coefficient, and order time. the LBP has outperformed the HOG include extraction strategy with a normal precision of 79.6% yet remains computationally costly. On the contrary, the HOG method has furnished a lower characterization time with a normal precision of 59.3 % as uncovered from our outcomes. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Emotion recognition

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Optical Character recognition using Backpropagation Neural Network for Handwritten Digit Characters 7

Optical Character Recognition using Backpropagation Neural N...

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7th international conference on Software Engineering and Computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Yap, Mei Ing Moorthy, Kohbalan Daud, Kauthar Mohd Ernawan, Ferda Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Fakulti Teknologi Dan Sains Maklumat Bangi43600 Malaysia

ISBN: (纸本)9781665414074

Recognizing handwritten characters, the accuracy of the optical character recognition is usually not relatively high due to every person having their unique way of writing characters. therefore, we focus on finding a high recognition accuracy of optical character recognition by using a backpropagation neural network. the input layer of the backpropagation neural network is the pixel number of the one-character image, which is 784 input nodes that will be the input layer of the neural network. then the output layer of the neural network will be the 10-digit characters which are 0 to 9. the dataset that used for this research has a total of 280, 000 data. the output of the neural network will a computerized digit representing the recognized digit characters. the performance measurement is the recognition accuracy where the recognized data and the expected output data are compared and calculated. Additionally, the dataset was applied with salt and pepper noise to represent the corrupted data and use a median filter to repair the image. the recognition accuracy for the corrupted image and the corrected image are obtained and discussed. © 2021 IEEE.

关键词： Salt and pepper noise

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Discovering Optimal patterns for Forensic pattern Warehouse 10th

Discovering Optimal Patterns for Forensic Pattern Warehouse

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10th international conference on Advanced computing and Communication Technologies (ICACCT)

作者： Agarwal, Vishakha Tiwari, Akhilesh Gupta, R. K. Singh, Uday Pratap Madhav Inst Sci & Technol Dept CSE & IT Gwalior India Madhav Inst Sci & Technol Dept Appl Math Gwalior India

ISBN: (纸本)9789811046032;9789811046025

As the need of investigative information is increasing at an exponential rate, extraction of relevant patterns out of huge amount of forensic data becomes more complex. Forensic pattern mining is a technique that deals with mining of the forensic patterns from forensic pattern warehouse in support of forensic investigation and analysis of the causes of occurrence of an event. But, sometimes those patterns do not provide certain analytical results and also may contain some noisy information with them. An approach through which optimal patterns or reliable patterns are extracted from forensic pattern warehouse which strengthen the decisions-making process during investigations has been proposed in the paper.

关键词： Data mining Forensic investigation Genetic algorithms pattern warehousing Optimal forensic patterns

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the Design and Implementation of New Chemistry Classroom Instructional Model under the Condition of 'Internet Plus' 10

The Design and Implementation of New Chemistry Classroom Ins...

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10th international conference of Educational Innovation through Technology, EITT 2021

作者： Zhang, Yuanyuan Yu, Liang Shen, Chao Wang, Jing Southwest University Faculty of Education Chongqing China

ISBN: (纸本)9781665427579

Under the background of 'Internet Plus Education', this study designed a new chemistry classroom instructional model based on several theories. then, an one-semester experiment was carried out on the 'Internet plus' typical school to prove the practical effect of the model, including experimental class (N=48) and control class (N=48). Results showed that, the new model significantly increased the students' problem-solving ability, while there was no significant difference between two classes' academic performances. To this end, we made a summary and recommendations about the model. © 2021 IEEE.

关键词： Education computing

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Feature Engineering and Stacked Echo State Networks for Musical Onset Detection 25

Feature Engineering and Stacked Echo State Networks for Musi...

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25th international conference on pattern recognition (ICPR)

作者： Steiner, Peter Jalalvand, Azarakhsh Stone, Simon Birkholz, Peter Tech Univ Dresden Inst Acoust & Speech Commun Dresden Germany Univ Ghent Imec IDLab Ghent Belgium

ISBN: (纸本)9781728188089

In music analysis, one of the most fundamental tasks is note onset detection - detecting the beginning of new note events. As the target function of onset detection is related to other tasks, such as beat tracking or tempo estimation, onset detection is the basis for such related tasks. Furthermore, it can help to improve Automatic Music Transcription (AMT). Typically, different approaches for onset detection follow a similar outline: An audio signal is transformed into an Onset Detection Function (ODF), which should have rather low values (i.e. close to zero) for most of the time but with pronounced peaks at onset times, which can then be extracted by applying peak picking algorithms on the ODF. In the recent years, several kinds of neural networks were used successfully to compute the ODF from feature vectors. Currently, Convolutional Neural Networks (CNNs) define the state of the art. In this paper, we build up on an alternative approach to obtain a ODF by Echo State Networks (ESNs), which have achieved comparable results to CNNs in several tasks, such as speech and image recognition. In contrast to the typical iterative training procedures of deep learning architectures, such as CNNs or networks consisting of Long-Short-Term Memory Cells (LSTMs), in ESNs only a very small part of the weights is easily trained in one shot using linear regression. By comparing the performance of several feature extraction methods, pre-processing steps and introducing a new way to stack ESNs, we expand our previous approach to achieve results that fall between a bidirectional LSTM network and a CNN with relative improvements of 1.8 % and -1.4 %, respectively. For the evaluation, we used exactly the same 8-fold cross validation setup as for the reference results.

关键词： Reservoir computing echo state networks note onset detection

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Pattem structure analysis and identification of cuttlefish iris by using various geometrical feature selection methods 4

Pattem structure analysis and identification of cuttlefish i...

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4th IEEE international conference on computing Communication and Automation, ICCCA 2018

作者： De, Parthasarathi Bhattacharya, Diptendu Biswas, Debrshita CSE NIT Agartala Agartala India CSE ICFAI University Triura Agartala India

ISBN: (纸本)9781538669471

Iris pattern matching is accepted worldwide as an efficient way of a personal identification and recognition by analyzing different patterns which are statistically unique [1-2, 10]. In this paper presented a large number of data set attributes are selected based on certain geometrical features such as shape, size, length and breadth. the geometrical features represented a vector to describe the cuttlefish object for identification and recognition in a meaningful manner. the classifier distinguishes the patterns based on the feature vector and performs classification or clustering that can be performed to recognize the cuttlefish eye. © 2018 IEEE.

关键词： pattern matching

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Efficient Mining of Regional Semantic patterns from Semantic Trajectories on Cloud computing 9

Efficient Mining of Regional Semantic Patterns from Semantic...

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9th international conference on Advanced Cloud and Big Data, CBD 2021

作者： Liu, Yushi Han, Jingjing Zhao, Bin Nanjing Normal University School of Computer and Electronic Information School of Artificial Intelligence Nanjing China Jiangsu Open University Quality Assurance Office Nanjing China

ISBN: (纸本)9781665407458

Semantic trajectory pattern mining is becoming more and more important with the efficient semantic enrichment process methods, which is the process of semantic annotation for spatiotemporal trajectories. Existing works on semantic trajectory pattern mining focus on sequential patterns that occur frequently globally in the semantic trajectory data, ignoring the ones that are locally significant within a region, which is called regional semantic pattern. As a consequence, the existing regional semantic pattern mining algorithm RegMiner is time consuming. Based on this motivation, we propose an improved efficient regional semantic pattern mining algorithm called MRSP, which consists of three parts: candidate semantic category sequence discovery, regional semantic pattern recognition, and parallelization. the innovations of MRSP lie in that we use an improved neighborhood pruning algorithm (NPSP) and an improved neighborhood query algorithm (NSSI) in candidate semantic category sequence discovery mentioned above, and adopt a parallelization algorithm to do calculation based on Hadoop platform. Finally, we evaluate our approach on real dataset in Shanghai. Compared with RegMiner, MRSP has the same effect in mining regional semantic patterns, and the efficiency is greatly improved. © 2022 IEEE.

关键词： pattern recognition

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Ontology of XSS Vulnerabilities and its Detection using XENOTIX Framework 10

Ontology of XSS Vulnerabilities and its Detection using XENO...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Manhas, Saloni University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9781665439701

Web technologies are framed for the purpose of catering the need of ubiquitousness. there is no doubt that web applications are providing number of advantages to the masses, but everything comes with certain vulnerabilities. Exploitation of these vulnerabilities can change the game completely by providing fatal results, instead of giving fruitful results. Cross site scripting attack is also a result of mishandling of vulnerabilities located in web applications. In this paper, XENOTIX framework from OWASP has been used for the detection of cross site scripting attack and practices to curb XSS are discussed. © 2021 IEEE.

关键词： Security of data

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2021 10th IFIP international conference on Performance Evaluation and Modeling in Wireless and Wired Networks, PEMWN 2021

2021 10th IFIP International Conference on Performance Evalu...

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10th IFIP international conference on Performance Evaluation and Modeling in Wireless and Wired Networks, PEMWN 2021

ISBN: (纸本)9783903176416

the proceedings contain 11 papers. the topics discussed include: a new taxonomy of physical-layer network coding techniques in two way relay channel model with single antenna;an efficient convolution method to compute the stationary transition probabilities of the G/M/c model and its variants;surveying and analyzing privacy issues in contact tracing apps;multi-attribute monitoring for anomaly detection: a reinforcement learning approach based on unsupervised reward;ANGELA: HTTP adaptive streaming and edge computing simulator;an analytical model for a class of receiver-initiated mac protocols for energy harvesting wireless sensor networks;and impact analysis of greedy behavior attacks in vehicular ad hoc networks.

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Scalable and Adaptive Software Defined Network Management for Cloud-hosted Group Communication Applications 17

Scalable and Adaptive Software Defined Network Management fo...

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10th international conference on Utility and Cloud computing (UCC) / 4th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Patil, Prithviraj Hakiri, Akram Shekhar, Shashank Gokhale, Aniruddha MathWorks Inc Natick MA 01760 USA Univ Carthage ISSAT Mateur Bizerte Tunisia Vanderbilt Univ Dept EECS 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781450351492

Group communications form the primary communication pattern for many cloud-hosted applications and cloud infrastructure management services, such as system health monitoring, multimedia distribution, collaborative applications and distributed databases. Although IP multicast has been used to support group communication semantics in diverse Internet-based distributed applications, its deployment in cloud Data Center Networks (DCNs) has been limited due to its higher resource consumption, scalability, and stability issues, which in turn degrades the utility of the cloud. Software Defined Networking (SDN) has enabled the re-engineering of multicast capabilities to overcome these limitations. To that end, this paper presents an autonomous, dynamic and flexible middleware solution called SDN-based Multicast (SDMC), which provides both network load-aware and switch memory-efficient group communication semantics in DCNs. thus, SDMC improves DCN resource utilization while allowing applications to remain agnostic to the underlying group communication semantics by efficiently toggling between unicast and multicast in accordance with changing network bandwidth and switch memory usage. Empirical studies comparing SDMC with traditional IP multicast shows up to 60% better latency performance for different DCNs topologies, and up to 50% better performance in the switch memory utilization for multicast groups exceeding size 30.

关键词： Cloud computing Adaptive Multicast Software Defined Network OpenFlow Data Center Networks

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