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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

3rd International Conference on Smart Innovations in Communi...

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

ISBN: (纸本)9789811553448

The proceedings contain 29 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Multipath routing using improved grey wolf optimizer (igwo)-based ad hoc on-demand distance vector routing (aodv) algorithm on manet;analysis and prediction of fuel oil in a terminal;an android malware detection method based on native libraries;fire early warning system based on precision positioning technology;the construction of learning resource recommendation system based on recognition technology;review of ultra-low-power cmos amplifier for bio-electronic sensor interface;an enhancement of underwater images based on contrast restricted adaptive histogram equalization for image enhancement;hybridization of social spider optimization (sso) algorithm with differential evolution (de) using super-resolution reconstruction of video images;enhanced convolutional neural network (ecnn) for maize leaf diseases identification;facial expression recognition using improved local binary pattern and min-max similarity with nearest neighbor algorithm;visibility improvement of hazy image using fusion of multiple exposure images;comparative analysis of consensus algorithms and issues in integration of blockchain with iot;use of hadoop for sentiment analysis on twitter’s big data;enhanced mobility management model for mobile communications;multi-objective-based lion optimization algorithm for relay selection in wireless cooperative communication networks;embpred30: Assessing 30-days readmission for diabetic patients using categorical embeddings;prediction of different classes of skin disease using machine learning techniques;feature selection and classification based on swarm intelligence approach to detect malware in android platform;preface;trust-based fuzzy bat optimization algorithm for attack detection in manet;transfer learning: Survey and classification.

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Identification on standard connection modes of distribution network based on topological features and pattern recognition 3

Identification on standard connection modes of distribution ...

引用

3rd international conference on Air Pollution and Environmental Engineering

作者： Wu, Tianchen Lv, Bing Southeast Univ Sch Elect Engn Nanjing Peoples R China

The distribution network has multiple points, long lines, wide coverage, complex structure, and intersecting management links. In order to improve the quality of the analysis and evaluation of the standard grid structure of the distribution network, this approach uses the 10kV medium voltage distribution network as the object to identify the standard connection Research. First, abstract the wiring diagram of the distribution network, extract the characteristics of standard connection types, establish a eigenvector expression model and standard connection type feature library. Second, use breadth-first search to form the network topology structure, and check data to detect the transfer capacity of closed loops, islands, and contact switches. Then use the principle of statistics to calculate the content of the eigenvector expression. Finally, use the pattern recognition method to match the eigenvector expression of the connection group to be identified with the standard wiring type feature library. Based on the statistical standard connection characteristics, this approach adopts the method of pattern recognition to judge the standard grid, which has a good recognition effect and accuracy on the actual distribution network wiring, and is of great significance to the later power grid transformation.

关键词： Eigenvalues and eigenfunctions

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Automatic Differentiation Between Legitimate and Fake News Using Named Entity recognition 20

Automatic Differentiation Between Legitimate and Fake News U...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Sen Xu, Bo Tsai, Chih Ming Xiamen Int Sch 262 Xingbei San Lu Xiamen Peoples R China Hsing Wu Univ 101Sec 1Fenliao Rd New Taipei Taiwan

ISBN: (纸本)9781450375511

Today, the increasing ease of publishing information online combined with a gradual shift of paradigm from consuming news via conventional media to non-conventional media calls for a computational and automatic approach to the identification of an article's legitimacy. In this study, we propose an approach for cross-domain fake news detection focusing on the identification of legitimate content from a pool of articles that are of varying degrees of legitimacy. We present a model as a proof of concept as well as data gathered from evaluating the model on Fake-News AMT, a dataset released for cross-domain fake news detection. The results of our model are then compared against a baseline model which has served as the benchmark for the dataset. We find all results in support of our hypothesis. Our proof-of-concept model has also outperformed the benchmark in the domains Technology and Entertainment as well as when it was run on the whole dataset at once.

关键词： Fake News Named Entity recognition (NER) Natural Language Processing (NLP)

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Secure and Efficient IoT Networks: An AI and ML-based Intrusion Detection System

Secure and Efficient IoT Networks: An AI and ML-based Intrus...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Mohammed A. Mahdi Department of Information and Computer Science College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The growing deployment of IoT devices has led to unprecedented interconnection and information sharing. However, it has also presented novel difficulties with security. Using intrusion detection systems (IDS) that are based on artificial intelligence (AI) and machine learning (ML), this research study proposes a unique strategy for addressing security issues in Internet of Things (IoT) networks. This technique seeks to address the challenges that are associated with these IoT networks. The use of intrusion detection systems (IDS) makes this technique feasible. The purpose of this research is to simultaneously improve the present level of security in ecosystems that are connected to the Internet of Things (IoT) while simultaneously ensuring the effectiveness of identifying and mitigating possible threats. The frequency of cyber assaults is directly proportional to the increasing number of people who rely on and utilize the internet. Data sent via a network is vulnerable to interception by both internal and external parties. Either a human or an automated system may launch this attack. The intensity and effectiveness of these assaults are continuously rising. The difficulty of avoiding or foiling these types of hackers and attackers has increased. There will occasionally be individuals or businesses offering IDS solutions who have extensive domain expertise. These solutions will be adaptive, unique, and trustworthy. IDS and cryptography are the subjects of this research. There are a number of scholarly articles on IDS. An investigation of some machine learning and deep learning techniques was carried out in this research. To further strengthen security standards, some cryptographic techniques are used. Problems with accuracy and performance were not considered in prior research. Furthermore, further protection is necessary. This means that deep learning can be even more effective and accurate in the future.

关键词： Deep learning Accuracy Simulation Intrusion detection Real-time systems pattern recognition Internet of Things

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Estimation of Human Age and Gender Based on LBP Features Using Two Level Decision by SVM 3rd

Estimation of Human Age and Gender Based on LBP Features Usi...

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3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

作者： Raghavendra, S.P. Adarsh, M.J. Ahamed, Shoieb Shree Hari, J. Department of College of Engineering JNN College of Engineering Shimoga Karnataka India Shimoga Karnataka India Siemens Technology and Services India Pvt Ltd. Bangalore Karnataka India

ISBN: (纸本)9789811605062

Automated face and age recognition is becoming an important technology due to the huge amount of biometric transactions will be carried and processed daily by Authorization agencies. Now a days Estimation of human age is a very significant characteristic in terms of identity authentication. In order to assess one’s individual age, selection of features from the human face is significant and vital with the perspective of experimental accuracy. Some of the Real world Age estimation applications are Biometrics, Security management, 3D face construction and cosmetology. Statistical and regression based are the traditional and earlier techniques commonly used age estimation technique which works on results of interrelationship and correlation among the different values of age for face images. The current work is an enhancement of existing techniques used previously for feature extraction using Local binary patterns with regional features of 2D Wavelet transformation image and age estimation can be accomplished by using SVM Classifier. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Performance Analysis of Keystroke Dynamics Using Classification Algorithms 3

Performance Analysis of Keystroke Dynamics Using Classificat...

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3rd international conference on Information and Computer Technologies (ICICT)

作者： Darabseh, Alaa Pal, Doyel CUNY Dept Math Engn & Comp Sci LaGuardia Community Coll Long Isl City NY 11101 USA

ISBN: (纸本)9781728172835

Authentication is the process of verifying the identity of a user. Biometric authentication assures user identity by identifying users physiological or behavioral traits. Keystroke dynamics is a behavioral biometric based on users typing pattern. It can be used to authenticate legitimate users based on their unique typing style on the keyboard. From a pattern recognition point of view, user authentication using keystroke dynamics is a challenging task. It can be accomplished by using classification algorithms - two-class and one-class classification algorithms. In this paper, we study and evaluate the effectiveness of using the one-class classification algorithms over the two-class classification algorithms for keystroke dynamics authentication system. We implemented and evaluated 18 classification algorithms (both two-class and one-class) from the literature of keystroke dynamics and pattern recognition. The result of our experiments is evaluated using 28 subjects with the total of 378 unique comparisons for each classifier. Our results show that the top-performing classifiers of one-class are not very different from two-class classifiers and can be considered to use in the real-world authentication systems.

关键词： keystroke dynamics one-class classifier two-class classifier biometrics pattern recognition

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Low-error star pattern recognition based on point cloud matching algorithm 3

Low-error star pattern recognition based on point cloud matc...

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2020 3rd international Symposium on Power Electronics and Control Engineering, ISPECE 2020

作者： Liang, Luo Jing Bo, Yu Cheng Min, Luo Chongqing University of Technology Chongqing China

Aiming at the problem that the matching rate of traditional star map recognition methods is not high enough, this paper proposes a star map recognition method based on the ICP point cloud matching algorithm based on feature point extraction. Feature points are selected through the change of the local normal vector of the point cloud, and then used Common (ICP) algorithm to realize more accurate registration of point clouds. In order to further reduce the error rate of star map matching, an improved ICP algorithm based on feature points is used to accurately register the point cloud. Finally, the matching conditions of several star maps are obtained through MATLAB tool simulation and the matching rate error rate is calculated. Common ICP algorithm The minimum matching error of is 14.224, and the minimum matching error of the improved ICP algorithm based on feature points is 14.0715. Obviously, the star map matching error rate of the ICP algorithm based on feature points is lower, which also verifies the superiority of the improved ICP algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Stars

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Graph Neural Networks for Functional Connectivity Analysis: Uncovering Neural Network Disruptions in Parkinson’s Disease

Graph Neural Networks for Functional Connectivity Analysis: ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： R. Ayyappa C. H. Hussaian Basha S. Senthilkumar K. Balasubramanian S Arunkumar S. Venkatesh Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering SR University Telangana India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the field of Parkinson’s’ disease diagnosis and progression monitoring, functional and structural connection patterns in the brain, which can be observed by modalities such as functional magnetic resonance imaging (MRI) and electroencephalography (EEG) are becoming more recognized as promising bio markers. An innovative use of Graph Neural Network (GNNs) is proposed in this study for analyzing patterns of brain connectivity. By utilizing the graph structure of brain networks, the researchers identify disturbances that are unique to Parkinson’s disease conditions. In the proposed framework, brain regions are represented as nodes, and the edges of the graph correspond to functional or structural connections derived from either fMRI or EEG data, depending on the modality used for analysis. The investigation of the brain regions and the connection that are most pertinent to the diagnosis can be accomplished through the utilization of attention weights in feature importance analysis. In recognizing brain network changes in Parkinson’s disease, the findings indicates that the GNN perform much better than traditional machine learning methods. Additionally, these research offers significant advancements into mechanism of neuronal degeneration leads attention to certain regions of the brain and connected throughout the brain that are impacted by Parkinson’s disease. This work contributes to a more comprehensive knowledge of neural network disruptions in neurodegenerative disease and enhances the potential of GNN as diagnostic tool.

关键词： Accuracy Functional magnetic resonance imaging Motors Graph neural networks Electroencephalography Telecommunication computing Planning pattern recognition Biological neural networks Diseases

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Efficient redundancy processing framework of association rules model based on hypergraph in information pattern recognition 3

Efficient redundancy processing framework of association rul...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： DIng, Jie Shaanxi Polytechnic Institute Xianyang712000 China

ISBN: (纸本)9781728170893

Efficient redundancy processing framework of the association rules model based on hypergraph in the information pattern recognition is designed in this paper. For big data analysis or pattern recognition problems, data sets have the characteristics of the diverse formats and sparse information, it is essential to extract the vital information to obtain the efficient analysis of the data sets. The interval value attribute data set is a data set containing multiple attribute data. These data are clustered together, and the types are huge, and it is difficult to reach agreement when classifying. Hence, his feature is used in the research work to construct the novel analytic framework. The experiment results have proven that the proposed algorithm can effectively process the data with higher accuracy. © 2020 IEEE.

关键词： Association rules

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Computer data processing mode in the era of big data: From pattern recognition to intelligent sensing 3

Computer data processing mode in the era of big data: From p...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： Huaiyong, Deng Qing, Ma ChongQing Water Resources and Electric Engineering College Chong qing402160 China

ISBN: (纸本)9781728170893

Computer data processing mode in the era of big data from pattern recognition to intelligent sensing is discussed in this paper. Intelligence refers to the interconnection between things and things and people through the Internet of Things and mobile Internet, combined with the general big data computing, communication, artificial intelligence and other technologies. Neighborhood dependence reflects the proportion of approximate samples to all samples under decision class in mixed information system, and it is also a measure to describe the degree of relationship between conditional attribute set and decision attribute, so it can be used as a heuristic function to reduce the attribute of mixed information system. This paper designed the model from the collection to processing, from pattern analysis to the intelligent sensing. The experimental results have proven that the proposed model can sense the data effectively. © 2020 IEEE.

关键词： pattern recognition

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