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The English Language Scientific Literature Classification Based on Abstract Using Rocchio Algorithm 3

The English Language Scientific Literature Classification Ba...

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3rd international conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Andayani, U. Arisandi, D. Hasugian, Misbah Syahputra, M.F. Siregar, B. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The need for the documents in the form of journals or scientific articles is currently increasing which means the need of documents available also increasing, in another side it makes difficult to find and present journals. Therefore, a method is needed to classify journals automatically according to the categories of the journal. One method that can help organize documents according to their categories is classification. In this study, Rocchio algorithm is used as a method to classify journals. The data used comes from Google Scholar. Journal consists of 4 (four) categories: Computer Vision and pattern recognition, Artificial Intelligent, Data Mining and Analysis, and Computer System. System testing is carried out by taking 160 journals for training data and 48 journals for testing data. The results of this study produced a web-based journal classification system and showed the method used was able to classify journals with accuracy result is 93%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： pattern recognition

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Creating A Large-Scale Financial News Corpus for Relation Extraction

Creating A Large-Scale Financial News Corpus for Relation Ex...

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international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Haoyu Wu Qing Lei Xinyue Zhang Zhengqian Luo University of International Business and Economics Beijing China

ISBN: (数字)9781728197418

ISBN: (纸本)9781728197425

As the data accessible is growing exponentially, the unstructured information has become the most common and main resource for decision makers. News, one of the most basic resource containing rich information covers from the company reports to market dynamics, has been regarded as one of the most important information resources. However, the richness of information in the huge volume of news also leads to the difficulty for the managers to get the key information benefit for their decisions, for which the processing on unstructured information is crucial and fundamental. This paper describes the establishment of a Chinese corpus for researches on financial entity recognition and financial relation extraction based on a large financial news data set. We propose a mixed pattern with POS tagging (Part of Speech Tagging) and BIES (Begin, Inside, End, Single) annotation for Generating quadruples (entity1, entity2, relation, text) from unstructured text. To the best of our knowledge this is the first attempt at extracting financial relation concepts. The average accuracy that was achieved by mixed pattern was (88.88%) by manually verification on random sampling dataset. Evaluation of the corpus gives good results and can be used for finance knowledge extraction.

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Detecting SQL injection attacks using grammar pattern recognition and access behavior mining 3

Detecting SQL injection attacks using grammar pattern recogn...

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3rd IEEE international conference on Energy Internet, ICEI 2019

作者： Gao, Hongcan Zhu, Jingwen Liu, Lei Xu, Jing Wu, Yanfeng Liu, Ao College of Computer Science Nankai University Tianjin China College of Software Nankai University Tianjin China College of Artificial Intelligence Nankai University Tianjin China

ISBN: (纸本)9781728114934

SQL injection attacks are a kind of the greatest security risks on Web applications. Much research has been done to detect SQL injection attacks by rule matching and syntax tree. However, due to the complexity and variety of SQL injection vulnerabilities, these approaches fail to detect unknown and variable SQL injection attacks. In this paper, we propose a model, ATTAR, to detect SQL injection attacks using grammar pattern recognition and access behavior mining. The most important idea of our model is to extract and analyze features of SQL injection attacks in Web access logs. To achieve this goal, we first extract and customize Web access log fields from Web applications. Then we design a grammar pattern recognizer and an access behavior miner to obtain the grammatical and behavioral features of SQL injection attacks, respectively. Finally, based on two feature sets, machine learning algorithms, e.g., Naive Bayesian, SVM, ID3, Random Forest, and K-means, are used to train and detect our model. We evaluated our model on these two feature sets, and the results show that the proposed model can effectively detect SQL injection attacks with lower false negative rate and false positive rate. In addition, comparing the accuracy of our model based on different algorithms, ID3 and Random Forest have a better ability to detect various kinds of SQL injection attacks. © 2019 IEEE.

关键词： Decision trees

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A Novel Local Feature Extraction Algorithm Based on Gabor Wavelet Transform 19

A Novel Local Feature Extraction Algorithm Based on Gabor Wa...

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Proceedings of the 2019 3rd international conference on Advances in Image Processing

作者： Jin Liu Zilu Wu Qi Li Qian Pu School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781450376754

Aiming at the problem that the feature extraction method based on Gabor wavelet transform makes the feature vector dimension higher, a novel method named GCLBP (Gabor-CSLBP) is proposed in this paper. Based on Gabor wavelet transform, the proposed algorithm is a local feature extraction method, which extracted a new kind of feature through applying the idea of CS-LBP (Center-Symmetric Local Binary pattern) into the resulted sub-images of Gabor transform. The feature vector obtained by the GCLBP method combines the advantages of Gabor wavelet transform and CS-LBP, which not only reduces the dimension of the feature vector, but also improves the robustness of image variation. The proposed method is evaluated by extensive experiments on benchmark databases CMU PIE, and Extended Yale B. The experimental results show that the proposed method -- GCLBP, can significantly improve the face recognition rate under complex illumination.

关键词： Feature extraction complex illumination center-symmetric local binary pattern LBP Gabor wavelet transform

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7th EAI international conference on Arts and Technology, Interactivity, and Game Creation, ArtsIT 2018 and 3rd EAI international conference on Design, Learning and Innovation, DLI 2018

7th EAI International Conference on Arts and Technology, Int...

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7th EAI international conference on Arts and Technology, Interactivity, and Game Creation, ArtsIT 2018 and 3rd EAI international conference on Design, Learning and Innovation, DLI 2018

ISBN: (纸本)9783030061333

The proceedings contain 58 papers. The special focus in this conference is on Arts and Technology, Interactivity, and Game Creation. The topics include: Using Motion Expressiveness and Human Pose Estimation for Collaborative Surveillance Art;creative Approaches on Interactive Visualization and Characterization at the Nanoscale;contemporary Installation Art and Phenomenon of Digital Interactivity: Aha Experiences – recognition and Related Creating with and for Affordances;memorial Design pattern Catalogue – Design Issues for Digital Remembrance;cyberella – Design Issues for Interactive 360 Degree Film;smart, Affective, and Playable Cities;serious Game for Teaching Statistics in Higher Education: Storyboard Design;speculative Design for Development of Serious Games: A Case Study in the Context of Anorexia Nervosa;ArtsIT/DLI History, Research and Network Development;multidisciplinary Experience in the Creation of Pervasive Games for Interactive Spaces;scentgraphy - Interactive Installation of Scent Communication;the Use of 360-Degree Video to Provide an Alternative Media Approach to Paralympic Sports;“I Didn’t Know, You Could Do That” - Affordance Signifiers for Touch Gestures on Mobile Devices;a Social App that Combines Dating and Museum Visiting Experiences;effects of Vibrotactile Feedback in Commercial Virtual Reality Systems;evolving Virtual Ecology;a Serious Game for Hemophobia Treatment Phobos: First Insights;inside the Geometry - Double Language;citizen Science and Game with a Purpose to Foster Biodiversity Awareness and Bioacoustic Data Validation;interfaces for Science: Conceptualizing an Interactive Graphical Interface;to Design with Strings for Playability in Cities;a Storytelling Smart-City Approach to Further Cross-Regional Tourism;fostering Social Interaction in Playful Cities.

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Research on the Boundary recognition of Self-Walking Agriculture Machine Based on Vision 18

Research on the Boundary Recognition of Self-Walking Agricul...

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3rd international conference on Robotics, Control and Automation (ICRCA) / 3rd international conference on Robotics and Machine Vision (ICRMV)

作者： Li, Yibo Wang, Qiushi Shenyang Aerosp Univ Shenyang Liaoning Peoples R China

ISBN: (纸本)9781450365307

The boundary recognition algorithm of self-walking agricultural machine based on vision was proposed in this paper, which combined 2D-gabor with uniform pattern of LBP to extract the texture features of boundary area. After that, an algorithm that fuzzy extreme learning machine based on KFCM was presented to improve the accuracy of boundary recognition. The experiment result shows that the proposed method is effective in classifying the boundary of the working area in the process of self-walking agriculture tractor, and owns highaccuracy.

关键词： Gabor Uniform pattern of LBP KFCM Fuzzy weighted Extreme Learning Machine

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Study of Effective Feature Combination for Fault Diagnosis of Bearings in Motors

Study of Effective Feature Combination for Fault Diagnosis o...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： B R Nayana P Geethanjali Sir M Visvesvaraya Institute of Technology Bengaluru India SELECT VIT University Vellore India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Condition monitoring for bearing faults is remaining more vital in the present scenario. This study presents a simple approach for diagnosing various bearing faults of induction motor, which uses effective statistical time domain features. The statistical time domain features like mean absolute value, zero crossing, waveform length, slope sign changes, simple sign integral and Willison amplitude are considered. The performance of features is studied with 6 feature ensembles (FEs) to identify the effective FE for fault diagnosis in the induction motor. This identification is based on the classifications that are performed by using linear discriminant analysis (LDA), naïve Bayes (NB), support vector machine (SVM) and logistic model tree (LMT) classifiers. Each feature ensemble is investigated for 5 datasets derived from a bench mark database for a maximum of 48 faulty conditions. The results indicate the FE constituting six features with SVM classifier outperforms other fault diagnosis techniques.

关键词： Feature extraction Support vector machines Iron Time-domain analysis Fault diagnosis pattern recognition Logistics

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Imagined Speech Decoding From EEG: The Winner of 3rd Iranian BCI Competition (iBCIC2020)

Imagined Speech Decoding From EEG: The Winner of 3rd Iranian...

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Iranian conference of Biomedical Engineering (ICBME)

作者： Nastaran Hamedi Susan Samiei Mehdi Delrobaei Ali Khadem K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9781665419550

ISBN: (纸本)9781665419567

Brain-computer interface (BCI) is defined as the combination of machine and brain signals to control a device or computer to improve the quality of life, e.g., for people with paralysis. In this paper, we focus on people with speech disorders and investigate the capability of electroencephalogram (EEG) signals to discriminate four classes, including the speech imagination of three Persian words corresponding to the English words "rock," "paper," and "scissors," in addition to the resting state. We used the data available from the 3rd Iranian BCI competition (iBCIC2020), acquired from 10 healthy participants in a randomized study. Initially, the mutual information (MI) was used to find the optimum frequency band. Then, features were extracted from the data using the Common Spatial pattern (CSP) algorithm. Afterward, the most discriminative features were selected using the neighborhood component analysis (NCA). These features were then fed to a meta-classifier based on the stacking ensemble learning. The results show that working on an optimum frequency band will enhance the results compared with the fixed frequency band. It is also worth mentioning that the optimum frequency band is subject dependent; therefore, it is substantial to be selected accurately. Our method achieved an average classification accuracy of 51.90%±2.73 across all participants, which is promising compared with the results of previous studies in the field of imagined speech recognition in subject dependent BCI systems with randomized order of the stimPuli.

关键词： Feature extraction Electroencephalography Band-pass filters Biomedical engineering Stacking Visualization Training data

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Automatic recognition and Classification of Granite Tiles Using Convolutional Neural Networks (CNN) 19

Automatic Recognition and Classification of Granite Tiles Us...

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Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Muhammad Ather Babar Khan Zhijie Wang Guangxiao Song Iqbal Hussain College of Information Science & Technology Donghua University Shanghai China Department of Electrical Engineering Usman Institute of Technology Karachi Pakistan College of Information Science & Technology Donghua University Shanghai China

ISBN: (纸本)9781450372534

In recent years, the manual recognition and classification of natural stones have become a multifaceted challenge due to the similar patterns and visual appearance. Therefore in the current study, a robust and more effective system has been developed for an automatic classification of natural stones i.e. granite tiles using the Convolutional Neural Networks (CNNs). This approach is based on fine-tuning pre-trained networks such as AlexNet and VGGNet. The techniques of data augmentation such as reflection and rotation are incorporated to reduce the probability of overfitting. Efforts have been made to distinguish the performance of training from scratch and fine-tuning pre-trained models. It is observed that the classification, based on CNNs incorporating color and texture both at the same time, is highly reliable, effective and better than the other conventional methods using visual features separately. The research findings also demonstrated that transfer learning based on fine-tuning a network produced better results in accuracy for the classification task of granite tiles.

关键词： pattern recognition convolutional neural networks transfer learning granite tile

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Toward a Deep Smart Waste Management System based on pattern recognition and Transfer learning

Toward a Deep Smart Waste Management System based on Pattern...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： Aissam Jadli Mustapha Hain SISM Team I2SI2E Laboratory ENSAM Casablanca University Hassan II Casablanca Morocco

ISBN: (数字)9781728187044

ISBN: (纸本)9781728187051

Smart cities essentially combine the use of Information and communications technology (ICT) to provide services for better living conditions inside. The recent developments in the Internet of Things (IoT) opened vast opportunities for researchers and developers to implement various systems and applications in the field of Smart Cities and Intelligent Transportation Systems (ITS). It is a diverse topic of discussion with several application areas such as smart traffic management, smart street lights or gas and water leak detection. Among these applications, Efficient Waste Collection (WC) is considered a fundamental service for helping maintain a healthier environment for the citizens while reducing operating costs. This paper proposes a new architecture for a smart wastes management system based on artificial intelligence techniques and focuses on the combined use of the Internet of Things (IoT) and surveillance systems as an assistive technology for high Quality of Service (QoS) in waste collection. By integrating deep learning techniques in this sector, important cost reductions can be made while maintaining optimal performance.

关键词： Machine learning Training Feature extraction Waste management Support vector machines Urban areas Task analysis

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