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2020 3rd international conference on Intelligent Autonomous Systems, ICoIAS 2020

2020 3rd International Conference on Intelligent Autonomous ...

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3rd international conference on Intelligent Autonomous Systems, ICoIAS 2020

ISBN: (纸本)9781728160788

The proceedings contain 25 papers. The topics discussed include: complete scene parsing for autonomous navigation in unstructured environments;semi-supervised meta-learning via self-training;value iteration solver networks;research on pulsar classification based on machine learning;bias and raising threshold algorithm using learning agents for the best proportion-searching problem;a study on layers of deep neural networks;motion-blur-free high-frame-rate vision system with frame-by-frame visual-feedback control for a resonant mirror;complete scene parsing for autonomous navigation in unstructured environments;pattern recognition of dynamic social network;and a comparative study on HSV-based and deep learning-based object detection algorithms for pedestrian traffic light signal recognition.

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Proceedings - 3rd international conference on Multimedia Information processing and Retrieval, MIPR 2020

Proceedings - 3rd International Conference on Multimedia Inf...

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3rd international conference on Multimedia Information processing and Retrieval, MIPR 2020

ISBN: (纸本)9781728142722

The proceedings contain 82 papers. The topics discussed include: a hybrid temporal modeling phoneme recognized network for real-time speech animation;two-stage method for effective user-guided video segmentation;learning adaptive receptive fields for point clouds;enhancing deep multimedia recommendations using graph embeddings;abnormal traffic congestion recognition based on video analysis;precision UAV landing control based on visual detection;MER-GCN: micro-expression recognition based on relation modeling with graph convolutional networks;multi-task image-based dietary assessment for food recognition and portion size estimation;exploring the use of machine learning as game mechanic - demonstrative learning multiplayer game prototype;end-edge-cloud collaborative system: a video big data processing and analysis architecture;and lane extraction and quality evaluation: a Hough transform based approach.

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Distinguishing Political Parties From Inaugural Speeches Using machine learning 3

Distinguishing Political Parties From Inaugural Speeches Usi...

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IEEE 3rd international conference on Multimedia Information processing and Retrieval (IEEE MIPR)

作者： Lim, SeungJin Han, Hyoil Merrimack Coll 315 Turnpike St N Andover MA 01845 USA Illinois State Univ Campus Box 5150 Normal IL 61790 USA

ISBN: (纸本)9781728142722

A massive amount of text data is available online;applying machine learning techniques to analyze the data helps human beings to grasp the status of previous and current eras. We analyze lexically all US presidential inaugural speeches from 1789 until now and use machine learning algorithms to learn patterns to categorize the authors of the speeches into their respective parties. We apply four different supervised learning approaches (Multinomial Naive Bayes, SVMs, Random Forest, and Logistic Regression) and evaluate their classification performance. The outcome is a 100% accurate classification based on our choice of lexical features (such as unigram and stopwords removal) and the parameters of SVM with linear, polynomial, and RBF kernels. Our study shows that the selected supervised machine learning algorithms can produce highly accurate association between political parties inaugural speech text without requiring any additional information such as metadata for authors or parties or semantics.

关键词： Inaugural speeches machine learning Text classification

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Study on gamma spectrum wavelet noise reduction based on scintillation detector

Study on gamma spectrum wavelet noise reduction based on sci...

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2021 3rd international conference on Applied machine learning and Data Science, ICAMLDS 2021

作者： Yu, Yingchi Ge, Liangquan Li, Fei Hu, Chuanhao College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China

Wavelet transform is a time-scale signal analysis method, which holds the characteristics of the same window size, arbitrary shape change and multi-resolution analysis. Most applications of wavelet transform need to be based on signal decomposition and reconstruction, that is, through the combination of low-pass and high-pass filters to achieve frequency division, the signal is decomposed into signal detail components and large-scale approximate components. In the measurement process of the original energy spectrum data, the denoting process plays a role in reducing the incidence of statistical fluctuations to a certain extent, which is of profound significance to improve the data accuracy. In this paper, wavelet transform is used as the theoretical basis to denoise the actual energy spectrum, at the same time based on the MATLAB platform, simulation of signal with noise, using the different parameters on the signal processing. Firstly, the signal-to-noise ratio (SNR) of the hard and soft thresholds to the signal processing under the same conditions was compared, and then the change of the SNR was observed when the number of decomposition layers was changed under the wavelet basis of db3 (soft threshold). The experimental results show that: 1) under the same conditions, the soft threshold method has higher SNR than hard threshold;2) The number of decomposition layers and the elimination moment affect the signal-to-noise ratio and smoothness of the processed signal respectively, and when the number of decomposition layers is 7-8 layers, the signal-to-noise ratio is about 29, the signal processing effect is the best. This study has shown that Wavelet method is an effective way to process energy spectrum. © Published under licence by IOP Publishing Ltd.

关键词： signal to noise ratio

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Analysis of Changing Trends in Textual Data Representation 3rd

Analysis of Changing Trends in Textual Data Representation

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3rd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2020

作者： Singh, Ksh. Nareshkumar Dorendro, A. Devi, H. Mamata Mahanta, Anjana Kakoti Department of Computer Science Manipur University Imphal India Department of Computer Science Gauhati University Guwahati India

ISBN: (纸本)9789811605062

The most efficient way to convey human knowledge is through natural language text. In order to manage the exponential growth of digital data, it is high time to build a robust text information system. The system performs various techniques such as classification, clustering, summarization, etc. to organize the huge unstructured text data into well-defined forms. Text representation is the fundamental task in text mining and text retrieval. Just like a backbone gives structure to human body, text representation lays the foundation step for various other downstream tasks of Natural Language processing (NLP), text mining and text retrieval. This paper concentrates on the analysis of various text representation models and also discusses major changing trends in textual data representation. This paper could be beneficial to those who wish to study and work on text data in NLP domain. The challenges and drawbacks of the existing text representation models are also discussed in this paper. © 2021, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Mushroom Classification Using Feature-Based machine learning Approach 3rd

Mushroom Classification Using Feature-Based Machine Learning...

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

作者： Maurya, Pranjal Singh, Nagendra Pratap MMM Univ Technol Dept CSE Gorakhpur 273010 Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

Mushroom is an important fungus which contains a good source of vitamin B and a large amount of protein when compared to all other vegetables. It helps to prevent cancer, useful in weight loss and increases the immunity power of human. On the other hand, some mushrooms are toxic and can prove dangerous if we eat them. Therefore, it is a prominent task to differentiate, the edible and poisonous mushrooms. This paper focuses on developing a method for classification of mushroom using its texture feature, which is based on the machine learning approach. The performance of the proposed approach is 76.6% by using SVM classifier, which is found better with respect to the other classifiers like KNN, Logistic Regression, Linear Discriminant, Decision Tree, and Ensemble classifiers.

关键词： Decision trees

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Pose-Invariant Face Recognition in Surveillance Scenarios Using Extreme learning machine Based Domain Adaptation 3rd

Pose-Invariant Face Recognition in Surveillance Scenarios Us...

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

作者： Bhattacharjee, Avishek IIT Madras Dept CS&E Chennai Tamil Nadu India

ISBN: (纸本)9789813290884;9789813290877

Face Recognition (FR) under adversarial conditions has been a big challenge for researchers in the computer vision community. FR performance deteriorates in surveillance condition due to poor illumination, blur, noise, and pose variation in test samples (probe), when compared to training samples (gallery). Even recent deep learning methods fail to perform well in such conditions. This paper proposes a novel framework called PIFR-EDA (Pose-Invariant Face Recognition using Extreme learning machine based Domain Adaptation) that performs pose-invariant face recognition (PIFR) in cross-domain settings. It consists of two stages where the first stage performs face frontalization using a single unmodified 3D facial model and the second stage performs the task of robust domain adaptation by simultaneously learning a category transformation matrix and an l(1,1)-regularized sparse extreme learning machine classifier. The proposed method outperforms state-of-the-art shallow and deep methods (in terms of rank-1 recognition rates) when experimented on three real-world face datasets captured using surveillance cameras.

关键词： Face recognition l(1,1)-regularized sparse extreme learning machine Face frontalization Domain adaptation

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MPCIT 2020 - Proceedings: IEEE 3rd international conference on "Multimedia processing, Communication and Information Technology"

MPCIT 2020 - Proceedings: IEEE 3rd International Conference ...

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3rd IEEE international conference on Multimedia processing, Communication and Information Technology, MPCIT 2020

ISBN: (纸本)9781665419871

The proceedings contain 27 papers. The topics discussed include: multiband rectangular microstrip patch antenna operating at C, X & Ku bands;PCB-fire: automated classification and fault detection in PCB;PZM and DoG based feature extraction technique for facial recognition among monozygotic twins;kidney stone recognition and extraction using directional emboss & SVM from computed tomography images;microscopic blood smear RBC classification using PCA and SVM based machine learning;deep learning based smart garbage monitoring system;and design and implementation of automated image handwriting sentences recognition using hybrid techniques on FPGA.

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CNN-based Mask Detection System Using OpenCV and MobileNetV2

CNN-based Mask Detection System Using OpenCV and MobileNetV2

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international conference on signal processing and Communication (ICSPC)

作者： G. Harriat Christa Jesica J Anisha K K. Martin Sagayam Karunya Institute of Technology and Sciences Coimbatore India

this paper establishes a `Safety system for mask detection during this COVID-19 pandemic'. Face mask detection has seen an overwhelming growth in the realm of Computer vision and deep learning, since the unprecedented COVID-19 global pandemic that has mandated wearing masks in public places. To tackle the situation, machine learning engineers have come up with several algorithms and techniques to identify unmasked individuals using various mask detection models. The proposed approach in this paper adopts frameworks of deep learning, TensorFlow, Keras, and OpenCV libraries to detect face masks in real time. The trained MobileNet model, presented in this paper, yielded an accuracy score of 0.99 and an F1 score of 0.99 in the training data. This user-friendly model can be incorporated with several existing technologies such as face detection, biometric authentication and facial expression detection for further advancements in the future.

关键词： Deep learning COVID-19 Pandemics Biological system modeling Training data signal processing algorithms Predictive models

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machine and Deep learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System 3

Machine and Deep Learning Based Comparative Analysis Using H...

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

作者： Rashid, Azam Siddique, Muhammad Jawaid Ahmed, Shahid Munir Board Intermediate & Secondary Educ Lahore Comp Dept Lahore Pakistan Pakistan Inst Management Fac Informat Technol Lahore Pakistan Univ Punjab Inst Business Adm Lahore Pakistan

ISBN: (纸本)9781728142357

Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (machine / Deep learning) classification algorithmic approaches such as SVM, Naive Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naive Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.

关键词： Deep learning machine learning Intrusion Detection Intrusion Detection System (IDS) Cyber Security

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