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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 732 条记录，以下是421-430 订阅

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Detection of Down Syndrome Using Deep Facial Recognition 3rd

Detection of Down Syndrome Using Deep Facial Recognition

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

作者： Mittal, Ankush Gaur, Hardik Mishra, Manish Graph Era Deemed Univ Dehra Dun Uttarakhand India

ISBN: (纸本)9789813290884;9789813290877

Down syndrome is a genetic disorder that affects 1 in every 1000 babies born worldwide. The cases of Down syndrome have increased in the past decade. It has been observed that humans with Down syndrome generally tend to have distinct facial features. This paper proposes a model to identify people suffering from Down syndrome based on their facial features. Deep representation from different parts of the face is extracted and combined with the aid of Deep Convolutional Neural Networks. The combined representations are then classified using a Random Forest-based pipeline. The model was tested on a dataset of over 800 individuals suffering from Down syndrome and was able to achieve a recognition rate of 98.47%.

关键词： KNN CNN HOG machine learning Computer vision Random forest Healthcare Down syndrome

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Physiological signals analysis, recognition and classification using machine learning algorithms 3

Physiological signals analysis, recognition and classificati...

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3rd international Workshop on Computer Modeling and Intelligent Systems, CMIS 2020

作者： Krak, Iurii Pashko, Anatolii Khorozov, Oleg Stelia, Oleg Taras Shevchenko National University of Kyiv 64/13 Volodymyrska str 01601 Ukraine Glushkov Cybernetics Institute 40 Glushkov avenue Kyiv03187 Ukraine

The spatial and time-frequency domain features analysis are major approaches for signal classification. For non-stationary signals classification, the selection of features is paramount to the robustness of the recognition systems. Among machine learning algorithms, convolutional neural networks (CNNs) have achieved significant performance in many pattern recognition tasks, along with traditional approaches such as Random Forest or Gradient Boosting. Wavelet transformations allow utilizing spectral components that are useful in signal processing tasks. The performance of the wavelet applications for signal classification was evaluated. The experiments show that wavelet transformations can achieve better accuracy than existing algorithms while having significantly fewer parameters than conventional CNNs. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： signal analysis

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Satellite signal Blind Detection Method Based on Time-Frequency Diagrams Semantic Segmentation 3rd

Satellite Signal Blind Detection Method Based on Time-Freque...

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Dong, Zheyu Luo, Hao Wu, Zhigang Zhang, Shuzhang Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030624620

signal blind detection is an important signal processing function of software radio, especially in satellite signal detection. However, threshold selection, high density and low energy signals are still substantially difficult issues to resolve. This paper proposes a signal blind detection method based on time-frequency diagrams semantic segmentation. First draw the time-frequency diagram of the signal. On this basis, the generalization ability of deep learning is used to transform the radio signal recognition problem into a semantic segmentation problem in the field of image recognition, which improves the intelligence and anti-interference ability of blind signal detection. Finally, combined with spectrum analysis, the result graph of semantic segmentation is post-processed to complete the blind detection of the signal. The experimental results show that the accuracy of the validation set composed of two communication satellite samples reaches 97%, the errors of the center frequency and bandwidth are 0.005 MHZ and 0.012 MHZ, which can well satisfy production requirements. © 2020, Springer Nature Switzerland AG.

关键词： Spectrum analysis

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machine learning Prediction of New York Airbnb Prices 3

Machine Learning Prediction of New York Airbnb Prices

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3rd international conference on Artificial Intelligence for Industries (AI4I)

作者： Zhu, Ang Li, Rong Xie, Zehao Univ Waterloo Comp Sci Waterloo ON Canada WeCloudData Inc Toronto ON Canada Univ British Columbia Math & Econ Vancouver BC Canada

ISBN: (数字)9781728187013

ISBN: (纸本)9781728187013

The sharing economy emerged just twenty years ago. It was hard to envision its importance for many industries back then, but today, it is closely incorporated into our daily lives. Airbnb is a prime example of this phenomenon;it brought a brand new business model to the hospitality industry. The Airbnb platform has created millions of micro-businesses with a distinctive pricing strategy. Understanding the pricing strategy will provide insights into this new business model. This paper analyzes a sample of 48 896 listings in New York City from ***, and builds a price prediction model with natural language processing and machine learning techniques. Different methods ranging from linear regression, to a generalized additive model, to a deep neural network, to random forest, to XGBoost and bagging (merging the previous five methods) were explored for the creation of the final prediction model. Of these, bagging, XGBoost and random forest demonstrated the strongest performance with the test data collected from the Airbnb website on January 28th, 2020.

关键词： machine learning Generalized Additive Model Deep Neural Network Random Forest Airbnb

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Evaluation of Content Using machine learning 3rd

Evaluation of Content Using Machine Learning

引用

3rd international conference on Smart Computing and Informatics (SCI)

作者： Thakur, Manish Kumar Goundkar, Raju Ramakrishna VTU Acharya Inst Technol Bengaluru India CMR Univ Bengaluru India

ISBN: (纸本)9789813296909;9789813296893

machine learning is one of the most widely used approaches in current day for effective analysis and prediction purpose in the domain of image processing applications. This paper thus makes a study of existing image retrieval methods. It has been a challenge to implement human recognition method in machines for a long time now. In this process number of methods and procedures are developed to implement the human recognition system in machines. It is however still not possible to achieve the accuracy human are bestowed with. There are number of ways in which different researchers have attempted this task. The paper describes the method along with the parameters used, processing method, and the accuracy achieved. The latest method being machine learning, this study concentrates on the development of supervised learning and thereof the progress made by the unsupervised learning methods. The two techniques Text-based Image Retrieval and Content-Based Image Retrieval are the important retrieval methods dealt with.

关键词： machine learning Image retrieval TBIR CBIR

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A Deep learning Framework Approach for Urban Area Classification Using Remote Sensing Data 3rd

A Deep Learning Framework Approach for Urban Area Classifica...

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

作者： Nijhawan, Rahul Jindal, Radhika Sharma, Himanshu Raman, Balasubramanian Das, Josodhir Graph Era Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Indian Inst Technol Roorkee Dept Earthquake Engn Roorkee Uttar Pradesh India Jaypee Inst Informat Technol Noida India Natl Inst Technol Hamirpur Hamirpur India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

The main aim of this study is to propose a Deep learning framework approach for Urban area classification. The research proposes a multilevel Deep learning architecture to detect the Urban/Non-Urban Area. The support models/parameters of the structure are Support Vector machine (SVM), convolution of (Neural Networks) NN, high resolution sentinel 2 data, and several texture parameters. The experiments were conducted for the study region Lucknow which is a fast-growing metropolis of India, using Sentinel 2 satellite data of spatial resolution 10-m. The performance observed by the proposed ensembles of CNNs outperformed those of current state of art machine algorithms viz;SVM, Random Forest (RF) and Artificial Neural Network (ANN). It was observed that our Proposed Approach (PA) furnished the maximum classification accuracy of 96.24%, contrasted to SVM (65%), ANN (84%) and RF (88%). Several statistical parameters namely accuracy, specificity, sensitivity, precision and AUC, have been evaluated for examining performance during training and validation phase of the models.

关键词： Deep learning Convolution neural network Remote sensing Support vector machine Urban area classification

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Confidence Estimation Using machine learning in Immersive learning Environments 3

Confidence Estimation Using Machine Learning in Immersive Le...

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

作者： Tao, Yudong Coltey, Erik Wang, Tianyi Alonso, Miguel, Jr. Shyu, Mei-Ling Chen, Shu-Ching Alhaffar, Hadi Elias, Albert Bogosian, Biayna Vassigh, Shahin Univ Miami Dept Elect & Comp Engn Coral Gables FL 33124 USA Florida Int Univ Sch Comp & Infoimat Sci Miami FL 33199 USA Florida Int Univ Coll Commun Architecture & Arts Miami FL 33199 USA

ISBN: (纸本)9781728142722

As the development of Virtual Reality and Augmented Reality (VR/AR) technology rapidly advances, learning in an artificial immersive environment becomes increasingly feasible. Such emerging technology not only facilitates and promotes an efficient learning process, but also reduces the cost of access to learning materials and environments. Current research mainly focuses on the development of immersive learning environments and the adaptive learning methods based on interactions between trainees and the environment. However, valuable human biometric data available in immersive environments, such as eye gaze and controller pose, have not been explored and utilized to help understand the affective state of the trainees. In this paper, we propose a machine-learning based research framework to estimate trainees' confidence about their decisions in immersive learning environments. Using this framework, we designed an experiment to collect biometric data from a multiple-choice question and answer session in an immersive learning environment. This includes collecting answers from 10 participants on 35 questions and their self-reported confidence in their answers. A Long Short-Term Memory neural network model was used to analyze the data and estimate the confidence with 85.6% accuracy.

关键词： immersive learning confidence estimation immersive environment deep neural network machine learning

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Proceedings of the 3rd international conference on Intelligent Sustainable Systems, ICISS 2020

Proceedings of the 3rd International Conference on Intellige...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

ISBN: (纸本)9781728170893

The proceedings contain 273 papers. The topics discussed include: intelligent automation based gas valve control mechanism in biogas plant;analysis of guiding quality evaluation model based on regional ecological safety performance evaluation and information mining;review on materials and methods for supercapacitors;performance analysis of current-fed DAB converter for DC microgrid with active power control;computer data processing mode in the era of big data: from pattern recognition to intelligent sensing;road conditions and obstacles indication and autonomous braking system;a study of big data analytics using apache spark with python and Scala;implementing a DC UPS with battery’s state of charge estimation based on coulomb-counting method;cascaded GSM detector-jammer design;diabetes prediction by using big data tool and machine learning approaches;and an efficient design of fault tolerant reversible multiplexer using QCA technology.

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Microscopic Blood Smear RBC Classification using PCA and SVM based machine learning 3

Microscopic Blood Smear RBC Classification using PCA and SVM...

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

作者： Kannadaguli, Prashanth Dhaarini Academy F Technical Education Technical Trainer and Freelancer Bengaluru India

ISBN: (纸本)9781665419871

The therapeutic analysis f microscopic blood smear begins with recognizing blood cells f various categories as well as estimating cell count in blood sample. The distinctive blood cell grading and counting furnish priceless knowledge to the pathologist about sundry infections. This exercise can be easily concluded if the shapes f blood cells are pinpointed first and using the shapes, we classify the blood cells. In this research work we build and test an automatic microscopic blood smear red blood cell (RBC) classification by using Principal Component Analysis (PCA) and Support Vector machine (SVM) based machine learning. We train and test the statistical data models based n probabilistic pattern recognition to classify the blood smear RBC into Normal Cells, Echinocytes, Elliptocytes and Sickle cells. The H-minimum Transform (HmT) and Watershed Transform (WT) are used in pre-processing f images to increase the accuracy if segmentation shape extraction f the blood cells. Then the Bag f Features (BoF) created considering the 500 strongest features f each type f blood cell after K-Means clustering. Training takes place through Image Category Classifier (ICC) whose performance measured by using Mean Average Precision (mAP) justifies that SVM based classifiers provide audacious results. © 2020 IEEE.

关键词： Support vector machines

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Automatic Questionnaire and Interactive Session Generation from Videos 3rd

Automatic Questionnaire and Interactive Session Generation f...

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3rd IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2020

作者： Skanda, V.C. Jayaram, Rachana Bukitagar, Viraj C. Kumar, N.S. Department of CSE PES University Bengaluru India

ISBN: (纸本)9783030634667

In this paper, we present a tool that interleaves lengthy lecture videos with questionnaires at optimal moments. This is done to keep students’ attention by making the video interactive. The student will be presented with MCQ type questions based on the topic covered so far in the video, at regular intervals. The questions are generated based on the transcript of the video lecture using machine learning and natural language processing techniques. In order to have continuity and proper flow of teaching, a LDA-based (Latent Dirichlet Allocation) model has been proposed to insert those generated questions at appropriate points called logical points. © 2020, IFIP international Federation for Information processing.

关键词： machine learning

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