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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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A machine learning Algorithm for Solving Hidden Object-Ranking Problems 2nd

A Machine Learning Algorithm for Solving Hidden Object-Ranki...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Mondonneix, Gaël Chabrier, Sébastien Mari, Jean Martial Gabillon, Alban University of French Polynesia Géopôle du Pacifique Sud EA4238 LABEX CORAIL Punaauia France

ISBN: (纸本)9789811391804

Hidden object-ranking problems (HORPs) are object-ranking problems stated in instance-ranking terms. To our knowledge, there is no algorithm able to process these problems with the appropriate bias. This lack is not significant as long as the size of the dataset makes it possible to capture enough information by mining more data;however, when the data are scarce, any information lying in the data is worth exploiting and such an algorithm would become useful. We explicit the appropriate bias for object-ranking problems and propose an algorithm able to apply this bias to cases where these problems arise in an instance-ranking form. The theoretical foundations of the algorithm are discussed and the algorithm is tested on scarce real data, yielding better results (94.4% accuracy) than traditional algorithms (92.6% accuracy for the best case). © 2019, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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proceedings of the Sixth international conference on Green and Human Information Technology 1

引用

丛书名： Lecture Notes in Electrical Engineering

2019年

作者： Seong Oun Hwang Syh Yuan Tan Franklin Bien

ISBN: (数字)9789811303111

ISBN: (纸本)9789811303104;9789811343841

This volume presents the proceedings of the Sixth international conference on Green and Human Information Technology (ICGHIT), held in Chiang Mai, Thailand, Jan 31-Feb 2, 2018.;ICGHIT is the unique global conference for researchers, industry professionals, and academics interested in the latest development of green and human information technology. Its broad scope ranges from electronics to communications, computers, multimedia and signal processing, control and intelligent systems, IC and convergence technologies, which are related to green and human issues such as energy saving and human welfare. Specially in this volume, ICGHIT covers state-of-the-art technologies for the 4th industrial revolution, for example, cyber security, big data and cloud service, smart medical system, machine learning and its applications.

关键词： Communications Engineering, Networks Computer Communication Networks Big Data Computational Intelligence

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RNN-LSTM Based Indoor Scene Classification with HoG Features 2nd

RNN-LSTM Based Indoor Scene Classification with HoG Features

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2nd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Verma, Ambica Sharma, Shilpa Gupta, Priyanka Lovely Profess Univ Sch Comp Sci & Engn Jalandhar Punjab India

ISBN: (纸本)9789811331404;9789811331398

The machine learning and artificial intelligence models have evolved with unenvisaged swiftness over the past decade. The machine learning and artificial intelligence models work on the basis of the mathematical models, which are capable of processing the financial, image, video, audio, audio-visual and several other forms of data. In this paper, the work has been carried upon the bulk image data representing the variety of indoor scenes. The goal is to detect the type of indoor scene, which can be utilized by variety of artificial intelligent application for various purposes. The indoor scene recognition has been performed over the computer vision & pattern recognition (CVPR), 2009 dataset, which is consisted of 15620 images. The deep learning mechanism known as recurrent neural network (RNN) has been incorporated for the classification of the indoor scene data over the histogram of oriented gradient (HoG) based features. Specifically, the forget gates based recurrent mode called Long short-term memory (LSTM) has been incorporated for the classification of the indoor scene data. The performance of the proposed model has been analyzed over the CVPR09 dataset using 50 and 100 randomly drawn test cases. The proposed model has been found 92% accurate in comparison with SVM (88%), KNN (80%) and Naive Bayes (86%). The F1-measure based performance assessment also proves the robustness of LSTM based model with 96% accuracy over 92% of Naive Bayes, 93% of SVM and 88% of KNN.

关键词： Deep learning Recurrent neural network Long-short term memory LSTM Indoor scene recognition CVPR'09

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Secured, Intelligent Blood and Organ Donation Management System - “LifeShare”

Secured, Intelligent Blood and Organ Donation Management Sys...

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Advancements in Computing (ICAC), 2019 international conference on

作者： P.L. Wijayathilaka P.H. Pahala Gamage K.H.B. De Silva A.P.P.S. Athukorala K.A.D.C.P. Kahandawaarachchi K.N. Pulasinghe Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

The scarcity and exigency for blood and organs has created many discrepancies in current approaches. These have created the criteria for malpractices such as organ trafficking and black market selling. This research presents a solution with a secured-smart blood and organ donation web developed system, allowing both patients and healthcare providers to access information about the blood and organ processing records. The database would be managed using the Blockchain technology which could be only accessed by authorized users. Finally, tracking all registered donors, the proposed system generates a smart identity developed by Ethereum Smart Contract (ESC). System predicts blood demand for the future ten years using Linear Regression Model with 0.998 of high R-squared accuracy value. This reduces shortages and wastage of blood. Also, using global positioning system and K-Nearest Neighbors machine learning algorithm, the system finds the best matches among donors and seekers according to the nearest location. Further, the system will automatically send questionnaires for registered users to identify and evaluate their awareness and issues about organ donation. Overall, this study aims for a secured and transparent web application. Thus, it facilitates an innovative and a productive blood donation and organ transplantation process in Sri Lankan healthcare sector.

关键词： machine learning algorithms Smart contracts Medical services Blockchain Predictive models Prediction algorithms Blood

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Review on Deep learning Methods Used for Computer-aided Lung Cancer Detection and Diagnosis 19

Review on Deep Learning Methods Used for Computer-aided Lung...

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proceedings of the 2019 2nd international conference on Algorithms, Computing and Artificial Intelligence

作者： Firdaous Essaf Yujian Li Seybou Sakho Mesmin J. Mbyamm Kiki School of Computer Science and Technology Beijing University of Technology Beijing China School of Artificial Intelligence Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (纸本)9781450372619

Cancer is one of the diseases with high mortality rates in the 21st century, with lung cancer being the first in all cancer morbidity and mortality rates. In recent years, with the large rise of data and artificial intelligence researches, the auxiliary diagnosis of lung cancer based on deep learning has gradually become a hot research *** the available and public datasets for lung cancer are mainly CT scans images with lung nodules annotations, the work on the assisted diagnosis of lung cancer using deep learning is mainly based on image data preprocessing, Pulmonary nodule segmentation, and lesion analysis and ***-aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. So our aim is to help the development of a new CAD system with higher performance than the existent ones to assist lung cancer detection in early stages. This paper presents an overview of the deep learning methods used for computer-aided lung cancer detection and diagnosis. It is mainly focused on the important processing and analyzing methods for the pulmonary image data obtained by medical instrument imaging, and which we can summarize into these 4 steps: Medical image data preprocessing, Pulmonary nodule segmentation, pulmonary nodule detection, and finally lesion diagnosis A full description of the CAD systems steps is given along with an overview of the state of art deep learning medical image processing methods.

关键词： computer-aided diagnosis Deep learning Medical Imaging processing Lung cancer

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Fully Convolutional Network for 3D Human Skeleton Estimation from a Single View for Action Analysis

Fully Convolutional Network for 3D Human Skeleton Estimation...

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IEEE international conference on Multimedia and Expo (IEEE ICME)

作者： Lie, Wen-Nung Lin, Guan-Han Shih, Lung-Sheng Hsu, Yuling Thang Huu Nguyen Quynh Nguyen Quang Nhu Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI CIRAS Dept Elect Engn Chiayi Taiwan Natl Chung Cheng Univ Dept Elect Engn Chiayi Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi Taiwan Natl Chung Cheng Univ CIRAS Chiayi Taiwan Univ Sci & Technol Univ Danang FAST Hanoi Vietnam

ISBN: (纸本)9781538692141

Elderly health monitoring is important nowadays. This paper proposes to extract 3D skeleton for elderly for monitoring their daily behavior (such as walk, fall-down, etc.). Our technique uses two "Fully Convolutional Networks" (FCNs) to estimate 3D Human pose (skeleton) from the corresponding 2D pose (skeleton), which can be estimated from a single RGB-image. Our FCNs contain two-stages: the 1st stage is to estimate a 3D Anchor Pose (i.e., the most similar and frequently occurring pose in the dataset) from the 2D skeleton, while the 2nd stage is to further regress/refine the 3D anchor pose to its final state (parameters). We also apply global or object-centered normalization methods as a pre-processing step so as to be applicable when cameras of different FOV (Field of View), focal length, or object distances, are encountered. According to experiments, our two-stage FCNs are capable of achieving an MPJPE (Mean per joint position error) of 38.84 mm (better than other methods in comparison, 45.5 mm) when a 2D ground truth pose is used as the input. When cascaded with other 2D pose estimator (e.g., stacked Hourglass model), the average MPJPE is about 67.71 mm. Tis can be further improved if a 17-joints human skeleton model is adopted and re-trained based on the dataset H36M.

关键词： 2D human skeleton 3D human skeleton 2D pose 3D pose Deep learning Fully-convolutional network Human-machine interaction Anchor pose

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Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer

Candidate Selection for the Interview using GitHub Profile a...

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Advancements in Computing (ICAC), 2019 international conference on

作者： R.G.U.S. Gajanayake M.H.M. Hiras P.I.N. Gunathunga E.G. Janith Supun Anuradha Karunasenna Pradeepa Bandara Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic performance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile, CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language processing to identify the Big Five personality traits, CV and GitHub insights, candidate's skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented and shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.

关键词： machine learning algorithms Organizations Programming Feature extraction Software Interviews Software development management

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Query-Based machine learning Model for Data Analysis of Infrasonic signals in Wireless Sensor Networks 2018

Query-Based Machine Learning Model for Data Analysis of Infr...

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2nd international conference on Digital signal processing (ICDSP) / international conference on Computer Graphics and Virtuality (ICCGV)

作者： Chang, Ray-I Huang, Chien-Chang Lai, Liang-Bin Lee, Chia-Yun Natl Taiwan Univ Dept Engn Sci & Ocean Engn Taipei 106 Taiwan Chunghwa Telecom Co Ltd Taipei 100 Taiwan

ISBN: (纸本)9781450364027

As infrasonic signals can through objects and propagate at a long distance, infrasound sensors are widely applied in wireless sensor networks to monitor environment events of a large area. The signal conditions are usually complex and have various characteristics while monitoring the large area. Different features in both time and frequency domains should be extracted and considered. Big data increases the computation complexity, and the wrong selection of features may decreases the accuracy in event prediction. To overcome this problem, a query-based learning method is applied to select the proper features for smart edge computing in machine learning. Experimental results show that the proposed method provides good performance when comparing with previous feature selection methods.

关键词： signal processing systems sensor networks feature selection data analytics machine learning

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signal Demodulation without Channel Equalizer Using machine learning Techniques

Signal Demodulation without Channel Equalizer Using Machine ...

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international conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： S Muthulakshmi Renu Jose ACIS ECE Rajiv Gandhi Institute of Technology KOTTAYAM INDIA

With the unprecedented availability of computing and data resources, there has been a widespread trend in using machine learning techniques in the field of communication as well. In the existing communication system, signal demodulation in multipath channel works with the aid of equalizers to mitigate the effect of inter-code cross talk and inter-symbol interference. Owing to the ability other machine learning models to learn the complex structure in feature space, deep learning architectures such as Deep Belief Network(DBN) and Stacked Auto Encoders(SAE) are used for demodulation in this paper. The experiments were conducted in a multipath Rayleigh channel. Simulation results shows the feasibility and hegemony of the deep learning model over the conventional demodulation techniques using equalization.

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PIMD signal Modeling Based on FTDNN

PIMD Signal Modeling Based on FTDNN

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Beomhee Jang Sungbin Im Donggeon Lee Chonghoon Kim Seungmo Hong Jinzi Song School of Electronic Engineering Soongsil University Seoul Korea

In this paper, modeling of the third-order passive intermodulation distortion (PIMD) caused by the downlink signal in the frequency division duplex (FDD) wireless communication system is developed with the Focused Time Delay Neural Network (FTDNN). PIMD is an interference signal generated due to the nonlinearity of a passive element in a wireless communication environment, and degrades the noise level of a receiver, resulting in a deterioration of communication quality. Offline learning and testing of the proposed approach are investigated with measured PIMD signals and transmitted downlink signals. As a result, it is confirmed that FTDNN structure is suitable for learning nonlinearity of the PIMD generation process.

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