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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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Image processing Techniques for Identification of Fish Disease 2

Image Processing Techniques for Identification of Fish Disea...

引用

2nd IEEE international conference on signal and Image processing (ICSIP)

作者： Malik, Shaveta Kumar, Tapas Sahoo, A. K. Lingayas Univ Nachauli India Reverie Ecol & Fisheries Div Biodivers Lab Barakpur India

ISBN: (纸本)9781538609699

Fish disease diagnosis is a difficult process and needs high level of expertise. Any attempt of developing the system dealing with the fish disease diagnosis and to overcome various difficulties is in it has still not met any extra ordinary success. Identification of diseased fish at early stage is necessary step to prevent from spreading disease. This paper recognizes and identifies the EUS (Epizootic Ulcerative syndrome) disease which is caused by bn Aphanomyces invadans, a fungal pathogen. It is a red spot disease and looks like ulcer hence it is generally misidentified by the people. The paper is divided into two parts, in the first part segmentation has been applied to enhance the image, various edge detection techniques have applied to get the useful information and Morphological operations have been applied on the EUS diseased fish image. In second part features are extracted from the EUS infected fish image through different feature Descriptors i.e. HOG (Histogram of Gradient), FAST (Features from Accelerated Segment Test) and classify the EUS infected and Non-EUS infected fish image through machine learning Algorithms and find the classification accuracy through Classifier. If PCA applied after feature extraction then it increases the accuracy as it is a dimensional reduction. The proposed combination of techniques gives better accuracy as compared to the others. The Experimentation has been done on MATLAB environment on real images of EUS infected fish database.

关键词： HOG (histogram of gradient) FAST (features from accelerated segment test) PCA (principle component analysis) the EUS (epizootic ulcerative syndrome)

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A Study on Software Vulnerability Prediction Model 2

A Study on Software Vulnerability Prediction Model

引用

2nd IEEE international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Shamal, P. K. Rahamathulla, K. Akbar, Ali Govt Engn Coll Comp Sci & Engn Trichur Kerala India Govt Engn Coll Comp Sci & Engn Wayanad Kerala India

ISBN: (纸本)9781509044429

Developing a secure software is time consuming and a complex activity. The main source of insecurity is vulnerabilities in the software. So the prediction of software vulnerability plays important role in software engineering, especially in web application development. A software vulnerability prediction model forecasts whether a software component is vulnerable or not. This paper describes various software vulnerability prediction models. Mainly two types of software vulnerability models are used to predict the vulnerability component in software. In software metrics based prediction model, different software metrics are used as an indicator of software vulnerability. In text analysis based method, source code of the software is used as input to the prediction model. Source code is converted into tokens and frequencies. These are used to predict the vulnerability.

关键词： Web application software vulnerability prediction machine learning text mining software metrics

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Convolutional Neural Network Based Vehicle Turn signal Recognition 2

Convolutional Neural Network Based Vehicle Turn Signal Recog...

引用

2nd international conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Yoneda, Keisuke Kuramoto, Akisue Suganuma, Naoki Kanazawa Univ Inst Frontier Sci Initiat Kanazawa Ishikawa Japan

ISBN: (纸本)9781509066643

This Automated driving is an emerging technology in which a car performs recognition, decision making, and control. Recognizing surrounding vehicles is a key technology in order to generate a trajectory of ego vehicle. This paper is focused on detecting a turn signal information as one of the driver's intention for surrounding vehicles. Such information helps to predict their behavior in advance especially about lane change and turn left-or-right on intersection. Using their intension, the automated vehicle is able to generate the safety trajectory before they begin to change their behavior. The proposed method recognizes the turn signal for target vehicle based on mono-camera. It detects lighting state using Convolutional Neural Network, and then calculates a flashing frequency using Fast Fourier Transform.

关键词： Automated Vehicle Environmental Recognition Image processing machine learning

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Classification of Low Resolution Astronomical Images using Convolutional Neural Networks 2

Classification of Low Resolution Astronomical Images using C...

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2nd IEEE international conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Patil, Jyoti S. Pawase, Ravindra S. Dandawate, Y. H. VIIT Dept Elect & Telecommun Engn Pune Maharashtra India VIIT Dept Engn & Appl Sci Pune Maharashtra India

ISBN: (纸本)9781509037049

Currently deep machine learningtechniques are widely adopted by computer vision and signal processing communities. Deep learning, in particular, the Convolutional Neural Networks (CNN), are the most impressive classifiers widely used for image classification in recent years. CNN model allows the machine to learn automatically about the complex image features from its representation, minimizing the need of human experts in feature extraction. Such a hierarchical representation learning of the images makes CNN a more promising model for classification of different kinds of images as compared to the traditional machine learning models. In this paper, one such successful implementation of CNN is performed for classifying low resolution radio astronomical images containing objects like 'Radio Halos and Relics', and several other ` Point Radio Sources'. For such images, low resolution makes feature extraction a difficult task. Hence, a CNN based classification model proved more efficient in this casegiving a classification accuracy of 88%.

关键词： image classification machine learning convolutional neural networks deep learning

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Contemporary Stegnalysis Schemes for Reliable Detection of Steganography 2

Contemporary Stegnalysis Schemes for Reliable Detection of S...

引用

2nd IEEE international conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Babu, G. John Sridevi, R. Sreekavitha Engn Coll Khammam Telangana India JNTUH Coll Engn Hyderabad Telangana India

ISBN: (纸本)9781509044429

Steganalysis is the process of detecting the hidden information in the carrier. Most used carriers for steganography are images due to the redundant information present in the images and frequency of their use on the Internet. Steganalysis methods are classified into two categories, Targeted steganalysis and universal steganalysis. Targeted steganalysis is based on analysis of individual and known steganographic scheme. Blind steganalysis methods detect steganographic schemes created by unknown random stego-systems. The objective of steganalysis algorithms is to distinguish stego images from pure images. A classifier is built based on stego and pure images. When the knowledge of steganographic scheme is not available, a general steganalyzer is built, which is trained with a set of pure images and a set of stego images generated by various steganographic algorithms. The performance of steganalysis algorithm depends on three important aspects, preprocessing technique, feature selection & extraction and classification. This paper presents the contemporary steganalysis schemes discussing the details and comparing various aspects of these methods.

关键词： Image steganalysis Feature Selection Feature Extraction machine learning

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Does Race Play a Role in Invasive Procedure Treatments? An Initial Analysis 17

Does Race Play a Role in Invasive Procedure Treatments? An I...

引用

2nd IEEE/ACM international conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Hammarlund, Noah Natarajan, Sriraam Indiana Univ Sch Publ & Environm Affairs Bloomington IN 47405 USA Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9781509047222

This study applies machine learning to a popular inpatient data set to identify whether black patients are treated differently than other patients in the invasive heart treatment decision. We use reverse machine learning to predict patient race using treatment and comorbidities in a matched patient sample. Our finding is suggestive that treatment choice only moderately depends on patient race.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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2017 IEEE 2nd international conference on signal and Image processing, ICSIP 2017

2017 IEEE 2nd International Conference on Signal and Image P...

引用

2nd IEEE international conference on signal and Image processing, ICSIP 2017

ISBN: (纸本)9781538609682

The proceedings contain 97 papers. The topics discussed include: improved k-d tree-segmented block truncation coding for color image compression;an error detection and recovery technique for images compressed with the CCSDS compression algorithm;adaptive compressive-sensing of 3D point clouds;compressed sensing MRI with total variation and frame balanced regularization;medical image fusion based on GPU accelerated nonsubsampled shearlet transform and 2D principal component analysis;a novel approach on classification of infant activity post surgery based on motion vector;automatic localization of optic disc based on deep learning in fundus images;a novel compensation algorithm of aerial image registration;three-dimensional positioning using ALOS/prism triple linear-array satellite images;digital anthropometry for human body measurement on android platform;stacked hidden Markov model for motion intention recognition;image processing algorithm for extracting the phase map from structured lights;supervised 3D graph-based automated epidermal thickness estimation;optimizing cognitive analysis sensitivity of photospheres using cube maps;detecting AMD caused vision scotoma through eye tracking;and learning visual odometry for unmanned aerial vehicles.

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machine learning resistant arbiter PUFs scheme based on polynomial reconstruction 2

Machine learning resistant arbiter PUFs scheme based on poly...

引用

2nd IEEE international conference on signal and Image processing, ICSIP 2017

作者： Chen, Shuai Li, Bing Dan, Fukui Chen, Jian Advanced Cloud-System Research Center School of Microelectronics Southeast University Nanjing China

ISBN: (纸本)9781538609682

Physical Unclonable Functions (PUFs) are emerging as an important building block in hardware security. It has been widely used in key generation and authentication. However, Strong PUFs are vulnerable to the machine learning (ML) attack by modeling the challenge-response pairs (CRPs) behavior. The traditional resistant methods based on non-linear blocks (e.g. XOR) have a negative impact on the reliability of Arbiter PUFs (APUFs). In this paper, we proposed a novel error-Tolerant countermeasure to resist this attack. In our Randomized Arbiter PUFs (R-APUFs) scheme, the mapping of CRPs is randomized based on the unordered feature of points in polynomial reconstruction to meet the randomness requirements of ML resistance. Moreover, the error-Tolerant feature of this scheme will improve the reliability of R-APUFs when used in authentication. We exemplified the Evolution Strategy (ES)-based ML attacks on APUFs and R-APUFs. The experimental result indicates that the modeling accuracy can be reduced from over 93% to below 60%, the percentage of authentication success based on R-APUFs in 1000 tests is 100%. Therefore, this scheme can be a good supplement for the existing ML resistance work. © 2017 IEEE.

关键词： Polynomials

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Towards learning Efficient Intervention Policies for Wearable Devices 17

Towards Learning Efficient Intervention Policies for Wearabl...

引用

2nd IEEE/ACM international conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Saponaro, Matthew Wei, Haoran Decker, Keith Univ Delaware Dept Comp Sci Newark DE 19711 USA

ISBN: (纸本)9781509047222

To serve large user populations, autonomous intervention systems (i.e. intelligent agents) are being developed to play more active roles such as fitness coaches and clinical disease prevention aids. Although generic user models have been developed, users may require extensive individualization to meet their personal needs. machine learning techniques may be applied to learn tailored intervention policies for users. However, traditional machine learning requires significant amounts of data to learn an optimal policy. For wearable technology, this may mean probing the user to perform some activity and gauging user response. This paper presents a feasible intervention system model and discusses learners for tailoring user intervention policies. We examine how similar the general user model has to be with respect to the tailored model in order for our learner to perform well.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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Boosting for Postpartum Depression Prediction 17

Boosting for Postpartum Depression Prediction

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2nd IEEE/ACM international conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Natarajan, Sriraam Prabhakar, Annu Ramanan, Nandini Baglione, Anna Connelly, Kay Siek, Katie Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA Univ Cincinnati Sch Informat Technol Cincinnati OH 45221 USA

ISBN: (纸本)9781509047222

Pregnancy and childbirth are important transitional life events for women. Like many other transitional life events, the effects of pregnancy and childbirth can have significant impact on a mother's physical and mental well-being. Sometimes they can even lead to Postpartum Depression (PPD). If left untreated, PPD can be debilitating for the mother and can adversely affect her ability to take care of herself and her infant. Since PPD is not clinically diagnosable, we consider the problem of predicting PPD from survey data about demographics, depression, and pregnancy etc. We adapt the successful functional-gradient boosting algorithm that can handle class imbalance in a principled manner. Our results demonstrate that the proposed machine learning approach can outperform the baseline classifiers and, consequently, demonstrate the potential of machine learning in predicting PPD.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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