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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"

共 1347 条记录，以下是661-670 订阅

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Fault Diagnosis of Spur Gear System through Decision Tree Algorithm Using Vibration signal 2

Fault Diagnosis of Spur Gear System through Decision Tree Al...

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2nd international conference on Materials Manufacturing and Modelling (ICMMM)

作者： Gunasegaran, V Muralidharan, V BSA Crescent Inst Sci & Technol Dept Mech Engn Chennai 600048 Tamil Nadu India

Online condition monitoring of machineries, equipment and rotating components in modem manufacturing is used to find the working condition, identify the fault and determine the remaining useful life of a component. The focused benefit of condition monitoring is to increase the reliability, functionality, machining efficiency and surface property of the component and to reduce the downtime and power consumption. Monitoring of the system can be done through vibration, temperature and sound. Vibration analysis on rotating component will give an efficient output for fault diagnosis. This paper deals with vibration signals;the signals are acquired from rotating spur gear system using an accelerometer and the statistical features are extracted for classifications by mathematical software. Distinguished vibration signals as good and faulty are used for fault diagnosis of spur gear with the help of machine learning approach for online condition monitoring. The predominant features were given as input to the classifier J48 algorithm. Accuracy of classification was observed as of 90.18 %. Hence J48 algorithm can be practically utilized to monitor the condition of spur gear system. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Condition monitoring Fault diagnosis Spur gear Classification Decision tree

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A Secure Received signal Strength based Indoor Localization Algorithm Using OS-ELM and Clustering Analysis Technique 2

A Secure Received Signal Strength based Indoor Localization ...

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IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Lu, Peibin Cao, Yanhua Yan, Jun Nanjing Univ Posts & Telecommun Coll Commun & Informat Engn Nanjing Peoples R China Water Resources Dept Jiangsu Prov Nanjing Project Construct Bur Nanjing Peoples R China

ISBN: (纸本)9781538692981

In this paper, a secure machine learning based indoor localization algorithm is proposed, when the received signal strength (RSS) measurement fingerprint based training data set is given by chunk-by-chunk and contains the attacked training data samples. In the off-line phase, the hierarchical clustering approach is proposed to distinguish the attacked training data and the attacked-free training data, firstly. By the above data pre-processing, the attacked RSS measurements is found and can be deleted. Then, the online sequential extreme learning machine (OS-ELM) algorithm is used to training the attacked-free data in turn. In on-line phase, according to the obtained RSS measurements, the obtained regression models are straightly used for final position estimation. Field tests are carried out to show the advantage of the proposed secure localization algorithm over traditional OS-ELM based approach.

关键词： Localization received signal strength (RSS) machine learning OS-ELM clustering

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An Inclusive Study of Several machine learning Based Non-functional Requirements Prediction Techniques 2nd

An Inclusive Study of Several Machine Learning Based Non-fun...

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2nd international conference on Futuristic Trends in Networks and Computing Technologiess, FTNCT 2019

作者： Handa, Naina Sharma, Anil Gupta, Amardeep Lovely Professional University PhagwaraPunjab India DAV College DasuhaPunjab India

ISBN: (纸本)9789811544507

Requirement Engineering is a critical area in the arena of Software Engineering. Non Functional Requirements (NFRs) are very important as Functional Requirements but often ignored. Especially the automated correct prediction and prioritization of Non Functional Requirements (NFRs) is highly needed. Non Functional Requirements can be predicted with the help of machine learning models. The machine learning models have shown more promising output than Natural Language processing. In this paper, the comprehensive review on the recently proposed NFRs prediction techniques has been presented. The output of the review showed that the different authors have used different Clustering and Classification algorithms. But Naïve Bayes algorithm has used by most of the researchers. The most of researchers have used Precision and Recall as their performance metric. machine learning techniques like Ensembling and Parameter Tuning has neglected by researchers. The overall objective is to evaluate the numerous shortcomings in machine learning based NFRs prediction techniques and to draw suitable future directions. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Software engineering

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2019 2nd international conference on signal processing and Information Security, ICSPIS 2019

2019 2nd International Conference on Signal Processing and I...

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2nd international conference on signal processing and Information Security, ICSPIS 2019

ISBN: (纸本)9781728138732

The proceedings contain 15 papers. The topics discussed include: identifying optimal features for multi-channel acoustic scene classification;analysis of CNN architectures for pose estimation of noisy 3-D face images;meteorite hunting using deep learning and UAVs;real-time dynamic security for ProSe in 5G;image steganography using YCbCr color space and matrix pattern;cost effective real time vision interface for off line simulation of Fanuc robots;smart healthcare systems on improving the efficiency of healthcare services;time-domain color mapping for color vision deficiency assistive technology;and spatio-temporal analysis and machine learning for traffic accidents prediction.

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Pattern Recognition in Analog Wafermaps with Multiple Ensemble Approaches

Pattern Recognition in Analog Wafermaps with Multiple Ensemb...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Abdullah Md. Habibur Rahman Shamim Akhter AISIP Lab International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

A wafer is a thin slice or substrate of semiconductors used for the fabrication of microelectronics devices. Thus, a large scale of precision is needed to make the microdevices work properly and to meet the requirements. Wafer test is an important step in wafer manufacturing to validate the activity of the microdevices and produces pictures of each wafer based on the electrical measurement of each of the devices on the wafer. Wafer shows different patterns while testing based on the requirement gap present on some of the devices on the wafer. To detect this error pattern manually is almost an impossible and time-consuming act. Therefore, we are presenting a wafer map pattern recognition system based on some ensemble approaches. Three (3) different ensemble including bagging, boosting, and voting approaches are implemented. Bagging performs relatively better than the others.

关键词： Radio frequency Semiconductor device modeling Performance evaluation Training machine learning algorithms Pattern recognition Bagging

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Influence of Language-Specific Features for Author Identification on Indian Literature in Marathi 2nd

Influence of Language-Specific Features for Author Identific...

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2nd international conference on Soft Computing and signal processing, ICSCSP 2019

作者： Kale, Sunil Digambarrao Prasad, Rajesh S. Smt. Kashibai Navale College of Engineering PuneMaharashtra India Pune Institute of Computer Technology PuneMaharashtra India Sinhgad Institute of Technology and Science PuneMaharashtra India

ISBN: (纸本)9789811524745

Author identification (AI) is a process of investigating author of an anonymous text document. AI has a great help in digital forensic, copyright issues, plagiarism detection, etc. for making the law process quick and efficient. This paper presents AI on Indian regional language Marathi. Insted of it kindly replce it by this scentence: In this research paper, we proposed 21 language-specific lexical features. Validation of these proposed features is done on "Author wise Marathi Language Typewritten Text Corpus" published by us at Indian Language Technology Proliferation and Deployment Center. Experimentation is performed with proposed 21 features. Performance is compared through various machine learning algorithms like Naïve Bayes, k-Nearest Neighbor and Sequential Minimal Optimization. k-Nearest Neighbor performs well over Naïve Bayes and Sequential Minimal Optimization with average accuracy achieved as 82.06 on comedy articles and 85.44 on mixed articles. Proposed language-specific features provide significant improvement in result of accuracy over traditional features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Sequential Segmentation of EEG signals for Epileptic Seizure Detection using machine learning 2

Sequential Segmentation of EEG Signals for Epileptic Seizure...

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2nd international conference on signal processing and Communication, ICSPC 2019

作者： Ahmad, Zeba Karin Singh, Vikram Khan, Yusuf Uzzaman Aligarh Muslim Unversity Department of Electrical Engineering Aligarh202002 India

ISBN: (纸本)9781728118499

The problem of epilepsy has grown exponentially and is now considered as one of the most prevailing neurological disorders affecting around 50 million people around the globe. Epilepsy is identified by analyzing the interictal activity present in the EEG signal. Visual analysis of EEG is a tedious process and subject to human error. This work proposes a robust method to ease the burden of intractable seizures by automatic recognition of ictal epileptiform activity in the EEG of epileptic patients. The classification between EEG having an epileptic seizure and non-seizure is done using various machine learning algorithms. The classifiers used are Simple Decision tree, Quadratic Discriminant, Medium Gaussian SVM, Bagged Trees, and Subspace k-NN. The performance is assessed using 10-fold cross-validation. © 2019 IEEE.

关键词： Electroencephalography

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Critical Dimension of Word2Vec 2

Critical Dimension of Word2Vec

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2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Das, Shuvayanti Ghosh, Sohan Bhattacharya, Shubham Varma, Rajat Bhandari, Dinabandhu Heritage Institute of Technology Kolkata India

ISBN: (纸本)9781728107448

Word embeddings are an efficient way of representing text such that they can be used by different machine learning Algorithms. Word2Vec is one such word embedding model. Although it is highly efficient, this model can take up a lot of space to store the vector representations. So defining the appropriate dimension of this model is very important to improve its performance in memory restricted devices. In this work, we present an empirical approach to decide the dimension of the word embeddings for a specific set of documents (corpora) to a critical value such that the representation of the words still preserve their original semantic and syntactic meanings. In this context, principal component analysis (PCA) has been used to compare the precision of vectors represented in different dimensions. © 2019 IEEE.

关键词： Principal component analysis

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A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques

A Comparative Study on Fake Job Post Prediction Using Differ...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Sultana Umme Habiba Md. Khairul Islam Farzana Tasnim Green University of Bangladesh Dhaka Bangladesh Khulna University of Engineering & Technology Khulna Bangladesh International Islamic University Chittagong Dhaka Bangladesh

ISBN: (数字)9781665415767

ISBN: (纸本)9781665415774

In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different data mining techniques and classification algorithm like KNN, decision tree, support vector machine, naïve bayes classifier, random forest classifier, multilayer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing 18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post.

关键词： Support vector machines Deep learning machine learning algorithms Neural networks Data mining Task analysis Random forests

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Management and Exploration of Digital signal processing Course Practical Teaching 2nd

Management and Exploration of Digital Signal Processing Cour...

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2nd international conference on Decision Science and Management, ICDSM 2019

作者： Wang, Shiqiang Gao, Caiyun Bai, Juan Zhang, Qin Zeng, Huiyong Zong, Binfeng Cai, Jiliang Air and Missile-Defence College Air Force Engineering University Xi’an710051 China Department of Basic Sciences Air Force Engineering University Xi’an710051 China

ISBN: (纸本)9789811535871

Digital signal processing is a required course for professional communication in navigation, navigation, radar engineering, etc. Although the professional basic status of the course is beyond doubt, some problems in the teaching of this course still affect and restrict its professional basic teaching effect. This article starts from the practical teaching of how the instructors can carry out the basic course of digital signal processing for different professions, and through the corresponding teaching research, promotes the young teachers to improve the practical teaching effect, and lays a solid foundation for the students of many majors before taking up the post in the study position. Strive to truly achieve teaching and learning and effectively improve the quality of teaching. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Digital signal processing

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