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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351-360 订阅

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Optimal vowels measurements for Obstructive Sleep Apnea Detection Using Speech signals 3

Optimal vowels measurements for Obstructive Sleep Apnea Dete...

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3rd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Pang, Kang-Gao Hsung, Tai-Chiu Law, Alex Ka-Wing Choi, Winnie W. S. Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Univ Hong Kong Fac Dent Hong Kong Peoples R China

ISBN: (纸本)9781728188232

In Obstructive Sleep Apnea (OSA) detection using speech signal during awake, traditional speech-based methods adopt speech features such as Formants and MFCC. As the OSA voice is pathological, the parameters for normal speech processing/recognition is not optimal for the detection. In this paper, we investigate the effects of Linear Predictive coder (LPC) order to the OSA detection. We further propose to adopt dual LPC for feature extractions. In the simulation using 66 OSA patients' voice signals, we achieve the best accuracy of 95.45% and 86.36% with the proposed parameters using quadratic discriminant analysis classifier for multi-class (4 levels) OSA severity classification using resubstitution and leave-one-out method respectively. As compared to the typical parameters setting, the improvement of resubstitution and leave-one-out are 6.06% and 9.09% respectively.

关键词： Obstructive Sleep Apnea speech analysis Linear Predictive coder (LPC) machine learning

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A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence 3

A Study on Medicinal Plant Leaf Recognition Using Artificial...

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3rd international conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Ayumi, Vina Ermatita, Ermatita Abdiansah, Abdiansah Noprisson, Handrie Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019-2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction;and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector machine (SVM), and Transfer learning (VGG19) as classifier. © 2021 IEEE.

关键词： Image processing

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Analysis of Gender Identification in Bahasa Indonesia using Supervised machine learning Algorithm 3

Analysis of Gender Identification in Bahasa Indonesia using ...

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3rd international conference on Information and Communications Technology, ICOIACT 2020

作者： Tanuar, Evawaty Abdurachman, Edi Gaol, Ford Lumban Lukas Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia

ISBN: (纸本)9781728173566

Gender classification or identification is an interesting research area in speech or voice signal processing. It is a promising research area, and there still a room for improvements, especially in the localization context. There are not many researches related gender identification in Bahasa Indonesia. Most of the research found are in English, some are in Chinese, Korea, Arab, France. This paper will used primary data, self-collected in Bahasa Indonesia to identify the gender using the supervised machine learning algorithm. MFCC is used as the feature extraction algorithm for input in the machine learning. After comparing several algorithms: Artificial Neural Network, SVM and K-Nearest Neighbors (KNN) algorithm, ANN shows more promising result then others. There are 2.735 primary data used in this research. The result in this research will be used in future experiment about the impact of gender classification in voice recognition in Bahasa Indonesia. © 2020 IEEE.

关键词： Speech recognition

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Potentials of machine learning for Data analysis in IoT: A Detailed Survey 3

Potentials of Machine Learning for Data analysis in IoT: A D...

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： Vasoya, Shreya Patel, Nishi Ramoliya, Dipak Patel, Khushi Department of Information Technology Changa Gujarat India

ISBN: (纸本)9781728170893

machine learning (ML) and Internet of Things (IoT) has become a pioneering and radical assistant in a wide range of applications and fields of data management. Currently, looking towards the rapid expansion of various trends and communication technology, it is surveyed that almost all the Sensor enabled devices are now connected with each other through the Internet. The widespread use of IoT devices and sensors produces huge amounts of raw data which also increases published data that increases the need of efficient data analysis and data processing mechanisms. The article contributes the detailed survey of different data processing and analyzing techniques of machine learning which can be applied to data generated by IoT devices to offer various services. In addition, the challenges and potential of machine learning (ML) for data analysis in IoT are also explored. The machine learning methods to overcome various challenges of IoT are also accessed. © 2020 IEEE.

关键词： Internet of things

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Research on Radar signal Recognition Technology Based on Deep learning 3

Research on Radar Signal Recognition Technology Based on Dee...

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3rd IEEE international conference on Electronics Technology, ICET 2020

作者： Wu, Yuze Mao, Yan Chen, Xu Wuhan University of Technology School of Automation Wuhan China

ISBN: (纸本)9781728162836

Focusing on the current problem of sorting and recognition of radar signals, the application of the time- frequency analysis method to signal recognition was studied. Compared with other radar signal recognition methods, this method can effectively analyze non-stationary signals, and at the same time offer image processing and deep learning. Through comparison of simulation experiments, this method was found to effectively identify the radar signal recognition target. Through experiments, PWVD is an ideal time- frequency analysis method, and through image preprocessing and deep learning, it can finally achieve a high recognition accuracy rate. © 2020 IEEE.

关键词： Deep learning

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Feature Selection using machine learning Techniques Based on Search Engine Parameters 2020

Feature Selection using Machine Learning Techniques Based on...

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3rd international conference on signal processing and machine learning, spml 2020

作者： Portier, Willy K. Li, Yujian Kouassi, Bonzou A. Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781450375733

In the last two decades, Internet visibility became mandatory for any companies wishing to get exposure and get revenues. Among many ways to be visible on the Internet, one of the most important is to be on top of search engines' results for keywords relative to companies' business. It is the art of Search Engine Optimization (SEO), which is a collection of techniques to get more traffic from a search engine. More a website is SEO optimized, thus more search engines give it a high ranking on results' pages for a maximal exposure. So, Google, with 90% market share worldwide, is the main search engine outside of China (Baidu) and Russia (Yandex), and its algorithm is like a black box all marketers want to discover. Google claims to have more than 200 features in his algorithm made to rank results for queries among billions of pages. This article tries different machine learning methods to determine the most important parameters using a selection of 30 features in a dataset made with around 28,000 observations. A binary classification approach was done to detect if a keyword can be found or not in Top10 search engine result. During the simulation, the importance of features was determined to find the most important parameters used for building related search results. According to the research result, it leads that there are three kinds of parameters which influence the process of ranking the results on search engine Google for web pages: editorial features, notoriety features and technical features. Moreover, few features with minimum importance were found, for example, the low importance of using "https"protocol in a web resource. © 2020 ACM.

关键词： Websites

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Intelligent Irrigation System Using machine learning Technologies and Internet of Things (IoT) 3rd

Intelligent Irrigation System Using Machine Learning Technol...

引用

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

作者： Patil, Sarika Naik, Radhakrishana Dr. BAM University Aurangabad India

ISBN: (纸本)9789811604928

Scare water resources necessitates technological involvement in irrigation scheduling, that can help to manage water according to the weather condition of different seasons, crop growth stage and landscape information. The proposed method calculates actual water required using machine learning model and Evapotranspiration. The model is trained using real time weather data to predict actual water requirement. Reference Evapotranspiration is calculated with the help of Penman-Monteith Method. Before starting with this real time system proposed model is implemented with the help of past 10 years web scraped weather data. Proposed algorithms of water requirement and Irrigation scheduling is executed on scrapped data. After successful results system is implemented for real time use. Furthermore, system consists of eight Arduino nodes that acting as a slave to read weather, soil landscape and rain data. In addition, three Raspberry-pi equipped with the Wi-Fi module, acting as server to send collected data to a remote web server. These databases are used as input for machine learning algorithm. As per the observations of proposed system water usage is getting reduced in large quantity as compared to the traditional irrigation system used for irrigation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crops

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Demystifying the Rumbling Noise Generated from the Elevator machine Room 3

Demystifying the Rumbling Noise Generated from the Elevator ...

引用

3rd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Shi, Alex Shanghai High Sch Int Div Shanghai Peoples R China

ISBN: (纸本)9781728188232

When an elevator is operating, there is often a cacophonous rumbling sound emanating from its machine room. Commonly the elevator companies claim no responsibility, citing that the overall sound pressure level (SPL) is legally compliant. However, clearly, SPL alone cannot fully represent the perceivable sound quality of the noise from the machine room. A smart phone has been used to record this noise, and the audio clip has been converted into time-domain signals. These digitalized signals have then been comprehensively analyzed by tools such as, but not limited to Fast Fourier Transformation (FFT), Short-time Fourier Transformation (STFT), and Wavelet functions. This visualized noise analysis in turn points to the fact that it is the tonality and fluctuations in the noise signal that make the acoustic noise of elevator machine so jarring to the ears despite its acceptable SPL level, prompting the negative psychoacoustic reactions of the residents. The resulting analysis demystifies the noise and has convinced elevator companies to replace their machines, paving a new path to be used for victims globally suffering from discomforting elevator noise. Furthermore, elevator companies should use similar analysis to improve its designs and manufacturing so as to improve overall perceived sound quality.

关键词： elevator traction machine noise analysis STFT wavelet transformation modelling signal processing

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Functional Annotation and Identification of Putative Drug Target in VV 3

Functional Annotation and Identification of Putative Drug Ta...

引用

3rd international conference on signal processing and Information Security, ICSPIS 2020

作者： Singh, Yashbir Deepa, S. Mansoor, Wathiq Biomedical Engineering Chung Yuan Christian University Zhongli Taiwan Engineering Information Technology University University of Dubai Dubai United Arab Emirates

ISBN: (纸本)9781728189987

Recent progress in computational biology has led to the identification of new genes in the genome of different organisms. In silico approaches enable characterization of structure and function of hypothetical proteins. Vaccinia virus has been used broadly for human immunization. Analysis of Vaccinia virus data determined that 45% of proteins are conserved hypothetical proteins whose function has not been determined. This analysis provides a platform to establish sequence-function relationships and to better understand the molecular machinery of organisms. In this study, we predicted the probable functions of Hypothetical proteins (HPs) and classified all HPs on the basis of sequence similarity, protein family, and domain assignment. The outcome of this work will be helpful for understanding mechanisms of pathogenesis, finding new therapeutic targets, and understanding adaptability to host. © 2020 IEEE.

关键词： machine learning

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Fossa Optimization Algorithm with Convolution Neural Network Based Brain Tumor Segmentation and Classification in MRI Images

Fossa Optimization Algorithm with Convolution Neural Network...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Veeresh Shivaprakash Ramy Read Hossain Aruna T M Tatiraju.V.Rajani Kanth Sir M Visvesvaraya College of Engineering Raichur India Affiliated to Visvesvaraya Technological University Belagavi India Government Engineering College Raichur India Visvesvaraya Technological University Belagavi India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India TVR Consulting Services Private Limited Gajularamaram Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Magnetic Resonance Image (MRI) brain tumor segmentation is critical and significant in a clinical field that assist in diagnosis, complete growth prediction and tumor mass rates required for patients. The complexity in segmenting brain tumors are challenging due to the various structures, shapes, locations, visual characteristics, frequency, texture and contrast. To overcome this issue, this research proposes a Fossa Optimization Algorithm (FOA) with Convolution Neural Network (CNN) for brain tumor classification. The Geodesic Active Contour (GAC) is used for segmentation which identifies tumor boundaries precisely in noisy and intricate data. FOA utilizes dynamic search strategy by its hunting behavior, balancing exploration and exploitation effectively. Its adaptive step size enables better convergence when avoiding local optima. Furthermore, its ability to capture edges and patterns makes it effective for classification. CNN are better to diverse in scale, noise and rotation because of its shared weights and local connectivity reduce the complexity thereby leading to higher accuracy and quick processing in classification. The FOA-CNN obtains 99.92% accuracy, 99.20% f1-score, 99.07% sensitivity and 99.34% precision for BRATS2020 dataset which is superior than Spatial Transformer Network and Non-local Attention Mechanism (STN-NAM).

关键词： Image segmentation Accuracy Sensitivity Convolution Brain tumors Search problems Classification algorithms Complexity theory Optimization Biological neural networks

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