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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是3691-3700 订阅

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Reliable machine learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care 4

Reliable Machine Learning Approach to Predict Patient Satisf...

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4th international conference on Communication and Electronics Systems, ICCES 2019

作者： Sabarmathi, G. Chinnaiyan, R. Computer Science Christ Academy Institute for Advanced Studies Bangalore India CMR Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9781728112619

In this paper a unique approach towards decision making process and better quality care inPati healthcare applications is developed by using the machine learning (ML) concepts as an alternative mode for identifying the characteristics and patient satisfaction (PS) in the proposed healthcare system. Extracting the information from raw data using some algorithmic approach is known as Data mining. In this a ML approach is used in determining the patient satisfaction in health care sector. Applied regression models to determine the patient satisfaction and also correlation methods is identified as an important attribute to be considered in determining the better quality of health care application models. the data set is taken based on the opinion on three types of data such as a) patient opinion towards hospital care b) nurse opinion towards workplace and c) Administrative aspects of healthcare. Our findings revealed high accuracy in Regression (88%), that helps in concluding by considering the administrative and workplace attributes related to patient satisfaction. the results are validated using traditional statistical methods like binomial correlation and linear regression. © 2019 IEEE.

关键词： Correlation methods

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Grid monitoring alarm event recognition method integrating expert rules and deep learning 4

Grid monitoring alarm event recognition method integrating e...

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4th international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Zhang, Ming Shen, Peifeng Zhao, Yang Liu, Yi State Grid Jiangsu Elect Power Co Ltd Nanjing Power Supply Co Nanjing Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing Peoples R China

ISBN: (纸本)9781728146898

SCADA collects a large amount of alarm information during the power system operation. If the controller cannot quickly judge the monitoring alarm events, it may cause the expansion of the accident. therefore, we propose a grid monitoring alarm event recognition method combining expert rules and deep learning. the method includes two stages: expert knowledge base determination and deep learning recognition model determination. In the first stage, the characteristics and rules related to monitoring alarm events are sorted, the expert knowledge base is constructed, and the alarm events are initially identified. In the second stage, the preliminary judgment results and the alarm information are combined into the convolutional neural network (CNN) recognition model, thus the final recognition result is obtained.

关键词： grid monitoring expert rules convolutional neural network alarm event recognition

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proceedings - 2019 4th international conference on Internet of things: Smart Innovation and Usages, IoT-SIU 2019

Proceedings - 2019 4th International Conference on Internet ...

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4th international conference on Internet of things: Smart Innovation and Usages, IoT-SIU 2019

ISBN: (纸本)9781728112534

the proceedings contain 81 papers. the topics discussed include: securing IoT devices and securely connecting the dots using REST API and middleware;experimental study for Gurmukhi handwritten character recognition;design issues in artificial neural network (ANN);multi-feature based automated flower harvesting techniques in deep convolutional neural networking;IoT based electrical device surveillance and control system;performance analysis of 4:1 multiplexer with DTMOS technique;comparative study and analysis of wireless mesh networks on AODV and DSR;CRAIoT : concept, review and application(s) of IoT;a comparative analysis on e-voting system using blockchain;and an opinion mining for Indian Premier League using machine learning techniques.

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Emotional Speech Classification application Development Using Android Mobile applications 4

Emotional Speech Classification Application Development Usin...

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4th international conference on Information Management and Technology, ICIMTech 2019

作者： Hidajat, Monica Supria Luwinda, Ferdinand Ariandy Sanjaya, Hengky Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Customer Relationship Management (CRM) has become popular as it is believed to be able to improve company performance. One of the features of CRM are customer satisfaction level analyses. It is a verbal communication means of sending or receiving information. there are several intonations spoken during verbal communication that reflect the emotional state. therefore, it can be used to get information regarding the level of customer satisfaction. Sound is one type of digital data that can be processed for an emotional state of classification. In this research, the authors decided to develop a mobile application that is integrated with machine learning to record a sound and classify it into a particular emotional state. this application is believed to be able to improve the user experience and simplify the installation to improve communication engagement. © 2019 IEEE.

关键词： Customer satisfaction

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Mobile application for Storage and Retrieval of e-learning videos Using Hadoop 4

Mobile Application for Storage and Retrieval of e-learning v...

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4th international conference on Communication and Electronics Systems, ICCES 2019

作者： Parsola, Jyoti Gangodkar, Durgaprasad Mittal, Ankush Graphic Era University Dept of Computer Applications Dehradun India Dept of Computer Science and Engineering Graphic Era University Dehradun India

ISBN: (纸本)9781728112619

With the rapid advancement of low-cost mobile phones and fast internet access, students/learners are using smart phones for the academic purposes. Mobile learning (m-learning) has been recognized as a competent method of seeking knowledge. As the quantity of users and lecture video contents is increasing, the deployment of m-learning becomes dubious because of limited memory space in mobile phones. the learning materials like ppt., docs, occupy less space but lecture videos consume a lot of memory in a mobile device. therefore, there should be some measure where students can store as well as access lecture videos. Hadoop has been evolved as a promising technology to overcome the problems in m-learning. It provides reliable, customized environments for end-users. We present a new mobile application for a mobile learning system, which is integrated with Hadoop, for storage and retrieval of lecture videos from mobile phone to the Hadoop Distributed File System (HDFS). Registered user of the application can perform the functions like upload video, search a video file and download a video file. We have calculated computational time to upload, search and download various size of video files (maximum size is 1.3 GB). the proposed application can be utilized where there is a high demand of exhaustive teaching and learning in higher education. © 2019 IEEE.

关键词： learning systems

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Second-Order Networks in PyTorch 4th

Second-Order Networks in PyTorch

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4th international SEE conference on Geometric Science of Information (GSI)

作者： Brooks, Daniel Schwander, Olivier Barbaresco, Frederic Schneider, Jean-Yves Cord, Matthieu Thales Land & Air Syst Adv Radar Concepts Limours France Sorbonne Univ LIP6 CNRS Lab Informat Paris 6 F-75005 Paris France

Classification of Symmetric Positive Definite (SPD) matrices is gaining momentum in a variety machine learning application fields. In this work we propose a Python library which implements neural networks on SPD matri... 详细信息

ISBN: (纸本)9783030269807;9783030269791

关键词： SPD matrix Covariance Second-order neural network Riemannian machine learning

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A novel method based on deep learning for herniated lumbar disc segmentation 4

A novel method based on deep learning for herniated lumbar d...

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4th international conference on Advanced Systems and Emergent Technologies, IC_ASET 2020

作者： Mbarki, Wafa Bouchouicha, Moez Frizzi, Sebastien Tshibasu, Frederick Ben Farhat, Leila Sayadi, Mounir Laboratoire Signal Image et Maitrise de l'Energie University of Sousse Eniso Sousse Tunisia Aix Marseille Univ Universite de Toulon Cnrs Lis Toulon France Université de Toulon Département Génie Biologie-IUT La Garde83957 France Cliniques Universitaires de Kinshasa République Démocratique du Congo Service Imagerie Mdicale Mongi Slim Hospital La Marsa Tunisia

ISBN: (纸本)9781728163567

Lower Back pain (LBP) is a common disease. therefore, a common cause of leg pain and lower back is a lumbar disc herniation. Herniated lumbar disc represents a displacement of disc material (annulus fibrosis or nucleus pulpous). In most cases, the pain goes away within days to weeks;however, it can last for three months or more. Segmentation and Detection are the two most important tasks in computer aided diagnosis system (CAD) [24]. Extraction of herniated lumbar disc from magnetic (MRI) resonance imaging is a difficult task for radiologist. Detection of herniated disc was achieved by different methods such as region growing, active contours, watershed technique and thresholding. In our case, to detect intervertebral disc from lumbar MRI we developed an approach using convolutional neural networks in order to find the type of herniated lumbar disc [24] such as median, foraminal or post lateral [24]. We proposed to explore the importance of axial view MRI to find the type of herniation. Previous works were concentrated only on the sagittal View. the main objective of this paper is to automatically detect the intervertebral disc in magnetic resonance images(MRI) with bounding boxes and their classes which can facilitate diagnoses based on axial view MRI [40]. therefore, the aim of this study is to assist detection using lumbar axial view MRI. A novel method is proposed in this paper based on deep convolutional neural networks. this study introduces the application of the convolutional neural network model. A framework was developed that enables the application of shape priors in the healthy part of intervertebral disc anatomy, with user intervention when the priors were inapplicable. © 2020 IEEE.

关键词： Semantic Segmentation

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Surveillance System for Abnormal Driving Behavior Detection 4

Surveillance System for Abnormal Driving Behavior Detection

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4th international conference on Information Technology (InCIT)

作者： Chaisiriprasert, Parkpoom Yongsiriwit, Kern Rangist Univ Coll Digital Innovat & Informat Technol Pathum Thani Thailand

ISBN: (纸本)9781728110196

the objective of this research is to detect abnormal car driving behaviors that may result in accidents. the behaviors include exhaustion from driving for a long time, taking eyes off the road, and the abnormal eye-blink from the sleepiness. the researchers are interested to develop a mobile application that analyzes car driving behaviors using a face detection algorithm. Our application is easy to install and to use on any Android based smartphone. Concretely, our mobile application considers three factors: (1) time of driving;(2) abnormal eye-blinking;and (3) taking eyes off the road. We integrate these factors with the acceleration of the driving vehicle to analyze and calculate abnormality level. therefore, we send a voice notification to the driver when the calculated abnormality level exceeds 10. We perform experiments and experimental results show that our mobile application is feasible in realistic situations.

关键词： Driving Surveillance Driver Behavior machine learning Facial detection Smart phone

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Facial Recognition using machine learning Algorithms on Raspberry Pi 4

Facial Recognition using Machine Learning Algorithms on Rasp...

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4th international conference on Electrical, Electronics, Communication, computer Technologies and Optimization Techniques (ICEECCOT)

作者： Singh, Seema Ramya, R. Sushma, V Roshini, S. R. Pavithra, R. BMS Inst Technol & Management Dept Elect & Telecommun Engn Bangalore Karnataka India

ISBN: (纸本)9781728132617

Facial recognition is a non-invasive method of biometric authentication and useful for numerous applications. the real time implementation of the algorithm with adequate accuracy is required, with hardware timing into consideration. this paper deals with the implementation of machine learning algorithm for real time facial image recognition. Two dominant methods out of many facial recognition methods are discussed, simulated and implemented using Raspberry Pi. A rigorous comparative analysis is presented considering various limitations which may be the case required for innumerable application which utilize facial recognition. the drawbacks and different use cases of each method is highlighted. the facial recognition software uses algorithms to compare a digital image captured through a camera, to the stored face print so as to authenticate a person's identity. the Haar-Cascade method was one of the first methods developed for facial recognition. the HOG (Histogram of Oriented Gradients) method has worked very effectively for object recognition and thus suitable for facial recognition also. Both the methods are compared with Eigen feature-based face recognition algorithm. Various important features are experimented like speed of operation, lighting condition, frontal face profile, side profiles, distance of image, size of image etc. the facial recognition model is implemented to detect and recognize faces in real-time by means of Raspberry Pi and Pi camera for the user defined database in addition to the available databases.

关键词： Facial recognition Haar Cascade HOG Comparison Raspberry Pi

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Deep learning application for plant diseases detection 19

Deep Learning application for plant diseases detection

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proceedings of the 4th international conference on Big Data and Internet of things

作者： F. Jakjoud A. Hatim A. Bouaaddi Laboratory of Energy Materials and systems Ibn Zohr University Agadir Morocco National School of Applied Sciences Cady Ayad University Marrakech Morocco

ISBN: (纸本)9781450372404

the use of deep learning for automatic detection of plant diseases is an innovative solution for agricultural application. In this paper we present a Convolutional neural network model based on VGGnet16 architecture for the recognition of sick and healthy leaves, Several optimizer are tested to examine accuracy and model stability, the best results are obtained with Adadelta and SGD optimizer. those models are tested on a computer and on a Raspberry pi model B

关键词： Convolutional neural network

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