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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

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Ensemble of Hybrid CNN-ELM Model for Image Classification 5

Ensemble of Hybrid CNN-ELM Model for Image Classification

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5th international conference on signal processing and Integrated Networks (SPIN)

作者： Kannojia, Suresh Prasad Jaiswal, Gaurav Univ Lucknow ICT Res Lab Dept Comp Sci Lucknow Uttar Pradesh India

ISBN: (纸本)9781538630457

To leverage feature representation of CNN and fast classification learning of ELM, Ensemble of Hybrid CNN-ELM model is proposed for image classification. In this model, image representation features are learned by Convolutional Neural Network (CNN) and fed to Extreme learning machine (ELM) for classification. three hybrid CNN-ELMs are ensemble in parallel and final output is computed by majority voting ensemble of these classifier's outputs. the experiments show this ensemble model improves the classifier's classification confidence and accuracy. this model has been benchmarked on MNIST benchmark dataset and effectively improved the accuracy in comparison of single hybrid CNN-ELM classifier with accuracy upto 99.33%. Proposed ensemble model has been also compared with Core CNN, Core ELM, Hybrid CNN-ELM and achieves competitive accuracy.

关键词： Enesemble model CNN-ELM Convolutional neural network Extreme learning machine Image classification

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machine learning based soil moisture prediction for Internet of things based Smart Irrigation System

Machine Learning based soil moisture prediction for Internet...

引用

IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Gursimran Singh Deepak Sharma Amarendra Goap Sugandha Sehgal A K Shukla Satish Kumar CSE UCoE Punjabi University Patiala Punjab India MIS CSIR-CSIO Chandigarh India CSE Thapar Institute of Engineering & Technology Patiala Punjab India AMS CSIR-CSIO Chandigarh India

ISBN: (纸本)9781728139890

Internet of things (IoT) and machine learning (ML) based solution are revolutionizing many fields of humankind like transportation, environment, business and agriculture. the fresh water resources, which are already stressed, are being used extravagantly in many countries. the Internet of things and machine learning techniques can be used to optimize the water usage in irrigation. this paper presents the application of ML techniques to optimize the irrigation water usage by predicting the future soil moisture of a field in an IoT driven smart irrigation framework. the field data collected from the deployed sensors (air temperature, air humidity, soil moisture, soil temperature, radiation) and the weather forecast data from the Internet are used for predicting the future soil moisture. Multiple ML techniques are analyzed for predicting future soil moisture and the results obtained using GBRT are quiet encouraging. the proposed techniques could be a crucial research front for optimizing the water usage in irrigation.

关键词： agriculture hydrological techniques Internet of things irrigation learning (artificial intelligence) moisture power engineering computing regression analysis soil water resources weather forecasting Internet of things Water utilization hydrological techniques Power engineering computing weather forecasting Soil moisture soil water content Water Resources irrigation Lavage agriculture regression analysis Moisture Internets thing

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E-Health Approaches for Developing Countries

E-Health Approaches for Developing Countries

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IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Amandeep Kaur Anuj Kumar Gupta IKGPTU KAPURTHALA PUNJAB Computer Science & Engg CGC LANDRAN INDIA

ISBN: (纸本)9781728139890

Online and telecommunication services reduce the gap between level of health care service quality between villages and cities. In this research work, a study of five e-health models has been done. these models are simultaneously in use these days in developing countries such as India. the models are discussed in terms of their evolution and their application in various kinds of eHealth services. the paper gives definitive information on the technical standards and stacks required to build such eHealth communication models and services. Our study has found that most of these models are running concurrently in most of the developing countries and there is no absolute winner when they are compared with each other. It all depends upon the level of development in the respective country and commitment of their governments to provide affordable health care services to the masses. the paper also discusses the use of Cloud-based medical sensors, IoT and Fog computing approach as well the use of data mining and machine learning modelling to construct an e-Health System. In the second part of this paper, mathematical models and algorithms to diagnose the onset of health problems are given. the outcome of this study shows that there is a need for statistical as well as machine learning models to build eHealth solutions in developing countries. After this research a new model has been proposed which aims at providing better and affordable healthcare services and coincidentally benefits various other sectors.

关键词： cloud computing data mining distributed processing health care Internet of things learning (artificial intelligence) medical computing statistical analysis data mining medical computing Distributed processing Internet of things Telerehabilitation Developing Countries Cloud Computing Health Care Statistical methods Statistical analysis Legal Services Health Services telecommunication services

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Envisaging Ventricular Arrhythmia from an ECG by Using machine learning algorithm

Envisaging Ventricular Arrhythmia from an ECG by Using Machi...

引用

international conference on Advanced Computing and Communication Systems (ICACCS)

作者： E. Ramya R. Prabha J. Jayageetha M. Keerthana S. Swetha N. Lakshmi SNS College of Technology Coimbatore India

the proposed methods presents for the prediction of abnormal heart rhythm expending customary of electro cardio graph topographies and a radial basis algorithm. Generally and changeable techniques are used for find out and separation of the P-QRS-T waves. And also detect all intervals in the ECG signal and compared with stored record. Ventricular arrhythmia is predicted by analyzing ECG signal. Four methods are used to analysis the ECG signal namely the methods are ECG preprocessing, QRS complex detection, feature extraction and classification. By using above methods an accurate noise free ECG signal is obtained. Proposed architecture of this project analysis the logic size, area and power consumption.

关键词： Electrocardiography Heart beat Feature extraction Classification algorithms signal processing algorithms Prediction algorithms Filtering

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Gait-based Gender Classification Using Smartphone Accelerometer Sensor

Gait-based Gender Classification Using Smartphone Accelerome...

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international conference on Frontiers of signal processing (ICFSP)

作者： Azhin T. Sabir Halgurd S. Maghdid Safar Maghdid Asaad Mohammed H. Ahmed Aras T. Asaad Faculty of Engineering Koya University University Park Koya-Erbil Kurdistan Region-F.R. Iraq University of Raparin Ranyah Kurdistan Region Iraq University of Buckingham UK

People gender and activities recognition are becoming a hot topic in our daily applications through gait information. the very well-known applications are safety-health, security, entertainment and billing. Numerous data mining and machine learning algorithms have been proposed for such issue. Equally, many technologies could be used to observe the people activities to identify their gender and activities. However, the existing solutions and applications suffer from privacy and cost to deploy the solution and their obtained accuracy. For example, when the CCTV camera or Kinect sensors technology are used to identify people, such technologies will violate the privacy since most of the people do not want to take their pictures or videos during their daily activities. therefore, to tackle such issue, this paper presents a new scheme to identify the gender of the people via onboard Smartphone sensors including accelerometer sensor. Such a scheme requires little interaction with the people; individuals would simply have to hold his/her smartphone and walk normally. Four different data mining techniques and machine learning algorithms are used to identify people gender including: Decision Tree (DT), Support Vector machine (SVM), k-Nearest Neighbor (k-NN) and Deep learning algorithm (recurrent-neural-network long-short-term-memory `RNN-LSTM'). Further, a set of experiments are conducted via Android-based smartphones (to read smartphone accelerometer sensor) and MATALB-2018a packages used to perform the validity of the scheme. the obtained results from the experiments show that the accuracy of the gender identification is about 94.11% via deep learning algorithm (RNN-LSTM) and is around 83.75% by using DT algorithm.

关键词： Feature extraction Accelerometers machine learning algorithms Support vector machines Data mining

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A Reduced KELM model using DBSCAN Clustering algorithm for centroid selection 5

A Reduced KELM model using DBSCAN Clustering algorithm for c...

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5th international conference on signal processing and Integrated Networks (SPIN)

作者： Jain, Sukirty Shukla, Sanyam Maulana Azad Natl Inst Technol Bhopal 462003 India

ISBN: (纸本)9781538630457

Extreme learning machine (ELM) is a single layer feedforward neural network (SLFN) and a popular classifier for classification and regression problems. It is unstable due to random initialization of weights between the hidden layer and the input layer. To overcome this problem of instability, kernelized ELM with kernels has been developed. Gaussian kernel Extreme learning machine (KELM) is one of the stable but computationally complex variant of ELM in which the number of hidden layer neurons is equal to the number of input instances. therefore, to reduce this computational complexity, a new variant of KELM i.e. reudced KELM has been proposed in the literature. It randomly selects the number of centroids from the training data set such that the number of centroids is always less than the number of input instances. this work develops a new reduced version of KELM using density-based clustering algorithm (DBSCAN). DBSCAN is a non-spherical clustering algorithm which is used to identify the number of concepts. Each concept is represented using the centroid of the cluster. In proposed KELM model, the number of hidden layer neurons is equal to the number of clusters formed by the DBSCAN clustering algorithm. Experiments have been performed on 16 datasets drawn from the KEEL data set repository. the result shows that the developed reduced KELM model gives better performance as compared to the traditional KELM in terms of reduced training time and testing time.

关键词： Extreme learning machine Kernel Extreme learning machine Kernel matrix DBSCAN clustering algorithm Data mining

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Indoor Micro-Activity Recognition Method Using Ubiquitous WiFi Devices 5

Indoor Micro-Activity Recognition Method Using Ubiquitous Wi...

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5th IEEE conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)

作者： Al-qaness, Mohammed A. A. Wuhan Univ Comp Sch Wuhan Hubei Peoples R China

ISBN: (纸本)9781538637555

this paper presents a wireless device-free WiFibased micro-activity recognition system by leveraging the Channel State Information (CSI) of WiFi signals. Unlike previous CSI-based micro-activity recognition methods, the proposed method extracts both amplitude and phase information of CSI, which provides more information and increased the detection accuracy. the proposed approach presents an efficient signal processing method to expose unique patterns of each activity. Moreover, a machine learning algorithm is applied to classify the proposed micro-activities. the proposed prototype has been evaluated in different indoor environments, and the empirical results show the efficiency of the proposed prototype with several volunteer users.

关键词： CSI WiFi Device-free Micro-Activity recognition machine learning

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Online Newton Step for Portfolio Selection with Side Information 5

Online Newton Step for Portfolio Selection with Side Informa...

引用

5th international conference on Information Science and Control Engineering (ICISCE)

作者： Yang, Fanfan Li, Xiangming Yang, Jie Ye, Neng Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China

ISBN: (纸本)9781538655009

Online portfolio selection, as a research hotspot in financial signal processing, has been widely studied with machine learning perspective in recent years. Online Newton Step (ONS), which generates portfolio with low-complexity online convex optimization and achieves the same asymptotic wealth as the Best Constant-Rebalanced Portfolio in hindsight, is one promising algorithm among various portfolio selection strategies. However, ONS does not consider the downside risk, which leads to large investment loss in some market environments. To overcome this limitation, this paper proposes a novel portfolio selection method, namely ONS with Side Information (ONS-SI), which incorporates ONS with the side information derived from the market, to reduce the investment risk. the performance of ONS-SI is evaluated on the Chinese A-share market. Experiment results show that the proposed ONS-SI achieves higher wealth and lower downside risk than ONS.

关键词： portfolio selection online newton step side information machine learning

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Handwritten Bengali Numeral Recognition using HOG Based Feature Extraction Algorithm 5

Handwritten Bengali Numeral Recognition using HOG Based Feat...

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5th international conference on signal processing and Integrated Networks (SPIN)

作者： Choudhury, Amitava Rana, Hukam Singh Bhowmik, Tanmay Indian Insritute Engn Sci & Technol Sibpur India UPES SoCS Dehra Dun Uttar Pradesh India UPES Sch Comp Sci Dehra Dun Uttar Pradesh India

ISBN: (纸本)9781538630457

Hand written character recognition is widely used in modern research. Recognize the characters from an image has always been active research area in the computer vision community. Apart from English, there are several regional languages available worldwide. In India there are 22 official languages, and Bengali is one of them. Bengali script has its own writing pattern. Like any other language number system, Bengali numeral system has ten different digits to indicate 0-9. It has its own alphabets and numerals. In this paper, Histogram of oriented gradient (HOG) and color histogram for selection of features algorithm is proposed. HOG are used as the feature set to represent each digit sample at the feature space and Support Vector machine (SVM) used to produce the output from input image. Proposed algorithm is efficient and gives an accuracy of 98.05 on CMATERDB3.1.1 dataset. this type of numeric image recognition can be use in the post offices to acknowledge the pin code written in Bengali.

关键词： OCR machine learning Support vector machine HOG features

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Mixture of Deep-Based Representation and Shallow Classifiers to Recognize Human Activities

Mixture of Deep-Based Representation and Shallow Classifiers...

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international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Ali Beikmohammadi Karim Faez Mohammad Hosein Mahmoodian Mohammad Hosein Hamian Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Human action recognition is one of today's investigate issues that has engaged many researchers due to its significant and large applications. Newly, for the sake of the fame and success of the utilization of deep learning-based approaches in diverse fields such as machine vision, object-detection, and natural language processing, investigators have traveled from conventional hand-crafted techniques to deep learning methods. In this investigation, a higher-performance method is proposed using the transfer learning technique for recognizing human action, where a pre-trained deep neural network model is first used to extract features from the target dataset and then, the softmax classifier is used to classify the actions. It can be noticed that the transfer of learning from a huge dataset to the task of action diagnosis with the limited dataset, is well done. the suggested approach is evaluated on two datasets namely Kth and UCF Sports action. Comparative study shows that the suggested approach is superior to hand-crafted extraction-based techniques and other deep learning-based techniques, in terms of accuracy. Also, due to fewer parameters, it is possible to use this method for applications of human action recognition in mobile.

关键词： Feature extraction Training Neural networks learning systems machine learning Task analysis

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