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ML based Non-Invasive Diabetes Detection System using Pulse Decomposition Analysis of PPG signal 8

ML based Non-Invasive Diabetes Detection System using Pulse ...

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8th international conference on signal processing and Communication, ICSC 2022

作者： Shaan, Bhavya Yadav, Jyoti Prabha, Anju Dept. of Instrumentation and Control Engineering Delhi India Dept. of Instrumentation and Control Engineering Delhi India

ISBN: (纸本)9781665454308

In this paper, Pulse Decomposition Analysis (PDA) of Photoplethysmography (PPG) and physiological parameters is used to develop Non-Invasive (NI) Diabetes Detection system. For this study, dataset of 217 participants from normal, diabetes and prediabetes subjects are used. Further signals are segmented in 5sec window which is filtered using bandpass filter to remove noise. After filtration PDA features are extracted from PPG signal. A total of seven features are extracted. In addition to this physiological parameter such as age, weight and height of the participants are also used as features. For classification a matrix of (7886 times 10) is formed containing seven PPG features and three physiological parameters. Dataset is classified under three classes i.e., Diabetic, Pre-diabetic, and Normal. Two commonly known machine learning classification learning algorithm i.e., ensemble and decision Trees are used for classification. the results shows that 99.3% accuracy is obtained using ensemble tree method. the study shows feasibility of wearable device for non-invasive diabetes detection. © 2022 IEEE.

关键词： Decision trees

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Experimental Study of Visible Light Localization Based on machine learning and Multiple Photo Diodes

Experimental Study of Visible Light Localization Based on Ma...

引用

Emerging Technology (INCET), international conference for

作者： Xiaoyun Yang China Acknowledgments Jingchu University of Technology Jingmen City Hubei Province China

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In view of the limitations of the traditional Search for excellent algorithm in the research of System for locating visible light resources, a research scheme of localization experimental study based on machine learning together with multiple photo diodes was proposed. Firstly, the influencing factors is accurately located through signal processing theories, and the indicators is reasonably divided to reduce interference, and the machine learning together with multiple photo diodes is used to construct a research scheme for localization experimental study. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Search for excellent algorithm in terms of research accuracy and processing time of influencing factors of localization experimental study, and has obvious advantages. the research of localization experimental study plays an extremely important role in visible light, which can accurately predict and optimize the growth characteristics and product generation of visible light. However, the traditional Search for excellent algorithm has some limitations in solving the problem of positioning experiment simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of localization experimental study based on machine learning together with multiple photo diodes to better solve this problem. the scheme accurately locates the influencing factors through signal processing theories, so as to determine the division of indicators, and uses the machine learning together with multiple photo diodes to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. therefore, the simulation scheme based on machine learning together with multiple photo diodes in the research of System for locating visible light resources can bet

关键词： Location awareness Accuracy machine learning algorithms signal processing algorithms machine learning signal processing Prediction algorithms

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machine learning and Deep learning for Maize Leaf Disease Classification: A Review 5

Machine Learning and Deep Learning for Maize Leaf Disease Cl...

引用

5th international conference on Electrical, Electronics, Informatics, and Vocational Education, ICEELINVO 2022

作者： Setiawan, W. Rochman, E.M.S. Satoto, B.D. Rachmad, A. Department of Informatics Faculty of Engineering University of Trunojoyo Bangkalan Jawa Timur Madura Indonesia

Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. the classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. this article compare machine learning and deep learning for maize leaf disease classification. there are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. the number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles. © Published under licence by IOP Publishing Ltd.

关键词： Image classification

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Human-Like Networking Based on Big Data and machine learning 11th

Human-Like Networking Based on Big Data and Machine Learning

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11th international conference on signal and Information processing, Network and Computers, ICSINC 2023

作者： Dai, Jinyou Yu, Shaohua Cheng, Chen Cheng, Xinzhou Lyu, Jianxin Wang, Xueshun China Information Communication Technology Group Wuhan China Pengcheng Laboratory Shenzhen China Research Institute China United Network Communications Corporation Beijing China Fiberhome Telecommunication Technologies Co. Ltd. Wuhan China

ISBN: (纸本)9789819721238

It can bring about innovation of network and new network architecture for big data technologies, artificial intelligence technologies, network awareness technologies, network self-restructuring technologies and other technologies to be applied into network area. Introducing human-like features into network can make the network make full use of advantages of human being and bring a new network architecture named human-like networking to come into being. this paper specifies the architecture, features and mechanism of human-like networking. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network architecture

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A Less Traditional Approach to Biomedical signal processing for Sepsis Prediction 5th

A Less Traditional Approach to Biomedical Signal Processing ...

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5th international conference on Nanotechnologies and Biomedical Engineering, ICNBME 2021

作者： Iapăscurtă, Victor Department of Anesthesia and Intensive Care N. Testemițanu State University of Medicine and Pharmacy Chișinău Moldova

ISBN: (纸本)9783030923273

Most of the data generated by monitors in a clinical setting represent time series data which can be visualized and subsequently used for decision making. this usually is the simplest part. A more challenging aspect is using this data for more complex task like machine learning with the same goal – computer assisted decisions. Within this challenge raw biomedical signal data need to be preprocessed before being passed to the machine learning algorithm. this can be done by a multitude of methods. A number of such methods comes from the field of Algorithmic Complexity and although of a promising nature, these particular methods are poorly explored yet. the current research presents an example of applying the Block Decomposition Method to data routinely generated by patients in a modern Intensive Care Unit. the final goal of a larger research, the actual research being part of, is building a system for early sepsis prediction. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Applications of machine learning to Skin Cancer Detection 5

Applications of Machine Learning to Skin Cancer Detection

引用

5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Prakash, Anju J. Satpathy, Gargibala Raman, Ramakrishnan Manikandan, E. Buddhi, Dharam Kapila, Dhiraj Sree Buddha College of Engineering Department of CSE Pattoor Alappuzha India Sree Balaji Medical College and Hospital Bharath Institute of Higher Education and Research Chrompet Department of Biotechnology Tamil Nadu Chennai India Symbiosis Institute of Business Management Maharashtra Pune India Thiruvalluvar University Thennangur Solid-State Nanoscale Laboratory Department of Physics TUCAS Campus Vellore India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350398267

Owing to shortage of awareness regarding signs and measures for prophylaxis, skin cancer (SC), among the most severe kinds of carcinoma (Ca), is actively destroying far more people than it used to. Consequently, in order to halt the progression of Ca, timely identification during an early phase is crucial. Melanoma, baseline-cell, as well as squamous cellular Ca are the deadliest subtypes of SC. Under this research, distinct SC kinds will be identified and classified utilizing machine learning and image processing techniques. Dermoscopy imaging are taken into consideration as input during the pre-processing phase. All undesirable hairs on skin lesion are removed using dull razor technique, and the picture is then smoothed using a Gaussian filtering. Median filter is employed to reduce noisiness and protect lesion's borders. Colour-based k-means grouping is carried out in the segmentation process because colour is a key element in determining the kind of malignancy. With the help of Asymmetric, Boundary, Colour, Diameter (ABCD) and Gray Level Cooccurrence (GLC) matrix, statistic and textural retraction of features is carried out. the ISIC 2019 datasets, which consists of 8 distinct kinds of dermoscopy pictures, is used for investigation conducted. Multi-class Support Vector machine (SVM) was used for classifying with an accurateness of roughly 96.25 percent. © 2022 IEEE.

关键词： Color

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EEGWavelet Classification for Fall Detection with Genetic Programming 22

EEGWavelet Classification for Fall Detection with Genetic Pr...

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15th ACM international conference on Pervasive Technologies Related to Assistive Environments (PETRA)

作者： Bird, Jordan J. Nottingham Trent Univ Nottingham Computat Intelligence & Applicat Res Grp CIA Nottingham England

ISBN: (纸本)9781450396318

the ability to autonomously detect a physical fall is one of the many enabling technologies towards better independent living. this work explores how genetic programming can be leveraged to develop machine learning pipelines for the classification of falls via EEG brainwave activity. Eleven physical activities (5 types of falls and 6 non-fall activities) are clustered into a binary classification problem of whether a fall has occurred or not. Wavelet features are extracted from the brainwaves before machine learning models are explored and tuned for better k-fold classification accuracy, precision, recall, and F1 score. Results show that solutions discovered through genetic programming can detect falls with a mean accuracy of 89.34%, precision of 0.883, recall of 0.908, and an F1-Score of 0.895 from EEG brainwave data alone. All three genetic programming solutions chose a further step of Principal Component Analysis for additional feature extraction from the computed wavelet features, each with iterated powers of 6, 3, and 7, and all with a randomised Singular Value Decomposition approach. the best model is finally analysed via the Receiver Operating Characteristic and Precision-Recall curves. Python code for each of the genetic programming pipelines are provided.

关键词： Fall Detection EEG signal processing signal Classification

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Performance Analysis of Parametric and Non-parametric Classifier Models for Predicting the Liver Disease 1

引用

4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Mahesh, Vijayalakshmi G. V. Kumar, M. Mohan Raj, Alex Noel Joseph Department of ECE BMS Institute of Technology and Management Bangalore India Department of ECE Yenepoya Institute of Technology Moodbidri India Department of Electronic Engineering Shantou University Shantou China

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Liver plays a major role in maintaining the overall health of the body, so it is important to know the functionality or well-being of the liver. Various tests conducted on the blood samples reveal the health status of the liver;then, an appropriate treatment will be suggested if tests indicate affliction. this paper presents a framework that uses the measurements obtained from blood samples after a series of tests and applies machine learning algorithms to predict the liver disease. the proposed work utilized the Indian Liver Patient Dataset with 583 samples with parametric and non-parametric machine learning algorithms for evaluating the performance of the methodology. the simulation results analyzed with the performance metric: sensitivity, specificity, precision, negative predicted value (NPV), false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), accuracy (ACC), F1-score, Matthews correlation coefficient (MCC), chi-square value, Kappa, Cramer's V, and confidence intervals (CI) of 95% indicated the better performance of the non-parametric classifier with reduced type I and type II errors. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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machine learning Recognition Mechanism Based on WI-FI signal Optimization in the Detection of Driver’s Emotional Fluctuations 5th

Machine Learning Recognition Mechanism Based on WI-FI Signal...

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5th international conference on Recent Innovations in Computing, ICRIC 2022

作者： Jinnuo, Zhu Goyal, S.B. Nanchang Institute of Science & Technology Nanchang China City University Petaling Jaya Malaysia

ISBN: (纸本)9789811998751

With the development of the automobile industry, the use of vehicles has become the most basic means of transportation in people’s daily life. However, due to the rapid increase in the number of vehicles, more and more drivers are causing traffic accidents due to emotional fluctuations during driving. In order to reduce the occurrence of this problem, the artificial intelligence machine learning mechanism under Industry 4.0 technology can currently capture and detect the instantaneous facial emotions of drivers during driving. through the literature survey, it is found that in the machine learning mode, the existing mechanism mainly relies on human vision and biological signals sensors to identify the driver’s emotional fluctuations during driving. However, due to the frequent distortion of visual detection methods and the problems of invasiveness and privacy invasion of biological signals, the investment cost is relatively high. In order to solve the problems under the existing algorithm, this paper firstly formulates the corresponding emotional fluctuation recognition model according to the existing WI-FI signal detection principle and designs the antenna position according to the Fresnel zone to achieve the best signal acquisition effect. In addition, the driver’s action status while using the brake and accelerator is collected. the emotion recognition coefficient is calculated by the collected data, and the fluctuation recognition is performed by using the recognition wash and LSTM emotion discriminator. Finally, according to the evaluation data, the recognition rate of about 85% in the real scene is achieved. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： signal detection

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Early Prognosis of Preeclampsia Using machine learning 6th

Early Prognosis of Preeclampsia Using Machine Learning

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6th IFIP TC 5 international conference on Computer, Communication, and signal processing, ICCSP 2022

作者： Sivaram, E. Vadivu, G. Sangeetha, K. Sugumaran, Vijayan SRMIST Tamilnadu Kattankulathur Chengalpet603203 India Oakland University RochesterMI48309 United States

ISBN: (纸本)9783031116322

Preeclampsia is a type of hypertension condition that can be induced by a variety of circumstances during pregnancy. Typically, a diagnosis is made after 20 weeks of gestation. Several investigations employing machine learning techniques have been undertaken to diagnose preeclampsia. SVM, KNN, random forest, gradient boosting methods, and deep learning approaches are examples of these. these techniques can be implemented to detect preeclampsia earlier in an efficient way for preventing the complications caused. this paper demonstrates how hyperparameter tuning of Support vector classification of the various factors involved in the classification of preeclampsia helps in efficiently separating the patients who are prone to have preeclampsia. the selection of the hyperparameter is done through the Grid Search CV algorithm by iterative trialing of the different hyperparameters. © 2022, IFIP international Federation for Information processing.

关键词： Diagnosis

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