Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach

Impedance pneumography has been suggested as an ambulatory technique for the monitoring of respiratory diseases. However, its ambulatory nature makes the recordings more prone to noise sources. It is important that such noisy segments are identified and removed, since they could have a huge impact on the performance of data-driven decision support tools. In this study, we investigated the added value of machine learning algorithms to separate clean from noisy bio-impedance signals. We compared three approaches: a heuristic algorithm, a feature-based classification model (SVM) and a convolutional neural network (CNN). The dataset consists of 47 chronic obstructive pulmonary disease patients who performed an inspiratory threshold loading protocol. During this protocol, their respiration was recorded with a bio-impedance device and a spirometer, which served as a gold standard. Four annotators scored the signals for the presence of artefacts, based on the reference signal. We have shown that the accuracy of both machine learning approaches (SVM: 87.77 ± 2.64% and CNN: 87.20 ± 2.78%) is significantly higher, compared to the heuristic approach (84.69 ± 2.32%). Moreover, no significant differences could be observed between the two machine learning approaches. The feature-based and neural network model obtained a respective AUC of 92.77±2.95% and 92.51±1.74%. These findings show that a data-driven approach could be beneficial for the task of artefact detection in respiratory thoracic bio-impedance signals.

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CNN based Image Classification Model

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1225.09811s19 ◽

2019 ◽

Vol 8 (11S) ◽

pp. 1106-1114

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Classification ◽

Error Rates ◽

Machine Learning Algorithms ◽

Classification Model ◽

The Internet ◽

Network Algorithms ◽

Media Companies ◽

Automatic Tagging

Images are the fastest growing content, they contribute significantly to the amount of data generated on the internet every day. Image classification is a challenging problem that social media companies work on vigorously to enhance the user’s experience with the interface. The recent advances in the field of machine learning and computer vision enables personalized suggestions and automatic tagging of images. Convolutional neural network is a hot research topic these days in the field of machine learning. With the help of immensely dense labelled data available on the internet the networks can be trained to recognize the differentiating features among images under the same label. New neural network algorithms are developed frequently that outperform the state-of-art machine learning algorithms. Recent algorithms have managed to produce error rates as low as 3.1%. In this paper the architecture of important CNN algorithms that have gained attention are discussed, analyzed and compared and the concept of transfer learning is used to classify different breeds of dogs..

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Developing a data-driven ocean model

10.5194/egusphere-egu2020-19339 ◽

2020 ◽

Author(s):

Rachel Furner ◽

Peter Haynes ◽

Dan Jones ◽

Dave Munday ◽

Brooks Paige ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

General Circulation Model ◽

General Circulation ◽

Climate Models ◽

Circulation Model ◽

Machine Learning Algorithms ◽

Data Driven ◽

Geophysical Research ◽

Weather And Climate

The recent boom in machine learning and data science has led to a number of new opportunities in the environmental sciences. In particular, climate models represent the best tools we have to predict, understand and potentially mitigate climate change, however these process-based models are incredibly complex and require huge amounts of high-performance computing resources. Machine learning offers opportunities to greatly improve the computational efficiency of these models.Here we discuss our recent efforts to reduce the computational cost associated with running a process-based model of the physical ocean by developing an analogous data-driven model. We train statistical and machine learning algorithms using the outputs from a highly idealised sector configuration of general circulation model (MITgcm). Our aim is to develop an algorithm which is able to predict the future state of the general circulation model to a similar level of accuracy in a more computationally efficient manner.We first develop a linear regression model to investigate the sensitivity of data-driven approaches to various inputs, e.g. temperature on different spatial and temporal scales, and meta-variables such as location information. Following this, we develop a neural network model to replicate the general circulation model, as in the work of Dueben and Bauer 2018, and Scher 2018.We present a discussion on the sensitivity of data-driven models and preliminary results from the neural network based model.&#160;Dueben, P. D., & Bauer, P. (2018). Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development, 11(10), 3999-4009.Scher, S. (2018). Toward Data&#8208;Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning. Geophysical Research Letters, 45(22), 12-616.

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Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Advances in Meteorology ◽

10.1155/2018/1824317 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Xianming Dou ◽

Yongguo Yang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Water Vapor ◽

Data Driven ◽

Machine Learning Techniques ◽

Support Vector ◽

Group Method ◽

Generalized Regression Neural Network ◽

Learning Approaches ◽

Inference System

Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.

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Crop Recommendation System by Artificial Neural Network

10.21203/rs.3.rs-874525/v1 ◽

2021 ◽

Author(s):

Bangaru Kamatchi S ◽

R. Parvathi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Recommendation System ◽

Climatic Factors ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Crop Type ◽

Artificial Neural

Abstract The agriculture yield mostly depends on climate factors. Any information associated with climatic factors will help farmers in foreordained farming. Choosing a right crop at right time is most important to get proper yield. To help the farmers in decision making process a classification model is built by considering the agro climatic parameters of a crop like temperature, relative humidity, type of soil, soil pH and crop duration and a recommendation system is built based on three factors namely crop, type of crop and the districts. Predicting the districts is the novel approach in which crop pattern of 33 districts of Tamilnadu is marked and based on that classification model is built. Thorough analysis of machine learning algorithms incorporating pre-processing, data augmentation and comparison of optimizers and activation function of ANN. Log loss metric is used to validate the models. The results shows that artificial neural network is the best predictive model for classification of crops crop type and district based on agrometeorological climatic condition. The accuracy of artificial neural network model is compared with five different machine learning algorithms to analyse the performance.

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Development of Rainfall Prediction Models Using Machine Learning Approaches for Different Agro-Climatic Zones

Advances in Data Mining and Database Management - Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science ◽

10.4018/978-1-7998-6659-6.ch005 ◽

2021 ◽

pp. 72-94

Author(s):

Diwakar Naidu ◽

Babita Majhi ◽

Surendra Kumar Chandniha

Keyword(s):

Neural Network ◽

Machine Learning ◽

Large Scale ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Learning Approaches ◽

Learning Models ◽

Climatic Zones ◽

Environmental Prediction ◽

Machine Learning Models

This study focuses on modelling the changes in rainfall patterns in different agro-climatic zones due to climate change through statistical downscaling of large-scale climate variables using machine learning approaches. Potential of three machine learning algorithms, multilayer artificial neural network (MLANN), radial basis function neural network (RBFNN), and least square support vector machine (LS-SVM) have been investigated. The large-scale climate variable are obtained from National Centre for Environmental Prediction (NCEP) reanalysis product and used as predictors for model development. Proposed machine learning models are applied to generate projected time series of rainfall for the period 2021-2050 using the Hadley Centre coupled model (HadCM3) B2 emission scenario data as predictors. An increasing trend in anticipated rainfall is observed during 2021-2050 in all the ACZs of Chhattisgarh State. Among the machine learning models, RBFNN found as more feasible technique for modeling of monthly rainfall in this region.

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Machine Learning for Financial Stability

Data Science for Economics and Finance ◽

10.1007/978-3-030-66891-4_4 ◽

2021 ◽

pp. 65-87

Author(s):

Lucia Alessi ◽

Roberto Savona

Keyword(s):

Machine Learning ◽

Financial Stability ◽

Financial Risk ◽

Global Financial Crisis ◽

Predictive Performance ◽

Empirical Models ◽

Machine Learning Algorithms ◽

Data Driven ◽

Machine Learning Techniques ◽

Learning Approaches

AbstractWhat we learned from the global financial crisis is that to get information about the underlying financial risk dynamics, we need to fully understand the complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of financial risks. Advanced machine learning techniques provide several advantages over empirical models traditionally used to monitor and predict financial developments. First, they are able to deal with high-dimensional datasets. Second, machine learning algorithms allow to deal with unbalanced datasets and retain all of the information available. Third, these methods are purely data driven. All of these characteristics contribute to their often better predictive performance. However, as “black box” models, they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.

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Application of Feature-Learning Methods Toward Product Usage Context Identification and Comfort Prediction

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4037435 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 4

Author(s):

Dipanjan Ghosh ◽

Andrew Olewnik ◽

Kemper Lewis

Keyword(s):

Neural Network ◽

Machine Learning ◽

Product Design ◽

Feature Learning ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Product Usage ◽

Feature Based ◽

Product Interaction ◽

Usage Context

Usage context is considered a critical driving factor for customers' product choices. In addition, physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g., level of comfort). In the emerging internet of things (IoT), this work hypothesizes that it is possible to understand product usage and level of comfort while it is “in-use” by capturing the user-product interaction data. Mining this data to understand both the usage context and the comfort of the user adds new capabilities to product design. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of feature-learning methods for the identification of product usage context and level of comfort is demonstrated, where usage context is limited to the activity of the user. A novel generic architecture using foundations in convolutional neural network (CNN) is developed and applied to a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural network and support vector machines (SVM)) and demonstrate the benefits of using the feature-learning methods over the feature-based machine-learning algorithms. To demonstrate the generic nature of the architecture, an application toward comfort level prediction is presented using force sensor data from a sensor-integrated shoe.

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Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

Current Pharmaceutical Design ◽

10.2174/1381612825666191107092214 ◽

2020 ◽

Vol 25 (40) ◽

pp. 4296-4302 ◽

Cited By ~ 2

Author(s):

Yuan Zhang ◽

Zhenyan Han ◽

Qian Gao ◽

Xiaoyi Bai ◽

Chi Zhang ◽

...

Keyword(s):

Machine Learning ◽

Inclusion Bodies ◽

Cross Validation ◽

Independent Set ◽

K562 Cells ◽

Machine Learning Algorithms ◽

Learning Approaches ◽

Validation Test ◽

Excess Number ◽

Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

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Derivation of Respiratory Metrics in Health and Asthma; Machine Learning Methodology (Preprint)

10.2196/preprints.25178 ◽

2020 ◽

Author(s):

Joseph Prinable ◽

Peter Jones ◽

David Boland ◽

Alistair McEwan ◽

Cindy Thamrin

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Reference Signal ◽

Machine Learning Algorithms ◽

Training Dataset ◽

Local Health ◽

Strongly Correlated ◽

Human Research Ethics ◽

Expiration Time ◽

Wide Variability

BACKGROUND The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. OBJECTIVE Examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. METHODS Pulse oximetry data was collected from 11 healthy and 11 asthma subjects who breathed at a range of controlled respiratory rates. UNET and Long Short-Term memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. RESULTS The UNET vs LSTM model provided breathing metrics which were strongly correlated with those from the reference signal (all p<0.001, except for inspiratory:expiratory ratio). The following relative mean bias(95% confidence interval) were observed: inspiration time 1.89(-52.95, 56.74)% vs 1.30(-52.15, 54.74)%, expiration time -3.70(-55.21, 47.80)% vs -4.97(-56.84, 46.89)%, inspiratory:expiratory ratio -4.65(-87.18, 77.88)% vs -5.30(-87.07, 76.47)%, inter-breath intervals -2.39(-32.76, 27.97)% vs -3.16(-33.69, 27.36)%, and respiratory rate 2.99(-27.04 to 33.02)% vs 3.69(-27.17 to 34.56)%. CONCLUSIONS Both machine learning models show strongly correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g. by increasing the size of the training dataset at the lower breathing rates. CLINICALTRIAL Sydney Local Health District Human Research Ethics Committee (#LNR\16\HAWKE99 ethics approval).

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