Machine learning-based prediction of outdoor ambient sound levels: Ensemble averaging and feature reduction

2018 ◽  
Vol 144 (3) ◽  
pp. 1791-1791
Author(s):  
Katrina Pedersen ◽  
Kent L. Gee ◽  
Mark K. Transtrum ◽  
Brooks A. Butler ◽  
Michael M. James ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Reduction ◽  
Ensemble Averaging ◽  
Sound Levels ◽  
Ambient Sound

scholarly journals Machine learning-based ensemble model predictions of outdoor ambient sound levels

2018 ◽  
Author(s):  
Katrina Pedersen ◽  
Mark K. Transtrum ◽  
Kent L. Gee ◽  
Brooks A. Butler ◽  
Michael M. James ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ensemble Model ◽  
Sound Levels ◽  
Ambient Sound ◽  
Model Predictions

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

scholarly journals Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches

2021 ◽  
Vol 35 (1) ◽  
pp. 11-21
Author(s):  
Himani Tyagi ◽  
Rajendra Kumar
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Detection System ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Testing Time ◽  
Learning Approaches ◽  
Reduction Techniques ◽  
Share Data

IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

Coregulation in Salivary Cortisol During Maternal Holding of Premature Infants

2008 ◽  
Vol 10 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Madalynn Neu ◽  
Mark L. Laudenslager ◽  
JoAnn Robinson
Keyword(s):  
Salivary Cortisol ◽  
Neonatal Intensive Care ◽  
Sound Level ◽  
Holding Time ◽  
Sound Levels ◽  
Ambient Sound ◽  
Antenatal Steroids ◽  
Postconceptional Age ◽  
Research Questions ◽  
Cortisol Levels

Purpose: The purpose of this study was to examine coregulation between mothers and preterm infants in hypothalamic-pituitary-adrenocortical (HPA) system activity, as indicated by salivary cortisol levels, while mothers held their infants. The research questions were (a) does mother—infant coregulation in HPA activity occur during holding? and (b) if mother— infant coregulation in HPA activity exists during holding, do type of holding, antenatal steroids, sound level, and maternal touch influence this coregulation? Sample: The sample consisted of 20 mother— infant dyads with infants at a mean postconceptional age of 34.7 weeks (+0.7) and average postnatal age of 15 days (+9) at the time of cortisol sampling. Design: The design was exploratory using convenience sampling. Maternal and infant cortisol levels were obtained at Time 1 (baseline) and Time 2 (end of holding); at each time, the absolute differences in levels between mother and infant were determined. Coregulation was operationalized as less difference between maternal-infant cortisol levels immediately after holding (Time 2) as compared to before holding (Time 1). Results: The two variables with the highest correlation with the Time 1/Time 2 difference score included antenatal steroids and ambient sound level, which were entered into a linear regression equation as predictor variables. A coregulatory relationship in cortisol levels existed between mothers and infants during holding, which was moderated by sound levels. Nurses in the neonatal intensive care unit (NICU) can facilitate the mother—infant relationship, as reflected in coregulatory measures, by promoting a quiet environment, particularly around mothers who are holding their infants.

Classification of Sleep Apnea Using ECG Signals With Machine Learning Techniques

2021 ◽  
pp. 184-203
Author(s):  
Karthik R. ◽  
Ifrah Alam ◽  
Bandaru Umamadhuri ◽  
Bharath K. P. ◽  
Rajesh Kumar M.
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Soft Tissues ◽  
Amplitude Distribution ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Feed Forward Neural Network ◽  
Ecg Signals

In this chapter, the authors use various signal processing techniques to analyze and gain insights on how ECG signals for patients suffering from sleep apnea (sleep apnea or obstructive sleep apnea occurs when the muscles that support the soft tissues in the throat, such as tongue and soft palate, relax temporarily) disease vary with respect to a normal person's ECG. The work has three stages: firstly, to identify waves, complexes, morphology in an ECG which reflect the presence of the disease; second, feature extraction techniques to extract features of ECG such as duration of the wave, amplitude distribution, and morphology classes; and third, detailed clustering (unsupervised) algorithm analysis of the extracted features with efficient feature reduction methodologies such as PCA and LDA. Finally, the authors use supervised machine learning algorithms (SVM, naive Bayes classifier, feed forward neural network, and decision tree) to distinguish between ECG signals with sleep apnea and normal ECG signals.

Music Therapy in the Medical Care of Infants

2015 ◽  
Author(s):  
Helen Shoemark ◽  
Trish Dearn
Keyword(s):  
Music Therapy ◽  
Medical Care ◽  
Neonatal Intensive Care ◽  
Infant Development ◽  
Developmental Care ◽  
Physical Context ◽  
Sound Levels ◽  
Ambient Sound ◽  
Nutritive Sucking ◽  
History Of

This chapter describes the ways in which music therapy can be provided to preterm infants or full-term who require medical care for complex issues that require hospitalization after birth. The history of Newborn Music Therapy research includes the application of music to change pain, non-nutritive sucking, feeding, with kangaroo care, and in developmental care. The modalities include recorded music and singing, and live singing and gentle instrument playing. Underpinning the application of music in the Neonatal Intensive Care Unit (NICU) are considerations of the ambient sound levels of the NICU, the age of the infant, the physical context, timing of session. The significance of the parents’ experience in family-centerd music therapy in hospital is highlighted, as is the pivotal role of the music therapist to stimulate and facilitate music as part of healthy infant development.

scholarly journals Feature Snatching and Performance Assessment for Connoting the Admittance Likelihood of student using Principal Component Reduction

2019 ◽  
Vol 8 (2) ◽  
pp. 4800-4807
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Dimensionality Reduction ◽  
Admission Rate ◽  
Principal Component ◽  
Feature Reduction ◽  
Experimental Result ◽  
Sparse Pca ◽  
Reduction Methods ◽  
Incremental Pca

Recently, engineers are concentrating on designing an effective prediction model for finding the rate of student admission in order to raise the educational growth of the nation. The method to predict the student admission towards the higher education is a challenging task for any educational organization. There is a high visibility of crisis towards admission in the higher education. The admission rate of the student is the major risk to the educational society in the world. The student admission greatly affects the economic, social, academic, profit and cultural growth of the nation. The student admission rate also depends on the admission procedures and policies of the educational institutions. The chance of student admission also depends on the feedback given by all the stake holders of the educational sectors. The forecasting of the student admission is a major task for any educational institution to protect the profit and wealth of the organization. This paper attempts to analyze the performance of the student admission prediction by using machine learning dimensionality reduction algorithms. The Admission Predict dataset from Kaggle machine learning Repository is used for prediction analysis and the features are reduced by feature reduction methods. The prediction of the chance of Admit is achieved in four ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly contributing to the prediction of chance of admit. Thirdly, the Admission Predict dataset is subjected to dimensionality reduction methods like principal component analysis (PCA), Sparse PCA, Incremental PCA , Kernel PCA and Mini Batch Sparse PCA. Fourth, the optimized dimensionality reduced dataset is then executed to analyze and compare the mean squared error, Mean Absolute Error and R2 Score of each method. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the CGPA, GRE Score and TOEFL Score are highly correlated features in predicting the chance of admit. The execution of performance analysis shows that Incremental PCA have achieved the effective prediction of chance of admit with minimum MSE of 0.09, MAE of 0.24 and reasonable R2 Score of 0.26.

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