Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements

Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a study where physiological measurements are combined with machine learning algorithms to dynamically estimate the engagement of individual dyads. Sixteen dyads completed 15-min naturalistic conversations and self-reported their engagement on a visual analog scale every 60 s. Four physiological signals (electrocardiography, skin conductance, respiration, skin temperature) were recorded, and both individual physiological features (e.g., each participant’s heart rate) and synchrony features (indicating degree of physiological similarity between two participants) were extracted. Multiple regression algorithms were used to estimate self-reported engagement based on physiological features using either leave-interval-out crossvalidation (training on 14 60-s intervals from a dyad and testing on the 15th interval from the same dyad) or leave-dyad-out crossvalidation (training on 15 dyads and testing on the 16th). In leave-interval-out crossvalidation, the regression algorithms achieved accuracy similar to a ‘baseline’ estimator that simply took the median engagement of the other 14 intervals. In leave-dyad-out crossvalidation, machine learning achieved a slightly higher accuracy than the baseline estimator and higher accuracy than an independent human observer. Secondary analyses showed that removing synchrony features and personality characteristics from the input dataset negatively impacted estimation accuracy and that engagement estimation error was correlated with personality traits. Results demonstrate the feasibility of dynamically estimating interpersonal engagement during naturalistic conversation using physiological measurements, which has potential applications in both conversation monitoring and conversation enhancement. However, as many of our estimation errors are difficult to contextualize, further work is needed to determine acceptable estimation accuracies.

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Fractional baud-length coding

Annales Geophysicae ◽

10.5194/angeo-29-1189-2011 ◽

2011 ◽

Vol 29 (6) ◽

pp. 1189-1196

Author(s):

J. Vierinen

Keyword(s):

Estimation Error ◽

Estimation Accuracy ◽

Target Range ◽

Estimation Errors ◽

Range Resolution ◽

Error Covariance ◽

Novel Approach ◽

Amplitude Estimation ◽

Radar Measurements ◽

Low Altitude

Abstract. We present a novel approach for modulating radar transmissions in order to improve target range and Doppler estimation accuracy. This is achieved by using non-uniform baud lengths. With this method it is possible to increase sub-baud range-resolution of phase coded radar measurements while maintaining a narrow transmission bandwidth. We first derive target backscatter amplitude estimation error covariance matrix for arbitrary targets when estimating backscatter in amplitude domain. We define target optimality and discuss different search strategies that can be used to find well performing transmission envelopes. We give several simulated examples of the method showing that fractional baud-length coding results in smaller estimation errors than conventional uniform baud length transmission codes when estimating the target backscatter amplitude at sub-baud range resolution. We also demonstrate the method in practice by analyzing the range resolved power of a low-altitude meteor trail echo that was measured using a fractional baud-length experiment with the EISCAT UHF system.

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A REVIEW ON GAS CONSUMPTION AND GAS PREDICTION IN ETHEREUM SMART CONTRACTS

EPRA International Journal of Multidisciplinary Research (IJMR) ◽

10.36713/epra8349 ◽

2021 ◽

pp. 54-58

Author(s):

Kuldeep Nageshawar ◽

Rupali chourey ◽

Dr. Ritu Shrivastava

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Smart Contracts ◽

Transaction Data ◽

Regression Algorithms ◽

Gas Consumption

This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction

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Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

Sustainability ◽

10.3390/su11143822 ◽

2019 ◽

Vol 11 (14) ◽

pp. 3822 ◽

Cited By ~ 2

Author(s):

Fahad Alrukaibi ◽

Rushdi Alsaleh ◽

Tarek Sayed

Keyword(s):

Machine Learning ◽

Real Time ◽

Statistical Approach ◽

Penetration Rate ◽

Time Travel ◽

Machine Learning Algorithms ◽

Estimation Accuracy ◽

Travel Times ◽

Urban Networks ◽

The Impact

The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals.

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Machine Learning for Modeling the Singular Multi-Pantograph Equations

Entropy ◽

10.3390/e22091041 ◽

2020 ◽

Vol 22 (9) ◽

pp. 1041

Author(s):

Amirhosein Mosavi ◽

Manouchehr Shokri ◽

Zulkefli Mansor ◽

Sultan Noman Qasem ◽

Shahab S. Band ◽

...

Keyword(s):

Machine Learning ◽

Fuzzy Logic ◽

Intelligent Systems ◽

Estimation Error ◽

Computational Cost ◽

Accurate Solution ◽

Machine Learning Algorithms ◽

Novel Approach ◽

Cubature Kalman Filter

In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.

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Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

Remote Sensing ◽

10.3390/rs13183560 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3560

Author(s):

Xiao Sun ◽

Yunlin Zhang ◽

Yibo Zhang ◽

Kun Shi ◽

Yongqiang Zhou ◽

...

Keyword(s):

Machine Learning ◽

Organic Matter ◽

Dissolved Organic Matter ◽

Absorption Coefficient ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Chromophoric Dissolved Organic Matter ◽

Estimation Accuracy ◽

Inland Waters ◽

Landsat 8

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

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Rice Disease Classification using Deep Convolutional Neural Network

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

10.35940/ijitee.d1112.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 849-857

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Disease Classification ◽

Asian Rice ◽

Input Dataset ◽

Classification Of Diseases ◽

Rice Disease ◽

Processing Techniques

For an Agro-based country like India where agriculture acts as a main source of livelihood for more than 50% of the population, crop diseases are a major threat to food security. Hence, digital image processing along with proper machine learning algorithms can be utilized for the classification of diseases from the images of a plant. In this paper, a comparative study on the effects of different machine learning models on crop disease prediction has been done. Since Convolutional Neural Network (CNN) proved to be the best for image classification techniques, models based on CNN alone were considered in this study. We compared the performance of smallCNN with three pre-trained CNN models namely, AlexNet, ResNet-50, and VGG-16. SmallCNN is the CNN model built by us with fewer parameters and suitable for small datasets. The crop tested in this research is Oryza Sativa (Asian Rice) commonly referred to as paddy which is cultivated in abundance in India. The input dataset was fed into the model after performing appropriate pre-processing techniques followed by segmentation. The best accuracy of 66.67% was achieved in the case of ResNet-50 with Adam as the optimizer at a learning rate of 0.0001.

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EMOTION CLASSIFICATION BASED ON PHYSIOLOGICAL RESPONSES INDUCED BY NEGATIVE EMOTIONS - Discrimination of Negative Emotions by Machine Learning Algorithms

Proceedings of the International Conference on Bio-inspired Systems and Signal Processing ◽

10.5220/0003871304530457 ◽

2012 ◽

Keyword(s):

Machine Learning ◽

Negative Emotions ◽

Physiological Responses ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Emotion Classification

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Analysis of Stator Resistance Effects in Carrier Signal Injection Based Sensorless Control of Permanent Magnet Synchronous Machine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5951 ◽

2011 ◽

Vol 383-390 ◽

pp. 5951-5957 ◽

Cited By ~ 1

Author(s):

Jian Min Wang ◽

Shi Xia Tian

Keyword(s):

Sensorless Control ◽

Estimation Error ◽

Position Estimation ◽

Estimation Accuracy ◽

Estimation Errors ◽

Injection Method ◽

Signal Injection ◽

Carrier Signal Injection ◽

Rotor Position Estimation ◽

Stator Resistance

This paper analyzes the effects of stator resistance on rotor position estimation accuracy in carrier signal injection based sensorless control of PMSM. Carrier current expressions are derived for both rotating and pulsating voltage injection method when the stator resistance is taken into account. Position estimation errors resulted from stator resistance are analyzed theoretically and investigated by simulation. It is shown that the influences of stator resistance on above two injection methods are quite different. The stator resistance will result in a position estimation error in the rotating voltage injection method. But it does not affect the position estimation accuracy in the pulsating voltage injection method as long as a suitable signal extracting method is used.

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Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches

Journal of Healthcare Engineering ◽

10.1155/2018/1548647 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 26

Author(s):

Syed Ghufran Khalid ◽

Jufen Zhang ◽

Fei Chen ◽

Dingchang Zheng

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Cardiovascular Diseases ◽

Learning Algorithms ◽

Regression Tree ◽

Machine Learning Algorithms ◽

Estimation Accuracy ◽

Online Database ◽

Iso Standard ◽

Device Validation

Introduction. Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the most useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and a photoplethysmograph (PPG) that make them unsuitable for true wearable applications. Therefore, developing a single PPG-based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods. The University of Queensland vital sign dataset (online database) was accessed to extract raw PPG signals and its corresponding reference BPs (systolic BP and diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5 s for each) were extracted, preprocessed, and normalised in both width and amplitude. Three most significant pulse features (pulse area, pulse rising time, and width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (regression tree, multiple linear regression (MLR), and support vector machine (SVM)). A 10-fold cross-validation was applied to obtain overall BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (normotensive, hypertensive, and hypotensive). Finally, they were compared with the ISO standard for noninvasive BP device validation (average difference no greater than 5 mmHg and SD no greater than 8 mmHg). Results. In terms of overall estimation accuracy, the regression tree achieved the best overall accuracy for SBP (mean and SD of difference: −0.1 ± 6.5 mmHg) and DBP (mean and SD of difference: −0.6 ± 5.2 mmHg). MLR and SVM achieved the overall mean difference less than 5 mmHg for both SBP and DBP, but their SD of difference was >8 mmHg. Regarding the estimation accuracy in each BP categories, only the regression tree achieved acceptable ISO standard for SBP (−1.1 ± 5.7 mmHg) and DBP (−0.03 ± 5.6 mmHg) in the normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion. This study developed and compared three machine learning algorithms to estimate BPs using PPG only and revealed that the regression tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the regression tree algorithm achieved acceptable measurement accuracy only in the normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories.

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An Optimized way to Solve Regression Problems

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e2873.0810621 ◽

2021 ◽

Vol 10 (6) ◽

pp. 61-65

Author(s):

Jyothi Vishnu Vardhan Kola ◽

Keyword(s):

Machine Learning ◽

Data Cleaning ◽

Machine Learning Algorithms ◽

Complex Data ◽

Learning Approaches ◽

Continuous Variables ◽

Regression Algorithms ◽

Working Principle ◽

Regression Problems ◽

Incomplete Datasets

In many real world scenarios, regression is a commonly used technique to predict continuous variables. In case of noisy(inconsistent) and incomplete datasets, a large number of previous works adopted complex non traditional machine learning approaches in order to get accurate predictions. However, compromising on time and space overheads. In this paper, we work with complex data yet by using traditional machine learning regression algorithms by working on data cleaning and data transformation according to the working principle of those machine learning algorithms.

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