Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data

Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.) and this particular structure should be taken into account in machine learning algorithms used in EMA like decision trees and its variants. We propose a new algorithm called BBT (standing for Bagged Boosted Trees) that is enhanced by a over/under sampling method and can provide better estimates for the conditional class probability function. Experimental results on a real-world dataset show that BBT can benefit EMA data classification and performance

Using Explainable Boosting Machine to Compare Idiographic and Nomothetic Approaches for Ecological Momentary Assessment Data

Previous research on EMA data of mental disorders was mainly focused on multivariate regression-based approaches modeling each individual separately. This paper goes a step further towards exploring the use of non-linear interpretable machine learning (ML) models in classification problems. ML models can enhance the ability to accurately predict the occurrence of different behaviors by recognizing complicated patterns between variables in data. To evaluate this, the performance of various ensembles of trees are compared to linear models using imbalanced synthetic and real-world datasets. After examining the distributions of AUC scores in all cases, non-linear models appear to be superior to baseline linear models. Moreover, apart from personalized approaches, group-level prediction models are also likely to offer an enhanced performance. According to this, two different nomothetic approaches to integrate data of more than one individuals are examined, one using directly all data during training and one based on knowledge distillation. Interestingly, it is observed that in one of the two real-world datasets, knowledge distillation method achieves improved AUC scores (mean relative change of +17\% compared to personalized) showing how it can benefit EMA data classification and performance.Comment: 13 pages, 2 figures, accepted on the symposium 'Intelligent Data Analysis' (2022

Predicting patients who will drop out of out-patient psychotherapy using machine learning algorithms

Background: About 30% of patients drop out of cognitive-behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous. Aims: This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables. Method: The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation. Results: The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = -2.93, 95% CI (-3.95, -1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale. Conclusions: Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not

Comparison of subjective and physiological stress levels in home and office work environments

Work stress is a major problem to individuals and society, with prolonged periods of stress often leading to health issues and reduced productivity. COVID-19 has increased the incidence of individuals working in a mixture of home and office-based environments, with each location presenting its own stressors. Identification of stress levels in each environment will allow individuals to better plan how to mitigate stress and boost productivity. In this project, differences in stress levels are predicted in each work environment from individuals’ physiological responses and subjectively reported stress and productivity. Initial work on the project focused upon development of a system for the detection of dementia-related difficulties through the wearable-based tracking of physiological indicators. As such, a review of the available commercial and laboratory devices available for tracking physiological indicators of dementia-related difficulties was conducted. Furthermore, no publicly available physiological dataset for predicting difficulties in dementia currently exists. However, a review of the methods for collecting such a dataset and the impact of COVID-19 found that it is impractical and potentially unethical to conduct an experiment with people with dementia during the pandemic. As such, a pivot in research was necessitated. Comparing the stress levels of individuals working in home and office environments was selected. A data collection experiment was then performed with 13 academics working in combinations of home and office environments. Descriptive statistical features were then extracted from both the physiological and questionnaire data, with the relationships between attributes and features calculated using various advanced data analytics and statistical approaches. The resultant correlation coefficients and statistical summaries of stress were used to evaluate relationships between stress and work environment at different times of day, different days of the week, and while performing different activities. A bagged tree machine learning model was trained over the data, achieving 99.3% accuracy when evaluated using 10-fold cross validation. When tested on the purely unseen instances it achieved 56% accuracy corresponding to inter-class stress classification, however a testing accuracy of 73.7% was achieved using principal component analysis for dimensionality reduction and the dataset is balanced using Synthetic Minority Oversampling Technique

CLASSIFYING SOIL MOISTURE CONTENT USING REFLECTANCE-BASED REMOTE SENSING

The ability to quantify soil moisture spatial variability and its temporal dynamics over entire fields through direct soil observations using remote sensing will improve early detection of water stress before crop physiological or economic damage has occurred, and it will contribute to the identification of zones within a field in which soil water is depleted faster than in other zones of a field. The overarching objective of this research is to develop tools and methods for remotely estimating soil moisture variability in agricultural crop production. Index-based and machine learning methods were deployed for processing hyperspectral data collected from moisture-controlled samples. In the first of five studies described in this dissertation, the feasibility of using “low-cost” index-based multispectral reflectance sensing for remotely delineating soil moisture content from direct soil and crop residue measurements using down-sampled spectral data were determined. The relative reflectance from soil and wheat stalk residue were measured using visible and near-infrared spectrometers. The optimal pair of wavelengths was chosen using a script to create an index for estimating soil and wheat stalk residue moisture levels. Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples; however, this index worked poorly on estimating stalk residue moisture. In the second of five studies, 20 machine learning algorithms were applied to full spectral datasets for moisture prediction and comparing them to the index-based method from the previous objective. Cubic support vector machine (SVM) and ensemble bagged trees methods produced the highest composite prediction accuracies of 96% and 93% for silt-loam soil samples, and 86% and 93% for wheat stalk residue samples, respectively. Prediction accuracy using the index-based method was 86% for silt-loam soil and 30% for wheat stalk residue. In the third study, a spectral measurement platform capable of being deployed on a UAS was developed for future use in quantifying and delineating moisture zones within agricultural landscapes. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous reflectance data acquisition. A similar ground-based system was developed to keep track of ambient light during reflectance target measurement. The systems were tested under varying ambient light conditions during the 2017 Great American Eclipse. In the fourth study, the data acquisition system from the third study was deployed for recognizing different targets in the grayscale range using machine learning methods and under ambient light conditions. In this study, a dynamic method was applied to update integration time on spectrometers to optimize sensitivity of the instruments. It was found that by adjusting the integration time on each spectrometer such that a maximum intensity across all wavelengths was reached, the targets could be recognized simply based on the reflectance measurements with no need of a separate ambient light measurement. Finally, in the fifth study, the same data acquisition system and variable integration time method were used for estimating soil moisture under ambient light condition. Among 22 machine learning algorithms, linear and quadratic discriminant analysis achieved the maximum prediction accuracy. A UAS-deployable hyperspectral data acquisition system containing three portable spectrometers and an embedded computer was developed to classify moisture content from spectral data. Partial least squares regression and machine learning algorithms were shown to be effective to generate predictive models for classifying soil moisture

Using Deep Learning Predictions of Smokers’ Behaviour to Develop a Smart Smoking-Cessation App

The number of new smoking-cessation apps had increased in recent years. Although these offer accessible and low-cost support to smokers, they often lack scientific understanding of nicotine addiction, and rely on smokers’ self-reporting their cravings / environmental factors; a method widely acknowledged to be unreliable. This PhD presents two novel deep-learning models for automatic smoking events prediction. Both models combine machine-learning with Control Theory Model of Smoking (CTMoS), to enable the prediction of smoking events based on both internal (nicotine level) and external (e.g. location) factors. This offers a way to overcome limitations of previous apps. The first model, combined CTMoS with a 1D Convolutional Neural Network, using raw accelerometer and GPS coordinates as input. Result indicated good prediction of internal craving factors (e.g. nicotine level and craving); but smoking events prediction required improvement, as the f1-score were 0.06, 0.14, 0.24, and 0.4 for predicting a smoking event 5, 15, 30, and 60 -min (respectively) prior to its occurrence. The second model combined 1D Convolutional Neural Network with the Bidirectional Long Short-Term Memory method, to create a deep learning model with Genetic Algorithm for hyperparameter selection. The model used the same 3- accelerometer values as input, but the 3-GPS coordinates were replaced with coded location data (five most smoked locations). These changes improved smoking events prediction with average f1-score of 0.32, 0.59, 0.71, and 0.8 for predicting a smoking event 5, 15, 30, and 60 -min (respectively) prior to its occurrence. This PhD achieved its three goals: minimize user input (by using data collected from phone sensors); improve scientific understanding of factors that influence smokers’ behaviour (by evaluating the relative contribution of different factors), and developing a state-of-the-art model that enables the automatic prediction of smoking events. As such, outcomes of this PhD lay the foundation for future development of smart and personalized apps that can provide real-time personalized support for smokers

Modelling loudness: Acoustic and perceptual correlates in the context of hypophonia in Parkinson’s disease

Hypophonia (quiet speech) is a common speech symptom associated with Parkinson’s disease (PD), and is associated with reduced intelligibility, communicative effectiveness, and communicative participation. Studies of hypophonia commonly employ average speech intensity as the primary dependent measure, which may not entirely capture loudness deficits. Loudness may also be affected by the frequency components of speech (i.e. spectral balance) and speech level variability. The present investigation examined relationships between perceived loudness and intelligibility with acoustic measures of loudness, speech intensity, and spectral distribution in individuals with hypophonia secondary to Parkinson’s disease (IWPDs) and neurologically healthy older adults (HOAs). Samples of sentence reading and conversational speech from 56 IWPDs and 46 HOAs were presented to listeners for ratings of perceived loudness and intelligibility. Listeners provided ratings of loudness using visual analogue scales (VAS) and direct magnitude estimation (DME). Acoustic measures of speech level (e.g. mean intensity), spectral balance (e.g. spectral tilt), and speech level variability (e.g. standard deviation of intensity) were obtained for comparison with perceived characteristics. In a spectral manipulation experiment, a gain adjustment altered the spectral balance of sentence samples while maintaining equal mean intensity. Listeners provided VAS ratings of perceived loudness of these manipulated samples. IWPDs were quieter, less intelligible, and had a relatively greater concentration of low-frequency energy than HOAs. Speech samples with weaker contributions of mid- (2-5 kHz) and high-frequency (5-8 kHz) energy were perceived as quieter. Results of the spectral manipulation experiment indicated that increases in the relative contribution of 2-10 kHz energy were associated with increases in perceived loudness. The acoustic time-varying loudness model (TVL) demonstrated stronger associations with perceived loudness and larger differences between IWPDs and HOAs, and successfully identified differences in loudness in the spectral manipulation experiment. Loudness ratings provided with VAS and DME were consistent, both providing excellent reliability. Findings of this investigation indicate that perceived loudness, acoustic loudness, and spectral balance are important components of hypophonia evaluation. Incorporating spectral manipulation in amplification by increasing high-frequency energy may improve efficacy of amplification devices for hypophonia management

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Pioneering Conservation in Alaska

"Designed as a companion to his "Environmental Conflict in Alaska" (2001), which presented the environmental issues of Alaska's statehood period, the newest study by Ross provides an in-depth view of the resource management controversies in Alaska up to statehood in 1958. Ross's chapters on predator control, when wildlife managers offered bounties not just for wolves but for eagles, and another on attempted translocations of ungulates, reveal astounding efforts to manipulate ecosystems. Especially useful is his history of the successful efforts to preserve the Arctic National Wildlife Refuge." CHOICE Magazin