165 results found
Tsai C-Y, Huang H-T, Liu M, et al., 2023, Associations of fine particulate matter exposure with sleep disorder indices in adults and mediating effect of body fat, Atmospheric Pollution Research, Vol: 14, Pages: 1-10, ISSN: 1309-1042
Exposure to particulate matter (PM) may be a risk factor for obstructive sleep apnea (OSA) and obesity. However, whether body fat accumulation exerts mediating effects on the association between air pollutant exposure and OSA aggravation remains unclear.This study retroactively acquired the polysomnographic data (sleep variables) and body composition information from 2893 patients in a northern Taiwan sleep center. The levels of exposure to various air pollutants were estimated using an adjusted method based on data from governmental air quality monitoring stations near registered residential addresses instead of only referencing the nearest station. The sleep disorder indices and body fat metrics, which served as the outcomes of interest, were transformed using the Box-Cox transformation. Multiple linear regression models and causal mediation analysis were employed to investigate the associations between the analyzed parameters and the estimated air pollutant exposure at various time scales (1-, 6-, and 12-month).Significant associations were observed between the increased interquartile range (IQR) of short-term (1-month) exposure to PM ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), and the apnea–hypopnea index (AHI), oxygen desaturation index (ODI), and arousal index (ArI). Short-term (1-month) exposure to PM10 and PM2.5 was significantly associated with increased trunk fat percentage. Causal mediation analysis revealed that short-term (1-month) exposure to PM10 and PM2.5 affected trunk fat percentage, thereby partially meditating the elevations in AHI, ODI, and ArI.PM exposure may directly increase sleep disorder indices and alter body fat, thereby mediating the worsening of OSA manifestations (i.e., increased AHI, ODI, and ArI).
Tsai C-Y, Su C-L, Wang Y-H, et al., 2023, Impact of lifetime air pollution exposure patterns on the risk of chronic disease, Environmental Research, Vol: 229, Pages: 1-9, ISSN: 0013-9351
Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal erro
Tsai C-Y, Cheong H-I, Houghton R, et al., 2023, Predicting fatigue-associated aberrant driving behaviors using a dynamic weighted moving average model with a long short-term memory network based on heart rate variability, Human Factors: The Journal of the Human Factors and Ergonomics Society, ISSN: 0018-7208
ObjectiveThis study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks.BackgroundFatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages.MethodThis study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance.ResultsSignificant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values.ConclusionHRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs.ApplicationThe established models can be used in realistic driving scenarios.
Tsai C-Y, Huang H-T, Liu M, et al., 2023, Associations between air pollution, intracellular-to-extracellular water distribution, and obstructive sleep apnea manifestations, Frontiers in Public Health, Vol: 11, Pages: 1-11, ISSN: 2296-2565
Background: Exposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations.Objectives: This study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution.Methods: This retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined.Results: Significant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA.Conclusion: Because exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the re
Hsu W-H, Yang C-C, Tsai C-Y, et al., 2023, Association of low arousal threshold obstructive sleep apnea manifestations with body fat and water distribution, Life, Vol: 13, Pages: 1-14, ISSN: 2075-1729
Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.
Cheong H-I, Macias JJE, Karamanis R, et al., 2023, Policy and strategy evaluation of ridesharing autonomous vehicle operation: a london case study, Transportation Research Record: Journal of the Transportation Research Board, Pages: 1-31, ISSN: 0361-1981
To understand the dynamics of an autonomous ridesharing transport mode from the perspectives of different stakeholders, a single model of such a system is essential, because this will enable policymakers and companies involved in the manufacture and operation of shared autonomous vehicles (SAVs) to develop user-centered strategies. The model needs to be based on real data, network, and traffic information and applied to real cities and situations, particularly those with complex public transportation systems. In this paper, we propose a new agent-based model for SAV deployment that enables the parametric assessment of key performance indicators from the perspective of potential SAV users, vehicle manufacturers, operators, and local authorities. This has been applied to a case study of three regions in London: central, inner, and outer. The results show there is no linear correlation between an increased ridesharing acceptance level and average trip duration. Without a fleet rebalancing algorithm, over 80% of SAVs’ energy expenditure is on picking up customers. By reducing pickup distance, SAVs could be a contender for a nonpersonal transportation system based on trip energy comparisons. The results provide a picture of future SAV systems for potential users and offer suggestions as to how operators can devise an optimal transportation strategy beyond the question of fleet size and how policymakers can improve the overall transport network and reduce its environmental impact based on energy consumption. As a result of its flexibility and parametric capability, the model can be utilized to inform any local authority how SAV services could be deployed in any city.
Tsai CY, Lin Y, Liu WT, et al., 2023, Aberrant Driving Behavior Prediction for Urban Bus Drivers in Taiwan Using Heart Rate Variability and Various Machine Learning Approaches: A Pilot Study, Transportation Research Record, Pages: 1304-1320
Objective: Aberrant driving behavior (ADB) decreases road safety and is particularly relevant for urban bus drivers, who are required to drive daily shifts of considerable duration. Although numerous frameworks based on human physiological features have been applied to predict ADB, the research remains at an early stage. This study used heart rate variability (HRV) parameters to establish ADB occurrence prediction models with various machine learning approaches. Methods: Twelve Taiwanese urban bus drivers were recruited for four consecutive days of naturalistic driving data collection (from their routine routes) between March and April 2020; driving behaviors and physiological signals were obtained from provided devices. Weather and traffic congestion information was determined from public data, while sleep quality and professional driving experience were self-reported. To develop the ADB prediction model, several machine learning models—logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)—were trained and 10-fold cross-validated by using the testing data. Results: Most drivers with ADB reported deficient sleep quality ( ł 80%), with significantly higher mean scores on the Karolinska Sleepiness Scale and driver behavior questionnaire subcategory of lapses and errors than drivers without ADB. Next, HRV indices significantly differed between the measurement of a pre-ADB event and a baseline. The accuracy of the GRU models ranged from 78.84% 6 1.49% to 89.57% 6 1.31%. Conclusion: Drivers with ADB tend to have inadequate sleep quality, which may increase their fatigue levels and impair driving performance. The established time-series models can be considered for ADB occurrence prediction among urban bus drivers.
Liu W-T, Huang H-T, Hung H-Y, et al., 2023, Continuous positive airway pressure reduces plasma neurochemical levels in patients with OSA: a pilot study, Life, Vol: 13, Pages: 1-13, ISSN: 2075-1729
Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aβ42), and the product of the two (Aβ42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAβ42: 0.58 pg/mL; ΔAβ42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aβ42 decreased considerably in the CPAP group (ΔT-Tau: −2.22 pg/mL; ΔAβ42 × T-Tau: −44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea–hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.
Shipman A, Dezecache G, Majumdar A, 2023, A quantification of the reliability of self-reports following a simulated stressful event, International Journal of Disaster Risk Reduction, Vol: 86, Pages: 1-10, ISSN: 2212-4209
Interviews and surveys are the most commonly used data-gathering and data-generating techniques when investigating human behaviour in emergencies. However, these approaches suffer from several limitations, including potential errors in memory accuracy, a lack of quantitative reliability. This study focuses on a survey performed on participants who had taken part in a stressful experiment. The survey was carried out three months afterwards, asking them to recall their experience. Analysis of this data quantitatively assesses their recall, across multiple different domains. This study observed several differences between experimental and control group participants, as well as differences between participants in VR and Physical experimental groups. However, it observes no increase in confabulation as a result of increased stress. The outcome of this study is to provide insight into the quantitative reliability of interviews and surveys of people involved in emergencies.
Chen K-Y, Kuo H-Y, Lee K-Y, et al., 2023, Associations of the distance-saturation product and low-attenuation area percentage in pulmonary computed tomography with acute exacerbation in patients with chronic obstructive pulmonary disease, Frontiers of Medicine, Vol: 9, Pages: 1-13, ISSN: 1673-7342
Background: Chronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations.Methods: This retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals’ data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables.Results: The low-DSP group (<290 m%) had significantly higher values for the mMRC, CAT, AECOPD frequency, and LAA% at different lung volume scales (total, right, and left), whereas it had lower values of the PFT and 6MWT parameters compared to the high-DSP group. Significant associations (with high odds ratios) were observed of the mMRC, CAT, AECOPD frequency, and PFT with low- and high-DSP groupings. Next, the risk of having AECOPD was associated with the mMRC, CAT, DSP, and LAA% (for the total, right, and left lungs).Conclusion: A lower value of the DSP was related
Tsai C-Y, Liu W-T, Hsu W-H, et al., 2023, Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events, Digital Health, Vol: 9, ISSN: 2055-2076
OBJECTIVES: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. METHODS: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. RESULTS: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. CONCLUSIONS: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.
Singh S, Rawat SS, Gupta M, et al., 2023, Deep attention network for pneumonia detection using chest X-ray images, CMC-Computers Materials & Continua, Vol: 74, Pages: 1673-1691, ISSN: 1546-2218
In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attention-aware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attention-aware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module. In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F- score of 0.92, indicating that the suggested model outperformed against the baseline models.
Long F, Majumdar A, Carter H, 2023, Understanding levels of compliance with emergency responder instructions for members of the Public involved in emergencies: Evidence from the Grenfell Tower fire, International Journal of Disaster Risk Reduction, Vol: 84, Pages: 1-11, ISSN: 2212-4209
Purpose– It is essential to understand how members of the public make decisions during emergencies. Such understanding is crucial in order to understand how emergency services can best influence positive protective behaviours. Previous research in this area has indicated that members of the public will respond both to the threat from an incident such as a fire as well the way the threat is managed by emergency responders and that this management will be crucial in increasing public willingness to comply with emergency services instructions.AimsThe study aimed to identify factors that affected the way in which those involved in the Grenfell Tower Fire behaved and develop the understanding of factors that affect public behaviour during large scale emergencies.Design/methodology/approachThis paper used 72 transcripts from the Grenfell Tower Inquiry to examine how members of the public make decisions during emergencies. The study utilised a Framework Analysis to identify themes relating to how members of the public made decisions regarding protective actions and what factors influenced these decisions.FindingsThe study identifies several key factors which influenced individuals' decision making concerning protective actions:- Uncertainty and Anxiety.- Environmental Factors in Evacuation Decision Making.- Trust.- Helping and Co-Operative Behaviours in Emergencies.Originality/valueData involving real life emergencies is extremely useful in providing support to the development of emergency procedures and training for emergency services.The research identifies several key factors which can inform a better understanding of public behaviour during emergencies.
Singh S, Singh Rawat S, Gupta M, et al., 2023, Hybrid models for breast cancer detection via transfer learning technique, Computers, Materials and Continua, Vol: 74, Pages: 3063-3083, ISSN: 1546-2218
Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this. The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease. Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms. Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues. To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost. Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper. The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images.
Cheong H-I, Lyons A, Houghton R, et al., 2023, Secondary qualitative research methodology using online data within the context of social sciences, International Journal of Qualitative Methods, Vol: 22, Pages: 1-19, ISSN: 1609-4069
Qualitative research using interviews is a crucial and established inquiry method in social sciences to ensure that the study outputs represent the researched people and area rather than those who are researching. However, first hand primary data collection is not alwayspossible, often due to external circumstances. Additionally, the use of secondary data, particularly open data, is progressively preferred to increase efficiency and gain geographical breadth. Therefore, this paper proposes a new step-by-step secondary qualitative inquiry methodology for online, publicly available interview data. Such procedural approach can help increase rigor, explicitly consider and mitigate potentialpitfalls, and expand the research community’s datasets. The 7-step methodology is based on a hybrid approach with elements of pragmatic qualitative approach, discursive grounded theory, and narrative approach and refers to research ethical principles of autonomy,equity, and diversity. One of the proposed steps is data quality assessment, which consists of quality assessment of data context and data content with a total of 16 qualitydimensions to filter the collected data. The data analysis method comprises of content analysis for dataset categorization and thematic discourse analysis to answer the pre-set research questions. Additionally, the methodology covers the ethical and legal considerations relating to secondary online data and reporting the research findings of such data. By providing an example in the field of forced migration, we demonstrate how themethodology provides structure when conducting secondary qualitative research.
Shehzad F, Attique Khan M, Asfand E Yar M, et al., 2022, Two-stream deep learning architecture-based human action recognition, Computers, Materials and Continua, Vol: 74, Pages: 5931-5949, ISSN: 1546-2218
Human action recognition (HAR) based on Artificial intelligence reasoning is the most important research area in computer vision. Big breakthroughs in this field have been observed in the last few years; additionally, the interest in research in this field is evolving, such as understanding of actions and scenes, studying human joints, and human posture recognition. Many HAR techniques are introduced in the literature. Nonetheless, the challenge of redundant and irrelevant features reduces recognition accuracy. They also faced a few other challenges, such as differing perspectives, environmental conditions, and temporal variations, among others. In this work, a deep learning and improved whale optimization algorithm based framework is proposed for HAR. The proposed framework consists of a few core stages i.e., frames initial preprocessing, fine-tuned pre-trained deep learning models through transfer learning (TL), features fusion using modified serial based approach, and improved whale optimization based best features selection for final classification. Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets. The fusion process increases the length of feature vectors; therefore, improved whale optimization algorithm is proposed and selects the best features. The best selected features are finally classified using machine learning (ML) classifiers. Four publicly accessible datasets such as Ut-interaction, Hollywood, Free Viewpoint Action Recognition using Motion History Volumes (IXMAS), and centre of computer vision (UCF) Sports, are employed and achieved the testing accuracy of 100%, 99.9%, 99.1%, and 100% respectively. Comparison with state of the art techniques (SOTA), the proposed method showed the improved accuracy.
Tsai C-Y, Majumdar A, Wang Y, et al., 2022, Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers., Int J Occup Saf Ergon, Pages: 1-11
Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.
Abbas Y, Martinetti A, Houghton R, et al., 2022, Disentangling large scale technological projects: learning from ERTMS roll-out case study in the Netherlands, Research in Transportation Business & Management, Vol: 45, ISSN: 2210-5395
To avoid project delays and cost overruns, it is crucial that organisations' knowledge base on project management is expanded and that experiences in large-scale projects are more widely shared through effective management of lessons learned. However, the management of lessons learned from large scale projects remains a scientific and practical challenge. In addition to their multi-stakeholder setting, the natural loss of experienced knowledge through retirement and the presence of lessons in both tacit and explicit form make their management quite complex. This paper investigates the shortcomings in the current lessons learned management process and contributes to the literature by presenting a formal process for extracting and managing lessons learned from large scale projects. It does this by conducting a case study on the deployment of the European Traffic Management System (ERTMS) in the Netherlands. A nuanced five-step methodological approach is presented, whereby twenty-four structured interviews are conducted with key stakeholders from the Dutch railway sector. The interview data is analysed using the content analysis method and the results are then validated by three independent assessors. To ensure rigour and quality of gathered results, the interrater reliability is determined by calculating Gwet's AC1 coefficients. The paper provides an overview of the key lessons learned from the deployment of ERTMS in the Netherlands and sets out fourteen principles for effective systems management of ERTMS. Finally, the paper also offers six recommendations for policymakers related to their role, such as appointing an independent system integrator, and five recommendations for railways organisations on organisational learning and system integration. The results can be used both to facilitate communication for better decision-making and to develop strategies for effective management of lessons learned.
Tsai C-Y, Wu S-M, Kuan Y-C, et al., 2022, Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study., Frontiers in Neurology, Vol: 13, Pages: 1-11, ISSN: 1664-2295
OBJECTIVES: Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD. METHODS: Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aβ42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared. RESULTS: We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product. CONCLUSIONS: Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.
Mohan R, Kadry S, Rajinikanth V, et al., 2022, Automatic detection of tuberculosis using VGG19 with seagull-algorithm., Life (Basel), Vol: 12, Pages: 1-17, ISSN: 2075-1729
Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier.
Tsai C-Y, Huang H-T, Cheng H-C, et al., 2022, Screening for obstructive sleep apnea risk by using machine learning approaches and anthropometric features., Sensors (Basel, Switzerland), Vol: 22, Pages: 1-15, ISSN: 1424-8220
Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.
<sec> <title>BACKGROUND</title> <p>Obstructive sleep apnea (OSA) is sleep-disordered breathing and is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features (e.g., symptoms or risk factors of OSA).</p> </sec> <sec> <title>METHODS</title> <p>This retrospective study collected data on 3629 patients from Taiwan, who had undergone PSG for symptoms of OSA. Their baseline characteristics, anthropometric measures, and PSG data were obtained. The number of snoring events of PSG was further derived, and correlations among the collected variables were investigated. Next, this study utilized six common supervised machine learning techniques to establish OSA risk screening models, including random forest (RF), XGBoost, k-nearest neighbors, support vector machine, logistic regression, and naïve Bayes. First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach which had the highest accuracy in the training and validation phase was employed to perform the classification for the test dataset. Moreover, the feature importance of employed models was determined by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.</p> </sec> <sec> <title>RESULTS</title> <p>RF models manifested the
Tsai C-Y, Lin Y, Liu W-T, et al., 2022, Aberrant driving behavior prediction for urban bus drivers in Taiwan using heart rate variability and various machine learning approaches: a pilot study, Transportation Research Record, Pages: 1-17, ISSN: 0361-1981
Objective: Aberrant driving behavior (ADB) decreases road safety and is particularly relevant for urban bus drivers, who are required to drive daily shifts of considerable duration. Although numerous frameworks based on human physiological features have been applied to predict ADB, the research remains at an early stage. This study used heart rate variability (HRV) parameters to establish ADB occurrence prediction models with various machine learning approaches. Methods: Twelve Taiwanese urban bus drivers were recruited for four consecutive days of naturalistic driving data collection (from their routine routes) between March and April 2020; driving behaviors and physiological signals were obtained from provided devices. Weather and traffic congestion information was determined from public data, while sleep quality and professional driving experience were self-reported. To develop the ADB prediction model, several machine learning models—logistic regression, random forest, naive Bayes, support vector machine, and gated recurrent unit (GRU)—were trained and 10-fold cross-validated by using the testing data. Results: Most drivers with ADB reported deficient sleep quality (≤80%), with significantly higher mean scores on the Karolinska Sleepiness Scale and driver behavior questionnaire subcategory of lapses and errors than drivers without ADB. Next, HRV indices significantly differed between the measurement of a pre-ADB event and a baseline. The accuracy of the GRU models ranged from 78.84% ± 1.49% to 89.57% ± 1.31%. Conclusion: Drivers with ADB tend to have inadequate sleep quality, which may increase their fatigue levels and impair driving performance. The established time-series models can be considered for ADB occurrence prediction among urban bus drivers.
Tsai C-Y, Liu W-T, Lin Y-T, et al., 2022, Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile., Inform Health Soc Care, Vol: 47, Pages: 373-388
(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.
Abbas S, Attique Khan M, Alhaisoni M, et al., 2022, Crops leaf diseases recognition: a framework of optimum deep learning features, Computers, Materials and Continua, Vol: 74, Pages: 1139-1159, ISSN: 1546-2218
Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time.
Yang L, Majumdar A, van Dam KH, et al., 2022, Theories and practices for reconciling transport, public space, and people - a review, PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-MUNICIPAL ENGINEER, Vol: 175, Pages: 187-203, ISSN: 0965-0903
Magnusdottir EH, Johannsdottir KR, Majumdar A, et al., 2022, Assessing cognitive workload using cardiovascular measures and voice, Sensors, Vol: 22, Pages: 1-17, ISSN: 1424-8220
Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.
Long F, Carter H, Majumdar A, 2022, Casualty behaviours during incidents involving hazardous materials, Safety Science, Vol: 152, Pages: 105758-105758, ISSN: 0925-7535
PurposeHuman Behaviour during emergency situations is a crucial component of any response. The ability of responders to effectively engage with casualties is critical to ensuring that any instructions given are followed and in doing assist rather than hinder the response. In order to improve the likelihood of this occurring it is essential to understand what drives decision making during emergencies in order to be able to effectively influence these. This paper will seek to establish what these behaviours are likely to be and what is likely to influence these in order to inform responder tactics and training.Design/methodology/approachThis paper seeks to develop a psychological model of casualty behaviour during a hazardous materials evacuation. The study utilises a survey of members of the public evacuated from their homes or places of work due to a fire impacting an ammonia tank in February 2019 at the Ocado distribution warehouse in Andover. The results of this survey were used to validate a hypothesised psychological model utilising Path Analysis.FindingsThe research identifies the importance of recognising the ability of casualties involved in emergency situations to remain rational and utilise information and instructions given to them. The paper highlights the importance which trust plays in engaging with casualties in order to provide effective information and instructions and how trust is constructed of both legitimacy and competency and influenced by the communications of responders. Most crucially the paper identified how trust during an emergency situation is the key driver of whether casualties are likely to co-operate with instructions and emergency responders.Originality/valueThe research utilised real world data to validate findings demonstrating the need for emergency responders to effectively engage with casualties and has implications for both guidance and training of emergency responders in managing casualties.
Rahman AU, Saeed M, Mohammed MA, et al., 2022, Supplier selection through multicriteria decision-making algorithmic approach based on rough approximation of fuzzy hypersoft sets for construction project, Buildings, Vol: 12, Pages: 1-20, ISSN: 2075-5309
The suppliers play a significant role in supply chain management. In supplier selection, factors like market-based exposure, community-based reputation, trust-based status, etc., must be considered, along with the opinions of hired experts. These factors are usually termed as rough information. Most of the literature has disregarded such factors, which may lead to a biased selection. In this study, linguistic variables in terms of triangular fuzzy numbers (TrFn) are used to manage such kind of rough information, then the rough approximations of the fuzzy hypersoft set (FHS-set) are characterized which are capable of handling such informational uncertainties. The FHS-set is more flexible as well as consistent as it tackles the limitation of fuzzy soft sets regarding categorizing parameters into their related sub-classes having their sub-parametric values. Based on these rough approximations, an algorithm is proposed for the optimal selection of suppliers by managing experts’ opinions and rough information collectively in the form of TrFn-based linguistic variables. To have a discrete decision, a signed distance method is employed to transform the TrFn-based opinions of experts into fuzzy grades. The proposed algorithm is corroborated with the help of a multi-criteria decision-making application to choose the best supplier for real estate builders. The beneficial facets of the put forward study are appraised through its structural comparison with few existing related approaches. The presented approach is consistent as it is capable to manage rough information and expert’s opinions about suppliers collectively by using rough approximations of FHS-set.
Abbas T, Ali SF, Mohammed MA, et al., 2022, Deep learning approach based on residual neural network and SVM classifier for driver’s distraction detection, Applied Sciences, Vol: 12, Pages: 6626-6626, ISSN: 2076-3417
In the last decade, distraction detection of a driver gained a lot of significance due to increases in the number of accidents. Many solutions, such as feature based, statistical, holistic, etc., have been proposed to solve this problem. With the advent of high processing power at cheaper costs, deep learning-based driver distraction detection techniques have shown promising results. The study proposes ReSVM, an approach combining deep features of ResNet-50 with the SVM classifier, for distraction detection of a driver. ReSVM is compared with six state-of-the-art approaches on four datasets, namely: State Farm Distracted Driver Detection, Boston University, DrivFace, and FT-UMT. Experiments demonstrate that ReSVM outperforms the existing approaches and achieves a classification accuracy as high as 95.5%. The study also compares ReSVM with its variants on the aforementioned datasets
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