Engineering and Information Technology Collected Works - Research Publications
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ItemA Lightweight Window Portion-Based Multiple Imputation for Extreme Missing Gaps in IoT Systems(Institute of Electrical and Electronics Engineers (IEEE), 2023)Intelligent techniques, including artificial intelligence and deep learning, normally perform on complete data without missing data. Multiple imputation is indispensable for addressing missing data resulting in unbiased estimates and dealing with uncertainty by providing more valid results. Most state-of-the-art techniques focus on high missing rates (around 50%-60%) and short missing gaps, while imputation for extreme missing gaps and missing rates is an important challenge for multivariate time-series data generated through the Internet of Things (IoT). Hence, we propose a Lightweight Window Portion-based Multiple Imputation (LWPMI) based on multivariate variables, correlation, data fusion, regression, and multiple imputations. We conduct extensive experiments by generating extreme missing gaps and high missing rates ranging from 10% to 90% on data generated by sensors. We also investigate different sets of feature to examine how LWPMI works when features have high, weak, or a mixture of high and weak correlation. All the obtained results prove LWPMI outperforms baseline techniques in preserving pattern, structure, and trend in both 90% extreme missing gap and missing rates.
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ItemPeter Lilienthal. Von Koffern und Tätern(Carl Hanser Verlag, 2023)
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ItemNo Preview AvailableTeatro Lautaro in Deutschland gestern und heute. Körperlichkeit, Rhythmus und Farbe(Theater der Zeit, 2023)
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ItemActress, Ballerina... Engineer?( 2023)Engineering needs more diversity, but there are almost no role models for women engineers in popular culture.
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ItemGuest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning(Institute of Electrical and Electronics Engineers (IEEE), 2023-08)We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in machine learning. Every machine-learning technique has hyperparameters that can be tuned using evolutionary computation and optimization, considering normally multiple criteria, such as bias, variance, complexity, and fairness in model selection. Multiobjective evolutionary optimization can help meet these criteria for optimizing machine-learning models. Some of the existing approaches address these multiple criteria by transforming the problem into a single-objective optimization problem. However, multiobjective optimization models are able to outperform single-objective ones in contributing to multiple intended objectives (criteria). In recent years, evolutionary computation has been shown to be the premier method for solving multiobjective optimization problems (MOPs), producing both optimal and diverse solutions beyond the capabilities of other heuristics. This is particularly true for very large solution spaces, which is the case in real-world machine-learning problems with many features.
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ItemEvaluation of the Early Intervention Physiotherapist Framework for Injured Workers in Victoria, Australia: Data Analysis Follow-Up(MDPI, 2023-08)PURPOSE: This study evaluates the performance of the Early Intervention Physiotherapist Framework (EIPF) for injured workers. This study provides a proper follow-up period (3 years) to examine the impacts of the EIPF program on injury outcomes such as return to work (RTW) and time to RTW. This study also identifies the factors influencing the outcomes. METHODS: The study was conducted on data collected from compensation claims of people who were injured at work in Victoria, Australia. Injured workers who commenced their compensation claims after the first of January 2010 and had their initial physiotherapy consultation after the first of August 2014 are included. To conduct the comparison, we divided the injured workers into two groups: physiotherapy services provided by EIPF-trained physiotherapists (EP) and regular physiotherapists (RP) over the three-year intervention period. We used three different statistical analysis methods to evaluate the performance of the EIPF program. We used descriptive statistics to compare two groups based on physiotherapy services and injury outcomes. We also completed survival analysis using Kaplan-Meier curves in terms of time to RTW. We developed univariate and multivariate regression models to investigate whether the difference in outcomes was achieved after adjusting for significantly associated variables. RESULTS: The results showed that physiotherapists in the EP group, on average, dealt with more claims (over twice as many) than those in the RP group. Time to RTW for the injured workers treated by the EP group was significantly lower than for those who were treated by the RP group, indicated by descriptive, survival, and regression analyses. Earlier intervention by physiotherapists led to earlier RTW. CONCLUSION: This evaluation showed that the EIPF program achieved successful injury outcomes three years after implementation. Motivating physiotherapists to intervene earlier in the recovery process of injured workers through initial consultation helps to improve injury outcomes.
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ItemUncertainty in Selective Bagging: A Dynamic Bi-objective Optimization Model(Society for Industrial and Applied Mathematics, 2023-01)Bagging is a common approach in ensemble learning that generates a group of classifiers through bootstrapping for classification tasks. Despite its wide applications, generating redundant classifiers remains a central challenge in bagging. In recent years, many selective bagging models have been presented to deal with this challenge. These models mostly focused on the accuracy of classifiers and the diversity among them. Despite the importance of uncertainty in the performance of ensemble classifiers, this criterion has been neglected in selective bagging models. In this paper, we propose a two-stage selective bagging model. In the first stage, we formalize the selective bagging problem as a bi-objective optimization model considering both the uncertainty and accuracy of classifiers. We propose an adaptive evolutionary Two-Arch2 algorithm, named Diverse-Two-Arch2, to solve the bi-objective model. The output of this stage is a subset of classifiers that are diverse, certain about correct predictions, and uncertain about incorrect predictions. While most selective bagging models focus on the selection of a fixed subset of classifiers for all test samples (static approach), our proposed model has a dynamic approach to the selection process. So, in the second stage of the model, we select only certain classifiers to make an ensemble prediction for each test sample. Experimental results on twenty data sets and comparing with two ensemble models, and five state-of-the-art dynamic selective bagging models show the outperformance of the proposed model. We also compare the performance of the proposed Diverse-Two-Arch2 to alternative evolutionary computation methods.
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ItemTime-Sensitive Cancellation Refund in Advance Booking: Effect of Online-to-Offline Marketing Policy(Elsevier BV, 2023-07)Due to heightened competition, current selling approaches are rapidly advancing as firms aim to enhance customer satisfaction to increase their market share and extend their customers’ zone. As an efficient advertising method, the online-to-offline (O2O) marketing strategy is extensively used by most of the competing vendors who tend to sell their commodities via advertisement in both virtual and traditional stores. By doing it this way, they can reach both online and offline-oriented customers, who prefer to buy the required commodities from online stores and real shopping centres respectively. In this research, we study the behaviour of two complementary firms in the airline and hotel industry using an advance booking policy. Here, it is supposed that cancellation is allowed and customers can enjoy a partial refund paid by the firms selling their products via both online and offline stores. The principal goal of this study is to investigate the behaviour of the partners of firms with/without an O2O strategy where the firms use partial refund for cancelled orders under time-sensitive cancellation rates. The optimal selling prices of commodities in advance and in a spot selling period are the decision variables, whose values are examined by numerical examples and are assessed using sensitivity analyses.
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ItemA deep reinforcement learning hyper-heuristic with feature fusion for online packing problems(PERGAMON-ELSEVIER SCIENCE LTD, 2023-11-15)In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning.
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ItemThe use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey.(Public Library of Science (PLoS), 2022-08)OBJECTIVES: To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups. METHODS: An online cross-sectional survey was conducted during June-September 2020. The survey was developed and reviewed independently by co-authors to establish face validity. Associations between using mobile apps and fitness trackers and health behaviors were examined using multivariate logistic regression models. Subgroup analyses were conducted using Chi-square and Fisher's exact tests. Three open-ended questions were included to elicit participants' views; thematic analysis was conducted. RESULTS: Participants included 552 adults (76.7% women; mean age: 38±13.6 years); 59.9% used mobile apps for health, 38.2% used fitness trackers, and 46.3% used COVID-19 apps. Users of mobile apps or fitness trackers had almost two times the odds of meeting aerobic physical activity guidelines compared to non-users (odds ratio = 1.91, 95% confidence interval 1.07 to 3.46, P = .03). More women used health apps than men (64.0% vs 46.8%, P = .004). Compared to people aged 18-44 (46.1%), more people aged 60+ (74.5%) and more people aged 45-60 (57.6%) used a COVID-19 related app (P < .001). Qualitative data suggest people viewed technologies (especially social media) as a 'double-edged sword': helping with maintaining a sense of normalcy and staying active and socially connected, but also having a negative emotional effect stemming from seeing COVID-related news. People also found that mobile apps did not adapt quickly enough to the circumstances caused by COVID-19. CONCLUSIONS: Use of mobile apps and fitness trackers during the pandemic was associated with higher levels of physical activity, in a sample of educated and likely health-conscious individuals. Future research is needed to understand whether the association between using mobile devices and physical activity is maintained in the long-term.