Engineering and Information Technology Collected Works - Research Publications
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ItemNo Preview AvailableEditorial: Special issue on operations research and machine learning(Taylor and Francis Group, 2024)Many machine learning techniques work through optimizing specific objective functions. Supervised learning techniques are to minimize the prediction error such as mean square error (MSE) and misclassification rate, or maximize the conditional likelihood, posterior probability, etc. Unsupervised learning techniques usually group instances into clusters in a way that instances within each group are optimally similar while they are distant from instances in other groups. In reinforcement learning, the goal of an agent is to maximize its cumulative reward. However, there is still room to exploit optimization and operations research (OR) in machine learning, and vice versa.
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ItemAn operational planning for emergency medical services considering the application of IoT(Springer, 2023)In the last two years, the worldwide outbreak of the COVID-19 pandemic and the resulting heavy casualties have highlighted the importance of further research in healthcare. In addition, the advent of new technologies such as the Internet of Things (IoT) and their applications in preventing and detecting casualty cases has attracted a lot of attention. The IoT is able to help organize medical services by collecting significant amounts of data and information. This paper proposes a novel mathematical model for Emergency Medical Services (EMS) using the IoT. The proposed model is designed in two phases. In the first phase, the data is collected by the IoT, and the demands for ambulances are categorized and prioritized. Then in the second phase, ambulances are allocated to demand areas (patients). Two main objectives of the proposed model are reducing total costs and the mortality risk due to lack of timely service. In addition, demand uncertainty for ambulances is considered with various scenarios at demand levels. Numerical experiments have been conducted on actual data from a case study in Kermanshah, Iran. Due to the NP-hard nature of the mathematical model, three meta-heuristic algorithms Multi-Objective Simulated Annealing (MOSA) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, and L-MOPSO have been used to solve the proposed model on medium and large scales in addition to the exact solution method. The results show that the proposed model significantly reduces mortality risk, in addition to reducing total cost. Data analysis also led to useful managerial insights.
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ItemA Diversity-Based Synthetic Oversampling Using Clustering for Handling Extreme Imbalance(Springer, 2023)Imbalanced data are typically observed in many real-life classification problems. However, mainstream machine learning algorithms are mostly designed with the underlying assumption of a relatively well-balanced distribution of classes. The mismatch between reality and algorithm assumption results in a deterioration of classification performance. One form of approach to address this problem is through re-sampling methods, although its effectiveness is limited; most re-sampling methods fail to consider the distribution of minority and majority instances and the diversity within synthetically generated data. Diversity becomes increasingly important when minority data becomes more sparse, as each data point becomes more valuable. They should all be considered during the generation process instead of being regarded as noise. In this paper, we propose a cluster-based diversity re-sampling method, combined with NOAH algorithm. Neighbourhood-based Clustering Diversity Over-sampling (NBCDO) is introduced with the aim to complement our previous cluster-based diversity algorithm Density-based Clustering Diversity Over-sampling (DBCDO). It first uses a neighbourhood-based clustering algorithm to consider the distribution of both minority and majority class instances, before applying NOAH algorithm to encourage diversity optimisation during the generation of synthetic instances. We demonstrate the implementation of both cluster-based diversity methods by conducting experiments over 10 real-life datasets with ≤ 5% imbalance ratio and show that our proposed cluster-based diversity algorithm (NBCDO, DBCDO) brings performance improvements over its comparable methods (DB-SMOTE, MAHAKIL, KMEANS-SMOTE, MC-SMOTE).
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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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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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ItemEnergy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization(NATURE PORTFOLIO, 2023-01-05)In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov-Smirnov, Wilcoxon, and Kruskal-Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems.
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ItemA parametric similarity measure for extended picture fuzzy sets and its application in pattern recognition(University of Sistan & Baluchestan, 2022-11-01)This article advances the idea of extended picture fuzzy set (E-PFS), which is especially an augmentation of generalised spherical fuzzy set (GSFS) by releasing the restricted selection of p in the description of GSFSs. Moreover, by the use of triangular conorm term in the description of E-PFS, it indeed widens the scope of E-PFS not only compared to picture fuzzy set (PFS) and spherical fuzzy set (SFS), but also to GSFS. In the sequel, a given fundamental theorem concerning E-PFS depicts its more ability in comparison with the special types to deal with the ambiguity and uncertainty. Further, we propose a parametric E-PFS similarity measure which plays a critical role in information theory. In order for revealing the advantages and authenticity of E-PFS similarity measure, we exhibit its applicability in multiple criteria decision making entitling the recognition of building material, the recognition of patterns, and the selection process of mega project(s) in developing countries. Furthermore, through the experimental studies, we demonstrate that E-PFS is able to handle uncertain information in real-life decision procedures with no extra parameter, and it has a prominent role in decision making whenever the concepts of PFS, SFS and GSFS do not make sense.
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ItemExpert-Machine Collaborative Decision Making: We Need Healthy Competition(IEEE COMPUTER SOC, 2022-09-01)Much has been written and discussed in previous years about human-AI interaction. However, the debate so far has mainly concentrated on "Aaverage" decision makers, neglecting important differences when it is experts who require support. In this article, we are going to talk about expert-machine collaboration for decision-making. We investigate the current approaches for expert decision support and exemplify the inefficiency of this approach for a real clinical decision-making problem. We propose two solutions for expert-machine collaboration to overcome the shortcomings of the current state of the art. We think that the proposed approaches open new horizons for expert-machine collaborative decision-making.
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ItemA cooperative robust human resource allocation problem for healthcare systems for disaster management(Elsevier, 2022-08-01)Similar to other human-made institutes, healthcare systems often experience post-disaster disruptions in performance, which can pose significant threats to the people's lives in the affected zone. In this study, we develop a cooperative game theory approach to alleviate the negative impacts of such catastrophic events, minimize normal hospital service levels, and reduce undesired expenses. Hence, we propose a linear robust formulation to enable the observation of collaborative behaviors among medical centers, including transferring staff, beds, and patients between hospitals. In our proposed model, information uncertainty is considered the right-hand side parameter (i.e., as coefficients for the decision variables of the constraints). Moreover, the existence of a core in the developed game structure is investigated to demonstrate the stability of the developed cooperative structure. Finally, we generated many numerical examples to evaluate the performance of the model under various circumstances and presented a number of managerial insights.