Dr Mohsen Naderpour is a Lecturer at the School of Information, Systems and Modelling in FEIT. Additionally, he is a core member of the Centre for Artificial Intelligence (CAI), and the Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS). Mohsen received his PhD degree from UTS, Master of Science degree in Industrial Engineering from the Iran University of Science and Technology (IUST), Tehran, Iran, and a Bachelor of Science degree in Applied Mathematics from Ferdowsi University of Mashhad, Mashhad, Iran. His research interests lie in the areas of human factors, decision support systems, uncertain information processing, and risk and safety-related systems.
Recently, Mohsen was awarded UTS’s top honours for teaching excellence, an Individual Teaching Award 2019, 'for developing risk awareness in engineering students to promote sustainability’.
Before joining UTS, Mohsen was with the National Iranian Oil Refining and Distribution Company, a part of Iran Ministry of Petroleum, for 4 years. He worked as a Senior Expert of Industrial Engineering in the Structural Engineering Division where his responsibilities mainly focused on developing, improving, implementing and evaluating systems and processes.
Prior to this, he worked as a Transportation Safety Expert for Iran Ministry of Roads and Urban Development. In the department of transportation safety, he has conducted several professional research studies on road and railway safety.
Can supervise: YES
- Decision Support Systems
- Uncertain Information Processing
- Risk and Safety-Related Systems
- Human Factors
- Systems Engineering for Managers (49004)
- Risk Management in Engineering (49006)
- Business Intelligence (32558)
Siami, M, Naderpour, M & Lu, J 2020, 'A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction', IEEE Transactions on Intelligent Transportation Systems, pp. 1-14.View/Download from: UTS OPUS or Publisher's site
Daniel, J, Naderpour, M & Lin, CT 2019, 'A Fuzzy Multilayer Assessment Method for EFQM', IEEE Transactions on Fuzzy Systems, vol. 27, no. 6, pp. 1252-1262.View/Download from: UTS OPUS or Publisher's site
© 1993-2012 IEEE. Although the European Foundation for Quality Management (EFQM) is one of the best-known business excellence frameworks, its inherent self-assessment approaches have several limitations. A critical review of self-assessment models reveals that most models are ambiguous and limited to precise data. In addition, the impact of expert knowledge on scoring is overly subjective, and most methodologies assume the relationships between variables are linear. This paper presents a new fuzzy multilayer assessment method that relies on fuzzy inference systems to accommodate imprecise data and varying assessor experiences to overcome uncertainty and complexity in the EFQM model. The method was implemented, tested, and verified under real conditions at a regional electricity company. The case was assessed by internal company experts and external assessors from an EFQM business excellence organization and the model was implemented using MATLAB software. When comparing the classical model with the new model, assessors and experts favored outputs from the new model.
© 2019, Springer Science+Business Media, LLC, part of Springer Nature. One of the significant challenges for cloud providers is how to manage resources wisely and how to form a viable service level agreement (SLA) with consumers to avoid any violation or penalties. Some consumers make an agreement for a fixed amount of resources, these being the required resources that are needed to execute its business. Consumers may need additional resources on top of these fixed resources, known as– marginal resources that are only consumed and paid for in case of an increase in business demand. In such contracts, both parties agree on a pricing model in which a consumer pays upfront only for the fixed resources and pays for the marginal resources when they are used. A marginal resource allocation is a challenge for service provider particularly small- to medium-sized service providers as it can affect the usage of their resources and consequently their profits. This paper proposes a novel marginal resource allocation decision support model to assist cloud providers to manage the cloud SLAs before its execution, covering all possible scenarios, including whether a consumer is new or not, and whether the consumer requests the same or different marginal resources. The model relies on the capabilities of the user-based collaborative filtering method with an enhanced top-k nearest neighbor algorithm and a fuzzy logic system to make a decision. The proposed framework assists cloud providers manage their resources in an optimal way and avoid violations or penalties. Finally, the performance of the proposed model is shown through a cloud scenario which demonstrates that our proposed approach can assists cloud providers to manage their resources wisely to avoid violations.
Sohaib, O, Naderpour, M, Hussain, W & Martinez, L 2019, 'Cloud Computing Model Selection for E-commerce Enterprises Using a New 2-tuple Fuzzy Linguistic Decision-Making Method', Computers & Industrial Engineering, vol. 132, pp. 47-47.View/Download from: UTS OPUS or Publisher's site
Naderpour, M, Rizeei, HM, Khakzad, N & Pradhan, B 2019, 'Forest fire induced Natech risk assessment: A survey of geospatial technologies', Reliability Engineering and System Safety, vol. 191.View/Download from: UTS OPUS or Publisher's site
Cruz, CG, Naderpour, M & Ramezani, F 2018, 'Water resource selection and optimisation for shale gas developments in Australia: A combinatorial approach', Computers & Industrial Engineering, vol. 124, pp. 1-11.View/Download from: UTS OPUS or Publisher's site
Kamyabniya, A, Lotfi, MM, Naderpour, M & Yih, Y 2018, 'Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach', Information Systems Frontiers, vol. 20, no. 4, pp. 759-782.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer Science+Business Media, LLC. Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.
Naderpour, M & Khakzad, N 2018, 'Texas LPG fire: Domino effects triggered by natural hazards', Process Safety and Environmental Protection, vol. 116, pp. 354-364.View/Download from: UTS OPUS or Publisher's site
Namvar, A, Siami, M, Rabhi, F & Naderpour, M 2018, 'Credit risk prediction in an imbalanced social lending environment', International Journal of Computational Intelligence Systems, vol. 11, no. 1, pp. 925-925.View/Download from: UTS OPUS or Publisher's site
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.
Khakzad, N, Naderpour, M & Reniers, G 2017, 'A Markov chain approach to domino effects in chemical plants', Journal of safety, health & environmental research, vol. 13, no. 2, pp. 360-369.View/Download from: UTS OPUS
Naderpour, M, Lu, J & Zhang, G 2016, 'A safety-critical decision support system evaluation using situation awareness and workload measures', RELIABILITY ENGINEERING & SYSTEM SAFETY, vol. 150, pp. 147-159.View/Download from: UTS OPUS or Publisher's site
Naderpour, M, Nazir, S & Lu, J 2015, 'The role of situation awareness in accidents of large-scale technological systems', PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, vol. 97, pp. 13-24.View/Download from: UTS OPUS or Publisher's site
Naderpour, M, Lu, J & Zhang, GQ 2015, 'An Abnormal Situation Modeling Method to Assist Operators in Safety-Critical Systems', Reliability Engineering and System Safety, vol. 133, pp. 33-47.View/Download from: UTS OPUS or Publisher's site
One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.
Naderpour, M, Lu, J & Zhang, G 2014, 'An Intelligent Situation Awareness Support System for Safety-Critical Environments', Decision Support Systems, vol. 59, pp. 325-340.View/Download from: UTS OPUS or Publisher's site
Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing error.However, existing systemsafety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of fourmajor elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to he...
Naderpour, M, Lu, J & Zhang, G 2014, 'The explosion at Institute: Modeling and analyzing the situation awareness factor.', Accident; analysis and prevention, vol. 73C, pp. 209-224.View/Download from: UTS OPUS or Publisher's site
In 2008 a runaway chemical reaction caused an explosion at a methomyl unit in West Virginia, USA, killing two employees, injuring eight people, evacuating more than 40,000 residents adjacent to the facility, disrupting traffic on a nearby highway and causing significant business loss and interruption. Although the accident was formally investigated, the role of the situation awareness (SA) factor, i.e., a correct understanding of the situation, and appropriate models to maintain SA, remain unexplained. This paper extracts details of abnormal situations within the methomyl unit and models them into a situational network using dynamic Bayesian networks. A fuzzy logic system is used to resemble the operator's thinking when confronted with these abnormal situations. The combined situational network and fuzzy logic system make it possible for the operator to assess such situations dynamically to achieve accurate SA. The findings show that the proposed structure provides a useful graphical model that facilitates the inclusion of prior background knowledge and the updating of this knowledge when new information is available from monitoring systems.
Naderpour, M & Lu, J 2013, 'A Human Situation Awareness Support System to Avoid Technological Disasters' in Vitoriano, B, Montero, J & Ruan, D (eds), Decision Aid Models for Disaster Management and Emergencies, Atlantis Press, Paris, France, pp. 307-325.View/Download from: UTS OPUS or Publisher's site
In many complex technological systems, accidents have primarily been attributed to human error. In the majority of these accidents the human operators were striving against significant challenges. They have to face data overload, the challenge of working with a complex system and the stressful task of understanding what is going on in the situation. Therefore, to design and implement complex technological systems where the information flow is quite high, and poor decisions may lead to serious consequences, Situation Awareness (SA) should be appropriately considered. A level 1 SA is highly supported in these systems through the various heterogeneous sensors and signal-processing methods but, for levels 2 and 3 there is still a need for concepts and methods. This work develops a system called the Human Situation Awareness Support System (HSASS) that supports the safety operators in an ever increasing amount of available risky status and alert information. The proposed system includes a new dynamic situation assessment method based on risk, which has the ability to support the operators understanding of the current state of the system, predict the near future, and suggest appropriate actions. The proposed system does not control the course of action and allows the human to act at his/her discretion in specific contexts.
Siami, M, Naderpour, M & Lu, J 2019, 'A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis', IEEE International Conference on Fuzzy Systems.View/Download from: UTS OPUS or Publisher's site
© 2019 IEEE. Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.
Dao, NHT, Daniel, J, Hutchinson, S & Naderpour, M 2017, 'Logistics and Supply Chain Management Investigation: A Case Study', Service Research and Innovation, Sixth Australasian Symposium on Service Research and Innovation, Springer International Publishing, Sydney, Australia, pp. 216-230.View/Download from: UTS OPUS or Publisher's site
This paper investigates several aspects of logistics and supply chain management such as advantages of a full model of logistics and supply chain management. In addition, it also details a series of challenges in logistics and supply chain management in general and in the computer and video game industry in particular. It also focuses on some popular models and the common trend in logistics and supply chain management. Especially, it analyses the logistics and supply chain model of Ubisoft Australia – a computer and video game publisher. By conducting interviews and observations together with gathering company internal records, it points out some potential problems of Ubisoft Australia with the software system, communication and information flow in inbound logistic and non-conforming returns. Finally, several recommendations are made for future improvements.
Namvar, A & Naderpour, M 2018, 'Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model', 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2018 Proceedings, IEEE International Conference on Fuzzy Systems, IEEE, Brazil.View/Download from: UTS OPUS or Publisher's site
Sohaib, O, Naderpour, M & Hussain, W 2018, 'SaaS E-Commerce Platforms Web Accessibility Evaluation', IEEE International Conference on Fuzzy Systems, International Conference on Fuzzy Systems, IEEE, Rio de Janeiro, Brazil.View/Download from: UTS OPUS or Publisher's site
Ramezani, F & Naderpour, M 2017, 'A fuzzy virtual machine workload prediction method for cloud environments', IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy.View/Download from: UTS OPUS or Publisher's site
Due to the dynamic nature of cloud environments, the workload of virtual machines (VMs) fluctuates leading to imbalanced loads and utilization of virtual and physical cloud resources. It is, therefore, essential that cloud providers accurately forecast VM performance and resource utilization so they can appropriately manage their assets to deliver better quality cloud services on demand. Current workload and resource prediction methods forecast the workload or CPU utilization pattern of the given web-based applications based on their historical data. This gives cloud providers an indication of the required number of resources (VMs or CPUs) for these applications to optimize resource allocation for software as a service (SaaS) or platform as a service (PaaS), reducing their service costs. However, historical data cannot be used as the only data source for VM workload predictions as it may not be available in every situation. Nor can historical data provide information about sudden and unexpected peaks in user demand. To solve these issues, we have developed a fuzzy workload prediction method that monitors both historical and current VM CPU utilization and workload to predict VMs that are likely to be performing poorly. This model can also predict the utilization of physical machine (PM) resources for virtual resource discovery.
Namvar, A, Ghazanfari, M & Naderpour, M 2017, 'A customer segmentation framework for targeted marketing in telecommunication', 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, China, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
Siaminamini, M, Naderpour, M & Lu, J 2017, 'Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model', Australasian Conference on Information Systems, Australasian Conference on Information Systems, ACIS, Hobart, Australia, pp. 1-8.View/Download from: UTS OPUS
In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area.
R. Kridalukmana, H. Y. Lu & M. Naderpour 2017, 'An object oriented Bayesian network approach for unsafe driving maneuvers prevention system', 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), International Conference on Intelligent Systems and Knowledge Engineering, IEEE, Nanjing, China, pp. 1-6.View/Download from: UTS OPUS or Publisher's site
As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers.
Sohaib, O & Naderpour, M 2017, 'Decision Making on Adoption of Cloud Computing in E-Commerce Using Fuzzy TOPSIS', Proceedings of 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Naples, Italy.View/Download from: UTS OPUS or Publisher's site
Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business.
Ramezani, F, Naderpour, M & Lu, J 2016, 'A multi-objective optimization model for virtual machine mapping in cloud data centres', 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, BC, Canada, pp. 1259-1265.View/Download from: UTS OPUS or Publisher's site
Modern cloud computing environments exploit virtualization for efficient resource management to reduce computational cost and energy budget. Virtual machine (VM) migration is a technique that enables flexible resource allocation and increases the computation power and communication capability within cloud data centers. VM migration helps cloud providers to successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, the VM migration process can affect the performance of applications unless it is supported by smart optimization methods. This paper presents a multi-objective optimization model to address this issue. The objectives are to minimize power consumption, maximize resource utilization (or minimize idle resources), and minimize VM transfer time. Fuzzy particle swarm optimization (PSO), which improves the efficiency of conventional PSO by using fuzzy logic systems, is relied upon to solve the optimization problem. The model is implemented in a cloud simulator to investigate its performance, and the results verify the performance improvement of the proposed model.
Alharbi, S & Naderpour, M 2016, 'E-commerce development risk evaluation using MCDM techniques', Proceedings of the Decision Support Systems VI - Addressing Sustainability and Societal Challenges (Lecture Notes in Business Information Processing), International Conference on Decision Support System Technology, Springer, Plymouth, UK, pp. 88-99.View/Download from: UTS OPUS or Publisher's site
© Springer International Publishing Switzerland 2016. Electronic commerce (EC) development takes place in a complex and dynamic environment that includes high levels of risk and uncertainty. This paper proposes a new method for assessing the risks associated with EC development using multi-criteria decision-making techniques A model based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to assist EC project managers and decision makers in formalizing the types of thinking that are required in assessing the current risk environment of their EC development in a more systematic manner than previously. The solution includes the use of AHP for analyzing the problem structure and determining the weights of risk factors. The TOPSIS technique helps to obtain a final ranking among projects, and the results of an evaluation show the usefulness performance of the method.
Ramezani, F, Naderpour, M & Lu, J 2015, 'Handling Uncertainty in Cloud Resource Management Using Fuzzy Bayesian Networks', Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE International Conference on Fuzzy Systems, IEEE, Istanbul, Turkey.View/Download from: UTS OPUS or Publisher's site
The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.
Naderpour, M & Lu, J 2013, 'A Hybrid Bayesian Network for Safety of Chemical Plants', Pacific Asia Conference on Information Systems 2013 Proceedings, Pacific Asia Conference on Information Systems, AIS Electronic Library (AISeL, Jeju Island, Korea, pp. 1-12.View/Download from: UTS OPUS
In today's process systems, operators must consider an overwhelming amount of information which is passed to them via automated systems, and make decisions very quickly. Since the decision-making in a time-critical situation is extremely complicated, the use of automated systems to aid decision making is highly recommended. This paper proposes a hybrid Bayesian network (HBN) to support process operators in hazardous situations. The proposed HBN includes three parts: an evidence preparation, a situational network, and risk estimation. The evidence preparation part provides soft evidence based on the online conditions and process monitoring system. The situational network is developed based on dynamic Bayesian networks to model the hazardous situations, and the risk estimation part calculates the risk level of every situation dynamically to show whether the risk level of situations is acceptable or not. The threefold HBN is explained through a case from U.S. Chemical Safety Board (CSB) investigation report. According to the CSB report, following an operator error at a paint manufacturing plant, the explosion and subsequent fire destroyed a facility, injured ten residents, and heavily damaged dozens of nearby homes and businesses. Finally a sensitivity analysis is presented to evaluate the proposed HBN.
Naderpour, M, Lu, J & Zhang, G 2013, 'A Fuzzy Dynamic Bayesian Network-Based Situation Assessment Approach', The Proceeding of IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Hyderabad, India, pp. 1-8.View/Download from: UTS OPUS or Publisher's site
Situation awareness (SA), a state in the mind of a human, is essential to conduct decision-making activities. It is about the perception of the elements in the environment, the comprehension of their meaning, and the projection of their status in the near future. Two decades of investigation and analysis of accidents have showed that SA was behind of many serious large-scale technological systems accidents. This emphasizes the importance of SA support systems development for complex and dynamic environments. This paper presents a fuzzy dynamic Bayesian network-based situation assessment approach to support the operators in decision making process in hazardous situations. The approach includes a dynamic Bayesian network-based situational network to model the hazardous situations where the existence of the situations can be inferred by sensor observations through the SCADA monitoring system using a fuzzy quantizer method. In addition to generate the assessment result, a fuzzy risk estimation method is proposed to show the risk level of situations. Ultimately a hazardous environment from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of proposed approach.
Naderpour, M & Lu, J 2012, 'A Fuzzy Dual Expert System for Managing Situation Awareness in a Safety Supervisory System', 2012 IEEE International Conference on Fuzzy Systems, IEEE International Conference on Fuzzy Systems, IEEE, Brisbane, Australia, pp. 715-721.View/Download from: UTS OPUS or Publisher's site
Safety supervisory systems continue to increase in degree of automation and complexity as operators are decreasing. As a result, each operator must be able to comprehend and respond to an ever increasing amount of available risky status and alert information. They generally have no difficulty in performing their tasks physically but they are stressed by the task of understanding what is going on in the situation. So in the last two decades, situation awareness has been recognized as a critical foundation for successful decision making across a broad range of complex and dynamic systems. This paper develops a fuzzy dual expert system based approach to enhance situation awareness. The proposed approach has ability to support the operators understanding and assessing the situations, and to deal with uncertainties, applying fuzzy risk assessment concepts.
Naderpour, M & Lu, J 2012, 'Supporting Situation Awareness Using Neural Network and Expert System', Uncertainty Modeling in Knowledge Engineering and Decision Making, International Fuzzy Logic and Intelligent technologies in Nuclear Science Conference, World Scientific, Istanbul, Turkey, pp. 993-998.View/Download from: UTS OPUS or Publisher's site
Situation awareness (SA) is a critical factor for human decision making and performance in dynamic environments. Actually SA is a mental model of the current state of the environment and includes many types of complex systems such as safety supervisory systems. The current paper employs two focus areas including neural network and expert system for maintaining SA in a safety supervisory system. The neural network components provide adaptive mechanisms for perception, and the expert system offers the ability to support comprehension and projection.
Naderpour, M, Lu, J & Kerre, EE 2011, 'A Conceptual Model for Risk-Based Situation Awareness', Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011), International Conference on Intelligent Systems and Knowledge Engineering, Springer, Shanghai, China, pp. 297-306.View/Download from: UTS OPUS or Publisher's site
Situation Awareness is the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future. It is a crucial factor in decision-making in a dynamic environment particularly with certain degrees of risk, called risk-based situation awareness. In this paper we first explore the most popular models in situation awareness, data fusion and risk assessment. We show how they complement each other in developing a conceptual model for risk-based situation awareness. We will also demonstrate how this model can be used to support decision-making in a dynamic environment.