Computing and Information Systems - Research Publications
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ItemNye tray vs sieve tray: A comparison based on computational fluid dynamics and tray efficiency(Wiley, 2021-10)Nye and sieve trays were hydrodynamically simulated and compared. The simulations were performed in a Eulerian‐Eulerian framework under unsteady (transient) conditions at industrial scale. Conducted on an air‐water system, the simulations included three dimensions and two phases. The velocity distribution across the tray, the height of clear liquid, the froth height, and the pressure drop were investigated and compared with experimental data. Péclet number was calculated using hydrodynamic and geometric parameters. The tray efficiencies were also predicted to further compare the two trays. The results showed that the liquid flow was steadier on the Nye tray rather than the sieve tray, possibly because of the special structure of the liquid and gas inlets for the Nye tray.
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ItemComputational Fluid Dynamics versus Experiment: An Investigation on Liquid Weeping of Nye Trays(Wiley, 2021-01)The weeping phenomenon was investigated using some experimental tests and a numerical model. The tests were performed within a 1.22‐m‐diameter pilot‐scale column including two chimney trays and two Nye test trays with an air‐water system. The rates of weeping were measured in the Nye trays with two heights of the weir and a hole area of 5 %. Moreover, the weeping rates in the outlet and inlet halves of the Nye tray and the total weeping rate were calculated. In the next step, an Eulerian‐Eulerian computational fluid dynamics (CFD) technique was used. The results show good agreement between the attained CFD findings and the experimental data.
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ItemPrivacy- and context-aware release of trajectory data(ACM, 2020-03)The availability of large-scale spatio-temporal datasets along with the advancements in analytical models and tools have created a unique opportunity to create valuable insights into managing key areas of society from transportation and urban planning to epidemiology and natural disasters management. This has encouraged the practice of releasing/publishing trajectory datasets among data owners. However, an ill-informed publication of such rich datasets may have serious privacy implications for individuals. Balancing privacy and utility, as a major goal in the data exchange process, is challenging due to the richness of spatio-temporal datasets. In this article, we focus on an individual's stops as the most sensitive part of the trajectory and aim to preserve them through spatio-temporal perturbation. We model a trajectory as a sequence of stops and moves and propose an efficient algorithm that either substitutes sensitive stop points of a trajectory with moves from the same trajectory or introduces a minimal detour if no safe Point of Interest (POI) can be found on the same route. This hinders the amount of unnecessary distortion, since the footprint of the original trajectory is preserved as much as possible. Our experiments shows that our method balances user privacy and data utility: It protects privacy through preventing an adversary from making inferences about sensitive stops while maintaining a high level of similarity to the original dataset.
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ItemExploiting patterns to explain individual predictions(Springer London, 2020-03)Users need to understand the predictions of a classifier, especially when decisions based on the predictions can have severe consequences. The explanation of a prediction reveals the reason why a classifier makes a certain prediction, and it helps users to accept or reject the prediction with greater confidence. This paper proposes an explanation method called Pattern Aided Local Explanation (PALEX) to provide instance-level explanations for any classifier. PALEX takes a classifier, a test instance and a frequent pattern set summarizing the training data of the classifier as inputs, and then outputs the supporting evidence that the classifier considers important for the prediction of the instance. To study the local behavior of a classifier in the vicinity of the test instance, PALEX uses the frequent pattern set from the training data as an extra input to guide generation of new synthetic samples in the vicinity of the test instance. Contrast patterns are also used in PALEX to identify locally discriminative features in the vicinity of a test instance. PALEX is particularly effective for scenarios where there exist multiple explanations. In our experiments, we compare PALEX to several state-of-the-art explanation methods over a range of benchmark datasets and find that it can identify explanations with both high precision and high recall.
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ItemPRESS: A personalised approach for mining top-k groups of objects with subspace similarity(Elsevier, 2020-07)Personalised analytics is a powerful technology that can be used to improve the career, lifestyle, and health of individuals by providing them with an in-depth analysis of their characteristics as compared to other people. Existing research has often focused on mining general patterns or clusters, but without the facility for customisation to an individual's needs. It is challenging to adapt such approaches to the personalised case, due to the high computational overhead they require for discovering patterns that are good across an entire dataset, rather than with respect to an individual. In this paper, we tackle the challenge of personalised pattern mining and propose a query-driven approach to mine objects with subspace similarity. Given a query object in a categorical dataset, our proposed algorithm, PRESS (Personalised Subspace Similarity), determines the top-k groups of objects, where each group has high similarity to the query for some particular subspace. We evaluate the efficiency and effectiveness of our approach on both synthetic and real datasets.
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ItemSTOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management(WILEY, 2022-12)Abstract Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.
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ItemMarket-inspired framework for securing assets in cloud computing environments(WILEY, 2022-09)Abstract Self‐adaptive security methods have been extensively leveraged for securing software systems and users from runtime threats in online and elastic environments, such as the cloud. The existing solutions treat security as an aggregated quality by enforcing “one service for all” without considering the explicit security requirements of each asset or the costs associated with security. Dealing with the security of assets in ultra‐large environments calls for rethinking the way we select and compose services—considering not only the services but the underlying supporting computational resources in the process. We motivate the need for an asset‐centric, self‐adaptive security framework that selects and allocates services and underlying resources in the cloud. The solution leverages learning algorithms and market‐inspired approaches to dynamically manage changes in the runtime security goals/requirements of assets with the provision of suitable services and resources, while catering for monetary and computational constraints. The proposed framework aims to inform the self‐adaptive security efforts of security researchers and practitioners operating in dynamic large‐scale environments, such as the Cloud. To illustrate the utility of the proposed framework it is evaluated using simulation on an application based scenario, involving cloud‐based storage and security services.
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ItemIoT-Pi: A machine learning-based lightweight framework for cost-effective distributed computing using IoT(JOHN WILEY & SONS LTD, 2022-05)It is possible to develop intelligent and self‐adaptive application on the edge nodes with rapid increase in computational capability of Internet of Things (IoT) devices. With the rapid growth of cloud technologies, the demand for hybrid architecture with cloud and IoT has also been boosted as well. To satisfy the critical and comprehensive requirements in the architecture evolution, we proposed a lightweight framework called IoT‐Pi to provide a 3‐phase (sample, learn, adapt) life cycle management of cloud resources with machine learning prediction working on IoT edge nodes using Raspberry Pi device. Compared to the traditional interference by human beings in the field of system administration, the accuracy rate of machine learning prediction in the proposed technique for some algorithms reached over 70%, which demonstrates the feasibility and effectiveness of running cloud resource management on an IoT devices such as Raspberry Pi.
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ItemRESCUE: Enabling green healthcare services using integrated IoT-edge-fog-cloud computing environments(WILEY, 2022-07)Abstract Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real‐life applications. To execute high‐end computations and data analytics, IoT and cloud‐based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay‐aware and energy‐efficient solution while providing time‐critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge‐based computing to provide healthcare services to users in exigency. Here, we propose an end‐to‐end framework namedRESCUE(enabling green healthcare services using integrated iot‐edge‐fog‐cloud computing environments), consisting efficient spatio‐temporal data analytics module for efficient information sharing, spatio‐temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd‐sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real‐time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency‐aware, power‐aware data‐driven analytics system helps to resolve any spatio‐temporal query more efficiently compared to the existing works for any time‐critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption.
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ItemOvercoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning(WILEY, 2022-05)Understanding antimicrobial usage patterns and encouraging appropriate antimicrobial usage is a critical component of antimicrobial stewardship. Studies using VetCompass Australia and Natural Language Processing (NLP) have demonstrated antimicrobial usage patterns in companion animal practices across Australia. Doing so has highlighted the many obstacles and barriers to the task of converting raw clinical notes into a format that can be readily queried and analysed. We developed NLP systems using rules-based algorithms and machine learning to automate the extraction of data describing the key elements to assess appropriate antimicrobial use. These included the clinical indication, antimicrobial agent selection, dose and duration of therapy. Our methods were applied to over 4.4 million companion animal clinical records across Australia on all consultations with antimicrobial use to help us understand what antibiotics are being given and why on a population level. Of these, approximately only 40% recorded the reason why antimicrobials were prescribed, along with the dose and duration of treatment. NLP and deep learning might be able to overcome the difficulties of harvesting free text data from clinical records, but when the essential data are not recorded in the clinical records, then, this becomes an insurmountable obstacle.