Analysing maintenance and renewal decision of sealed roads at city council in australia
- Authors: Shrestha, Kishan , Chattopadhyay, Gopi
- Date: 2024
- Type: Text , Conference paper
- Relation: 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Lulea, Sweden, 13-15 June 2023, International Congress and Workshop on Industrial AI and eMaintenance 2023 Conference proceedings p. 291-301
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- Description: Roads are one of the major physical infrastructures of Hepburn Shire Council (HSC) as of all other local councils. Every year HSC allocates and spends huge amount of budget on roads for maintenance and renewal. The road performance condition level has been the major priority for roads renewal selection. However, other criteria are under-considered, and there are gaps in significant analysis of the relation between roads age, condition, risk, and cost. In this study, decision-making framework or tool has developed using multi criteria technique (MCT) and analytic Hierarchy Process (AHP) for single objective optimisation i.e., to provide an agreed level of service optimising Maintenance and Renewal cost or improve the condition subjected to annual budget. This study adopted decision criteria as per community and council needs, by developing a model for criteria selection. Additionally, this study analysed the adopted HSC maintenance strategies, condition monitoring systems, performance conditions of the roads, and operational and renewal budget of HSC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Detection of anomalies and explanation in cybersecurity
- Authors: Samariya, Durgesh , Ma, Jiangang , Aryal, Sunil , Zhao, Xiaohui
- Date: 2024
- Type: Text , Conference paper
- Relation: 30th International Conference on Neural Information Processing, ICONIP 2023, Changsha, 20-23 November 2023, Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20-23, 2023, Proceedings, Part XIII Vol. 1967 CCIS, p. 414-426
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- Description: Histogram-based anomaly detectors have gained significant attention and application in the field of intrusion detection because of their high efficiency in identifying anomalous patterns. However, they fail to explain why a given data point is flagged as an anomaly. Outlying Aspect Mining (OAM) aims to detect aspects (a.k.a subspaces) where a given anomaly significantly differs from others. In this paper, we have proposed a simple but effective and efficient histogram-based solution - HMass. In addition to detecting anomalies, HMass provides explanations on why the points are anomalous. The effectiveness and efficiency of HMass are evaluated using comparative analysis on seven cyber security datasets, covering the tasks of anomaly detection and outlying aspect mining. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Knowledge graph completion via subgraph topology augmentation
- Authors: Huang, Haufei , Ding, Feng , Zhang, Fengyi , Wang, Yingbo , Peng, Ciyuan , Shehzad, Ahsan , Lei, Qihang , Cong, Lili , Yu, Shuo
- Date: 2024
- Type: Text , Conference paper
- Relation: 11th Chinese National Conference on Social Media Processing, SMP 2023, Anhui, China, 23-26 November 2023, Social Media Processing: 11th Chinese National Conference, SMP 2023, Anhui, China, November 23–26, 2023, Proceedings Vol. 1945 CCIS, p. 14-29
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- Description: Knowledge graph completion (KGC) has achieved widespread success as a key technique to ensure high-quality structured knowledge for downstream tasks (e.g., recommendation systems and question answering). However, within the two primary categories of KGC algorithms, the embedding-based methods lack interpretability and most of them only work in transductive settings, while the rule-based approaches sacrifice expressive power to ensure that the models are interpretable. To address these challenges, we propose KGC-STA, a knowledge graph completion method via subgraph topology augmentation. First, KGC-STA contains two topological augmentations for the enclosing subgraphs, including the missing relation completion for sparse nodes and the removal of redundant nodes. Therefore, the augmented subgraphs can provide more useful information. Then a message-passing layer for multi-relation is designed to efficiently aggregate and learn the surrounding information of nodes in the subgraph for triplet scoring. Experimental results in WN18RR and FB15k-237 show that KGC-STA outperforms other baselines and shows higher effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Process reliability analysis applied for continual improvement of large-scale alumina refineries
- Authors: Don, R. Welandage , Chattopadhyay, Gopi , Kamruzzaman, Joarder
- Date: 2024
- Type: Text , Conference paper
- Relation: 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Lulea, Sweden, 13-15 June 2023, International Congress and Workshop on Industrial AI and eMaintenance 2023 Conference proceedings p. 665-677
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- Description: Large-scale alumina refineries use strategic planning to forecast production plans for short, medium, and long term operational decisions. However, actual production deviates from the forecast due to reasons within Supplier, Input, Process, Output and Contractor (SIPOC) related variations including unplanned downtimes, issues with supply chain disruptions, availability of staff and demand fluctuations due to numerous factors including environmental changes, if any. The unreliable production process results in lost revenue and adversely affects the corporate image. This paper presents a statistical approach applying the Weibull model to identify the causes of production deviation and find improvement opportunities for reducing costs and risks while enhancing performance. An illustrative example from a chemical alumina refinery plant in Australia is presented. The various steps used in the analysis are discussed in this paper using illustrative example where production data is analysed and compared for diverse options of interventions for a robust and effective method for managers to better understand the gaps for monitoring and assuring plant performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
A brief guide to multi-objective reinforcement learning and planning JAAMAS track
- Authors: Hayes, Conor , Bargiacchi, Eugenio , Källström, Johan , Macfarlane, Matthew , Reymond, Mathieu , Verstraeten, Timothy , Zintgraf, Luisa , Dazeley, Richard , Heintz, Frederik , Howley, Enda , Irissappane, Aathirai , Mannion, Patrick , Nowé, Ann , Ramos, Gabriel , Restelli, Marcello , Vamplew, Peter , Roijers, Diederik
- Date: 2023
- Type: Text , Conference paper
- Relation: 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023, London, 29 May to 2 June 2023, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2023-May, p. 1988-1990
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- Description: Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple - often conflicting - objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper [4], serves as a guide for the application of explicitly multi-objective methods to difficult problems. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
A case for causal loop diagrams to model electronic health records ecosystems
- Authors: Hashmi, Mustafa , McInnes, Angelique , Sahama, Tony , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne, Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 238-239
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- Description: Causal loop diagrams (CLD) that emerged from systems thinking disciplines have been used to simulate complex inter-dependencies between causal factors in diverse phenomena. This paper highlights a process for generating a casual loop diagrams to represent the quality of electronic health record (EHR) ecosystem in a medical context. The quality inherent in the use of electronic health records for specific clinical purposes is taken to depend on factors including data integrity, reliability, relevance, timeliness and completeness. By improving the electronic health record ecosystem quality, health care providers can enhance their data sharing practices, and personalised patient care, while reducing the probabilities of medical errors. Ultimately the CLD can be used to run multiple simulations for several clinical case scenarios to understand the impact of various case phenomena on the quality of the electronic health record ecosystem. © 2023 ACM.
A hybrid PWM strategy for SMES integrated grid-feeding transformerless PV Inverters
- Authors: Mondal, Sudipto , Biswas, Shuvra , Bin Islam, Md Sabbir , Islam, Md Rabiul , Shah, Rakibuzzaman
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023, Tianjin, China, 27-29 October 2023, 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
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- Description: This paper presents a new hybrid pulse width modulation (PWM) strategy for improving the performance of superconducting magnetic energy storage (SMES) integrated solar photovoltaic (PV) based grid-feeding transformerless H6 inverter systems. The proposed PWM strategy offers significantly lower total harmonic distortion (THD) of output voltage compared to existing unipolar PWM (UPPWM), bipolar PWM (BPPWM), and some well-established advanced PWM techniques. A new carrier and reference signals are used to develop the proposed hybrid PWM strategy, which ensures low leakage current compared to existing counterparts. The presented inverter system is simulated in MATLAB/Simulink and PLECS computer simulation environment. Apart from simulated results, some preliminary experimental test results are also shown to validate the superiority of the proposed PWM strategy. © 2023 IEEE
A new tour on the subdifferential of the Supremum function
- Authors: Hantoute, Abderrahim , López-Cerdá, Marco
- Date: 2023
- Type: Text , Conference paper
- Relation: International Meeting on Functional Analysis and Continuous Optimization, IMFACO 2022, Elche, Spain, 16-17 June 2022, Functional Analysis and Continuous Optimization In Honour of Juan Carlos Ferrando's 65th Birthday, Elche, Spain, June 16–17, 2022 Vol. 424, p. 167-194
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- Description: This chapter is a survey presenting various characterizations of the subdifferential of the pointwise supremum of convex functions, as well as some featured applications. We gathered here the main outcomes we obtained in a series of recent papers, dealing with different models, assumptions and scenarios. Starting by the maximum generality framework, we move after to particular contexts in which some continuity and compactness assumptions are either imposed or inforced via processes of compactification of the index set and regularization of the data functions. Some relevant applications of the general results are presented, in particular to derive rules for the subdifferential of the sum, and for convexifying a general (unconstrained) optimization problem. The last section gives some specific constraint qualifications for the convex optimization problem with an arbitrary set of constraints, and also contains different sets of KKT-type optimality conditions appealing to the subdifferential of the supremum function. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Novel Common Grounded Type 1-\varphi Five-Level Boost PV Inverter with Reduced Device Count
- Authors: Ardashir, Jaber , Ghadim, Hadi , Heydari, Dorsa , Hu, Jiefeng
- Date: 2023
- Type: Text , Conference paper
- Relation: 8th International Conference on Technology and Energy Management, ICTEM 2023, Babol, Iran, 8-9 February 2023, 8th International Conference on Technology and Energy Management, ICTEM 2023
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- Description: Common-grounded multi-level inverters with the input voltage boost ability are more common for grid-tied PV system applications. In this paper, a new five-level inverter topology is introduced that can generate a five-level voltage at the output stage, statically increase the input voltage, fully transfer the reactive power, eliminate the leakage current due to common-grounded capability, reduce the number of the power electronic components, self-balancing capability of switched capacitors voltage based on operation modes, and equality of switched capacitors voltage. The operation of the proposed inverter along with its comparison with other similar inverter topologies and the simulation results of the proposed inverter under different loads are demonstrates in this paper. The results indicate that the performance of the proposed inverter in the grid-connected PV system is best than other compared topologies. Also, experimental results with a prototype have been presented, which validates the simulation results and proves the applicability of the proposed inverter in grid-tied PV systems. © 2023 IEEE.
A robust ensemble regression model for reconstructing genetic networks
- Authors: Gamage, Hasini , Chetty, Madhu , Lim, Suryani , Hallinan, Jennifer , Nguyen, Huy
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 International Joint Conference on Neural Networks, IJCNN 2023 Vol. 2023-June
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- Description: Genetic networks contain important information about biological processes, including regulatory relationships and gene-gene interactions. Numerous methods, using high-dimensional gene expression data have been developed to capture these interactions. These gene expression data, generated using high-throughput technologies, are prone to noise. However, most existing network inference methods are unable to cope with noisy data, making genetic network reconstruction challenging. In this paper, we propose a novel ensemble regression model combining quantile regression and cross-validated Ridge regression, RidgeCV, to infer interactions from noisy gene expression data. The application of quantile regression to GRN inference is novel, and its design makes it appropriate for noisy data. RidgeCV also addresses other important issues, such as data overfitting and multicollinearity. First, each regression method is independently applied to gene expression data and the output of these methods, in the form of ranked gene lists, is aggregated using a novel gene score-based method by considering the gene rank and model importance. The model importance score is evaluated based on an adjusted coefficient of determination. This method implicitly includes majority voting by averaging each gene score value across all models. The proposed model was tested on the DREAM4 datasets and publicly available small-scale real-world network datasets. Experiments with noisy datasets showed that the proposed ensemble model is more accurate and efficient than other state-of-the-art methods. © 2023 IEEE.
A study into the impact of data breaches of electronic health records
- Authors: Pilla, Ravi , Oseni, Taiwo , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 252-254
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- Description: The research study deals with electronic health records (EHRs) data breaches, their impact., Electronic health records play an important role in digital healthcare services. However, confidentiality and integrity of sensitive EHRs are critical to ensure patient privacy. Although the existing traditional cybersecurity practices provide some protection, they cannot prevent EHRs data breaches. Therefore, this research's primary focus will be critically reviewing the impact of data breaches and current cybersecurity practices. Finally, the paper's key findings highlight the type of cyberattacks and options to reduce them. © 2023 ACM.
A survey on image classification of lightweight convolutional neural network
- Authors: Liu, Ying , Xiao, Peng , Fang, Jie , Zhang, Dengsheng
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
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- Description: In recent years, deep neural networks have achieved tremendous success in image classification in both academic and industrial settings. However, the high hardware requirements imposed by their intensive and complex computations pose a challenge for deployment on low-storage devices. To address this challenge, lightweight networks provide a viable solution. This paper provides a detailed review of recent lightweight image classification algorithms, which can be categorized into low-redundancy network model design and neural network compression algorithms. The former reduces network computations by replacing traditional convolution with efficient lightweight convolution, while the latter reduces redundancy in the network by employing methods such as network pruning, knowledge distillation, and parameter quantization. We summarize the experimental results of some classical models and algorithms on ImageNet2012 and CIFAR-10 datasets, and analyze the characteristics, advantages and disadvantages of these models respectively. Finally, future research directions for lightweight algorithms in the field of image classification are identified. © 2023 IEEE.
Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study
- Authors: Siddiqui, Mustafa , Appuhamillage, Gayan , Hewawasam, Hasitha
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
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- Description: Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.
Autoregressive models for biomedical signal processing
- Authors: Haderlein, Jonas , Peterson, Andre , Burkitt, Anthony , Mareels, Iven , Grayden, David
- Date: 2023
- Type: Text , Conference paper
- Relation: 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023, Sydney, 24-27 July 2023, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
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- Description: Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters.Clinical relevance-This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy. © 2023 IEEE.
Bilateral insider threat detection : harnessing standalone and sequential activities with recurrent neural networks
- Authors: Manoharan, Phavithra , Hong, Wei , Yin, Jiao , Zhang, Yanchun , Ye, Wenjie , Ma, Jiangang
- Date: 2023
- Type: Text , Conference paper
- Relation: 24th International Conference on Web Information Systems Engineering, WISE 2023, Melbourne, 25-27 October 2023, Web Information Systems Engineering – WISE 2023, 24th International Conference, Melbourne, VIC, Australia, October 25–27, 2023, Proceedings Vol. 14306 LNCS, p. 179-188
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- Description: Insider threats involving authorised individuals exploiting their access privileges within an organisation can yield substantial damage compared to external threats. Conventional detection approaches analyse user behaviours from logs, using binary classifiers to distinguish between malicious and non-malicious users. However, existing methods focus solely on standalone or sequential activities. To enhance the detection of malicious insiders, we propose a novel approach: bilateral insider threat detection combining RNNs to incorporate standalone and sequential activities. Initially, we extract behavioural traits from log files representing standalone activities. Subsequently, RNN models capture features of sequential activities. Concatenating these features, we employ binary classification to detect insider threats effectively. Experiments on the CERT 4.2 dataset showcase the approach’s superiority, significantly enhancing insider threat detection using features from both standalone and sequential activities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Biosynthetic organic solar cell biorefinery to fulfil Australian baseload power demands
- Authors: Ghayur, Adeel
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, Wollongong, 3-6 December 2023, 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
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- Description: Renewable energy technologies are fundamental to mitigating climate change. However, the intermittent nature associated with wind and solar technologies is the biggest hurdle to their implementation in baseload grid. The other two issues are incorporation of fossil fuel derived materials in their synthesis and end-of-life recycling. These issues for solar panels have been addressed here. In this study, for the first time, a pathway for the incorporation of renewable organic materials in the synthesis of organic solar cells has been developed. While this novel biorefinery concept has been developed for Australia, it is just as applicable in other regions. In this concept, 650,000 metric tons of non-food bio-waste is consumed for the production of organic materials that manufacture solar cells with 14 GW nameplate capacity, annually. In the State of Victoria (Australia) this is sufficient for 2 GW of baseload capacity. In this baseload 12 GW is earmarked for electrolytic hydrogen production to generate 2 GW of fuel cell based power for 18 h, daily, at 50% roundtrip efficiency. The land area required for such a 2 GW baseload solar farm is 200 km2. These results show that less than 300,000 km2 of area (0.2% of Earth's surface) is needed to transition the entire planet's power grid to solar baseload and 150 biorefineries can produce enough organic solar panels to achieve this transition in ten years. At their end-of-life, these solar panels are easier to recycle, when compared to silicon solar panels due to their organic materials. © 2023 IEEE.
Causal deep operator networks for data-driven modeling of dynamical systems
- Authors: Nghiem, Truong , Nguyen, Thang , Nguyen, Binh , Nguyen, Linh
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Hybrid, Honolulu, 1-4 October 2023, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics p. 1136-1141
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- Description: The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality - a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control. © 2023 IEEE.
Clinically prioritized data visualization in remote patient monitoring
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew , Neupane, Arun
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, Montreal, Canada, 21-23 June 2023, International Conference on Wireless and Mobile Computing, Networking and Communications Vol. 2023-June, p. 5-12
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- Description: Understanding and integrating physiological data collected from wearable sensors in remote patient monitoring (RPM) is challenging. Data streams may be interrupted due to the sensor's sensitivity, movement, and electromagnetic interference leading to inconsistent, missing, and inaccurate data. Existing approaches to summarize data flows into a single score such as the traditional Modified early warning score (MEWS) is limited. Data visualization approaches have the potential to address this challenge, but few studies have focused on visualization of RPM streams. The study presents a transformation of observed raw RPM physiological data into parameters identified as trust, frequency, slope, and trend. This facilitated visualization and enabled automated assessments of prioritized alerts. Experimental results have shown that the transformations led to the prioritization of clinically significant conditions, and improved visualization has the potential to better support clinical decisions compared with traditional MEWS. © 2023 IEEE.
Comprehensive analysis of feature extraction techniques and runtime performance evaluation for phishing detection
- Authors: Nath, Subrata , Islam, Mohammad , Chowdhury, Abdullahi , Rashid, Mohammad , Islam, Maheen , Jabid, Taskeed , Naha, Ranesh
- Date: 2023
- Type: Text , Conference paper
- Relation: 6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023, Bandar Seri Bagawan, Brunei, 23-25 October 2023, 2023 6th International Conference on Applied Computational Intelligence in Information Systems: Intelligent and Resilient Digital Innovations for Sustainable Living, ACIIS 2023 - Proceedings
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- Description: The digital landscape is continually evolving, bringing with it numerous cybersecurity challenges, notably the rise of phishing websites targeting unsuspecting users. These deceptive websites jeopardize digital identities, emphasizing the critical need for precise detection mechanisms. This research provides a deep analysis of feature extraction nuances and critically evaluates the runtime performance of detection models. Through intensive refinement of Random Forest classification models, an integrative approach is adopted, which encompasses feature selection, outlier mitigation, and hyperparameter optimization using advanced data mining techniques. Leveraging a pre-established dataset with 87 distinct features from 11,430 URLs, this research narrows down the features to a pivotal set of 56. The outcome is a robust model that achieves an accuracy of 97.069% and a precision rate of 97.326%. A noteworthy aspect of this study is the incorporation of ensemble models, which amplify prediction accuracy by harnessing the capabilities of multiple algorithms. By employing the ensemble approach, the research ensures the model's heightened accuracy and adaptability, making it resilient against ever-changing phishing strategies. The findings underscore the symbiotic relationship between comprehensive feature extraction techniques and the paramount importance of runtime efficiency, laying the groundwork for a fortified digital landscape. © 2023 IEEE.
Data-efficient graph learning meets ethical challenges
- Authors: Tang, Tao
- Date: 2023
- Type: Text , Conference paper
- Relation: 16th ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February to 3 March 2023, WSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining p. 1218-1219
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- Description: Recommender systems have achieved great success in our daily life. In recent years, the ethical concerns of AI systems have gained lots of attention. At the same time, graph learning techniques are powerful in modelling the complex relations among users and items under recommender system applications. These graph learning- based methods are data hungry, which brought a significant data efficiency challenge. In this proposal, I introduce my PhD research from three aspects: 1) Efficient privacy-preserving recommendation for imbalanced data. 2) Efficient recommendation model training for Insufficient samples. 3) Explainability in the social recommendation. Challenges and solutions of the above research problems have been proposed in this proposal. © 2023 Owner/Author.