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Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

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RESOURCE ALLOCATION FOR AUSTRALIAN BUSHFIRE RESPONSE USING AGENT-BASED MODELLING AND SIMULATION 51

RESOURCE ALLOCATION FOR AUSTRALIAN BUSHFIRE RESPONSE USING A...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Miller, Deniz E. Chakrabortty, Ripon K. School of Engineering & Information Technology UNSW Canberra 2610 ACT Australia School of Systems & Computing UNSW Canberra 2610 ACT Australia

Using simulation and other advanced artificial intelligence (AI) models to allocate and optimise resources can significantly improve the response to bushfire disasters. Resource allocation for bushfire response is the process of distributing response materials. The optimisation of this process ensures that resources are distributed in accordance with the severity of the given disaster. Ultimately, this ensures that the mismanagement of resources does not constrict the response plan implemented by the disaster response workforce. An agent-based simulation model, with its potential to revolutionize resource allocation, is used in this study to conduct optimised resource allocation. The simulation includes the optimisation of Search And Rescue (SAR) teams and Fire Strike Teams (FST) to save the most lives. Its output is a disaster response plan for implementation by the workforce. The model will be based on a realistic scenario with specified constraints. Hence, it will be restricted in its real-world applications;however, this can later be expanded to include more complex scenarios. The model effectively chooses the optimal resource usage and provides a disaster response plan, including a summary of allocated resources and a vehicle routing plan. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Bushfire Optimisation Resource allocation Simulation

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Sepsis Risk Assessment with XGBoost: A Data-Intensive Approach

Sepsis Risk Assessment with XGBoost: A Data-Intensive Approa...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Gayathri, M. Kavitha, V. School of Computing Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India School of Computing Department of DataScience and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur India

ISBN: (纸本)9798350319200

In sepsis, immunological response to infection fails, causing systemic inflammation and organ failure. Sepsis progresses quickly and is commonly misdiagnosed, making early discovery and treatment crucial for patient outcomes. Augmented patient care and earlier intervention are possible using machine learning sepsis prediction systems. This research investigates if the well-known gradient boosting technique XGBoost can predict sepsis. Our goal is to construct a viable predictive model that can identify sepsis patients at risk of deterioration utilizing a huge dataset of EHRs from various healthcare facilities. The information includes vitals, test results, and patient details. Methodology includes "data preprocessing,""feature engineering,"and model development". Train the XGBoost model to predict sepsis onset and then build a threshold-based system to alert patients when their risk surpasses a certain level. Some indicators of model efficacy include sensitivity, specificity, and AUC-ROC. Sepsis prediction appears promising using the XGBoost model. If doctors can detect at-risk patients early and administer early treatments, fatality rates and patient outcomes may decrease. The study emphasizes the need of machine learning in healthcare to improve clinical decision support systems and assist critical care physicians. © 2023 IEEE.

关键词： Disease prediction Healthcare Machine learning Sepsis XGBoost

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Singe Image Dehazing using Deep Neural Networks 2

Singe Image Dehazing using Deep Neural Networks

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

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AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models

AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Larg...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hong, Xin Gong, Yuan Sethu, Vidhyasaharan Dang, Ting School of Computing and Information Systems University of Melbourne Melbourne Australia CSAIL SLS Massachusetts Institute of Technology Boston United States School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (纸本)9798350368741

Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent studies have shown LLMs' capability in recognizing emotions, but they often focus on single emotion labels and overlook the complex and ambiguous nature of human emotions. This study is the first to address this gap by exploring the potential of LLMs in recognizing ambiguous emotions, leveraging their strong generalization capabilities and in-context learning. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition. Experiments conducted using three datasets indicate significant potential for LLMs in recognizing ambiguous emotions, and highlight the substantial benefits of including context information. Furthermore, our findings indicate that LLMs demonstrate a high degree of effectiveness in recognizing less ambiguous emotions and exhibit potential for identifying more ambiguous emotions, paralleling human perceptual capabilities. © 2025 IEEE.

关键词： ambiguous emotion emotion recognition large language models multimodal prompt design

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Distributed Charging Scheduling and Pricing Strategy for Plug-in Electric Vehicles Based on Stackelberg-Nash and Multi-Cluster Aggregative Games

Distributed Charging Scheduling and Pricing Strategy for Plu...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Jing, Yuhao Chen, Jianguo Qiao, Li Mo, Huadong Dong, Daoyi School of Engineering and Technology University of New South Wales Canberra Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China School of Systems and Computing University of New South Wales Canberra Australia School of Engineering Australian National University Canberra Australia

ISBN: (纸本)9781665410205

In this paper, we propose a distributed and interactive Plug-in Electric Vehicle (PEV) charging scheduling approach, which is also combined with an optimal pricing strategy. This method tackles challenges such as fluctuations in charging currents, potential supply congestion, and uneven demand distribution that arise as PEV penetration increases. The objective is to improve the robust stability of the charging system while also reducing the costs for PEV users. This study designs a multi-cluster aggregative game mechanism to handle the competitive dynamics among operational clusters and the collective behavior of individual PEVs. Additionally, a strategic pricing method, based on Stackelberg game theory, is designed to refine the determination of basic electricity prices. We further introduce a distributed update method that efficiently seeks the Nash Equilibrium (NE) of the hierarchical game described. The effectiveness of the proposed architecture and solution methodology is validated through experimental studies. © 2024 IEEE.

关键词： State of charge

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

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4th International Conference on Smart systems: Innovations in computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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State of Health Prediction for Battery Energy Storage systems under Random Walk Operations 6

State of Health Prediction for Battery Energy Storage System...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Selim, Alaa Mo, Huadong Pota, Hemanshu Wu, Di Dong, Daoyi University of New South Wales School of Engineering and Technology Canberra Australia University of New South Wales School of Systems and Computing Canberra Australia Sichuan University Peking University China Australian National University Ciicada Lab School of Engineering ACT Australia

ISBN: (纸本)9798350356083

This study addresses the challenge of predicting State of Health (S o H) and capacity degradation in Battery Energy Storage systems (BESS) that experience random walk phenomena due to frequent control algorithm adjustments. Through meticulous feature engineering on diverse operational cycle data, this research identifies and utilizes critical predictors influencing battery behavior. We employ a Gaussian Process Regression model, renowned for its effectiveness in capturing complex, nonlinear interactions characteristic of BESS under dynamic conditions, which consistently achieves accurate prediction with R-squared value of 0.99. The results affirm the potential of advanced machine learning techniques to enhance the management and reliability of BESS, particularly in applications where control algorithms frequently induce battery cycling akin to random walk, thereby complicating SoH and capacity degradation assessments. © 2024 IEEE.

关键词： Battery storage

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DESIGNING AN INTEGRATED MULTI-ECHELON, MULTI-PERIOD HEALTHCARE SUPPLY CHAIN NETWORK 51

DESIGNING AN INTEGRATED MULTI-ECHELON, MULTI-PERIOD HEALTHCA...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Rahman, Rafat Sultana, Farzana Farhan, Mahmudur Rahman Ahmed, Sayem Chakrabortty, Ripon K. Department of Mechanical & Production Engineering Ahsanullah University of Science & Technology Dhaka Bangladesh School of Systems & Computing University of New South Wales CanberraACT2610 Australia

The healthcare sector faces unique supply chain management challenges because of the wide range of product life cycles, growing levels of outsourcing, and globalization. Selecting cooperators and forming alliances across different supply chain echelons is particularly crucial when designing a multi-echelon network. This study presents a mixed-integer linear programming (MILP) model to design a multi-echelon, multi-period healthcare supply chain network (HSCN) in the context of a developing country. The model aims to improve operational efficiency and cost-effectiveness by selecting optimal partners, production planning, and inventory control at each echelon. The results demonstrate that the proposed model achieves a 100% customer service level (CSL), ensuring that all demands are met without shortages. Additionally, the model minimizes the total supply chain costs, which are determined to be $183,329, while maintaining a low average inventory level of 9 units across all nodes. These findings underscore the effectiveness of the model in optimizing strategic, tactical, and operational planning for a highly efficient and competent healthcare supply chain. Finally, a 23 factorial experimental design is carried out, considering demand variability, capacity variability for selected partners, and transportation capacity variability as the main factors. Among these factors, demand variability has the largest positive effect on total cost, while both partner capacity variability and transportation capacity variability have negative effects on total cost. This framework offers valuable insights for decision-makers and provides the foundation for future research to explore more complex scenarios. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Decision making

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