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Sepsis Risk Assessment with XGBoost: A Data-Intensive Approach

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

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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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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Improving Multimodal Human Pose Estimation by Adversarial Modality Enhancement

Improving Multimodal Human Pose Estimation by Adversarial Mo...

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

作者： Xia, Jiangnan Wu, Qilong Guo, Yanyin Li, Yi Cheng, Jianghan Li, Junwei Zhang, Zhiyuan College of Information Science and Electronic Engineering Zhejiang University China School of Computing and Information Systems Singapore Management University Singapore NingboTech University China

ISBN: (纸本)9798350368741

Human pose estimation in computer vision predominantly focuses on the visible modality, with limited research on the infrared modality. No existing methods demonstrate robust performance across both modalities, missing their complementary strengths. This gap arises from the lack of a multimodal benchmark and the difficulty of developing robust multimodal capabilities. To address this, we introduce MMPD, a novel visible-infrared multimodal pose benchmark with high-quality annotations for both modalities. Leveraging MMPD, we expose the limitations of state-of-the-art methods due to modality variance. To overcome this challenge, we propose a novel method-agnostic scheme called AMMPE. By employing the Modality Adversarial Enhancement Stage and Modality Interaction Stage, AMMPE easily incorporates multimodal information without additional pose annotations and enhances effective modality interaction. Extensive experiments demonstrate that AMMPE improves performance in both visible and infrared modalities, achieving excellent modality robustness. The code and benchmark is avaible at: https://***/ICANDOALLTHINGSSS/AdversarialMulti-Modality-Pose-Estimation. © 2025 IEEE.

关键词： benchmark Human pose estimation multi-modality

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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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From Deep Learning to Interpretable and Explainable Deep Learning in Medical Image computing: Balancing Innovation with Ethics and Responsibilities 28th

From Deep Learning to Interpretable and Explainable Deep Lea...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Chehri, Abdellah Ahmed, Imran Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

The utilization of artificial intelligence (AI) and other cutting-edge techniques in the field of medical image analysis has exhibited significant potential. Nevertheless, a significant obstacle that impedes the extensive implementation of these models in the healthcare sector is their restricted interpretability. The concept of explainability is a subject of extensive discussion and debate within the context of utilizing artificial intelligence in the healthcare domain. Notwithstanding the empirical evidence demonstrating the superior performance of AI-driven systems compared to humans in certain analytical tasks, particularly in the field of medical image computing, these systems still encounter challenges due to their limited explainability. The present study provides a comprehensive assessment of the significance of explainability in the field of medical artificial intelligence and performs an ethical analysis of the influence of explainability on the incorporation of AI-driven tools in data engineering in medicine and health care. The paper examines various subjects including data security, confidentiality, privacy, fairness, and discrimination, among others. © 2024 The Authors.

关键词： Data Analytics Deep Learning Ethics Explainable AI Medical Imaging Responsibilities

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Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke Data

Application of Bayes Rule for Precise Analysis of Class-Imba...

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

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350319200

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid *** strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis. © 2023 IEEE.

关键词： Bayes rule Class imbalance probabilistic analysis Resampling Stroke data

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