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2024 IEEE/ACS 21st International Conference on computer systems and Applications, AICCSA 2024

作者： Ben Abdallah, Walid Njah, Hasna Jamoussi, Salma National Engineering School of Gabes Gabes University Tunisia Sfax University Multimedia InfoRmation Systems and Advanced Computing Laboratory Tunisia Higher Institute of Computer Sciences and Multimedia of Gabes Gabes University Tunisia Higher Institute of Computer Sciences and Multimedia of Sfax Sfax University Tunisia

ISBN: (纸本)9798331518240

Time-To-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-To-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-To-event processes. © 2024 IEEE.

关键词： Prediction models

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Grid-Forming Inverters for Enhancing Stability and Resilience in Distribution Networks Under Transients and Restoration

Grid-Forming Inverters for Enhancing Stability and Resilienc...

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Innovative Smart Grid Technologies (ISGT)

作者： Alaa Selim Junbo Zhao Gab-Su Seo Fei Ding Bai Cui Department of Electrical and Computer Engineering University of Connecticut Power Systems Engineering Center National Renewable Energy Laboratory Golden CO Department of Electrical and Computer Engineering Iowa State University

The increasing integration of distributed energy resources in distribution networks has significantly reduced system inertia, posing challenges to grid stability during transient events, such as Cold Load Pickup (CLPU). This paper proposes the implementation of grid-forming inverters (GFMs) to enhance the stability of low-inertia distribution systems. The paper also investigates the performance of the GFM-based approach in maintaining voltage profiles along the distribution feeder, especially during CLPU scenarios without synchronous machines. A comparison is made between the GFM-based system and the conventional synchronous machine setup. The synchronous machine is chosen as a reference point for evaluating the effectiveness of the GFM-based approach. The GFM-based system’s performance is assessed under various challenging scenarios, including short-circuit faults, islanding, and a combination of these through extensive simulations. The results demonstrate the potential of GFMs to stabilize a distribution system and outperform the traditional rigid synchronous machine during transient events.

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Securing a Generative AI-Powered Healthcare Chatbot

Securing a Generative AI-Powered Healthcare Chatbot

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2024 European Federation for Medical Informatics Special Topic Conference, EFMI STC 2024

作者： Feretzakis, Georgios Anastasiou, Athanasios Pitoglou, Stavros Paxinou, Evgenia Gkoulalas-Divanis, Aris Kalodanis, Konstantinos Tsapelas, Ioannis Kalles, Dimitris Verykios, Vassilios S. School of Science and Technology Hellenic Open University Patras Greece Biomedical Engineering Laboratory National Technical University of Athens Athens Greece Computer Solutions Sa Athens Greece Merative Healthcare Dublin Docklands Dublin 2 Ireland Harokopio University of Athens Kallithea Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9781643685540

In Generative Artificial Intelligence (AI), Large Language Models (LLMs) like GPT-4, Gemini, Claude, and Llama, significantly impact healthcare by aiding in patient care, medical research, and administrative tasks. AI-powered chatbots offer real-time responses and manage chronic diseases, improving patient outcomes and operational efficiency. However, these models pose security and ethical challenges, necessitating robust data privacy, adversarial training, and ethical guidelines. This paper proposes a secure, ethical pipeline for deploying AI healthcare chatbots, integrating advanced privacy-preserving techniques and continuous security assessments to enhance data privacy, resilience, and user trust. © 2024 The Authors.

关键词： Differential privacy

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Quorum sensing-based interactions among drugs,microbes,and diseases

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Science China(Life Sciences) 2023年第1期66卷 137-151页

作者： Shengbo Wu Shujuan Yang Manman Wang Nan Song Jie Feng Hao Wu Aidong Yang Chunjiang Liu Yanni Li Fei Guo Jianjun Qiao School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China School of Computer Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Key Laboratory of Systems Bioengineering Ministry of Education(Tianjin University)Tianjin 300072China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Department of Engineering Science University of OxfordOxford OX13PJUK Institute of Shaoxing Tianjin UniversityShaoxing 312300China

Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be *** existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate *** this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://***/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common *** interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental ***,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal *** QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse *** work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

关键词： quorum sensing drugs microbe-disease association microbe-host interaction gut microbiota personalized medicine

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Adaptive Optimal Output-Feedback Control of Discrete-Time systems based on Hybrid Iteration 63

Adaptive Optimal Output-Feedback Control of Discrete-Time Sy...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Sitong Gao, Weinan Jiang, Zhong-Ping Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Lianning Shenyang110819 China Tandon School of Engineering New York University Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9798350316339

In this paper, a novel adaptive dynamic programming (ADP) algorithm, named output-feedback hybrid iteration (HI),is proposed to address the adaptive optimal control problem of discrete-time linear systems. The proposed output-feedback HI strategy learns the optimal control policy through two phases. First, a novel data-driven value-iteration (VI) scheme is employed to learn an admissible output-feedback control policy using the input/output data without relying on the knowledge of system matrices. Then, with the obtained admissible control policy, the optimal output feedback-control policy is approximated with an accelerated convergence rate through output-feedback policy iteration (PI). Online input/output data are utilized to reconstruct the full state of the system and integrated into the new output-feedback HI algorithm. Simulation results are presented and demonstrate the efficacy and practicality of the proposed output-feedback HI approach in comparison with traditional PI and VI techniques. © 2024 IEEE.

关键词： Dynamic programming

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NeSyKHG: Neuro-Symbolic Knowledge Hypergraphs 2

NeSyKHG: Neuro-Symbolic Knowledge Hypergraphs

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Bhuyan, Bikram Pratim Singh, Thipendra P. Tomar, Ravi Ramdane-Cherif, Amar LISV Laboratory University of Paris Saclay 10-12 Avenue of Europe Velizy78140 France School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India School of Computer Science Engineering & Technology Bennett University Greater Noida201306 India Persistent Systems Pune411016 India

In the rapidly evolving landscape of artificial intelligence, there is an escalating demand for enhanced reasoning capabilities and robust representation of higher-order relationships. Despite their prowess in pattern identification and generalization, Neural networks often grapple with challenges linked to interpretability and the absence of explicit relational reasoning. Symbolic logic, on the other hand, excels in formal knowledge representation and inference yet remains limited in deriving knowledge directly from data. We introduce the Neuro-Symbolic Knowledge Hypergraphs (NeSyKHG) framework to bridge this gap. This innovative model synergistically merges the strengths of neural networks and symbolic logic, offering an optimized representation and reasoning system for higher-order relationships using knowledge hypergraphs. Our empirical evaluations on the Chinese Medical High-order Relational (CMHR) dataset revealed that NeSyKHG significantly outperforms several established baseline models. Specifically, when compared to models such as Hypergraph Neural Networks (HGNN), Hypergraph Convolutional Networks (HyperGCN), Logical Hypergraph Link Prediction (LHP), and Universal Graph Convolutional Networks (UniGCN), NeSyKHG achieved an F1-score of 0.845, an accuracy (ACC) of 0.947, and an area under the curve (AUC) of 0.965. These metrics underscore NeSyKHG's exceptional ability in higher-order relational learning and reasoning. © 2024 Elsevier B.V.. All rights reserved.

关键词： computer circuits

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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning

arXiv

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arXiv 2024年

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m = 2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k−1/3 for k observations, instead of O(k−1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k−(2m−1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k−1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. Copyright © 2024, The Authors. All rights reserved.

关键词： Kalman filters

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A Review of Genome to Chaos: Exploring DNA Dynamics in Security

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Multimedia Tools and Applications 2024年 1-28页

作者： Aashiq Banu, S. Rao, L. Koteswara Priya, P. Shanmuga Thanikaiselvan Hemalatha, M. Dhivya, R. Rengarajan, Amirtharajan Department of Systems Engineering Military Technological College Muscat Oman Department of ECE Koneru Lakshmaiah Education Foundation Telangana Hyderabad500043 India Department of Electronics and Communication Engineering Rajalakshmi Engineering College ThandalamChennai602105 India Vellore632 014 India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India School of Electrical & amp Electronics Engineering SASTRA Deemed University Thanjavur613401 India

One of the largest and fastest-growing industries globally is e-healthcare. Digital medical imaging constantly evolves, advancing from standard computed tomography to powerful multi-dimensional imaging. This medical imaging forms patients’ real-time electronic health records (EHR), making it a prime target for hackers. To counter cipher-attacks, it is necessary to secure medical imaging. This research thoroughly analyses the properties of DNA chaos-based digital medical image encryption and reviews various algorithms. Researchers have performed several statistical and differential metrics, conducting simulation experiments to determine which methodology provides the best security outcome. The paper concludes by analysing significant advances in medical image encryption and emphasising future challenges. This paper discusses the importance of securing medical images, the potential breaches, and the security needs, providing a comprehensive review of DNA-based chaos cryptography techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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Monitoring the impact of energy conservation measures with Artificial Neural Networks

Monitoring the impact of energy conservation measures with A...

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2022 ECEEE Summer Study on Energy Efficiency: Agents of Change

作者： Sarmas, Elissaios Marinakis, Vangelis Doukas, Haris Dimitropoulos, Nikos Zucika, Aija School of Electrical and Computer Engineering Decision Support Systems Laboratory National Technical University of Athens Athens Greece The Latvian Environmental Investment Fund Riga Latvia

ISBN: (纸本)9789198827002

Energy Conservation Measures are mandatory in order to improve buildings' energy performance by using upgraded technologies, systems and installations. However, the lack of accurate techniques for Measurement & Verification (M&V) imposes insurmountable barriers towards their extended financing. The development of precise M&V techniques to estimate energy savings is a critical issue that can be tackled through the adoption of predictive models for the adjusted baseline energy consumption in the reporting period. The most commonly used M&V practices to date are reported in the International Performance Measurement and Verification Protocol (IPMVP), where the most widespread techniques per case for calculating energy savings are defined. More specifically, the IPMVP indicates the adoption of linear regression methods to predict the adjusted baseline energy consumption of a building, exploiting outdoor temperature and heating degree days. In this paper, utilisation of Deep Learning for training energy consumption predictive models is examined, as vast amount of data from Internet of Things devices are available nowadays. Thus, the feedforward Artificial Neural Network (ANN) is proposed for predicting the adjusted baseline energy consumption, using the hour of the day, the day of the week and weather data as training features. The proposed models incorporate both linear and non-linear relationships, in contrast to linear regression methods. To validate the proposed method, an experimental application is implemented, applying the developed models on an educational institution in Latvia. The building has been renovated regarding its heating supply and ventilation system, as well as its enclosing structures insulation. The predictions from the ANN models are compared with the ones from the traditional degree days method, indicating that ANN achieves higher accuracy in energy savings estimation for electricity and diesel fuel consumption. © 2022 European Council for an Energ

关键词： Energy utilization

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