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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Simone Boesso Lorenzo Farina Manuela Petti Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Radiomics represents a specialized branch of medical imaging where quantitative features are extracted from images. Performing a classification using radiomics means solving two common problems: the imbalanced setting, and the large number of features that would increase the risk of overfitting. Moreover, since its main application and impact are in clinical field, there is the need of interpretable models for explaining their results. The aim of this study is to compare two modelling approaches: one based on a logistic regression model, known for its simplicity and interpretability, and RUSBoost, an ensemble method designed to handle class imbalance with potentially higher complexity, in order to answer the question whether higher complexity and lower interpretability are justified when dealing with radiomics data. Additionally, due to the large literature suggesting it, we analyze the impact of a feature selection step applied to these two classifiers. Test performances measured across 20 repeated splits on two datasets show how the RUSBoost approach is able to capture more detailed patterns of the data but this is highly dependent on the dataset at hand.

关键词： Logistic regression Analytical models Feature extraction Oncology Data models Complexity theory Mutual information Radiomics Overfitting Tumors

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Predicting the Time from Diagnosis to Treatment in Breast Cancer

Predicting the Time from Diagnosis to Treatment in Breast Ca...

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E-Health and Bioengineering Conference (EHB)

作者： Sergiu Ceică Ştefan-Daniel Achirei Dept. of Computer Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.

关键词： Medical services Predictive models Boosting Prediction algorithms Breast cancer Delays Resource management Socioeconomics Medical diagnostic imaging Regression tree analysis

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Home Care Assistance Solution based on a Software Multi-Agents System 24

Home Care Assistance Solution based on a Software Multi-Agen...

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24th International Conference on control Systems and computer Science, CSCS 2023

作者： Rasras, Mohammad Marin, Iuliana Radu, Serban University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania University Politehnica of Bucharest Dept. of Engineering in Foreign Languages Bucharest Romania University Politehnica of Bucharest Dept. of Computer Science Bucharest Romania

ISBN: (纸本)9798350313390

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ' age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home's residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies. © 2023 IEEE.

关键词： Multi agent systems

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Process-Model-Free Design of MPC via a Reference-Model-Based PI controller for the Gas Turbine 9

Process-Model-Free Design of MPC via a Reference-Model-Based...

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9th International Conference on Electrical engineering, control and Robotics, EECR 2023

作者： Shi, Gengjin Dai, Chengbo Ma, Miaomiao Li, Donghai Ding, Yanjun Tsinghua University State Key Lab of Power Systems Dept. of Energy & Power Engineering Beijing China North China Electric Power University School of Control & Computer Engineering Beijing China

ISBN: (纸本)9781665491204

Model predictive control (MPC) is a modern advanced control strategy which has great reputation because of its excellent reference tracking performance and the ability to deal with process constraints, time delay and multivariable problems. However, the gas turbine is difficult to model accurately and unmeasurable disturbances are unavoidable, which limits the application of MPC to the gas turbine. To solve these problems, a process-model-free design method for MPC via a reference-model (RM)-based proportional-integral (PI) controller-desired dynamic equational (DDE)-PI controller (MPC-DDE) is proposed in this paper. The DDE-PI provides the design model for the MPC and handle with unmeasurable disturbances while MPC provides a rolling optimized set point for DDE-PI. Simulation results illustrate the superiorities of the proposed hierarchical control strategy in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of practical application of MPC-DDE to the gas turbine. © 2023 IEEE.

关键词： Model predictive control

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry 5

Monitoring and Forecasting Quality Indicators of Predough Pr...

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Zhyltsov, Andrii Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Electromagnetic Systems The Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350384499

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them. ©2024 IEEE.

关键词： Bakeries

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Empowering citizens by a blockchain-Based Robinson list

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International Journal of computers and Applications 2022年第10期44卷 920-928页

作者： Cirillo, Albenzio Dalena, Vito Mauro, Antonio Mogavero, Francesco Pennino, Diego Pizzonia, Maurizio Vitaletti, Andrea Zecchini, Marco Fondazione Ugo Bordoni Roma Italy Roma Tre University–Eng. Dept. Roma Italy Sapienza University of Rome Department of Computer Control and Management Engineering Roma Italy

Robinson lists are adopted in many countries to protect phone subscribers against commercial spam calls. In its essence, they collect the denial of the subscribers to be contacted by marketing operators. Nowadays, all Robinson lists run as centralised services. Namely, citizens have to rely on trusted third parties for the management of their own choices. The Blockchain allows us to envision the development of a decentralised Robinson list in which citizens are finally empowered to autonomously manage their choices. In this paper, we present a proof-of-concept (PoC) of a decentralised Robinson list developed with the Algorand technology. To the best of our knowledge, this is the first attempt to implement a Robinson list on a blockchain technology. The PoC has been designed to support the requirements provided by Fondazione Ugo Bordoni, the manager of the Italian Robinson list. The evaluation of the PoC shows that our decentralised solution provides satisfactory performance, in terms of scalability, latency and costs, when compared to the current centralised service. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Optimization Scheduling of Multi-Energy Microgrid Based on the QUBO Quantum Computing Model 19th

Optimization Scheduling of Multi-Energy Microgrid Based on t...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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Learning on the Edge: Online Learning with Stochastic Feedback Graphs 36

Learning on the Edge: Online Learning with Stochastic Feedba...

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36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Esposito, Emmanuel van der Hoeven, Dirk Fusco, Federico Cesa-Bianchi, Nicolò Dept. of Computer Science Università degli Studi di Milano Italy Istituto Italiano di Tecnologia Italy Dept. of Computer Control and Management Engineering Sapienza Università di Roma Italy

ISBN: (纸本)9781713871088

The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erdős-Rényi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge. We prove nearly optimal regret bounds of order (Equation presented) (ignoring logarithmic factors), where αΕ and δΕ are graph-theoretic quantities measured on the support of the stochastic feedback graph G with edge probabilities thresholded at Ε. Our result, which holds without any preliminary knowledge about G, requires the learner to observe only the realized out-neighborhood of the chosen action. When the learner is allowed to observe the realization of the entire graph (but only the losses in the out-neighborhood of the chosen action), we derive a more efficient algorithm featuring a dependence on weighted versions of the independence and weak domination numbers that exhibits improved bounds for some special cases. © 2022 Neural information processing systems foundation. All rights reserved.

关键词： Stochastic systems

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Remaining Useful Life Prediction of Multi-Component System Clustering with Time-Varying Kernel Density

Remaining Useful Life Prediction of Multi-Component System C...

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2023 China Automation Congress, CAC 2023

作者： Li, Ke Fan, Xingle Shi, Hui Zhang, Xiaohong Sun, Chaoli Wang, Jianan Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Electronic and Information Engineering Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Economics and Management Taiyuan China Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Dept. of Computer Science and Technology Taiyuan China

ISBN: (纸本)9798350303759

Aiming at the uncertainty of the degradation procedure of a complex multi-component system in a time-varying environment and the influence of the random dependence between different components, a remaining useful life prediction model for time-varying adaptive kernel density estimation based on the dependence between multiple components is established. First, the random dependence between multi-component systems is clustered. Considering that different components in the same cluster are affected by the random dependence, the components between different clusters do not affect each other. Judge the dependence characteristics between components, and use the dependence characteristics of different components to conduct clustering degradation modelling. Secondly, the parameters of the system change with time. Considering that monitoring data near the current time has a greater impact on the remaining useful life prediction compared with historical data, data in the vicinity of the current time is accorded a more significant weight, and a time-varying kernel density estimation prediction method is designed, in which the nearest-neighbour adaptive window-width selection method is used. Finally, give an example to demonstrate the effectiveness by the proposed method. © 2023 IEEE.

关键词： Forecasting

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