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4th international conference on machine learning, Optimization, and data science, LOD 2018

ISBN: (纸本)9783030137083

The proceedings contain 46 papers. The special focus in this conference is on machine learning, Optimization, and data science. The topics include: Adaptive dimensionality reduction in multiobjective optimization with multiextremal criteria;REFINE: Representation learning from diffusion events;augmented design-space exploration by nonlinear dimensionality reduction methods;classification and survival prediction in diffuse large B-cell lymphoma by gene expression profiling;learning consistent tree-augmented dynamic bayesian networks;designing ships using constrained multi-objective efficient global optimization;a new approach to measuring distances in dense graphs;ant colony optimization for markov blanket-based feature selection. Application for precision medicine;Average performance analysis of the stochastic gradient method for online PCA;simple learning with a teacher via biased regularized least squares;Improving traditional dual ascent algorithm for the uncapacitated multiple allocation hub location problem: A RAMP approach;supervised learning approach for surface-mount device production;crawling in Rogue’s dungeons with (partitioned) A3C;decision of neural networks hyperparameters with a population-based algorithm;strong duality of the kantorovich-rubinstein mass transshipment problem in metric spaces;evolutionary construction of convolutional neural networks;improving clinical subjects clustering by learning and optimizing feature weights;a framework to automatically extract funding information from text;Speeding up budgeted stochastic gradient descent SVM training with precomputed golden section search;an unsupervised learning classifier with competitive error performance;feature based multivariate data imputation;A GRASP/VND heuristic for the max cut-clique problem;a modelica-based simulation method for black-box optimal control problems with level-set dynamic programming;a clonal selection algorithm for multiobjective energy reduction multi-depot vehicle r

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Understanding Stroke Risk Profiles in Middle-Aged Adults: A Genetic Algorithm-Based Feature Selection Aproach

Understanding Stroke Risk Profiles in Middle-Aged Adults: A ...

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18th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Gonçalves, Ligia Ferreira de Carvalho Fiorini, Caio Davi Rabelo Franca, Daniel Rocha Noronha, Marta Dias Moreira Song, Mark Alan Junho Galvez, Luis Enrique Zárate Data Science and Artificial Intelligence Pontifícia Universidade Católica de Minas Gerais Rua Claudio Manuel Belo Horizonte Brazil Computer Science Pontifícia Universidade Católica de Minas Gerais Rua Claudio Manuel Belo Horizonte Brazil Institute of Exact Sciences and Computer Science Pontifícia Universidade Católica de Minas Gerais Rua Claudio Manuel Belo Horizonte Brazil

data mining and machine learning techniques have been widely used in the knowledge extraction process of medical databases, one highlight being their use to improve diagnostic systems. Decision trees are supervised black box machine learning models that, although simple, are easy to interpret. In this work, we propose the use of these techniques to describe the profile of middle-aged adults (40-59) diagnosed with stroke, a disease that in Brazil was one of the main causes of death in previous years. The genetic algorithm was applied to extract the best characteristics so that the Decision Tree algorithm could then be used in the database provided by the 2019 National Health Survey to obtain the most comprehensive rules and identify the most relevant attributes for describing the profile of these individuals. The conclusions indicate that the rules generated for middle-aged adults are mainly about routine habits, such as work or salt consumption. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Adversarial machine learning

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Development and Validation of Risk Assessment Model for Pelvic Organ Prolapse Based on A Retrospective Study with machine learning Algorithms

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international UROGYNECOLOGY JOURNAL 2025年 1-9页

作者： Mei, Ling Gao, Linbo Wang, Tao Yang, Dong Chen, Weixing Niu, Xiaoyu Sichuan Univ West China Univ Hosp 2 Dept Obstet & Gynecol Chengdu 610041 Peoples R China Sichuan Univ West China Univ Hosp 2 Ctr Translat Med Key Lab Birth Defects & Related Dis Women & Childr Chengdu 610041 Peoples R China Sichuan Univ West China Univ Hosp 2 Lab Metabol & Gynecol Dis Res Chengdu 610041 Peoples R China Guangzhou AID Cloud Technol Co Ltd Dept Data Sci Guangzhou 510663 Peoples R China

Introduction and HypothesisWe aimed to develop and validate a clinically applicable risk assessment model for identifying women at a high risk of pelvic organ prolapse (POP) based on a retrospective *** study enrolled patients with and without POP between January 2019 and December 2021. Clinical data were collected and machine learning models were applied, such as multilayer perceptron, logistic regression, random forest (RF), light gradient boosting machine and extreme gradient boosting. Two datasets were constructed, one comprising all variables and the other excluding physical examination variables. Two versions of the machine learning model were developed. One was for professional doctors, and the other was for community-health providers. The area under the curve (AUC) and its confidence interval (CI), accuracy, F1 score, sensitivity, and specificity were calculated to evaluate the model's performance. The Shapley Additive Explanations method was used to visualize and interpret the model *** total of 16,416 women were recruited, with 8,314 and 8,102 in the POP and non-POP groups respectively. Eighty-seven variables were recorded. Among all candidate models, the RF model with 13 variables showed the best performance, with an AUC of 0.806 (95% CI 0.793-0.817), accuracy of 0.723, F1 of 0.731, sensitivity of 0.742, and specificity of 0.703. Excluding the physical examination variables, the RF model with 11 variables showed an AUC, accuracy, F1 score, sensitivity, and specificity of 0.716, 0.652, 0.688, 0.757, and 0.545 *** constructed a clinically applicable risk warning system that will help clinicians to identify women at a high risk of POP.

关键词： machine learning Pelvic organ prolapse Risk assessment model Shapley additive explanations method

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Analyzing COVID-19 sentiments on Twitter: A machine learning approach 4th

Analyzing COVID-19 sentiments on Twitter: A machine learning...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Aishwarya, V. Sanmuga Priyan, K. Vyshnavi, R. Azhagiri, M. Sathya Priya, S. SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781032911571

Throughout the outbreak, COVID-19 has been a hot issue on Twitter, providing a forum for people to voice their ideas and perspectives. Unfortunately, there isn’t a thorough rundown of sentiment analysis in Twitter discussions around COVID-19. By analyzing sentiment analysis utilizing COVID-19 Twitter data from both a machine learning and a behavioral and social science standpoint, this article fills this gap. Forty papers examining public attitudes were found in a study of the literature conducted between October 2019 and January 2022. Higher accuracy is shown by ensemble models, especially those that use BERT and RoBERTa. These models do not provide exact outcomes, despite their theoretical promise. Based on the COVIDSENTI dataset, the research emphasizes the need for government action on social media to counter false information. It highlights that, despite the continual examination of datasets, proactive public health actions are crucial in lowering unfavorable sentiments during a pandemic. © 2025 the Author(s).

关键词： Tweets

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Adaptive machine learning for Resource-Constrained Environments 1st

Adaptive Machine Learning for Resource-Constrained Environm...

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1st international Workshop on Discovering Drift Phenomena in Evolving Landscapes, DELTA 2024

作者： Ordóñez, Sebastián A. Cajas Samanta, Jaydeep Suárez-Cetrulo, Andrés L. Carbajo, Ricardo Simón University College Dublin Dublin Ireland

ISBN: (纸本)9783031823459

The Internet of Things is an example domain where data is perpetually generated in ever-increasing quantities, reflecting the proliferation of connected devices and the formation of continuous data streams over time. Consequently, the demand for ad-hoc, cost-effective machine learning solutions must adapt to this evolving data influx. This study tackles the task of offloading in small gateways, exacerbated by their dynamic availability over time. An approach leveraging CPU utilization metrics using online and continual machine learning techniques is proposed to predict gateway availability. These methods are compared to popular machine learning algorithms and a recent time-series foundation model, Lag-Llama, for fine-tuned and zero-shot setups. Their performance is benchmarked on a dataset of CPU utilization measurements over time from an IoT gateway and focuses on model metrics such as prediction errors, training and inference times, and memory consumption. Our primary objective is to study new efficient ways to predict CPU performance in IoT environments. Across various scenarios, our findings highlight that ensemble and online methods offer promising results for this task in terms of accuracy while maintaining a low resource footprint. Code is available at https://***/sebasmos/AML4CPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Privacy Assessment Methodology for machine learning Models and data Sources 19th

Privacy Assessment Methodology for Machine Learning Models ...

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19th international conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Erdei, Rudolf Pasca, Emil Delinschi, Daniela Avram, Anca Chereja, Ionela Matei, Oliviu Technical University of Cluj Napoca North University Centre of Baia Mare Baia Mare Romania

ISBN: (纸本)9783031750090

machine learning and data Mining imply notable privacy vulnerabilities since they have the potential to expose confidential details about people or collectives that have contributed to the data. This paper proposes a Generalised Privacy Assessment Methodology that can be universally applied to any machine learning model and its corresponding data Source. The suggested technique facilitates the quantification of the Vulnerability Score for any given data source or model. We demonstrate the practicality and efficacy of the concept by implementing it in several scenarios, ranging from a small-scale IoT project to a hypothetical extensive database, like those used by governmental entities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Construction and realization of modern Chinese vocabulary base driven by big data 2

Construction and realization of modern Chinese vocabulary ba...

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2nd international conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Luo, Zhong Faculty of Language and Literature Anhui Sanlian University Anhui Hefei230601 China

ISBN: (纸本)9781510689053

With the rapid advancement of big data technology, the construction of large-scale databases has become a critical focus in computer science. Existing research primarily explores the integration of big data with various computational methods, such as machine learning, data mining, and cloud computing, to enhance data processing efficiency and analytical capabilities. However, there is still a gap in the development of specialized databases that can effectively leverage big data for specific applications. This study aims to address this gap by proposing a novel approach to constructing a modern Chinese vocabulary database driven by big data techniques. The research focuses on the application of advanced computer methods, including data preprocessing, indexing, and retrieval algorithms, to optimize database performance. The ultimate goal is to develop a robust and scalable database system that can efficiently handle large volumes of data and provide accurate and timely information for various computational tasks. © 2025 SPIE.

关键词： data assimilation

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machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Han, Bo Hai, Junrui Jonathan Pan, Aixing Wang, Yingjie Hai, Yong Capital Med Univ Beijing Jishuitan Hosp Beijing Peoples R China Princeton Int Sch Math & Sci 19 Lambert Dr Princeton NJ 08540 USA Capital Med Univ Beijing Chao Yang Hosp Dept Orthoped Surg GongTiNanLu 8 Beijing 100020 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Capital Med Univ Joint Lab Res & Treatment Spinal Cord Injury Spina Beijing Peoples R China Capital Med Univ Clin Ctr Spinal Deform Beijing Peoples R China

We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients from our centre between 2007 and 2019 were retrospectively reviewed. Radiographic parameters include the cervical lordosis (CL), T1 slope, C2-C7 sagittal vertical axis (C2-7 SVA), primary curve Cobb angle, thoracic kyphosis (TK), C7-S1 sagittal vertical axis (C7-S1 SVA) and proximal junctional angle (PJA) were evaluated preoperatively, postoperatively and at the final follow-up. The parameters were analyzed using a t-test and chi 2 test. The machine learning methodology of a sparse additive machine (SAM) was applied to identify the risk factors that caused the cervical imbalance. 138 patients were enrolled in this study (96 male and 42 female). The mean thoracic curve Cobb angle was 67.00 +/- 22.74 degrees. The mean age at the first operation was 8.5 +/- 2.6yrs. The mean follow-up was 38.48 +/- 10.87 months. CL, T1 slope, and C2-7 SVA increased significantly in the final follow-up compared with the pre-operative data. (P < 0.05). The CL and T1 slope increased more significantly in the group of patients who had proximal junctional kyphosis (PJK) compared with the patients without PJK (P < 0.05). The location of the upper instrumented vertebrae (UIV) and single/dual growing rod had no significant influence on the sagittal cervical parameters (P > 0.05). According to the SAM analysis of machine learning algorithms, Postoperative PJK, more improvement of kyphosis, and T1 slope angle were identified as the risk factors of cervical sagittal imbalance during the treatment of growing rod surgery. The growing rod surgery in EOS significantly affected the cervical sagittal alignment. Postoperative PJK and more improvement of kyphosis and T1 slope angle would lead to a higher incidence of cervical sagittal imbalance.

关键词： Cervical alignment Early-Onset Scoliosis Growing rod Proximal junctional kyphosis machine learning algorithm

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Application of Deep learning for Wildfire Risk Management: Preliminary Results 4th

Application of Deep Learning for Wildfire Risk Management:...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： De Rango, Alessio D’Ambrosio, Donato Mendicino, Giuseppe Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

Due to ongoing climate change and the related increased risk of drought, wildfires in the Mediterranean are expected to become more frequent and intense. This note introduces the use of Deep learning to produce susceptibility maps for wildfire risk assessment to tackle this scenario. Indeed, thanks to the improved computational capabilities of modern GPUs, recent literature has pointed out that it is possible to train Deep learning models on big datasets by outperforming standard approaches in several application domains, including the one object of this study. An Artificial Neural Network (ANN) model has been developed and applied to the Calabria region (southern Italy) study area. The network has been trained and validated over ten years of data by considering both meteorological and geomorphological data, and human factors. Preliminary results are promising, evidencing a higher network’s accuracy to the operational state-of-the-art Fire Weather Index (FWI) method, currently applied for forecasting and mitigation purposes in the study area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Themes, Knowledge Evolution, and Emerging Trends Related to the Application of Artificial Intelligence in Personalized learning: A Scientometric Analysis in CiteSpace 14

Themes, Knowledge Evolution, and Emerging Trends Related to ...

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14th international conference on Educational and Information Technology, ICEIT 2025

作者： Qin, Qinggui Wang, Jiaming Zhang, Shuhan Macao Polytechnic University Faculty of Applied Sciences China GXNU School of Design Guangxi Normal University Guilin China

ISBN: (纸本)9798331540883

Artificial intelligence (AI) plays a pivotal role in the development and growth of personalized learning. Therefore, it is necessary to conduct scientific and in-depth research on the application of AI in this field, systematically analyzing its development trends and research hotspots to provide references for researchers. This study employs a visual bibliometric analysis as the primary research approach, selecting 922 articles from the Web of science spanning from 2004 to 2024. A comprehensive analysis was conducted using CiteSpace, primarily covering major highly co-cited literature and keywords. The notable findings of this study are as follows: Firstly, the study reveals three major themes related to the application methodology of AI in personalized learning, namely, machine learning, large language models, and personalized recommendation systems. Secondly, the visual timeline reveals the application of Personalized learning in AI across three phases: Part A (2016-2022), Part B (2019-2024), and Part C (2011-2024). Thirdly, the burst analysis illustrates the hotspots, especially 'intelligent tutoring system'. This study deepens our understanding of the fundamentals and cutting-edge research in the application of AI to personalized learning, thus facilitating the identification of future research directions, as well as teaching practice patterns and trends. © 2025 IEEE.

关键词： Teaching

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