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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33935 条记录，以下是1011-1020 订阅

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Prediction of Output Generated by a Steam Turbine Using machine learning 1

引用

3rd Biennial international conference on Future learning Aspects of Mechanical engineering, FLAME 2022

作者： Lad, Nishigandha Vamsikrishna, A. Pune India

ISBN: (数字)9789819923823

ISBN: (纸本)9789819923816

A steam turbine is one of the most widely used equipment to generate electricity. In this data-driven world, the development of predictive models for such equipment has become necessary. In this paper, machine learning models are developed to predict the output generated by the steam turbine. Five predictive models are developed and compared. It is found that a third-degree polynomial regression model with an R-squared value of 87% and Mean square error value of 94.9 is found to be the best-suited model as per the requirement. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steam turbines

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Automatic Detection of Learner’s learning Style 4th

Automatic Detection of Learner’s Learning Style

引用

4th international conference on data, engineering, and Applications, IDEA 2022

作者： Sharuni, S. Dhana Lakshmi, R. Murugappan, Abirami Department of Information Science and Technology College of Engineering Guindy Anna University Chennai India

ISBN: (纸本)9789819700363

learning style is crucial in assisting students in better understanding things learnt and in helping them retain them for extended periods of time. Utilising surveys, learning styles in traditional and online environments are identified. To automatically determine a learner’s preferred learning style without upsetting them, it is becoming more popular. machine learning is a branch of artificial intelligence that allows systems to learn and develop without being explicitly programmed. A learning style is the way that makes one learns best. It is based on the tastes and traits of the individual. Because different students learn in different ways, individual learning styles are vital to consider in effective teaching. learning style is important in assisting students in remembering topics for extended periods of time and improving their knowledge of concepts. The way that kids learn is crucial in helping them retain information for longer periods of time and advance their conceptual understanding. Surveys are used to determine learning preferences both offline and online. The goal is to recognise and apply characteristics that will automatically and unobtrusively determine a learner’s preferred learning style. Using Multi-nominal Naive Bayes the data acquired from students in various classes is used to train a model, which may then be used to predict a student’s learning style based on the suggested traits. The information gathered from learners is used to trained model, and it could be used for appropriately predict the learner’s learning style based on the recommended qualities. The accuracy in each dimension for the different machine learning algorithms was calculated using the data obtained for the VARK model (Visual, Aural, Read/Write, and Kinesthetic). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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Predicting Depression History from a Short Reward/Aversion Task with Behavioral Economic Features 1

引用

5th international conference on Biomedical and Health Informatics (ICBHI)

作者： Stefanopoulos, L. Lavlani, S. Kim, B. W. Vike, N. Bari, S. Azcona, E. Woodward, S. Block, M. Maglaveras, N. Katsaggelos, A. K. Breiter, H. Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Univ Cincinnati Dept Comp Sci & Biomed Engn Cincinnati OH USA Northwestern Univ Feinberg Sch Med Chicago IL USA Northwestern Univ Integrated Mkt Commun Evanston IL USA Aristotle Univ Thessaloniki Dept Med Thessaloniki Greece Massachusetts Gen Hosp Dept Psychiat Boston MA USA

ISBN: (数字)9783031592164

ISBN: (纸本)9783031592157;9783031592164

This paper presents a novel example of depression prediction, merging cognitive science with data-driven machine learning. Behavioral economic features were engineered from a short picture rating task. Relative Preference Theory was applied to rating data for quantifying the degree to which participants liked, disliked, or were neutral to several types of pictures;thus, behavioral economic variables including loss aversion, risk aversion, and 13 others that are amenable to psychological interpretation were mined. These variables were features of a logistic regression predictive model that targeted depression in a population-based sample (N = 281) with high test accuracy and no overfitting. Per our review of the literature, we cannot identify other papers that explicitly use behavioral economic features to predict depression with machine learning.

关键词： Depression prediction Behavioral Economic Features machine learning

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Vulnerabilities in AI Code Generators: Exploring Targeted data Poisoning Attacks 32

Vulnerabilities in AI Code Generators: Exploring Targeted Da...

引用

32nd IEEE/ACM international conference on Program Comprehension (ICPC)

作者： Cotroneo, Domenico Improta, Cristina Liguori, Pietro Natella, Roberto Univ Naples Federico II Naples Italy

ISBN: (纸本)9798400705861

AI-based code generators have become pivotal in assisting developers in writing software starting from natural language (NL). However, they are trained on large amounts of data, often collected from unsanitized online sources (e.g., GitHub, HuggingFace). As a consequence, AI models become an easy target for data poisoning, i.e., an attack that injects malicious samples into the training data to generate vulnerable code. To address this threat, this work investigates the security of AI code generators by devising a targeted data poisoning strategy. We poison the training data by injecting increasing amounts of code containing security vulnerabilities and assess the attack's success on different state-of-the-art models for code generation. Our study shows that AI code generators are vulnerable to even a small amount of poison. Notably, the attack success strongly depends on the model architecture and poisoning rate, whereas it is not influenced by the type of vulnerabilities. Moreover, since the attack does not impact the correctness of code generated by pretrained models, it is hard to detect. Lastly, our work offers practical insights into understanding and potentially mitigating this threat.

关键词： machine Translation Software security engineering

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Enhancing Solar Radiation Conversion in Floating Photovoltaic Systems through machine learning 9

Enhancing Solar Radiation Conversion in Floating Photovoltai...

引用

9th international conference on Power and Renewable Energy

作者： Peng, Qiao He, Xiang Yang, Zhile Liu, Kailong Shandong Univ Shenzhen Res Inst Shenzhen Peoples R China Queens Univ Belfast Belfast Antrim North Ireland Queens Univ Belfast Sch Nat & Built Environm Belfast Antrim North Ireland Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Overreliance on traditional energy sources leads to resource depletion and environmental pollution, posing serious challenges to global energy security. In response, Floating Photovoltaic (FPV) technology has emerged as a promising solution, offering an alternative to traditional ground-mounted Photovoltaic (PV) systems. This study utilizes data collected from Brookhall Estate between September 2022 and January 2023, employing six machine learning algorithms and the interpretable SHapley Additive exPlanations (SHAP) technique to analyze solar radiation convertibility. Random Forest is identified as the most reliable model for FPV prediction. The results highlight the nonlinear impact of factors such as temperature, conductivity, seasonal variations, water depth, and wind direction on solar radiation collection efficiency. Targeted strategies for FPV design and placement are proposed, contributing to overall FPV optimization in alignment with sustainable energy development goals and the pursuit of net-zero emissions.

关键词： floating photovoltaic systems solar radiation prediction environmental factors interpretable machine learning sustainable energy

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Hybrid Quantum machine learning Model for Power Allocation in NOMA 8

Hybrid Quantum Machine Learning Model for Power Allocation i...

引用

8th IEEE international conference on Information and Communication Technology, CICT 2024

作者： Nandi, Tanmoy Baghel, Amit Pathak, Abhishek Kumar Bhatia, Vimal Indian Institute of Technology Department of Electrical Engineering Indore India

ISBN: (纸本)9798331531539

Recent advances in quantum machine learning with its inherent properties of superposition, quantum parallelism, and quantum entanglement, have opened doors of possibilities in solving complex resource allocation problems in digital communication technologies. This study investigates power allocation in non-orthogonal multiple access (NOMA) systems using hybrid quantum neural networks (QNN) and compares with the classical neural network. A dataset is prepared for training the models and analysis of performance of the model is presented. Results show that the hybrid QNN achieves similar validation accuracy, while reducing overfitting and maintaining comparable performance in maximizing the sum rate. Despite fewer number of layers and parameters, the hybrid QNN achieves competitive results, demonstrating the potential of quantum-enhanced models in NOMA power allocation tasks. © 2024 IEEE.

关键词： Quantum entanglement

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Boosting Achieved-learning Outcomes with Maritime-Specific Projects in a machine learning Course

Boosting Achieved-Learning Outcomes with Maritime-Specific P...

引用

2024 ASEE Annual conference and Exposition

作者： Kump, Paul Marty August, Ian Kansas State University United States

In 2022, we developed a maritime-specific course in machine learning (ML) for undergraduate maritime engineering and naval architecture students in an effort to boost low levels of achieved student outcomes as articulated by the Accreditation Board for engineering and Technology (ABET). As a major component to the course, we designed a set of mini projects that all utilized the same maritime-related dataset-hypothesizing that domain-specific projects would increase student performance-and we made the projects and solutions publicly available for students and like-minded instructors. This work was met with high praise from colleagues and students, with several positive comments and solicitations for downloads and solutions. But just how important is domain-specific material to an ML course? In this paper, we report on the effects our course had on student learning. The results and lessons learned from our study are valuable information for course developers, instructional designers, and educators looking to boost student performance and craft a domain-specific ML course, be it maritime or other. We measure the effects of our course on student learning by applying the difference-in-differences (DiD) statistical technique to course data before and after the 2022 course redesign. Included in this analysis are the results of course surveys completed by students, achieved level of ABET learning outcomes, and students' final grades in the course. We find significant increase for ABET learning outcomes;a result that pleased ABET during our institution's most-recent review cycle. We also find a significant increase of +0.50 grade point average (out of 4.00) to students' final grades. With regard to student attitude and perception via course evaluations, a positive change is observed, but we are unable to conclude that the change is statistically significant. © American Society for engineering Education, 2024.

关键词： Naval architecture

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An adaptive time-convolutional network online prediction method for ocean observation data 36

An adaptive time-convolutional network online prediction met...

引用

36th international conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Li, Enjing Li, Xiang Zhao, Zhigang Wu, Lu Wang, Yinglong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction. However, there is a’concept drift’ problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios. For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios. We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell. Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams. The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Spatio-temporal data

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Bidirectional Adaptation for Robust Semi-Supervised learning with Inconsistent data Distributions 40

Bidirectional Adaptation for Robust Semi-Supervised Learning...

引用

40th international conference on machine learning

作者： Jia, Lin-Han Guo, Lan-Zhe Zhou, Zhi Shao, Jie-Jing Xiang, Yu-Ke Li, Yu-Feng Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Huawei Technol Consumer BG Shenzhen Peoples R China

Semi-supervised learning (SSL) suffers from severe performance degradation when labeled and unlabeled data come from inconsistent data distributions. However, there is still a lack of sufficient theoretical guidance on how to alleviate this problem. In this paper, we propose a general theoretical framework that demonstrates how distribution discrepancies caused by pseudo-label predictions and target predictions can lead to severe generalization errors. Through theoretical analysis, we identify three main reasons why previous SSL algorithms cannot perform well with inconsistent distributions: coupling between the pseudo-label predictor and the target predictor, biased pseudo labels, and restricted sample weights. To address these challenges, we introduce a practical framework called Bidirectional Adaptation that can adapt to the distribution of unlabeled data for debiased pseudo-label prediction and to the target distribution for debiased target prediction, thereby mitigating these shortcomings. Extensive experimental results demonstrate the effectiveness of our proposed framework.

关键词： Forecasting

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Using machine learning to Assess the Impact of Harsh Violent Discipline on Children and Adolescents in Lowand Middle-Income Countries: A Comparative Analysis Focusing on Its Relationship with Disabilities

Using Machine Learning to Assess the Impact of Harsh Violent...

引用

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

作者： Barreira, Milena S. Da Silva, Ariane C. B. Mannani, Hasheem Nobre, Cristiane N. Institute of Exact Sciences and Informatics Pontifical Catholic University of Minas Gerais Belo Horizonte Dom José Gaspar Brazil University College Dublin Ireland

Children’s exposure to violence has long been a social and cultural concern, manifesting in various forms across societies. According to UNICEF, approximately 300 million children worldwide, aged 2 to 4, experience regular violent discipline from caregivers, with around 250 million subjected to physical punishment. This study leverages data from the Multiple Indicator Cluster Survey to investigate the prevalence of severe violent discipline among children with and without disabilities in 54 lowand middle-income countries. Using machine learning algorithms, including Decision Tree, Random Forest, XGBoost, Support Vector machine (SVM), and Neural Networks, the analysis revealed that SVM outperformed other models, achieving the highest precision, recall, and F1-score (with values of 78% and 80% for the violence and non-violence classes, respectively). The results highlighted an increase in severe disciplinary violence correlated with the presence of disabilities, particularly in contexts involving the domain of ‘controlling behavior’. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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