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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Driven Multivariate Regression - Feature engineering with Random Forest and XGBoost for Accurate Weather Prediction

Driven Multivariate Regression - Feature Engineering with Ra...

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international conference on Cybernetics and Intelligent System (ICORIS)

作者： Nur Alamsyah Budiman Venia Restreva Danestiara Imannudin Akbar Arnold Ropen Sinaga Reni Nursyanti Faculty of Technology and Informatics UNIBI Bandung Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Fluctuations in weather conditions present a signif- icant challenge for accurate prediction, particularly when con- sidering multiple interdependent variables such as temperature and precipitation. To address this issue, this study proposes a multivariate regression approach driven by advanced feature engineering techniques. Specifically, we employed Random Forest and XGBoost models, leveraging temporal, interaction, and lag features to enhance the predictive accuracy. the proposed method was evaluated using a real-world weather dataset, where both models demonstrated substantial improvements in prediction per-formance. the Random Forest model achieved a Mean Squared Error (MSE) of 0.059 for temperature and 0.019 for precipitation, with an R-squared (R2) of 0.995 and 0.999, respectively. Similarly, the XGBoost model yielded an MSE of 0.043 for temperature and 1.826 for precipitation, with an R2 of 0.996 and 0.983. these results underscore the effectiveness of the feature engineering process combined with robust multivariate regression models, offering a promising approach for accurate weather prediction.

关键词： Precipitation Accuracy Fluctuations Weather forecasting predictive models Multivariate regression Boosting Intelligent systems Random forests Cybernetics

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RESEARCH ON KEY TECHNOLOGY AND APPLICATION OF SEVERE ACCIDENT SIMULATOR IN CANDU6 NUCLEAR POWER PLANT 30

RESEARCH ON KEY TECHNOLOGY AND APPLICATION OF SEVERE ACCIDEN...

引用

30th international conference on Nuclear engineering, ICONE 2023

作者： Huang, Xiong Gu, Yunfeng Zhou, Faru Wei, Wei Xie, Zhengquan Yang, Xujie Hou, Xueyan Ma, Guoyang China Nuclear Power Operation Tech. Co. Ltd. Wuhan China Nuclear Power Operation Management Co. LTD CNNP Jiaxing City China

ISBN: (纸本)9784888982566

the Full Scope Simulator (FSS) of CANDU6 is very important for operator training, but due to the lack of the severe accident models, the severe accident (SA) training cannot be carried out. therefore, it is urgent to expand the severe accident model on the CANDU6-FSS in QinShan III NPP. By coupling MAAP5-CANDU model to develop CANDU6 SA simulator module, the FSS of QinShan III NPP can provide richer and more complete accident scenarios for operator training and accident drills. this paper introduces the severe models (such as reactor core, primary system and containment) and the key technology of coupling MAAP5-CANDU software with FSS simulation platform. Based on the CANDU6 SA simulator and virtual reality technology, a severe accident 3D displays system (3D) is developed. By using the data of CANDU6 SA Simulator, the system can present the severe accident simulation results with 3D dynamic visualization to the audience. Furthermore, the digital severe accident management guidance (SAMG) system is developed, which successfully transforms manual processes into electronic and automated processes, such as paper document review, data search, logical judgment, and auxiliary calculations related to SAMG operations. In the meanwhile, it can reduce the work intensity of relevant personnel of QinShan III NPP and improve the efficiency of SAMG execution. In summary, the development of CANDU6 SA simulator is not only an effective means for nuclear power plant severe accident scenario drills, but also can be used as an auxiliary tool for SAMG verification and drill scenarios optimization, which provide technical support for the safe and stable operation of nuclear power plant in the future. © 2023 by ASME.

关键词： Nuclear power plants

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How Does Pre-trained Language Model Perform on Deep Learning Framework Bug Prediction?

How Does Pre-trained Language Model Perform on Deep Learning...

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international conference on software engineering - Companion, ICSE, Companion

作者： Xiaoting Du Chenglong Li Xiangyue Ma Zheng Zheng Shanghai Key Laboratory of Trustworthy Computing (East China Normal University) Beijing University of Posts and Telecommunications Beijing China Beihang University Beijing China

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Understanding and predicting bugs is crucial for developers seeking to enhance testing efficiency and mitigate issues in software re-leases. Bug reports, though semi-structured texts, contain a wealth of semantic information, rendering their comprehension a critical aspect of bug prediction. In light of the recent success of pretrained language models (PLMs) in the domain of natural language processing, numerous studies have leveraged these models to grasp various forms of textual information. However, the capability of PLMs to understand bug reports remains uncertain. To tackle this challenge, we introduce KnowBug, a framework with a bug report knowledge-enhanced PLM. In this framework, utilizing bug reports obtained from open-source deep learning frameworks as input, prompts are designed and the PLM is fine-tuned for evaluating KnowBug's ability to comprehend bug reports and predict bug types.

关键词： Deep learning Computer bugs Semantics predictive models Rendering (computer graphics) software Natural language processing

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Twitter-informed Prediction for Urban Traffic Flow Using Machine Learning 6

Twitter-informed Prediction for Urban Traffic Flow Using Mac...

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6th IEEE international conference on Universal Village, UV 2022

作者： Shoaeinaeini, Maryam Ozturk, Oktay Gupta, Deepak Wichita State University School of Computing Wichita United States Wichita State University Industrial Systems and Manufacturing Engineering Wichita United States

ISBN: (纸本)9781665474771

the current traffic system requires short-term traffic forecasting to manage and control the traffic flow. Irregular traffic events, such as road closures, accidents, and severe weather, reduce the accuracy of data-driven predictive models. Social media platforms, particularly Twitter can significantly help to realize a real-traffic flow system by representing traffic events. Combining traffic data with information about road disruptions posted on Twitter can improve urban traffic parameter prediction. this paper proposes an urban traffic flow prediction by combining massive traffic, calendar, and weather data with related tweet posts. As a case study, the model is implemented on an urban traffic dataset extracted from the California Performance Measurement System (PeMS) in the USA. To provide a reliable and accurate prediction, the proposed model is evaluated with several machine learning methods. the results from the empirical study show that when Twitter features are combined with traffic, weather, and calendar features, the prediction accuracy is enhanced. As a result, we obtain around 89 percent, 95 percent, 93 percent, 91 percent, 91 percent, and 95 percent R-squared from AdaBoost regression, Random Forest, Gradient Boosting, Artificial Neural Network, Decision Trees, and KNN Regression, respectively. © 2022 IEEE.

关键词： Decision trees

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A Method of Electrical Network Nodes Selection for Allocating Devices Mitigated Intermittent Voltage Unbalance

A Method of Electrical Network Nodes Selection for Allocatin...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Konstantin Shish Pavel Bordadyn Vladimir Tulsky Department of Theoretical Foundations of Electrical Engineering National Research University “Moscow Power Engineering Institute” Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

the paper is dedicated to intermittent voltage unbalance (IVU) in the electrical networks with traction load. Dynamic modeling of unbalanced modes in a network fragment in MATLAB Simulink was performed. A method of nodes selection for allocating devices mitigated intermittent voltage unbalance in the electrical networks with traction load was proposed. the software based on the method was developed.

关键词： Performance evaluation Substations software packages Static VAr compensators Medium voltage Control systems Mathematical models Automatic voltage control Inductors Load modeling

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Comparative Analysis: Machine Learning vs. Artificial Neural Networks for Animal Disease Prediction

Comparative Analysis: Machine Learning vs. Artificial Neural...

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international conference on Computational Intelligence and Networks (CINE)

作者： Zakiya Khan Pritam Raj Manni Kumar Department of Computer Science Engineering Chandigarh University Punjab India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

Accurate animal disease prediction is essential for improving livestock health and mitigating the impact of disease outbreaks, thereby safeguarding food security andreducing agricultural losses. this study presents a comparative analysis of two prominent approaches: Machine Learning (ML) and Artificial Neural Networks (ANN), for predicting animal diseases using symptoms and temperature data. Our experiments revealed that while both methods offer valuable insights, the ANN model, implemented with TensorFlow and Keras, outperforms ML in predictive accuracy. the Support Vector Classifier (SVC) achieved an accuracy of 79.59%, whereas the ANN model demonstrated superior accuracy and robustness, making it a more reliable tool for early disease detection and livestock health management.

关键词： Adaptation models Accuracy Static VAr compensators Artificial neural networks Machine learning predictive models Vectors Robustness Livestock Diseases

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Research on the application of artificial intelligence algorithms in drought prediction 6

Research on the application of artificial intelligence algor...

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6th international conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Liu, Mengxi Zai, Guangjun Wang, Guofu Shi, Weiwei Zhang, Guanqun School of Cyber Science and Engineering ZhengZhou University HeNan ZhengZhou450002 China National Climate Center Beijing100081 China Beijing SuperMap Software Co. Ltd 100085 China

ISBN: (纸本)9781450397148

With the aggravation of global warming, drought has caused more and more serious impact on society and national economy, so the accurate prediction of drought is also the most important thing to deal with the frequent drought problem. On the other hand, artificial intelligence technology has shown its advantages in various fields and has a broad application prospects, so it is gradually being used in drought prediction. In this paper, drought prediction algorithms in recent years are reviewed, and existing cases of drought prediction using different arithmetic models for different drought indices are studied. the advantages and disadvantages of different algorithms are compared, the challenges of drought prediction are summarized, and the future of drought prediction is prospected. © 2022 Association for Computing Machinery.

关键词： Machine learning

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Research on Photovoltaic Power Prediction Method based on LSTM-KAN

Research on Photovoltaic Power Prediction Method based on LS...

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international conference on Electronic engineering and Informatics (EEI)

作者： Shuoqi Gong Wenbo Chen Xuedong Jing Chun Wang School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

the effective integration of photovoltaic power stations in power grids necessitates accurate prediction of solar photovoltaic power generation. However, current prediction methodologies that depend on manual feature extraction, conventional modeling techniques, and isolated deep learning (DL) models fall short of satisfying the performance requirements for intricate and highly variable scenarios. therefore, a novel approach is proposed in this study that leverages environmental climate information for hourly photovoltaic power generation prediction. the prediction task is characterized as a structured output prediction problem wherein multiple inputs are concurrently employed. To account for correlations between consecutive hours, the proposed predictive model utilizes KAN network modified Long short-term memory (LSTM) networks during training. Experimental results using environmental datasets from Shanghai, China demonstrate that the proposed algorithm outperforms LSTM.

关键词： Photovoltaic systems Training Time series analysis predictive models Prediction algorithms Regulation Power grids Forecasting Long short term memory Standards

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DTG-LSTM: A Delaunay Triangulation Graph Network for Human Trajectory Prediction

DTG-LSTM: A Delaunay Triangulation Graph Network for Human T...

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software engineering and Computer Science (CSECS), international conference on

作者： Zhimiao Shi Yao Xiao School of Intelligent System Engineering Sun Yat-Sen University Shenzhen China

the prediction of pedestrian trajectories is an essential yet challenging aspect of intelligent transportation systems, particularly relevant for autonomous vehicles and complex pedestrian scenarios. Taking into account the complex dynamics of pedestrian motion, we suggest employing the Long Short-Term Memory network combined with the Delaunay triangulation graph (DTG-LSTM) as a novel approach to capturing these intricacies. the model uniquely integrates the geometric characteristics of Delaunay triangulation to better represent evolving topological relationships among pedestrians, potentially contributing to more realistic trajectory predictions. Our model incorporates an encoder-decoder structure: the encoder amalgamates temporal and interaction trajectory information, while the decoder implements a three-pronged strategy, recursion, teacher-forcing, and a novel mixed teacher-forcing technique, to enhance prediction performance. While there is room for further optimization, our integration of geometric, topological, and graph convolutional elements into a trajectory prediction framework shows potential. Preliminary evaluations on widely-used public datasets (Eth and UCY) suggest promising predictive accuracy. the DTG-LSTM model offers a new avenue for exploration in the field of intelligent transportation systems, inviting further inquiry and development.

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A Deep Feature Ensemble Methodology for 2D Biomedical Image Classification 6th

A Deep Feature Ensemble Methodology for 2D Biomedical Image ...

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6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Rajoria, Rajat Kanodia, Balmukund Saha, Debam Singh, Pawan Kumar Department of Information Technology Jadavpur University Salt Lake City West Bengal Kolkata India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Department of Information Technology Jadavpur University West Bengal Kolkata India Shinawatra University Pathum Thani Thailand

ISBN: (纸本)9789819780891

In recent years, the emergence of deep learning (DL) has triggered a transformative revolution within the realm of medical imaging. Its profound impact lies in its ability to confront complex medical image analysis tasks with unmatched precision and efficiency. this paper delves into the domain of DL, focusing on its application to optical coherence tomography (OCT), a critical modality in biomedical imaging. the core of this research revolves around the utilization of a multi-class dataset tailored specifically for OCT, featuring four distinct categories. In parallel, the study extends its reach to DermaMNIST, a comprehensive dataset featuring a diverse array of common pigmented skin lesions for multi-class classification. Experiments are conducted using MedMNIST v2, a comprehensive, lightweight benchmark for 2D biomedical image classification. It not only illuminates the untapped potential of DL within the realm of OCT and dermatological imaging but also underscores the pivotal role of harnessing AI-driven solutions in the ever-evolving landscape of medical imaging. this study focuses on a deep feature ensemble methodology, which combines distinctive features from two pre-trained DL models such as DenseNet121 and InceptionV3 to enhance predictive accuracy. the research presents a compelling narrative that emphasizes the broader implications and applications of DL in the intricate field of biomedical image classification, spanning both the domains of OCT and dermatology. the source codes related to this work are available at https://***/rajatrajoria/A-Deep-Feature-Ensemble-Methodology-for-2D-Biomedical-Image-Classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optical coherence tomography

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