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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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predictive Maintenance of Aircraft Engines: Machine Learning Approaches for Remaining Useful Life Estimation

Predictive Maintenance of Aircraft Engines: Machine Learning...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Deepika J Pappuru Manikanta Reddy Kothaluri Murari Boyana Rahul School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In the realm of predictive maintenance for aircraft engines, ensuring precise and timely predictions of Remaining Useful Life (RUL) is paramount to enhancing safety, reducing costs, and minimizing downtime. While deep learning models dominate this field, their complexity, high computational demand, and lack of interpretability can hinder practical deployment in real-world scenarios. this study demonstrates the potential of machine learning models as an efficient and interpretable alternative for RUL prediction, leveraging the publicly available C-MAPSS dataset from NASA. Using C-MAPSS dataset, we integrated PostgreSQL for scalable data storage and Flask for interactive real-time visualization, creating a seamless pipeline from data ingestion to result interpretation. Preprocessing steps, including normalization, noise removal via box plots, and feature engineering, ensured a clean and reliable dataset. Among the models evaluated-XGBoost, Random Forest, SVM, KNN, and Linear Regression-XGBoost emerged as the best performer, achieving an RMSE of 23.8 and an R2 score of 0.67. these results nearly matches the accuracy typically associated with deep learning models while maintaining computational efficiency. Feature importance analysis through Explainable AI (XAI) further revealed critical insights into sensor contributions, enhancing interpretability and trust in the predictions. this work underscores the practicality and effectiveness of machine learning in predictive maintenance, offering a robust, transparent, and resource-efficient approach to RUL prediction. the findings highlight its potential to meet industry needs where simplicity, accuracy, and interpretability are critical for decision-making and operational efficiency.

关键词： Deep learning Industries Accuracy Computational modeling Decision making Machine learning predictive models Safety Random forests predictive maintenance

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

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

引用

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...

引用

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 Photovoltaic Power Prediction Method based on LSTM-KAN

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

引用

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...

引用

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

Research on the application of artificial intelligence algor...

引用

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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A Study on Monthly Precipitation Prediction in Chengdu City Based on QPSO-LSTM

A Study on Monthly Precipitation Prediction in Chengdu City ...

引用

international conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Junchao Dai Yue Guan Anfu Wen Jiaxin Li Zihao Yang Jiashu Sun Department of computer and software Chengdu Jincheng College Sichuan China Department of intelligent manufacturing Chengdu Jincheng College Sichuan China

ISBN: (数字)9798331541798

ISBN: (纸本)9798331505615

In this paper, a precipitation prediction model based on QPSO-LSTM is proposed to optimize the hyperparameter combinations of the LSTM network through the QPSO algorithm to improve the prediction accuracy and efficiency of the model. the QPSO algorithm enhances the global searching ability, accelerates the speed of convergence, and avoids the local optimal solution efficiently, which makes the model have a significant advantage in capturing the seasonal changes of precipitation. Based on the historical precipitation data of Chengdu city, the experiment proves that the QPSO-LSTM model is superior to the traditional LSTM and PSO-LSTM models in terms of accuracy, which is of great theoretical and practical significance for future precipitation prediction.

关键词： Analytical models Precipitation Accuracy Urban areas Time series analysis predictive models Prediction algorithms Data models Long short term memory Convergence

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

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

引用

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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COMPUTATIONAL MODELING OF NANOSECOND LASER ABLATION OF TI-6AL-4V 18

COMPUTATIONAL MODELING OF NANOSECOND LASER ABLATION OF TI-6A...

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ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Shim, Sally Aguilar, Lesly Ma, J. Jahan, M. P. St Louis Univ Dept Aerosp & Mech Engn St Louis MO 63103 USA Miami Univ Dept Mech & Mfg Engn Oxford OH 45056 USA

ISBN: (纸本)9780791887240

Ti-6Al-4V is an alpha-beta titanium alloy having excellent strength, low modulus of elasticity, high corrosion resistance, good weldability and heat treatability. Consequently, it is the most commonly used titanium alloy with a wide use for applications in aerospace, automotive, and biomedical industries. However, Ti-6Al-4V is very difficult to be machined using traditional manufacturing processes. Laser machining is one nontraditional manufacturing process to machine Ti-6Al-4V. However, the laser-machined surface usually experiences phase transformation during the laser machining process because of the heating and cooling in laser machining. In this paper, the commercial Finite Element Method (FEM) software package ABAQUS is employed to assess the effect of nanosecond laser machining conditions and operating temperature on crater size, phase transformation in the Heat Affected Zone (HAZ), and residual stress distribution in laser machining of Ti-6Al-4V. the energy levels: 60mJ, 121mJ, 201mJ, 309mJ, 407mJ, 621mJ, 667mJ were tested. For the predictive modeling, three energy levels: 1000mJ, 2000mJ, and 4000mJ were tested. the phase transformation of Ti-6Al-4V at different temperatures was investigated to see material property change. the temperature used were: 200 degrees C, 400 degrees C, 600 degrees C, 800 degrees C, and 100C. Phase transformation kinetics is used to model the phase transformation of Ti-6Al-4V in laser machining process and rule of mixtures is employed to describe flow stress. Based on the results, the increasing the temperature will induce more HAZ. this research can provide insightful guidance for laser machining of Ti-6Al-4V.

关键词： Ti-6Al-4V Nanosecond laser FEM simulation phase transformation HAZ predictive modeling laser machining flat-top laser

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