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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2518 条记录，以下是281-290 订阅

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BRIDGING thE DATA ANALYTICS - DATA EXCELLENCE GAP: REQUIREMENTS FOR A DATA CATALOGUE FOR EFFICIENT CROSS-ORGANIZATIONAL MOBILITY AND SMART CITY DATA ANALYTICS 16

BRIDGING THE DATA ANALYTICS - DATA EXCELLENCE GAP: REQUIREME...

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16th international conference on ICT, Society and Human Beings, ICT 2023, the 15th international conference on e-Health, EH 2023, the 9th international conference on Connected Smart Cities, CSC 2023 and 8th international conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2023

作者： Sautter, Johannes Kräck, Jan Fischer, Rudolf Fähnrich, Nicolas Erlenhardt, Kai Soares, Marco Weitzel, Balthasar Fraunhofer-Institute for Industrial Engineering IAO Stuttgart Germany ifeu - Institut für Energie- und Umweltforschung Heidelberg Heidelberg Germany University of Stuttgart Institute of Human Factors and Technology Management IAT Stuttgart Germany Fraunhofer IAO Stuttgart Germany Municipality of Solingen Solingen Germany Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany

ISBN: (纸本)9789898704504

Data Analytics projects often suffer from low data availability, inconsistent data formats and data models. In mobility data projects this especially means there are inconsistent attributes for specifying time and space dimensions. Also, non-data management and data analytics experts have so far had little opportunity to obtain an overview of data products, since data repositories and catalogs usually do not present the data in a self-describing manner. Data mostly exists in organizational silos as well as inadequate quality. In organizational departments data literacy is often focused on the own work area and does not include an overreaching view on data management. Arguing from a holistic view including data analytics as well as data management knowledge, here a "data analytics - data excellence gap" exists. this paper describes and motivates a smart city and mobility data ecosystem data catalog as a core component to bridge this gap. © ICT *** rights reserved.

关键词： Data Analytics

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Capability Assessment of URANS Turbulence models for Simulation of Vortex Rope-Related Flow Defect in a Model Francis Turbine 9th

Capability Assessment of URANS Turbulence Models for Simulat...

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9th international and 49th National conference on Fluid Mechanics and Fluid Power, FMFP 2022

作者： Masoodi, Faiz Azhar Goyal, Rahul Department of Energy Science and Engineering IIT Delhi New Delhi110016 India

ISBN: (纸本)9789819710324

Off-design operation of hydraulic turbines is necessitated for the prevention of load imbalance in the electric power system. In particular, the part load operation of the turbine introduces a flow anomaly in the draft tube. the anomalous flow, a consequence of the vortex breakdown phenomenon, is comprised of a centrally located helical vortex structure that self-rotates as well as precesses about the draft tube central axis. this structure known as the rotating vortex rope, wrapped by a normal flow toward the draft tube periphery, is causative of low-frequency pressure fluctuation at a large scale. Disadvantage accompanies as loss of draft tube pressure recovery, reduction in turbine efficiency, and possible mechanical vibrations in event of resonance between frequencies of system and rotating vortex rope. this work assesses in detail the applicability of URANS-based standard k-Ε and SST k-ω turbulence models for the characterization of a part load vortex rope. Complete turbine transient simulations at PL, based on Francis-99 test case, are performed using the commercially available ANSYS CFX software. Obtained axial and radial velocity fields are compared with results from the experiment. A detailed spectral analysis is carried out using Fast Fourier Transforms to identify the dominant frequencies of pressure fluctuation in numerical and experimental data. Moreover, the pressure signal is decomposed into synchronous and asynchronous modes, the distribution of frequency within which is analyzed through Fast Fourier Transforms. A mismatch between experiment and simulation is identified and quantified to evaluate the utility of URANS models for investigations on RVR. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： software testing

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BEMAO: A novel adaptive and high-order BEM solver for steady-state acoustics-part 1: theory 30

BEMAO: A novel adaptive and high-order BEM solver for steady...

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30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Atak, O. Li, Y. Hamiche, K. Beriot, H. Desmet, W. Siemens Digital Industries Software Francis House 112 Hills Rd CambridgeCB2 1PH United Kingdom Siemens Digital Industries Software Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium DMMS Core Lab Flanders Make Belgium

ISBN: (纸本)9789082893151

this paper presents a novel adaptive and high order implementation of the Boundary Element Method (BEM) for steady-state acoustics, which we refer to as BEMAO. the method uses a priori error estimators to automatically adapt the p-order of the elements based on the target accuracy, edge lengths and input frequency. Using high order interpolation shape functions (up to order 6) compresses the model size substantially. In addition, BEMAO is also able to use a single non-homogeneous mesh to cover a wide frequency range while it provides optimal model size per frequency without the need for remeshing. the assembly cost is reduced via the use of multipole approximations. A direct solver is used to solve the compressed models. All these properties make BEMAO a novel and competitive BEM solver. the Part 1 of the BEMAO papers focuses on the theoretical aspects: the quadrature rules, the convergence rates of the high orders and the accelerated assembly routines. the computational power and accuracy of BEMAO is demonstrated on academic examples. © 2022 proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Boundary element method

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BIM-Based Parametric Modeling: Design Innovation and Project Demonstration of Special-Shaped Steel Structure engineering 9

BIM-Based Parametric Modeling: Design Innovation and Project...

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9th international Technical conference on Frontiers of Hydraulic and Civil engineering Technology, HCET 2024

作者： Chen, Yu Zheng, Shenglan Shanghai Urban Construction Vocational College Shanghai China

ISBN: (纸本)9781643685595

In the intricate landscape of special-shaped steel structure design, where complexity and uniqueness are paramount, the integration of Building Information Modeling (BIM) has proven to be a game-changer. Specifically, BIM significantly expedites the creation of steel structure models and streamlines the engineering drawing process, enhancing both accuracy and efficiency. this paper spotlights a case study from a subway station project in Shanghai, leveraging the inherent parametric modeling capabilities of Revit software to design complex steel structures. Our investigation explores the methodology and foundational principles guiding the development of parametric special-shaped steel structure models. We emphasize the critical importance of the initial design stages in laying the groundwork for project success. the advantages of employing parametric technology are underscored, notably increased productivity and reduced labor costs, illustrating its potential to streamline complex steel structure projects. Furthermore, the research illuminates how parametric technology can profoundly reshape the steel structure industry. By fostering a more agile and cost-effective design and construction process, it optimizes resource allocation and accelerates project timelines. this makes parametric technology an indispensable element in the ongoing modernization of construction practices. the insights gleaned from this study contribute to the expanding body of knowledge on the practical application of BIM and parametric modeling in complex engineering projects. Our findings offer a roadmap for future advancements, guiding the field towards more sophisticated and efficient design methodologies. © 2024 the Authors.

关键词： Subway stations

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An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction 4

An Applied Artificial Intelligence Technique for Early-Stage...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Ali, Hassan Imtiaz, Hussain Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Alshammeri, Menwa Mir, Azka Kumar, Himanshu University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait College of CIS Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

Alzheimer9;s disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which s... 详细信息

ISBN: (纸本)9798350353266

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. this increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. this research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. the insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's disease Classification Health care Machine Learning OASIS Prediction

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE international conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. this research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th international conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

this article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. the main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. the study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. the ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. this significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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Efficient black box time domain simulation of frequency domain based vibro-acoustic models 30

Efficient black box time domain simulation of frequency doma...

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30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Jonckheere, S. Elkafafy, M. Atak, O. Bériot, H. Janssens, K. Peeters, B. Desmet, W. Siemens Digital Industries Software Simulation and Test Solutions Interleuvenlaan 68 HeverleeB-3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium Siemens Digital Industries Software Francis House 112 Hills Rd CambridgeCB2 1PH United Kingdom DMMS Core Lab Flanders Make Belgium

ISBN: (纸本)9789082893151

Historically, simulations of the vibro-acoustic of products have been mainly performed in the frequency domain. Especially for damping, a key characteristic of vibro-acoustic systems, dedicated modelling formalisms exist (e.g. Perfectly/Automatically Matched Layer (P/AML) to account for unbounded domains, or dedicated frequency-dependent damping material models). However, with the growing interest in the time domain, stability of the modelling formalism is of the utmost importance for long-term stable simulations. Hereto, this paper proposes the Polymax and its combination with Maximum Likelihood estimation of a Modal Model (MLMM). A black-box Löwner approach supports this method by generating finely discretized frequency response curves. the proposed combined method not only provides an efficient computational framework, but also provides a framework that can automatically resolve the dynamics of the system and select the optimum number of frequency snapshots needed to generate a refined frequency domain response. As such, user expertise and involvement are minimized. © 2022 proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Maximum likelihood estimation

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Estimating Medical Insurance Cost using Linear Regression with HyperParameterization, Decision Tree and Random Forest models 13

Estimating Medical Insurance Cost using Linear Regression wi...

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13th international conference on Cloud Computing, Data Science and engineering, Confluence 2023

作者： Patidar, Sanjay Dudi, Saksham Rohit Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9781665462631

the total health expenditure refers to the total public and private funds spent on health services and amenities, medical and surgical bills and all other healthcare facilities provided. the financing for health is of great significance and plays a crucial role in health systems. To enhance the productivity of human capital, the efficiency and delivery of healthcare services must be uplifted. Reports have shown that from year 2000 to 2018 there had been a gradual escalation of global health expenses and is standing on around 10% of the total GDP of the world. Out of pocket expenses are also high in least developed nations that have lower per capita income. Even though the World Health Organization (WHO) sanctions loans to these countries, these nations are bound to use the money on industrialization only and not their healthcare, education and public welfare sectors. With inflation, the expenses of first-rate healthcare are also rising which makes it fundamental to have health and life insurance plans. Health insurance schemes insured around 514 million people in India in the year 2021, most of which were covered under government schemes only. Since the advent of COVID-19 people have realized the need for having a insurance plan. Most of the companies that are based on the health insurance sector use predictive modelling to improve their services and business process. Machine Learning (ML) algorithms are used to train a model and provide insurance costs estimations. Past data is searched for any pattern or trend in the behaviour history of consumers and then future estimations are evaluated. the proposed project is comprised of different regression models like Linear regression with hyperparameterization , regressors like Decision Tree and Random forest to estimate the approximate insurance expenditure. © 2023 IEEE.

关键词： Machine learning

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Comparative Analysis of Random Forest and Multilayer Perceptron Algorithms for Predicting Gross Domestic Product 4

Comparative Analysis of Random Forest and Multilayer Percept...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Adewale, Muyideen D. Omote, Patrick O. Onaolapo, Funmi O. Bakare, Idowo A. Tjiraso, Steven Namibia University of Science and Technology Department of Software Engineering Namibia Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Nigeria Crawford University Department of Computer and Mathematical Sciences Ogun StateNeria Nigeria

this study examines the efficacy of machine learning techniques, particularly Multilayer Perceptron (MLP), and Random Forest and in forecasting Nigeria9;s Gross Domestic Product (GDP). Traditional statistical metho... 详细信息

ISBN: (纸本)9798350353266

this study examines the efficacy of machine learning techniques, particularly Multilayer Perceptron (MLP), and Random Forest and in forecasting Nigeria's Gross Domestic Product (GDP). Traditional statistical methods frequently fail to capture intricate, nonlinear relationships, thus necessitating advanced techniques. the dataset employed in this research encompasses 22 instances from 2000 to 2021. It includes variables such as GDP, healthcare expenditure, population, and the index of economic freedom obtained from the World Bank as well as the Nigerian Perceptions Index for corruption. the data was prepared to undergo training and testing following the CRISP-DM methodology. the models were trained and evaluated using a five-member cross-validation method. Performance metrics indicated that MLP outperformed Random Forest, with MAE values of 16.8228 and 25.3074 and RMSE values of 23.2111 and 33.2027, respectively. these findings suggest that MLP offers more accurate GDP predictions for Nigeria, effectively managing complex data relationships. this study provides valuable insights for enhancing predictive accuracy and supporting more effective economic planning and decision-making. © 2024 IEEE.

关键词： Decision trees

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