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

共 3288 条记录，以下是11-20 订阅

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Optimization of Time for the Revision of Norms in BIM models of Schools, Based on Semi-automation of Rule Check 6th

Optimization of Time for the Revision of Norms in BIM Models...

引用

6th international conference on Civil engineering and Architecture (ICCEA)

作者： Condori Vargas, Max Sigfrid Fernando Ecos Soto, Hector Andre Ulloa, Karem Peruvian Univ Appl Sci Sch Civil Engn Lima Peru

ISBN: (纸本)9789819753178;9789819753154;9789819753147

Currently, the number of construction and/or rehabilitation projects of educational centers, due to the effects of natural disasters, is much higher than other sectors (such as health and drainage), around 57.36% of projects executed are schools. In this sense, the approval process for each project must be carried out as soon as possible to satisfy the demand;but the approval process of each project is slow because it is a task done manually. For this reason, this research focuses on optimizing the time required for the check of BIM models of schools by public entities, through the semi automation of rules using the Smart View tool of the BIMCollab Zoom software;it is carried out by means of data collection, identification of the check process executed, real time information recording for the development of the new proposal and the automation of the check of rules for the model under pre-established criteria.

关键词： Programming Semi automation Public entities BIM BIMCollab Zoom check

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Earthquake Resilient Management in Northeast Algeria: A Multivariate Time Series Forecasting based on LSTM-Attention Neural Network 6

Earthquake Resilient Management in Northeast Algeria: A Mult...

引用

6th international conference on Advanced Aspects of software engineering, ICAASE 2024

作者： Abdallah, Khababa Drif, Ahlem Khababa, Ghizlane Harous, Saad University of Ferhat Abbas Setif 1 Serif 19000 Algeria Department of Computer Science University of Ferhat Abbas Setif 1 Serif 19000 Algeria Department of Computer Science College of Computing and Informatics University of Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

Forecasting methods are critical to earthquake management by providing early warning systems and predictive insights into potential hazards. this work proposes a prediction framework for forecasting earthquake magnitude. the methodology involves collecting historical data from the seismic catalog, identifying geophysical characteristics of northeast Algeria, specifically the Setif (Algeria) region, and proposing a multivariate time series forecasting model based on LSTM with an attention mechanism. the attention neural network learns deeply the most relevant weights representing the subduction zone characteristics that influence earthquake prediction. We validate the proposed model based on real data-sets, and the results show an accurate magnitude forecasting for the northeast Algeria zone. © 2024 IEEE.

关键词： Prediction models

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Use of predictive models to Analyze Hospitalization for Cardiovascular Interventions 6th

Use of Predictive Models to Analyze Hospitalization for Card...

引用

6th international conference on Biomedical engineering

作者： D'Amore, Antonio D'Onofrio, Gaetano Sarnelli, Raffaele Triassi, Maria Marino, Marta Rosaria AORN Antonio Cardarelli Naples Italy Univ Naples Federico II Dept Publ Hlth Naples Italy Univ Naples Federico II Interdept Ctr Res Healthcare Management & Innovat Naples Italy

ISBN: (纸本)9783031803543;9783031803550

Cardiovascular disease remains the single most frequent cause of death among people over the age of 65, and it also represents an important and growing share of hospitalization and healthcare costs. therefore, it is important to evaluate the duration of the hospital stay for patients who are undergoing percutaneous cardiovascular surgery. In this work were considered the data of 1316 patients of the Hospital "A.O.R.N Antonio Cardarelli" of Naples (Italy) who have undergone a surgery on the cardiovascular system percutaneously in the years 2019 and 2020. the dataset has been studied by designing and implementing different Machine Learning (ML) models for improving the effectiveness of our analysis. the results obtained from the analysis are all accurate values, in fact, a precision of more than 75% has always been obtained. In particular, the accuracy values were about 83% for the Random Forest algorithm, while for the Decision Tree algorithm almost 80%. Hence, ML techniques have demonstrated to support further analysis in the medical field.

关键词： Machine Learning Length of Stay Cardiovascular Disease

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Welcome to the 16th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE 2020)

PROMISE 2020 - Proceedings of the 16th ACM International Con...

引用

PROMISE 2020 - proceedings of the 16th ACM international conference on predictive models and Data Analytics in software engineering, Co-located with ESEC/FSE 2020 2020年 III-IV页

作者： Minku, Leandro Menzies, Tim Nagappan, Mei

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Classification Algorithms to Predict Hospitalization for a Lower Limb Fracture: A Multicenter Analysis 6th

Classification Algorithms to Predict Hospitalization for a L...

引用

6th international conference on Biomedical engineering

作者： Marino, Marta Rosaria Borrelli, Anna Triassi, Maria Improta, Giovanni Univ Naples Federico II Dept Publ Hlth Naples Italy Federico II Univ Hosp Naples Italy Univ Naples Federico II Interdept Ctr Res Healthcare Management & Innovat Naples Italy

ISBN: (纸本)9783031803543;9783031803550

In this paper, machine learning algorithms have used to estimate the length of stay (LOS) of subjects with lower limbs fractures. the dataset used comes from the A.O. Federico II based in Naples and includes 132 patients. KNIME software were used to predict LOS using different type of Machine Learning (ML) classification models. the input variables were sex, age, the presence of comorbidities (0/1), the hospitalization regime (1-5) and mode of discharge (1/2). the very best result was attained with SVM and LR algorithms with an accuracy of 77.78%. these values obtained were compared with those coming from the analysis of data obtained from two other Hospital ("San Giovanni di Dio and Ruggi d'Aragona" of Salerno and at the "A.O.R.N. Antonio Cardarelli" of Naples) to evaluate the difference from the hospitals. For the future development, using the same algorithms, the best efficiency is obtained with the largest dataset.

关键词： Machine Learning KNIME software Algorithms LOS

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Groundwater Level Forecasting using Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost 6

Groundwater Level Forecasting using Spatio-temporal Graph Co...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

this research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. the periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. these methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. the integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

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A Curated Solidity Smart Contracts Repository of Metrics and Vulnerability 20

A Curated Solidity Smart Contracts Repository of Metrics and...

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20th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Ibba, Giacomo Aufiero, Sabrina Neykova, Rumyana Bartolucci, Silvia Ortu, Marco Tonelli, Roberto Univ Cagliari Cagliari Italy UCL London England Brunel Univ London England

ISBN: (纸本)9798400706752

Smart contracts (SCs) significance and popularity increased exponentially with the escalation of decentralised applications (dApps), which revolutionised programming paradigms where network controls rest within a central authority. Since SCs constitute the core of such applications, developing and deploying contracts without vulnerability issues become key to improve dApps robustness to external attacks. this paper introduces a dataset that combines smart contract metrics with vulnerability data identified using Slither, a leading static analysis tool proficient in detecting a wide spectrum of vulnerabilities. Our primary goal is to provide a resource for the community that supports exploratory analysis, such as investigating the relationship between contract metrics and vulnerability occurrences. Further, we discuss the potential of this dataset for the development and validation of predictive models aimed at identifying vulnerabilities, thereby contributing to the enhancement of smart contract security. through this dataset, we invite researchers and practitioners to study the dynamics of smart contract vulnerabilities, fostering advancements in detection methods and ultimately, fortifying the resilience of smart contracts.

关键词： Smart Contracts Ethereum Blockchain Vulnerability Detection software engineering Data Analysis

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Application and Certification of software models Used in Development 6

Application and Certification of Software Models Used in Dev...

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6th IEEE international conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Schuster, Gyorgy Obuda University Budapest Hungary Institute of Electronics and Communication Systems Hungary Kando Kalman Faculty of Electrical Engineering Department of Instrumentation and Automation Hungary

ISBN: (纸本)9798350328752

As the complexity of tasks increases, developers have to use different tools. We talked about this in details in some previous publications. In this paper, we will touch on a special area that is increasingly coming to the fore these days, which is the question of the application of models. However, the application of models in safety-critical systems raises the question of the reliability of the models and the questions of the certification of these software tools. © 2023 IEEE.

关键词： software reliability

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Semantic-aware Source Code Modeling 24

Semantic-aware Source Code Modeling

引用

39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Ding, Yangruibo Columbia Univ New York NY 10027 USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Source code modeling represents a promising avenue for automating software development, such as code generation, bug repair, and program analysis. this research direction aims to train deep neural nets to learn the statistical predictability inherent in human-written programs to enhance developer productivity, code quality, and the overall software development life cycle. Although existing code modeling approaches, particularly those underpinned by Transformer-based language models, have demonstrated effectiveness across various software engineering tasks, most of them have directly adopted learning schemes from natural language processing (e.g., data collection and processing, training objectives) to source code, primarily focusing on learning code text and syntax. However, such a direct transplant limits the models' capability to capture deep program semantics, such as code functionality, dependencies, and program states during execution. In this research proposal, we highlight the critical role of program semantics in source code modeling. We propose a range of innovative methodologies to bridge the gap between the text-based language models for large-scale code training and the requirement of deep semantic understanding to assist with software engineering tasks effectively. Furthermore, we showcase the efficacy of the proposed semantic-aware code modeling through a handful of published papers and preliminary results, with motivations to delve deeper into this avenue during doctoral research.

关键词： Training Codes Source coding Semantics predictive models Syntactics Transformers Data models software engineering software development management

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Empowering Energy Systems: Exploring the Intersection of Artificial Intelligence and predictive Maintenance 6

Empowering Energy Systems: Exploring the Intersection of Art...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Natarajan, Karthika Tekkali, Chandana Gouri Radhika, Vankayalapati School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Raghu Engineering College Andhra Pradesh Visakhaptnam India and AI & DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331522667

this article aims to improving and refining energy processes by applying diverse technologies, strategies, or methodologies of Artificial Intelligence (AI). AI plays an essential role in transforming and optimizing energy systems across various sectors. this article provides how advanced techniques in AI to analyze data, predict necessary issues, and enact proactive maintenance protocols, ultimately contributing to a more resilient and intelligent energy infrastructure. We have used various Machine Learning (ML) models to optimize energy consumption by learning from benchmark data i.e., energy consumption of 2020, and adjusting system parameters accordingly, leading to improved efficiency. this article contributed to sustainability, cost-effectiveness, and improved reliability in the generation, distribution, and consumption of energy and also helps in proactive maintenance to minimizing downtime, and improving overall system reliability. By harnessing the power of AI technologies, industries can proactively address challenges and optimize energy resources to meet the growing demands of the future. © 2025 IEEE.

关键词： predictive maintenance

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