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检索条件"任意字段=9th International Conference on Predictive Models in Software Engineering, PROMISE 2013"

共 124 条记录，以下是1-10 订阅

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AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Severity Prediction, and Post Effects 9

AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Sev...

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9th international conference on Information Technology Research

作者： Nivasheni, B. Fernando, V. G. S. O. Srihari, M. Sarah, M. I. L. Jayasinghearachchi, Vishan Rathnayake, Samadhi Chathuranga Sri Lanka Inst Informat Technol Dept Comp Sci Malabe Sri Lanka Sri Lanka Inst Informat Technol Dept Software Engn Malabe Sri Lanka

ISBN: (纸本)9798331511425;9798331511432

this paper aims to examine the applicability of machine learning models for aiding in the diagnosis and management of arboviral illnesses, especially Dengue. the study focuses on three key objectives: Diagnosis of Dengue, prediction of prognosis of severe disease, and the identification of the post dengue effects. Multiple strategies were used to address these challenges. they are feature engineering based multiclass classification of arboviral diseases, enhancement of standard conventional machine learning algorithms for binary classification of severe Dengue and finally MLSOL enhancing ensemble model for drawing future effects of Dengue infections. the comparison of traditional models included both Random Forest, XGBoost, and also ensemble method while applying the feature selection methods of SFA and RFE. this study presents the comparison of traditional individual models and optimal ensemble method for the multiclass classification in arboviral diseases. In the case of binomial classification, the best accuracy level was 0.93 achieved by the Random Forest with RFE feature selection compared to Logistic Regression as well as the Gradient Boosting. Furthermore, the study proposed an MLSOL augmented ensemble approach to handle label imbalance problem in the dataset which in turn substantially enhanced the prediction accuracy. this approach decreased Hamming Loss to 0. 12 and increased the F-measure to 0.82, this has the capability of handling the imbalanced dataset and improving the predictive accuracy and performance. this machine learning framework offers great potential for clinical use in such aspects as early intervention and in effects following Dengue fever. It is meant to help healthcare practitioners and policy makers on how to better prevent and combat Dengue in Sri Lanka.

关键词： Dengue Fever DENV Post Dengue effects Disease epidemiology Severe Dengue Arboviral Disease

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Research on software Fault Feature Data Extraction Method for software Fault Prediction Technology 9

Research on Software Fault Feature Data Extraction Method fo...

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9th international conference on Advances in Machinery, Materials Science and engineering Applications, MMSE 2023

作者： Yan, Ran Wang, Meichen Xu, Zhaowei Zhang, Kai China Shipbuilding Software Quality & Reliability Testing Center Beijing China

ISBN: (纸本)9781643684383

the correlation between software failure characteristics and software failure directly determines the predictive performance of the failure prediction model. the extraction of software fault features is crucial for building equipment software fault prediction models, and is an important process to ensure accurate prediction. However, the software fault feature data extraction method is often complicated to use and has no pertinence to the selected software fault feature data, and it takes a lot of time to complete the extraction steps. this paper summarizes software metrics and software defect types based on research at home and abroad, and selects software metrics and software defect types that are suitable for equipment software. Using regular expression technology and CSV technology research the automatic extraction way of software fault features, and finally constructs a fault data set that can be used for software fault prediction models. © 2023 the Authors.

关键词： Extraction

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Pipe Network Water Level Prediction Platform Coupled with SWMM and LSTM 9th

Pipe Network Water Level Prediction Platform Coupled with SW...

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9th international conference on Water Resource and Environment, WRE 2023

作者： Sheng, Zheng Zheng, Mengting Computer Applications and Software Huzhou University Zhejiang Huzhou313000 China

ISBN: (纸本)9789819709472

In this paper, a pipe water level prediction platform is proposed to address the low predictability issue of traditional drainage systems in combating waterlogging. Based on the drainage pipe network, the study integrates real-time data collected through the Internet of things (IoT) sensor system and predictive data from the Storm Water Management Model (SWMM) and Long Short-Term Memory (LSTM) neural network models to enhance the accuracy and speed of waterlogging forecasts. Additionally, a web-based and WeChat Mini Program application platform has been developed. the platform was tested and validated in a specific region in Huzhou. Experimental results demonstrate the platform’s high prediction accuracy and its potential for urban waterlogging risk monitoring and early warning, offering a novel approach to smart water management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods 9

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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9th IEEE international conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Elmazi, Donald Karras, Dimitrios A. Alkholidi, Abdulsalam Capari, Klea Faculty of Engineering Dept. of Electronics and Telecommunications Tirana Albania Dept. S/W Engineering Tirana Albania Dept. General School of Science Athens Greece Tirana Albania Faculty of Engineering Sana'a University Yemen University of Rijeka Faculty of Engineering Dept. of Computer Engineering Croatia Faculty of Engineering Cit Dept. of Software Engineering Tirana Albania

ISBN: (纸本)9798350333794

Presently, the global proliferation of Internet of things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. the special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. the statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

关键词： Internet of things

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Contact parameter estimation using a constrained extended Kalman filter 30

Contact parameter estimation using a constrained extended Ka...

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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： Mazzanti, L. Vivet, M. De Gregoriis, D. Tamarozzi, T. Blockmans, B. Siemens Digital Industries Software Interleuvenlaan 68 LeuvenB-3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium DMMS Core Lab Flanders Make Belgium

ISBN: (纸本)9789082893151

In structural dynamics simulations, sophisticated contact models are required to accurately describe contact interactions. these contact models typically depend on several, possibly uncertain, parameters. In recent years, significant efforts have been spent in developing hybrid techniques that enhance the predictive performance of numerical models with real-world measured data. the Virtual Sensing (VS) estimation framework uses a Kalman Filter-based approach to combine experimental measurement data with numerical models. In this work, an extension of VS for contact model parameter estimation is presented. the developed VS formulation is numerically validated using a lumped-parameter model of a mass-spring-damper system to demonstrate the potential of the methodology. the objective of the experiment is accurate joint state-contact parameter estimation, starting from an inaccurate system model. Results show that the inclusion of contact parameter in the estimation improves the accuracy of the overall estimation. © 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： Parameter estimation

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CVEfixes: Automated Collection of Vulnerabilities and their Fixes from Open-Source software 17

<i>CVEfixes</i>: Automated Collection of Vulnerabilities and...

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17th ACM international conference on predictive models and Data Analytics in software engineering (promise)

作者： Bhandari, Guru Naseer, Amara Moonen, Leon Simula Res Lab Oslo Norway

ISBN: (纸本)9781450386807

Data-driven research on the automated discovery and repair of security vulnerabilities in source code requires comprehensive datasets of real-life vulnerable code and their fixes. To assist in such research, we propose a method to automatically collect and curate a comprehensive vulnerability dataset from Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database (NVD). We implement our approach in a fully automated dataset collection tool and share an initial release of the resulting vulnerability dataset named CVEfixes. the CVEfixes collection tool automatically fetches all available CVE records from the NVD, gathers the vulnerable code and corresponding fixes from associated open-source repositories, and organizes the collected infonnation in a relational database. Moreover, the dataset is enriched with meta-data such as programming language, and detailed code and security metrics at five levels of abstraction. the collection can easily be repeated to keep up-to-date with newly discovered or patched vulnerabilities. the initial release of CVEfixes spans all published CVEs up to 9 June 2021, covering 5365 CVE records for 1754 open-source projects that were addressed in a total of 5495 vulnerability fixing commits. CVEfixes supports various types of data-driven software security research, such as vulnerability prediction, vulnerability classification, vulnerability severity prediction, analysis of vulnerability-related code changes, and automated vulnerability repair.

关键词： Security vulnerabilities dataset software repository mining vulnerability prediction vulnerability classification source code repair

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Hybrid Deep Learning Framework for Stock Price Prediction Incorporating Technical and Macroeconomic Indicators

Hybrid Deep Learning Framework for Stock Price Prediction In...

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international conference on Computer Science and engineering (UBMK)

作者： Ali Can Turan Vael Kokach Büşra Kocaçınar Öznur Şengel Fatma Patlar Akbulut Department of Computer Engineering Istanbul Kültür University Istanbul Türkiye Department of Software Engineering Istanbul Kültür University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

the complicated interaction of several elements makes accurate stock price prediction still difficult. this study suggests a hybrid deep learning model to improve stock market prediction by integrating Bidirectional Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs). Incorporating both technical and macroeconomic variables helps the model to adequately identify complex trends and interconnections inside the financial data. While the bidirectional LSTM models temporal dependencies, the CNN component shines in retrieving pertinent features from the input data. Experimental findings show that the suggested model beats conventional approaches, therefore offering traders and investors important new perspectives.

关键词： Deep learning Accuracy Computational modeling Computer architecture predictive models Feature extraction Data models Macroeconomics Convolutional neural networks Resilience

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Estimation of software Integration Test Duration via UML Statecharts

Estimation of Software Integration Test Duration via UML Sta...

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international conference on Computer Science and engineering (UBMK)

作者： Fehim Göler Tolga Ovatman ASELSAN AS Ankara Türkiye Department of Computer Engineering Istanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

In integration testing it can be very useful to predict how much time it will take to complete a set of integration test runs, especially during the version changes of software projects. Past studies usually focus on estimating the whole test suite effort rather than the test delta required to be applied during version changes which can be expected to increase the margin of error during the estimation process. In this study we apply well-knwon prediction techniques over software projects that make use of Model Driven engineering (MDE) and UML-statecharts per se, in order to estimate the duration of a single run of integration tests during version changes. In particular, we apply a linear regression model to estimate the exact duration and Support Vector Machine (SVM) classifiers to predict the duration by using three categories: “short”, “medium” and “long”. Our results give important hints about how to perform such analysis in the chosen domain.

关键词： Support vector machines Computer science Unified modeling language Linear regression Estimation predictive models Model driven engineering Open source software Testing

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Estimating the Manufacturing Cost of a Metal Part from Textual and Geometric Features

Estimating the Manufacturing Cost of a Metal Part from Textu...

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international conference on Computer Science and engineering (UBMK)

作者： Talha Rehman Abid Mert Daloğlu Cem Yldız Ali Erman Erten Kamer Kaya Dakik Software Technologies Ankara Türkiye ERMETAL Bursa Türkiye Sabanci University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

this work presents a comprehensive approach to estimating labor hours for machining complex industrial metal parts, leveraging a rich dataset with diverse features like geometric dimensions, material properties, and operational parameters. Advanced ML models, including XGBoost, LightGBM, CNN, GBM, Random Forest, Linear Regression, Ridge, SVR, and MLP, are employed to predict labor hours. these predictions are a foundation for accurate cost estimation and are critical for efficient production planning and competitive pricing in the manufacturing sector. Our experimental study performed on a real-life metal-part dataset obtained from ERMETAL shows that the proposed LightGBM model estimates the manufacturing cost of a metal part with only 10.3% mean absolute percentage error.

关键词： Costs Production planning Linear regression Metals Pricing Machining predictive models Manufacturing Random forests Material properties

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Comparative Study of Random Search Hyper-Parameter Tuning for software Effort Estimation 17

Comparative Study of Random Search Hyper-Parameter Tuning fo...

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17th ACM international conference on predictive models and Data Analytics in software engineering (promise)

作者： Villalobos-Arias, Leonardo Quesada-Lopez, Christian Univ Costa Rica San Pedro Costa Rica

ISBN: (纸本)9781450386807

Empirical studies on software effort estimation have employed hyper-parameter tuning algorithms to improve model accuracy and stability. While these tuners can improve model performance, some might be overly complex or costly for the low dimensionality datasets used in SEE. In such cases a method like random search can potentially provide similar benefits as some of the existing tuners, with the advantage of using low amounts of resources and being simple to implement. In this study we evaluate the impact on model accuracy and stability of 12 state-of-the-art hyper-parameter tuning algorithms against random search, on 9 datasets of the promise repository and 4 sub-datasets from the ISBSG R18 dataset. this study covers 2 traditional exhaustive tuners (grid and random searches), 6 bio-inspired algorithms, 2 heuristic tuners, and 3 model-based algorithms. the tuners are used to configure support vector regression, classification and regression trees, and ridge regression models. We aim to determine the techniques and datasets for which certain tuners were 1) more effective than default hyper-parameters, 2) more effective than random search, 3) which models(s) can be considered "the best" for which datasets. the results of this study show that hyper-parameter tuning was effective (increased accuracy and stability) in 862 (51%) of the 1,690 studied scenarios. the 12 state-of-the-art tuners were more effective than random search in 95 (6%) of the 1,560 studied (non-random search) scenarios. Although not effective in every dataset, the combination of flash tuning, logarithm transformation and support vector regression obtained top ranking in accuracy on the highest amount (8 out of 13) of datasets. Hyperband tuned ridge regression with logarithm transformation obtained top ranking in accuracy on the highest amount (10 out of 13) of datasets. We endorse the use of random search as a baseline for comparison for future studies that consider hyper-parameter tuning.

关键词： hyper-parameter tuning software effort estimation machine learning empirical study

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