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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 3308 条记录，以下是801-810 订阅

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Data-Driven Risk Measurement by SV-GARCH-EVT Model

Data-Driven Risk Measurement by SV-GARCH-EVT Model

引用

Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Shi Bo Minheng Xiao dept. Mathematics and Statistics xsBoston University Boston USA dept. Integrated System Engineering Ohio State University Columbus USA

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

this paper aims to more effectively manage and mitigate stock market risks by accurately characterizing finan-cial market returns and volatility. We enhance the Stochastic Volatility (SV) model by incorporating fat-tailed distributions and leverage effects, estimating model parameters using Markov Chain Monte Carlo (MCMC) methods. By integrating extreme value theory (EVT) to fit the tail distribution of standard residuals, we develop the SV-EVT-VaR-based dynamic model. Our empirical analysis, using daily S&P 500 index data and simulated returns, shows that SV-EVT-based models outperform others in backtesting. these models effectively capture the fat-tailed properties of financial returns and the leverage effect, proving superior for out-of-sample data analysis.

关键词： Reactive power Analytical models Pandemics Stochastic processes Tail predictive models Prediction algorithms Data models Stock markets Standards

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Performance Analysis of Machine Learning Algorithms Using Bagging Ensemble Technique for software Fault Prediction

Performance Analysis of Machine Learning Algorithms Using Ba...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Roshan Samantaray Himansu Das School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

software development comes with a lot of challenges. Developers face various issues with performance and bugs. these issues increase with the scale of the project and if fewer individuals work on the development. It has become necessary to fix various bugs during development to ensure better performance and reduce the chances of failure during the deployment of the software. As a result of this, faults in the software must be predicted during the earlier stages of development. this would help in reducing the cost of maintenance of the software post-deployment. Multiple software fault prediction (SFP) approaches have been proposed to tackle this problem. these approaches can be improved by implementing ensemble techniques. In this paper, we study the effect of the bagging technique and how it helps to improve the predictive capability across various datasets. these datasets are provided and made open source by NASA. Decision Tree Classifier (DTC), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Support Vector Classifier (SVC) were used as base classifiers for the bagging method. Random forest (RFC) is an ensemble learning algorithm that uses the bagging technique. Based on the outcome of the results, it was concluded that RFC was the best-performing algorithm.

关键词： Machine learning algorithms software algorithms NASA Support vector machine classification Static VAr compensators Gaussian processes Prediction algorithms

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Electricity Load Forecasting Based on Patch Channel-Mixing Transformer Model

Electricity Load Forecasting Based on Patch Channel-Mixing T...

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Jiwei Xing Xinying Xu Chao Liu Dongmei Hu School of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Recent research has demonstrated the strong potential of Transformer models in electricity load forecasting, due to their ability to effectively address the long-term memory modeling issues in the load forecasting process. However, there are several issues with Transformer models for load forecasting, including quadratic time complexity, difficulty in screening factors with electricity load, and false similarity points due to the modeling methods on point level. To address those issues, we propose an efficient design of Transformer-based model for electricity load forecasting. It is based on two components: (i) divide the load sequence and multi-factor sequence into subsequence level segments with multiple resolutions, and then use them as input markers for the Transformer model. (ii) channel mixing, where the load channel interacts with multiple factors channels to achieve explicit modeling of the relationship between load and multiple factors. Extensive experiments have demonstrated that our model significantly outperforms existing methods on two large-scale datasets, and provide a new solution to the load forecasting problem.

关键词： Training Accuracy Load forecasting Electricity Noise predictive models Transformers Time complexity Informatics Load modeling

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Traffic Flow Prediction Method Based on Hybrid Adaptive Graph Convolutional Network

Traffic Flow Prediction Method Based on Hybrid Adaptive Grap...

引用

international conference on Communications, Information System and Computer engineering (CISCE)

作者： Jianhua Ren Lei Gao School of Electronic and Information Engineering Liaoning Technical University Huludao Liaoning China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Aiming at the problems that the explicit graph structure in the short-time traffic flow prediction model does not necessarily reflect the real dependency relationship and cannot effectively discriminate the high-frequency information in the short-time sequence, a novel traffic flow prediction approach leveraging a hybrid adaptive graph convolutional network is introduced. Firstly, a spatial information extraction module that can obtain different relationship features among nodes is introduced, which is used to solve the problem of too single spatial information extraction. Secondly, a temporal convolution module with local high-frequency information extraction is proposed to better identify the high-frequency effects in the traffic flow. Lastly, the spatial and temporal extraction modules are connected in parallel to form the spatiotemporal network layer, which is finally connected to the output layer to obtain the prediction. three real traffic datasets are used for the experiments, and compared with the existing mainstream models, the MAE, RMSE, and MAPE indexes have been improved by an average of 4.480%,3.744%, and 1.913%. It fully demonstrates that the method in this paper is effective for short-term traffic flow prediction.

关键词： Adaptation models Adaptive systems Graph convolutional networks Convolution predictive models Information retrieval Feature extraction

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Evaluating Feature Prioritization in a Reduced Feature Set Using Explainable AI for Intracranial Aneurysm Classification

Evaluating Feature Prioritization in a Reduced Feature Set U...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Iram Abdullah Ali Javed Department of Computer Science Air University Kamra Pakistan Department of Software Engineering University of Engineering & Technology Taxila Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

the timely management of an unruptured aneurysm can prevent hemorrhage. Multiple anatomical, morphological, and hemodynamic features associated with an aneurysm can predict the risk of rupture and classify the nature of the aneurysm as either ruptured or unruptured. However, considering all features may create ambiguities in the model due to inter-dependencies, such as the correlation between aneurysm height and width to aneurysm area. In computer-aided medical diagnostics, focusing on relevant features that diagnose the disease is more crucial than using an abundance of features that may not contribute to the diagnosis and may cause unnecessary complexity. the use of independent or sparsely correlated features improves the accuracy of the classifier. the proposed research analyzes the interdependency of features by calculating the correlation between features. Based on the analyses, the high correlation threshold is determined through rigorous experimentation. From the highly correlated features, one feature is selected. Consequently, the reduced feature set is recommended. the reduced feature set performs better with simpler classifiers. Instead of choosing a classifier that struggles with a broad range of features, an appropriately optimized feature set can enhance performance and improve accuracy. the proposed approach is tested on a publicly available dataset of aneurysms, leading to an improvement in classification accuracy from 82.6% to 87%, due to the use of an appropriate feature set. Moreover, the best model was evaluated using two different explainable AI frameworks to assess the contribution of each feature in prediction. However, the conflicting feature prioritization in each explainable framework suggests that these models prioritize different features for the same trained model, leading to ambiguity. this inconsistency indicates that the explainable models lack generalization and fail to consistently analyze feature importance.

关键词： Accuracy Correlation Explainable AI Computational modeling Redundancy Training data Aneurysm predictive models Medical diagnosis Random forests

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Time Delay Estimation model and Robot Network Control Research

Time Delay Estimation model and Robot Network Control Resear...

引用

Internet of things, Automation and Artificial Intelligence (IoTAAI), international conference on

作者： Kemin Wang Feng Xu School of Electrical and Information Engineering Guangdong Baiyun University Guangzhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

When robots and affiliates work together, there is a practical demand for third-party software platform centralized scheduling control. In this paper, a robot control scheme based on maximum delay estimation model is proposed to solve the problem of network communication delay between robot controller and thid-party software platform. Firstly, the function model, data model, structure model and behavior mechanism of network control module ara analyzed, and the QTcpSocket class is used to establish communication connnection and send receive network packets. A set of network conmunication mechanism based on delay model is designed and developed. this scheme has been successfully applied in the 3D reconstruction projects of 3D scanner and robot. It realizes the stable control of the robot and the affiliated operation mechanism by the PC human-machine interface, and achieves the expected goals of the effectiveness of collaborative tasks, accurate command response and reasonable avoidance of misactions.

关键词： Analytical models three-dimensional displays Human-machine systems Robot control Estimation Delay estimation Data models software Internet of things Robots

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Online Test-Time Adaptation for Electronic Nose Drift Compensation

Online Test-Time Adaptation for Electronic Nose Drift Compen...

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Zhifang Liang Dongming Chen Liu Yang School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Sensor drift is a common problem in E-nose systems. In recent years, research on this problem has made significant progress, and various drift compensation methods have emerged. Although these methods have achieved notable performance in recognition accuracy, they seldom deal with the drift problem in the online scenarios. therefore, this paper proposes an E-nose drift compensation method based on online test-time adaptation. the method first removes abnormal data using data preprocessing methods to ensure the training quality of the model. then uses a mutually learning teacher-student distillation model with continuous updates to adapt the model to drift data. the online recognition of unlabeled drift data is finally realized. the ability of the test-time adaptation (TTA) to adaptively adjust to the unlabeled drift data also makes the model highly robust and adaptable. Experimental results on public datasets show that the proposed method is effective enough to deal with the drift problem in the online case, and it also exhibits a high recognition rate compared to other state-of-the-art approaches.

关键词： Training Adaptation models Accuracy Data preprocessing predictive models Electronic noses Data models Informatics

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Success Factors of Requirement engineering that Influence software Development Efficiency

Success Factors of Requirement Engineering that Influence So...

引用

Advancements in Computing (ICAC), international conference on

作者： thilini Yapa Dasuni Nawinna Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Due to the rapid advancement of technology, the software industry has become one of the major industries all over the world. Many organizations invest large amounts of money to develop software products. Requirement engineering is one of the major phases in the software development process. It is a challenging and time-consuming task which should carefully be done because requirement engineering plays a vital role in shaping a project's success and efficiency. software development teams face significant challenges in making functional software products that meet client's requirements due to lack of clear requirements. this study explores the critical success factors of requirement engineering that influence software development efficiency. A study was conducted to gather data from business analysts within a leading telecommunication organization and its subsidiaries across three countries. the data was thoroughly analyzed through a structured analysis using Smarts PLS. the success factors considered in this study are business analyst's skills, quality of the requirement, customer involvement and team capability. the results highlighted that software development efficiency is strongly influenced by requirements engineering factors, including the skills of the Business Analyst, customer involvement, and team capability, each with a significance value of less than 0.05. By addressing requirement engineering challenges and implementing standardized procedures derived fromcritical success factors, organizations can streamline their requirement engineering processes, and this study provides valuable insights to enhance requirement engineering best practices fostering software development efficiency.

关键词： Industries Computational modeling Standards organizations Companies software Mathematical models Requirements engineering Best practices Faces software development management

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Towards a Digital Twin Power System: A Case Study for Wind Power Forecasting

Towards a Digital Twin Power System: A Case Study for Wind P...

引用

Smart Power & Internet Energy Systems (SPIES), international conference on

作者： Cheng Chen Gang Yang Wenshan Hu Xiaoran Dai College of Electrical Engineering Naval University of Engineering Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

this paper presents a significant step towards realizing a digital twin power system, focusing on a case study for wind power forecasting. We propose a novel framework that integrates digital twin technology with a Gated Recurrent Unit (GRU) neural network, termed DT-GRU. this approach leverages real-time data processing capabilities and adaptive modeling to enhance wind power forecasting accuracy. Based on the digital twin based forecasting framework, the online learning mechanism is tailored to update the DT-GRU model in real time. Using real-world high-resolution data, we evaluate the DT-GRU model against traditional machine learning approaches for one-hour and two-hour ahead forecasts. Results demonstrate that our digital twin-based model consistently outperforms conventional methods across multiple error metrics, particularly in capturing rapid fluctuations in wind power output.

关键词： Adaptation models Renewable energy sources Neural networks Wind power generation predictive models Power system stability Real-time systems Data models Digital twins Forecasting

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Interpretation and Analysis of Machine Learning models for Brain Stroke Prediction

Interpretation and Analysis of Machine Learning Models for B...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Ritesh Kumari Hitendra Garg Department of Computer Engineering & Applications GLA University Mathura India

Brain stroke is a significant cause of death nowadays. As per WHO, 11% of the population dies yearly from it. However, early measures can save many lives. Machine Learning (ML) is used as a tool for early predictions in a human through their symptoms, lifestyle, and from medical history. With the advancement in machine learning, features responsible for brain stroke can be identified and ranked as per their effect. Such features for brain stroke are hypertension, smoking status, heart disease, body mass index, and sugar level. In this paper, various ML classifiers such as Neural Network (NN), Support Vector Machine (SVM), Random Forest (RMF), Decision Tree (DST), and Gradient Boost (GBST) are used to classify patients with brain stroke. the models are then compared for the best results. Lastly, Local Interpretable Model-agnostic Explanation (LIME) and SHAP (SHapley Additive exPlanations) are used for explanation to find the reason behind the decision taken by the best ML model. the results show that RMF (GBST after that) achieves the highest prediction accuracy.

关键词： Support vector machines Analytical models Computational modeling predictive models Stroke (medical condition) Brain modeling Decision trees

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