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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

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COMPARISON OF REINFORCEMENT learning METHODS FOR PRODUCTION CONTROL IN DISCRETE MANUFACTURING SYSTEMS 18

COMPARISON OF REINFORCEMENT LEARNING METHODS FOR PRODUCTION ...

引用

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： Yun, Lingxiang Wang, Jingwen Xiao, Minkun Li, Lin Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA

ISBN: (纸本)9780791887240

The widespread deployment of smart meters and communication technologies brings opportunities to improve the adaptability and flexibility of future manufacturing systems under growing complexity and uncertainty. As a result, data-driven approaches, especially Reinforcement learning (RL) methods, are gaining wide attention in recent years. Although in the current literature, RL approaches show superiority over traditional methods in many applications, a comprehensive review and comparison of different RL methods and their use in discrete manufacturing system control under changing environments have not yet been established. This paper first provides a literature review of RL algorithms for decision-making in discrete manufacturing systems, and then systematically discusses the underlying mechanisms of four most commonly used RL algorithms. In addition, the performance of these RL algorithms is compared by solving a production control problem, which aims to maximize the profit under time-varying production costs. The comparison results show that the single-agent RL methods with discrete action space can provide satisfactory results in small-scale systems, while multi-agent RL algorithms are more suitable to solve problems in complex, large-scale systems to remit the curse of dimensionality. This research sheds light on the impacts of RL method selection on the search for high-quality solutions in manufacturing systems, which is envisioned to drive future research on RL-supported manufacturing systems.

关键词： machine learning reinforcement learning discrete manufacturing systems production control

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software engineering Classification Model and Algorithm Based on Big Data Technology 3

Software Engineering Classification Model and Algorithm Base...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Hu, Tao Department of Intelligent Science and Engineering Yunnan Technology and Business University Yunnan Kunming China

With the development of internet technology, cloud computing is becoming increasingly popular, and it has a wide range of applications in various fields, such as mobile payments and the Internet of Things. The big data model is a very important and valuable set of useful and unique information. This article mainly introduces the establishment of an object-oriented architecture-based machine learning system classification model using big data analysis methods, as well as the use of neural network algorithms to construct machine learning system classification patterns. Through examples, a comparative experiment is conducted to verify the effectiveness of traditional manual annotation modeling methods combined with parallel processing. Its experimental results show that the model has high accuracy, with an accuracy rate above 92% and a recall rate above 94%, and its F1 value is infinitely close to 1, indicating that the average accuracy and precision of the model is very high. © 2023 The Authors. Published by Elsevier B.V.

关键词： software engineering

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software Fault Prediction Using FeatBoost Feature Selection Algorithm 2

Software Fault Prediction Using FeatBoost Feature Selection ...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Medicharla, Sirisha Kumar, Shubham Devarakonda, Praphul Agrawalla, Bikash Reddy, B Ramachandra Department of CSE National Institute of Technology Jharkhand Jamshedpur India

A critical aspect of software engineering is software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. © 2024 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Advanced EdTech Strategies for Predicting software Reliability: Harnessing ALM and SVM

Advanced EdTech Strategies for Predicting Software Reliabili...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Ritu Anchal Pandey, Shivam Chauhan, Aditya Mridul Sharma, Shreya Chandigarh University Department of Computer Science & Engineering Chandigarh Mohali India

ISBN: (纸本)9798350313987

software defect prediction is pivotal for ensuring the reliability of new software systems. This study introduces an innovative approach to the domain, leveraging software reliability prediction. This study is focused on solving two critical questions: the utility of historical failure data and the relevance of specific failure datatypes. Furthermore, this investigation examines the trade-off between prediction accuracy and timeliness in SVM and ELM-based models. Experimental results highlight the superior predictive power of ELM, offering enhanced metrics such as specificity, regression, accuracy, and F1 score. In addition to predictive models, this research presents a sophisticated feature selection framework utilizing ELM and SVM. The NASA Metrics dataset serves as a robust testing ground. To address dataset imbalances, resampling techniques are applied to optimize prediction accuracy prior to feature selection. In sum, this study offers a concise yet comprehensive approach to software reliability prediction, advancing the field's capabilities and contributing to elevated software quality assurance practices © 2023 IEEE.

关键词： Extreme learning machine Key phrases model for defect prediction software fault prognosis Support vector machine Top-notch software

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Application of Support Vector machine Algorithm in Real-time Bearing Fault Diagnostic Model for Single-phase Induction Motor using Hilbert-Huang Transform as Feature Extractor 7

Application of Support Vector Machine Algorithm in Real-time...

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7th international conference on Electrical, Telecommunication and Computer engineering, ELTICOM 2023

作者： Herrera, Francis Gabriel M. Mortera, Maurice Benjamin E. Ostia, Conrado F. Mapúa University Department of Electrical Electronics and Computer Engineering Manila City Philippines

ISBN: (纸本)9798350385526

The most common motor fault among electrical motors is a bearing fault. Different bearing faults produce different vibrations which can be recognized by machine learning algorithms. A real-time mechanical motor bearing fault diagnosis system using a Support Vector machine classifier and Hilbert-Huang Transform as feature extraction was proposed in this study. The real-time motor bearing fault diagnosis model system was run using MATLAB software to communicate with an accelerometer as a vibration sensor. Six (6) bearing fault types were used to simulate different bearing faults. The average accuracy obtained during the testing of the model was 78.96%. © 2023 IEEE.

关键词： Empirical mode decomposition

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A Statistical Method for API Usage learning and API Misuse Violation Finding 21

A Statistical Method for API Usage Learning and API Misuse V...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Panda, Deepak Basia, Piyush Nallavolu, Kushal Zhong, Xin Siy, Harvey Song, Myoungkyu University of Nebraska Department of Computer Science Omaha United States

ISBN: (纸本)9798350345889

A large corpus of software repositories enables an opportunity for using machine learning (ML) approaches to create new software engineering tools. In this paper, we propose a novel technique which leverages ML approaches for automating software engineering tasks and thus improves software quality. Our concrete goal is to (1) explore the abundance of predictable repetitive regularities of such a massive codebase, (2) develop an ML approach for training a statistical model to identify common patterns in software corpora, and then (3) use these patterns to statistically detect anomalous, likely buggy, program behavior that significantly deviates from these typical patterns. These internal regularities and repetitive properties of software can be captured as patterns to detect violations of these common patterns. Such violations have a critical impact on program behavior such as bugs, security vulnerabilities, or even program crashes. Our approach focuses on usage patterns of application programming interfaces (APIs). API usage patterns are commonly recurring, representative examples of how real-world applications use APIs in software corpora. These desirable patterns of API usage are learnable to validate or improve developers' implementations. This paper shows preliminary results that we use standard cross-entropy and perplexity to measure how surprising a test subject application is to a statistical model estimated from a software corpus. We continue to develop our approach and evaluate the effectiveness to focus on the following research questions. Are our ML models effectively trainable on large code corpora to learn desirable API usage patterns? How does the performance of our ML-based approach compare to state-of-The-Art language models for software when learning API usage for detecting API misuse violations? © 2023 IEEE.

关键词： software testing

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A Novel Dimensionality Reduction-based software Bug Prediction using a Bat-Inspired Algorithm 13

A Novel Dimensionality Reduction-based Software Bug Predicti...

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

作者： Gupta, Anurag Sharma, Mayank Srivastava, Amit Amity University Uttar Pradesh Auup Aiit Noida India Jaypee University Jiit Noida India

ISBN: (纸本)9781665462631

The major intention of software bug identification is to predict the defects in the software modules for increasing the performance of testing. It helps in analyzing the bugs before starting the real testing through a few fundamental project resources. It is highly preferable for identifying the bugs in the early stages owing to the expensive cost of achieving flawless codes in the testing phase. Therefore, various approaches and techniques are designed in recent years for predicting the defects in software. Thus, machine learning is vastly utilized in bug prediction models. It is broadly applied for giving precise analysis and results. In the primary stage, the software modules are collected from the standard public benchmark datasets. Then, these data are pre-processed and given to dimensionality reduction for increasing the performance of software bug prediction. Here, the Improved Fisher's Linear Discriminant (IFLD) technique is utilized and this parameter optimization is done through the Bat-Inspired Algorithm (BA). The software bug prediction is done by using the Enhanced Deep Neural Network (EDNN). Here, the improvement in DNN is tuned by the same BA technique. The analysis is carried out for investigating the comparative evaluation of various statistical approaches like Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA), Kernel Support-Vector machine (K-SVM), etc. © 2023 IEEE.

关键词： Deep neural networks

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Proceedings - 2023 IEEE international conference on software Maintenance and Evolution, ICSME 2023

Proceedings - 2023 IEEE International Conference on Software...

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39th IEEE international conference on software Maintenance and Evolution, ICSME 2023

ISBN: (纸本)9798350327830

The proceedings contain 71 papers. The topics discussed include: security and safety verification in IoT apps;summarize me: the future of issue thread interpretation;DeltaNN: assessing the impact of computational environment parameters on the performance of image recognition models;revisiting machine learning based test case prioritization for continuous integration;deploying deep reinforcement learning systems: a taxonomy of challenges;software bill of materials adoption: a mining study from GitHub;GPTCloneBench: a comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench;you augment me: exploring ChatGPT-based data augmentation for semantic code search;and slicing shared-memory concurrent programs the threaded system dependence graph revisited.

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software EFFORT ESTIMATION WITH METAHEURISTIC OPTIMIZED ENSEMBLE FOR software CROWDSOURCING 16

SOFTWARE EFFORT ESTIMATION WITH METAHEURISTIC OPTIMIZED ENSE...

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16th IADIS international conference on Information Systems 2023, IS 2023

作者： Yasmin, Anum Butt, Wasi Haider Islamabad Pakistan

ISBN: (纸本)9789898704481

software crowdsourcing (SWCS) is rapidly growing from past decade due to its flexible work environment. software effort estimation (SEE) is already renowned field in traditional software engineering, utilized in preliminary resource planning, budget, and time. SWCS platform can suffer from schedule, cost, and human resource uncertainty, which can be attained by estimating effort consumed on crowdsourced tasks. With more advent in SEE, ensemble effort estimation (EEE) is emerged providing unbiased results across different datasets. Recently, intelligent methods such as metaheuristic algorithms are used for ensemble by assigning optimal weights. This study aims to utilize prediction accuracy of EEE and metaheuristics on SWCS tasks to established accurate effort estimation model. In this work, ensembles are created using high predictive solo machine learning algorithms (RF, SVM, NeuralNet), whose weights are optimized with MO. TopCoder is selected as target SWCS platform, and this is first work to utilize TopCoder Design category tasks and contributes in dataset formation with relevant crowdsourced designing features. Results of proposed scheme clearly show that Metaheuristic-weight learning is giving more accurate ensembles with approximately 60% performance improvement compared to solo ML and other EEE techniques, proving it more suitable SEE technique for SWCS. © 2023 Proceedings of the 16th IADIS international conference on Information Systems 2023, IS 2023. All rights reserved.

关键词： Crowdsourcing

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2023 4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

2023 4th International Conference on Big Data and Artificial...

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4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

ISBN: (纸本)9798350329490

The proceedings contain 85 papers. The topics discussed include: tuberculosis screening with cough sounds using the deep learning models;ship trajectory compression in fish net area based on improved sliding window algorithm;critical system design based on high availability cluster technology;monthly rainfall prediction based on VMD-GRA-Elman model;light information access when operating a small drone;a convolutional neural network based method for masked face detection;control over distributed topology of wire-less sensor network based on power optimization;deep CNN-RNN with self-attention model for electric IoT traffic classification;machine learning modeling on acoustic emission leak detection of metal-sealed pressure vessel;a restricted embedding transfer model for hyperspectral anomaly detection;and text labels classification model based on BERT algorithm.

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