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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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Convolutional Neural Network based Digital Twin of Rolling Bearings for CNC machine Tools in Cloud Computing 10

Convolutional Neural Network based Digital Twin of Rolling B...

引用

10th international conference on Dependable Systems and Their Applications, DSA 2023

作者： Wang, Zhaoming Gong, Siqian Liu, Yang Beijing Jiaotong University School of Mechanical Electronic and Control Engineering Beijing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350304770

CNC machine tool is the mother of industry, and the realization of Intelligent CNC machine tool is an important link to promote the development of intelligent manufacturing. The fault diagnosis of key components of CNC machine tools is very important for the machining process. The traditional fault diagnosis methods have problems such as poor real-time performance and inaccurate prediction. Therefore, with the help of digital twin technology and cloud computing, this paper proposes a five-dimensional digital twin system architecture of CNC machine tool rolling bearing based on cloud computing to solve the above problems. And a deep learning technology for data processing is established, which is a hybrid network structure based on CNN-LSTM. Taking the fault diagnosis and prediction of rolling bearing as an example, it is verified that the prediction effect of the algorithm is good, and it can be used for system data processing. © 2023 IEEE.

关键词： Cloud computing

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Innovative Poverty Estimation through machine learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

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18th IEEE international conference on Emerging Technologies, ICET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 IEEE.

关键词： Forecasting

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A Deep learning Based Model for Defect Prediction in Intra-Project software 7

A Deep Learning Based Model for Defect Prediction in Intra-P...

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7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Sekaran, Kripa Annabel, L. Sherly Puspha St.Joseph's College of Engineering Chennai India

ISBN: (纸本)9798350397284

Testing of software ensures the supply of meaningful software and hence prediction of defects in producing high quality software has become an inevitable one. software defect prediction's main aim is to find out various bugs present in software and focus on testing efforts. Many of existing software defect prediction frameworks are much simple, making it difficult for developers to get detailed reference information. Nowadays, many deep learning models, like the Radial Base Functional Neural Network(RBF) and the Convolutional Neural Network (CNN), are applied to features which are created automatically from deep learning models and abstract syntax trees (AS Ts) to aid in the improved performance of predicting defects. But the results generated using RBF and CNN algorithms are not able to provide much accuracy due to its restricted size of dataset and improper baseline selections. To resolve these state-of-the-art problems, we have constructed a dataset taken from various defect datasets namely the Kamei Dataset, NASA Dataset and the PROMISE Source Code (PSC) dataset. In this research, the dataset is named as Combination Defect Analysis Dataset (CDA). Then, an Enhanced Convolutional Neural Network (ECNN) model is proposed for predicting defects in Intra-Project software (IPDP) and associated results to different models. Experimental results implied that Enhanced CNN(ECNN) model is efficient compared to the other associated models, along with it outclassing the other machine learning models suggested for IPDP. © 2023 IEEE.

关键词： Defects

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AI-IOT: Integrating Artificial Intelligence & Internet of Things for Healthcare Systems

AI-IOT: Integrating Artificial Intelligence & Internet of Th...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Joshi, Sunita Gupta, Neha Burmi, Sathak Sharma, Yash Manav Rachna International Institute of Research and Studies School of Computer Applications India

ISBN: (纸本)9798350344387

AI technology has made healthcare far more efficient, usable, and available. Artificial intelligence (AI) is a present day method of developing algorithms and programs to make computers intelligent and effective at performing jobs that frequently need competent human brains. machine learning (ML), deep learning (DL), classic neural networks, fuzzy logic, and speech recognition represent just a few of the artificial intelligence (AI) subsets with distinct characteristics and functionalities that might improve medical science performance. These cognitive technologies make it easier for humans to participate in clinical diagnostics, medical imaging, and decision- making. Around the same time period, the Internet of Medical Things (IoMT) emerged, which is a cutting-edge bio-analytical tool that integrates network-connected biomedical devices with a software program to improve human health. The paper will address this integration as AI-IoT approach. The paper addresses the scientific, engineering, and business opportunities for creating effective patient care biomedical systems that are appropriate for next-generation intelligent healthcare using AI-based integrated personalized IoT devices. © 2023 IEEE.

关键词： Artificial Intelligence Disease Prediction Healthcare Systems Internet of Things machine learning Algorithms Sensors

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Improving polygon image segmentation by enhancing U-Net architecture

Improving polygon image segmentation by enhancing U-Net arch...

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2023 international conference on machine learning and Automation, conf-MLA 2023

作者： Li, Da Software College Jiangxi Agricultural University Nanchang330045 China

The crucial task of polyp recognition in medical imaging plays a pivotal role in the early detection and prevention of colorectal cancer. Semantic segmentation, particularly utilizing sophisticated deep learning models such as U-Net, has demonstrated promising results in the realm of polyp segmentation. However, the traditional U-Net structure sometimes grapples with accurately delineating the edges of polyps, which subsequently impacts the overall performance of segmentation. To address this issue, the current study introduces a novel approach by proposing a modified framework of U-Net, equipped with an enhanced edge loss function. This function is designed to ameliorate the accuracy of segmentation within polyp images. The aim is to elevate the model's capacity to capture intricate details, specifically the edges, which is an area where standard U-Net structures often falter. Experimental outcomes of this study serve to underscore the effectiveness of the proposed approach in accomplishing superior segmentation of edges and improved overall performance in polyp recognition. By successfully tackling the challenges inherent to polyp edge segmentation, the modified U-Net model contributes significantly towards more precise diagnostic systems in the field of medical imaging. Consequently, this research is poised to make a valuable contribution to advancements in the prevention and early detection of colorectal cancer. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Medical imaging

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Improving Fine-tuning Pre-trained Models on Small Source Code Datasets via Variational Information Bottleneck 30

Improving Fine-tuning Pre-trained Models on Small Source Cod...

引用

30th IEEE international conference on software Analysis, Evolution and Reengineering (SANER)

作者： Liu, Jiaxing Sha, Chaofeng Peng, Xin Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665452786

Small datasets are common in software engineering tasks such as linguistic smell detection and code runtime complexity prediction, as crafting these datasets often involves expert knowledge. Prior work usually applies machine learning algorithms (e.g., logistic regression and SVM) with handcrafted features to tackle them, which could outperform neural models such as CNN. Recently, researchers have employed fine-tuning large pre-trained code models on various code-related tasks thanks to their transferability. However, it might be still instable and overfitting when fine-tuning on small datasets. In this paper, we firstly conduct an empirical study to fine-tune CodeBERT(a) on four code-related small datasets and observe the instability phenomenon. This could be induced by overcapacity and irrelevant features inherent in these large pre-trained code models with respective to those small datasets. To address this issue, we leverage variational information bottleneck to filter out irrelevant features when fine-tuning the models. The experiments demonstrate the out-performance of our method compared to standard fine-tuning and regularization method such as dropout and weight decay. We also experimentally study the stability of our method through varying dataset sizes. Our code and data are available at https://***/little-pikachu-hash/VIBCodeBERT.

关键词： pre-trained model fine-tuning small software engineering dataset information bottleneck

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Code Obfuscation based Adversarial Example Generation Method

Code Obfuscation based Adversarial Example Generation Method

引用

2022 international conference on Internet of Things and machine learning, IoTML 2022

作者： Li, Jin School of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing211100 China

ISBN: (纸本)9781510664913

Malware is still a big threat to network security, and various types of malware detectors are needed. Deep learning-based classifiers have substantially improved their ability to identify malware samples. However, these detectors suffer from adversarial examples. The samples were made by adding small, carefully selected perturbations to the normal software. Any vulnerability in malware detectors can pose a significant threat to the platforms they defend. However, existing attack methods may not meet the inherent limitations of malware. This paper proposes a new method to generate malware adversarial samples. The original malware is mutated into new samples through semantic analysis of malware, transplantation of code in the program and addition of code at the end. And used to fool the detector. Experiments show that compared with the existing methods, our method has a significant effect on the efficiency of generating adversarial examples and the success rate of the attack. © 2023 SPIE.

关键词： Malware

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Optimizing Continuous Integration by Dynamic Test Proportion Selection 30

Optimizing Continuous Integration by Dynamic Test Proportion...

引用

30th IEEE international conference on software Analysis, Evolution and Reengineering, SANER 2023

作者： Zhang, Long Cui, Binyi Zhang, Zhenyu Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Beijing China

ISBN: (纸本)9781665452786

Continuous integration is widely used in modern software engineering. However, it is an expensive practice. The proposed approaches focus on either intra- or inter-build cost reduction. Test case prioritization and selection (TCPS) techniques, typical intra-build techniques, are designed to save the high cost of CI by identifying failed test cases for failed builds. However, existing works are inadequate to distinguish characteristics of builds, but to apply an identical test selection proportion to different builds. Build-prediction techniques, typical inter-build techniques, are designed to save CI cost at the build level. If a build is deemed likely to pass based on the prediction of machine learning models, the whole test suite is skipped for it. Apparently, build in such a manner may miss some realistic failed test cases, if a machine learning model provides a false negative result. In this paper, we propose a dynamic test proportion selection technique DTPS, which incorporates intra- and inter-build cost reduction techniques. DTPS uses build features to construct machine learning models to predict the probability of a specific build failure and transform the probability into the necessary test proportion, with respect to a selected test case prioritization technique. Based on the output of machine learning model, it thus selects a prioritized test suite and a variable proportion of test cases with respect to a build. We constructed a large-scale dataset with approximately 115,000 builds, and conducted a controlled experiment using the dataset. The experiment shows that DTPS outperforms existing techniques significantly. It detects 19.9% to 32.5% more failed test cases, compared with state-of-the-art techniques evaluated in the experiment. At the same time, DTPS performs better than all three existing peer techniques on approximately 47% of projects. Moreover, the experiment also shows that our failure prediction model has an improvement of 0.15 in Area Under C

关键词： Cost reduction

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Traffic Prediction Model Using machine learning in Intelligent Transportation Systems

Traffic Prediction Model Using Machine Learning in Intellige...

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2023 international conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Sharma, Abhilasha Ranjan, Prabhat Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9798350333244

The term Traffic Environment refers to everything that has the potential to disrupt the flow of vehicular traffic on the road, but not particulates to accidents, traffic signals, rallies and even for the maintenance of roads, which may lead to backups. If an individual possesses prior knowledge that is similar to the above-mentioned factors, along with an understanding of other everyday variables that may influence traffic, they can make informed decisions as a driver or passenger. Advanced transport systems, tourist database systems, and traffic management systems now have much better traffic predictions because to the adoption of intelligent transportation systems. Therefore, the continuous improvement and implementation of these systems will continue to enhance traffic prediction accuracy. The goal of this study is to use modern communication technologies and to propose a methodology based on machine learning for improving transportation safety, mobility and productivity. According to the research, the suggested method (SVR) performs better than the competing approaches in terms of performance on all assessment metrics, regardless of the dataset. © 2023 IEEE.

关键词： Support vector machines

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A Review of Metamorphic Relation Representation Forms 10

A Review of Metamorphic Relation Representation Forms

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10th international conference on Dependable Systems and Their Applications, DSA 2023

作者： Zhao, Yin Li, Meng Liu, Jie University of South China School of Computing Hengyang China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment Hengyang China Cnnc Key Laboratory on High Trusted Computing Hengyang China

ISBN: (纸本)9798350304770

Metamorphic testing can effectively alleviate the problem of testing oracle. The lack of specification descriptions of metamorphic relations affects its generalized application. This text categorizes metamorphic relations into three types based on their representation forms: arithmetic expressions, predicate expressions, and other categories. Among the one hundred and sixty-two relations classified, arithmetic expressions account for sixty-four percent. Within geometry, numerical programs, and algorithms, the main to employ relations on arithmetic expressions, while machine learning and optimization algorithms are mainly inclined toward hiring predicate expression relations. Meanwhile, the research findings about the construction methods of metamorphic relations, the generation of original test cases, and the utilization of testing tools have been systematically organized to guide the application of the above techniques based on representation forms. Moreover, it lays the foundation for studying the fundamental nature of relationships, such as complexity, similarity, and relation composite. © 2023 IEEE.

关键词： machine learning

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