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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151-160 订阅

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

引用

6th international conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. the quest for more reliable and efficient diagnostic methods has led to the development of automated systems. this review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. the findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. the paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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MaLTeSQuE 2022 - proceedings of the 6th international Workshop on Machine Learning Techniques for software Quality Evaluation, co-located with ESEC/FSE 2022

MaLTeSQuE 2022 - Proceedings of the 6th International Worksh...

引用

6th international Workshop on Machine Learning Techniques for software Quality Evaluation, MaLTeSQuE 2022, co-located with the 30th ACM Joint Meeting on European software engineering conference and Symposium on the Foundations of software engineering, ESEC/FSE 2022

ISBN: (纸本)9781450394567

the proceedings contain 6 papers. the topics discussed include: the promise and perils of using machine learning when engineering software;neural language models for code quality identification;are machine programming systems using right source-code measures to select code repositories?;on the application of machine learning models to assess and predict software reusability;using machine learning to guide the application of software refactorings: a preliminary exploration;and DeepCrash: deep metric learning for crash bucketing based on stack trace.

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CodeMap: A Graphical Note-Taking Tool Cooperating with an Integrated Development Environment 22

CodeMap: A Graphical Note-Taking Tool Cooperating with an In...

引用

6th international conference on the Art, Science, and engineering of Programming (Programming)

作者： Taniguchi, Rikito Masuhara, Hidehiko Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781450396561

Program comprehension is an important yet difficult activity in a software development process. One of the main causes of the difficulty is its cognitive overhead for maintaining the mental models, which consist of roles of program elements and relationships between them. though researchers have been working on tools to help maintaining the mental models, existing tools have high adoption barrier and support only a few programming languages, which hinders wide-range of programmers from using the program comprehension tools. We propose CodeMap, a graphical note-taking tool for offloading the mental models onto a visual representation. We designed CodeMap by considering familiarity and availability for practitioners. Our tool allows programmers to extract interested information into a graphical note with a few keyboard/mouse operations, and support many programming languages by using the language server protocol. In this paper, we present the design and implementation of CodeMap, and discuss possible features that could be useful for program comprehension.

关键词： program comprehension code navigation software engineering

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Robust Learning of Deep predictive models from Noisy and Imbalanced software engineering Datasets 7

Robust Learning of Deep Predictive Models from Noisy and Imb...

引用

37th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Li, Zhong Pan, Minxue Pei, Yu Zhang, Tian Wang, Linzhang Li, Xuandong Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781450394758

With the rapid development of Deep Learning, deep predictive models have been widely applied to improve software engineering tasks, such as defect prediction and issue classification, and have achieved remarkable success. they are mostly trained in a supervised manner, which heavily relies on high-quality datasets. Unfortunately, due to the nature and source of software engineering data, the real-world datasets often suffer from the issues of sample mislabelling and class imbalance, thus undermining the effectiveness of deep predictive models in practice. this problem has become a major obstacle for deep learning-based software engineering. In this paper, we propose RobustTrainer, the first approach to learning deep predictive models on raw training datasets where the mislabelled samples and the imbalanced classes coexist. RobustTrainer consists of a two-stage training scheme, where the first learns feature representations robust to sample mislabelling and the second builds a classifier robust to class imbalance based on the learned representations in the first stage. We apply RobustTrainer to two popular software engineering tasks, i.e., Bug Report Classification and software Defect Prediction. Evaluation results show that RobustTrainer effectively tackles the mislabelling and class imbalance issues and produces significantly better deep predictive models compared to the other six comparison approaches.

关键词： predictive models Mislabelling Imbalanced Data Deep Learning

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Revolutionizing Project Collaborations: the Role of AI Integration 6

Revolutionizing Project Collaborations: The Role of AI Integ...

引用

6th Parul international conference on engineering and Technology, PICET 2024

作者： Monteiro, Malaika Dsilva, Ivan Abraham, Sarah Nadar, Athsa Dhamanskar, Prajakta Department of Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India

ISBN: (纸本)9798350369748

the Student-Business Collaboration Platform fosters innovative solutions by connecting businesses with ambitious students, addressing real-world challenges. Powered by AI mentorship, students receive personalized guidance throughout the project lifecycle. the Task Generator module, utilizing Large Language models (LLMs), aids in planning, ensuring timely task completion. the Meeting Assistant module facilitates progress updates and generates comprehensive reports. this collaborative platform promotes knowledge exchange and automates development processes, offering a transformative approach to student-business collaboration. © 2024 IEEE.

关键词： Artificial intelligence

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OptiNet: Leveraging Deep Learning Ensembles for Timely Detection of Visual Abnormalities in Children 6

OptiNet: Leveraging Deep Learning Ensembles for Timely Detec...

引用

6th Parul international conference on engineering and Technology, PICET 2024

作者： Reddy, Chethan Hariprada Manik, Nilkant Satish Shilpa, S. Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350369748

this research introduces an innovative approach to early detection of visual defects in children, integrating deep learning models (ResNet-50, VGG-19, EfficientNet) through transfer learning for enhanced diagnostic accuracy. Augmenting the dataset from Bajwa Eye Hospital addresses data limitations, while ensemble learning consolidates predictions for a high-accuracy outcome. the user-friendly React interface prioritizes accessibility, and FastAPI enables efficient server-side processing for real-time predictions. Deployed on a web server, the system proves widely accessible. Results from individual models and ensemble learning exhibit commendable accuracy, showcasing the proposed methodology as a promising tool for early detection in pediatric ophthalmology, addressing a critical public health concern. © 2024 IEEE.

关键词： Contrastive Learning

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Review on Simulation and Optimization of Vehicle Ride Comfort Based on Suspension Model 6th

Review on Simulation and Optimization of Vehicle Ride Comfor...

引用

conference of the-Efficiency-and-Performance-engineering-Network (TEPEN) / 6th international conference on Maintenance engineering (IncoME)

作者： Tang Jianghu Xiong Qing Zhu Yingmou He Zhuoyu Xihua Univ Sch Automobile & Transportat Chengdu 610039 Peoples R China State Key Lab Heavy Duty AC Drive Elect Locomot S Zhuzhou 412001 Peoples R China Xihua Univ Key Lab Automot Measurement Control & Safety Chengdu 610039 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

At present, commercial software of the multi-body dynamics is widely used in the research of vehicle ride comfort simulation and optimization. this paper reviews some literatures on vehicle ride comfort optimization based on ADAMS, and focuses on the research based on suspension models. the suspension rigid-flexible coupling models and the simulation research about optimizing suspension model parameters to achieve multi-objective optimization are the main areas of concern. Finally, the paper is summarized and the future trend of ADAMS applied to the simulation and optimization of vehicle ride comfort is prospected.

关键词： Ride comfort Suspension models Multi-body dynamics ADAMS

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, tharindu thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. the paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. this approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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Temperature Feedforward Model Based on Improved Stochastic Configuration Network for Coal Mill 6

Temperature Feedforward Model Based on Improved Stochastic C...

引用

6th international conference on Industrial Artificial Intelligence, IAI 2024

作者： Huang, Xiaodi Jiang, Yanchen Li, Qiansheng Wang, Yongfu School of Mechanical Engineering and Automation Northeastern University Liaoning Shenyang110819 China Dalian Power Plant Huaneng Power Intl Inc Dalian116100 China School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China

ISBN: (纸本)9798350356618

Aiming at the practical needs for feedforward mod-eling of the coal mill outlet temperature setpoint under blended coal operation conditions, an improved stochastic configuration network (ISCN) is introduced for predictive modeling. Firstly, the stochastic configuration of hidden node parameters based on the supervision mechanism leads to uncertainty and cannot guarantee parameter optimality. Consequently, an improved pelican optimization algorithm (IPOA) is introduced to optimize the configuration of hidden node parameters, which not only enhances network learning efficiency but also lightweights the network structure. On the other hand, fully supervised SCN mod-eling demands large amount of high-cost experimental data. To reduce modeling expenses, a semi-supervised learning approach based on small labeled data is employed to categorize unlabeled data and expand the training dataset. Nextly, considering that there are some irrationalities in determining the coal mill outlet temperature setpoint using traditional methods. the ISCN introced in this paper is applied to feedforward model of coal mill outlet temperature setpoint. Finally, the validity and practicality of the method are verified by experiments. © 2024 IEEE.

关键词： Stochastic models

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Analysis of College Students' Behavioral Intention to Use Online Healthcare Treatment under Digital Transformation 6

Analysis of College Students' Behavioral Intention to Use On...

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6th IEEE international conference on Computer and Communication engineering Technology, CCET 2023

作者： Liu, Yiman Bai, Bing Liang, Jiale Zhang, Lin Business School Jiangsu Normal University Xuzhou China

ISBN: (纸本)9798350340686

Under the background of digital transformation, online healthcare treatment has become a popular way. Based on the UTAUT model, this study took the national college students as the research object, retained the core factors, introduced perceived risk and service pricing, and built a structural equation model of influencing factors of college students' behavioral intention to use online healthcare treatment. SPSS and AMOS software were used to test the degree of model fit, and the path analysis of influencing factors was conducted. the results show that performance expectation, effort expectation, social impact and promote forcing have a positive predictive effect on college students' behavioral intention to use online healthcare treatment, while service pricing has a negative predictive effect. Studying the behavior intention of college students to use online healthcare treatment is conducive to promoting the online healthcare treatment platform to further improve its own construction, improve the medical experience of college students, and achieve sustainable development. © 2023 IEEE.

关键词： software testing

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