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

共 3227 条记录，以下是81-90 订阅

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Design of A Novel Model that Aids Task Estimation in Comparison with a Regression Tree 10

Design of A Novel Model that Aids Task Estimation in Compari...

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Wakudekar, Sachin Mohite, Sagar Vanjale, Sandeep Mohite, Snehal Jadhav, Swati College of Engineering Pune India Viswakarma Institue of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9789380544472

One of the sectors with the fastest recent growth is the software sector. Project management is key to the software industry's success. One of the most crucial tasks in software project management is to determine the cost and effort associated with software development in the initial stages of the project. There are various conventional techniques for estimating software development efforts. However, there is a lot of interest in using machine learning techniques to address the issue of effort estimates. Out of these machine learning techniques, regression tree-based models have been focused on more in this paper due to their ability to generalization and understandability. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Project management

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Analysis and Practice of Ideological and Political Education in Computer Public Basic Courses in Architecture Universities Based on machine learning and Deep learning 24

Analysis and Practice of Ideological and Political Education...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area international conference on Education Digitalization and Computer Science, EDCS 2024

作者： Lv, Cheng Department of Computer Science School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798400710360

In response to the shortcomings of ideological and political education in the computer basic course of our school, the teaching team has conducted in-depth research on the connotation of ideological and political education in the course, accurately grasped the direction of ideological and political education in the course, innovatively proposed the"progressive method of ideological and political education", and constructed an integrated curriculum system of ideological and political education in the course;Using Orange3 software and machine learning algorithms to deeply explore the ideological and political elements of the curriculum, constructing an analytical model, formulating personalized teaching and learning strategies, and constructing a double-layer teaching evaluation system that emphasizes both morality and ability. Through the analysis of course data, it is shown that the effectiveness of ideological and political education in the curriculum has been comprehensively improved, and the quality of teaching has been comprehensively enhanced. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Research on Steam Meter Calibration in Cigarette Factory Based on machine learning 3

Research on Steam Meter Calibration in Cigarette Factory Bas...

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3rd international conference on Testing Technology and Automation engineering, TTAE 2023

作者： Qin, Yuanli Zhao, Ningning Feng, Yulong Zhao, Shaolin Hebei Baisha tobacco Co. LTD Hebei Province Shijiazhuang City China

ISBN: (纸本)9781510674769

In the current industrial environment, electronic instruments have been widely used, but at the same time, electronic instruments also face many problems, such as zero temperature drift and sensitivity temperature drift caused by temperature changes. This article proposes an algorithm based on software proofreading that combines anomaly detection algorithms with neural networks. It is concluded that this method can indeed proofread instruments and maintain errors within an acceptable range. This method can further improve the accuracy and reliability of the current industry, ensuring process optimization and efficiency improvement. © 2024 SPIE.

关键词： machine learning

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NICHE: A Curated Dataset of Engineered machine learning Projects in Python 20

NICHE: A Curated Dataset of Engineered Machine Learning Proj...

引用

IEEE/ACM 20th international conference on Mining software Repositories (MSR)

作者： Widyasari, Ratnadira Yang, Zhou Thung, Ferdian Sim, Sheng Qin Wee, Fiona Lok, Camellia Phan, Jack Qi, Haodi Tan, Constance Tay, Qijin Lo, David Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9798350311846

machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

关键词： Engineered software Project machine learning Python Open Source Projects

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Automotive Perception software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges 2

Automotive Perception Software Development: An Empirical Inv...

引用

IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Heyn, Hans-Martin Habibullah, Khan Mohammad Knauss, Eric Horkoff, Jennifer Borg, Markus Knauss, Alessia Li, Polly Jing Chalmers Univ Gothenburg Gothenburg Sweden RISE Res Inst Sweden Gothenburg Sweden Zenseact AB Gothenburg Sweden Kogn AB Gothenburg Sweden

ISBN: (纸本)9798350301137

software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations. This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving.

关键词： accountability annotations data ecosystems machine learning requirements specification

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An Optimal Spacing Approach for Sampling Small-sized Datasets for software Effort Estimation 35

An Optimal Spacing Approach for Sampling Small-sized Dataset...

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35th international conference on software engineering and Knowledge engineering, SEKE 2023

作者： Abedu, Samuel Mensah, Solomon Boafo, Frederick Bushel, Eva Akuafum, Elizabeth Department of Computer Science and Software Engineering Concordia University Canada Department of Computer Science University of Ghana Accra Ghana Department of Computer Science Lancaster University Ghana

Context: There has been a growing research focus in conventional machine learning techniques for software effort estimation (SEE). However, there is a limited number of studies that seek to assess the performance of deep learning approaches in SEE. This is because the sizes of SEE datasets are relatively small. Purpose: This study seeks to define a threshold for small-sized datasets in SEE, and investigates the performance of selected conventional machine learning and deep learning models on small-sized datasets. Method: Plausible SEE datasets with their number of project instances and features are extracted from existing literature and ranked. Eubank’s optimal spacing theory is used to discretize the ranking of the project instances into three classes (small, medium and large). Five conventional machine learning models and two deep learning models are trained on each dataset classified as small-sized using the leave-one-out cross-validation. The mean absolute error is used to assess the prediction performance of each model. Result: Findings from the study contradicts existing knowledge by demonstrating that deep learning models provide improved prediction performance as compared to the conventional machine learning models on small-sized datasets. Conclusion: Deep learning can be adopted for SEE with the application of regularisation techniques. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Statistical methods

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software Requirement Classification Using machine learning Algorithms

Software Requirement Classification Using Machine Learning A...

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2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Patel, Vrutik Mehta, Priya Lavingia, Kruti Nirma University Institute of Technology Gujarat Ahmedabad India

ISBN: (纸本)9781665456272

Every software contains numerous processes for building a program, and each step is significant for software requirements. As the globe expands and develops quickly, so does the demand for software. Categorization of requirements can be done manually however doing so requires a lot of human effort, time, money, and risk of inaccurate results. As a result, numerous earlier studies have suggested automating the classification process but consumes lot of time. Here several ways are introduced such that this time taking process can be automated and software requirements can be classified using several machine learning algorithms into various categories. In the process of achieving this there were several algorithms that were taken into consideration which includes KNN, SVM, DT, Naïve Bayes to train dataset and their evaluation metrics were studied. © 2023 IEEE.

关键词： Requirements engineering

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A Novel approach to Handle Imbalanced Dataset in machine learning 8

A Novel approach to Handle Imbalanced Dataset in Machine Lea...

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8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Sapra, Taj Shubhama Meena, Shweta Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9798350334012

The world has seen an exponential rise in machine learning and artificial intelligence since the 1990s. We apply machine learning models to solve various real life problems like regression and classification. However, class imbalance is a very common issue faced for classification problems in machine learning. In this study, we propose new greedy resampling techniques to solve the problem of class imbalance. We shall also compare the results of these techniques with the Synthetic Minority Over-sampling Technique (SMOTE). © 2023 IEEE.

关键词： COVID-19

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machine learning Models for Condominium Appraisal with Result Tuning 20

Machine Learning Models for Condominium Appraisal with Resul...

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20th international Joint conference on Computer Science and software engineering, JCSSE 2023

作者： Posungnern, Sahassawat Tanachutiwat, Sansiri Anchaleechamaikorn, Thanit Lamjiak, Taninnuch The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand King Mongkut's University of Technology Thonburi Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350300505

Real estate appraisals are crucial in determining the value of properties. Condominium valuations, in particular, have mathematical formulas that are applied to determine their value. However, the use of machine learning in real estate appraisals, including condominium valuations, is still not widely trusted. This is because historical data is necessary for machine learning models to make accurate predictions. Additionally, the effectiveness of the most commonly used regression model in practice is limited, and most of the research conducted in this field focuses on appraisals of properties that have already been set a price. To increase the confidence in machine learning models used in real estate appraisals, we propose a modified method that involves feature engineering with a similar name and near area for new condominium appraisals. The goal of this method is to increase the capabilities of the machine learning model or reduce the Mean Absolute Percentage Error (MAPE). © 2023 IEEE.

关键词： Deep neural networks

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Analysis of Integrated learning Methods in Computer Aided Translation software 3

Analysis of Integrated Learning Methods in Computer Aided Tr...

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3rd international conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Guo, Yi Foreign Languages and Literature Wuhan Donghu University Hubei Province Wuhan China

ISBN: (纸本)9798350331578

In the era of intelligence today, computer translation has been equipped with the ability of self-memorization and self-learning. Coupled with continuous manual intervention and iterative optimization, the accuracy rate of many contents in computer translation has reached as high as 90%. Therefore, computer-Aided translation software plays a crucial role in the improvement of translation efficiency. Ensemble learning is a machine learning method that combines multiple learners to make collaborative decisions. It can effectively integrate the probability distribution predicted by multiple models in the process of machine translation task inference, so as to improve the accuracy of translation system. It is also the focus of this paper. © 2023 IEEE.

关键词： Iterative methods

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