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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 3232 条记录，以下是171-180 订阅

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Effects of Different Vaccines on Genetic Immune and Antibody Response Using machine learning methods

Effects of Different Vaccines on Genetic Immune and Antibody...

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2023 international conference on engineering, Science and Advanced Technology, ICESAT 2023

作者： Husham, Nuha Zaki, Sura College of Information Technology University of Babylon Software Department Babylon Iraq

ISBN: (纸本)9798350344400

machine learning models have shown promising results in analyzing genetic, immune, and antibody responses to vaccines. Studies have identified certain genes consistently activated in individuals with a robust immune response to the flu vaccine, and specific antibodies most effective in preventing infection. In a recent study using a dataset of 161 blood samples from individuals who received the BNT162b2 and ChAdOx1 vaccines, machine learning algorithms accurately predicted vaccine efficacy based on genetic, immune, and antibody responses. The AdaBoost model had the highest accuracy at 98.9%, followed by the Decision Tree model at 93.8%, and k-Nearest Neighbors at 87.7%. The AdaBoost model also performed the best overall, with the highest precision and recall for both vaccines. © 2023 IEEE.

关键词： Vaccines

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Smart Trash Classification machine 9

Smart Trash Classification Machine

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9th international conference on Mechatronics and Robotics engineering (ICMRE)

作者： Wiriwithya, Pathaphon Rungnarongruck, Siwach Pongamphai, Sasapol Puapattanakul, Saranyapong Chancharoen, Ratchatin Chulalongkorn Univ Dept Mech Engn Bangkok Thailand

ISBN: (纸本)9781665463263

Smart trash classification machine is a machine vision made to handle the problem of unsorted trash from its primary source. The machine is made to classify trash in a short time with high efficiency and consists of two components. The first component is software for constructing an algorithm using the TensorFlow library, built in the Python programming language, to produce an image of trash and store it in the library. The image will be divided into six types based on their characteristics and shape and will be automatically analysed using the deep learning ImageNet model. When the safety light curtain sensor detects trash, a signal is sent to the RGB camera, which captures an image of the trash. The acquired image will then be analysed by algorithm software that is compared to the library image in the database and then sends a signal to the hardware component indicating the type of trash. The second component is hardware, which consists of an RGB camera, light curtain safety sensors, a bin-driving motor, and garbage sorting bins.

关键词： trash classification tensorflow deep learning Programmable Logic Control (PLC) python Convolutional Neural Network (CNNs) ImageNet

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Towards Extracting Ethical Concerns-related software Requirements from App Reviews 24

Towards Extracting Ethical Concerns-related Software Require...

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39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Sorathiya, Aakash Ginde, Gouri Univ Calgary Calgary AB Canada

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

As mobile applications become increasingly integral to our daily lives, concerns about ethics have grown drastically. Users share their experiences, report bugs, and request new features in application reviews, often highlighting safety, privacy, and accountability concerns. Approaches using machine learning techniques have been used in the past to identify these ethical concerns. However, understanding the underlying reasons behind them and extracting requirements that could address these concerns is crucial for safer software solution development. Thus, we propose a novel approach that leverages a knowledge graph (KG) model to extract software requirements from app reviews, capturing contextual data related to ethical concerns. Our framework consists of three main components: developing an ontology with relevant entities and relations, extracting key entities from app reviews, and creating connections between them. This study analyzes app reviews of the Uber mobile application (a popular taxi/ride app) and presents the preliminary results from the proposed solution. Initial results show that KG can effectively capture contextual data related to software ethical concerns, the underlying reasons behind these concerns, and the corresponding potential requirements.

关键词： ethical concern user reviews app reviews knowledge graph mobiles apps software requirements analysis

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Exploring Research Opportunities to Apply Data Mining Techniques in software engineering Lifecycle 1

Exploring Research Opportunities to Apply Data Mining Techni...

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1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Anitha Gracy, J. Parthasarathy, S. Bagavathilaksmi, R. Thiagarajar College of Engineering Applied Mathametics and Computational Science Madurai India VELS Institute of Science Technology and Advanced Studies Department of Computer Application/Information Technology Chennai India

ISBN: (纸本)9798350342796

This software engineering (SE) is the core research area of the software industry, and numerous algorithms and frameworks are proposed every day. software engineering is the discipline that keeps up with recent developments in fields and techniques such as data mining, machine learning, natural language processing, and artificial intelligence. It is difficult to manage such a massive amount of data in a software engineering repository if data processing and big data analytics are not available. This paper provides an in-depth study of data mining techniques that to improve the efficiency of the software development lifecycle. The study emphasizes the practical applications and discoveries made by text mining, clustering, classification, and other methods. © 2023 IEEE.

关键词： Classification Clustering Data Mining SDLC software engineering Text Mining

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A Comprehensive Study of Real-World Bugs in machine learning Model Optimization 23

A Comprehensive Study of Real-World Bugs in Machine Learning...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Guan, Hao Xiao, Ying Li, Jiaying Liu, Yepang Bai, Guangdong Univ Queensland Brisbane Qld Australia Southern Univ Sci & Technol Shenzhen Peoples R China Microsoft Software Technol Ctr Asia Beijing Peoples R China

ISBN: (纸本)9781665457019

Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.

关键词： machine learning Model Optimization Bugs

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MLinter: learning Coding Practices from Examples Dream or Reality? 30

MLinter: Learning Coding Practices from Examples Dream or Re...

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

作者： Latappy, Corentin Perez, Quentin Degueule, Thomas Falleri, Jean-Remy Urtado, Christelle Vauttier, Sylvain Blanc, Xavier Teyton, Cedric Univ Bordeaux Bordeaux INP CNRS UMR5800LaBRI F-33400 Talence France Univ Montpellier EuroMov Digital Hlth Motion Ales France IMT Mines Ales Ales France IUF Paris France Promyze Bordeaux France

ISBN: (纸本)9781665452786

Coding practices are increasingly used by software companies. Their use promotes consistency, readability, and maintainability, which contribute to software quality. Coding practices were initially enforced by general-purpose linters, but companies now tend to design and adopt their own companspecific practices. However, these company-specific practices are often not automated, making it challenging to ensure they are shared and used by developers. Converting these practices into linter rules is a complex task that requires extensive static analysis and language engineering expertise. In this paper, we seek to answer the following question: can coding practices be learned automatically from examples manually tagged by developers? We conduct a feasibility study using CodeBERT, a state-of-the-art machine learning approach, to learn linter rules. Our results show that, although the resulting classifiers reach high precision and recall scores when evaluated on balanced synthetic datasets, their application on real-world, unbalanced codebases, while maintaining excellent recall, suffers from a severe drop in precision that hinders their usability.

关键词： software quality coding practices machine learning CodeBERT

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Towards Understanding Fairness and its Composition in Ensemble machine learning 23

Towards Understanding Fairness and its Composition in Ensemb...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Gohar, Usman Biswas, Sumon Rajan, Hridesh Iowa State Univ Dept Comp Sci Ames IA 50011 USA Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA USA

ISBN: (纸本)9781665457019

machine learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble? Can fair learners result in an unfair ensemble? Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers design fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: Bagging, Boosting, Stacking, and Voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.

关键词： fairness ensemble machine learning models

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Building a Classification Model based on Feature engineering for the Prediction of Wine Quality by Employing Supervised machine learning and Ensemble learning Techniques

Building a Classification Model based on Feature Engineering...

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international conference on Computer, Electrical and Communication engineering (ICCECE)

作者： Nandan, Mauparna Gupta, Harsh Raj Mondal, Moutusi Techno Main SaltLake Dept Comp Applicat Kolkata India Dr BC Roy Engn Coll Dept ECE Durgapur India

ISBN: (纸本)9781665452519

In today's world, consumers are more concerned regarding the quality of any product. Different approaches are being deployed by various industries to guarantee the excellent quality of their products. Thus, quality certification serve as a vital authentication mechanism for majority of the industries for promoting their numerous products in the market. In the past, only human specialists were employed to evaluate and measure quality. But, nowadays, the majority of validation jobs are automated by software, which curtails the workload of human experts by assisting them to predict the quality of the product and thereby leading to a considerable amount of time saving. Over the past few decades, there has been a sharp rise in wine consumption due to its intrinsic health benefits, particularly for the human heart, as well as for recreational reasons. The main focus of this study is two-fold: the first objective is to predict the quality of wine based upon the correlation between the various physicochemical factors in order to determine the most prominent factors which play a significant role for determining the quality of wine by implementing several supervised machine learning and ensemble learning techniques and the final results being confirmed by employing a variety of quantitative indicators and the second objective is the classification of wine into 3 categories, namely, Best, Good and Poor in order to rank the quality of wine. However, during testing the models with the test dataset, it has been observed that the Random Forest classifier outperformed the other machine learning classifiers with an accuracy of 98%.

关键词： Supervised machine learning wine quality classification ensemble learning feature engineering

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software Susceptibility Detection Using Deep learning Techniques 10

Software Susceptibility Detection Using Deep Learning Techni...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Kumar, K. Sudheer Sahu, Dillip Narayan Takore, Tamirat Tagesse Singh, Navdeep Durga, Csl Vijaya Kapila, Dhiraj School of Cs & Ai Sr University Telangana Warangal India Gangadhar Meher University Department of Mca Odisha India Wachemo University Department of Electrical and Computer Engineering Ethiopia Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350382471

software susceptibilities are flaws in the source code that could be used to do havoc or collect information. A variety of automated machine learning methods have been suggested in prior research to find them. The majority of these methods operate at the release level, which means they try to foresee which files might be vulnerable in a future version. Manually detecting insecure code is both challenging and costly in both time and money. Therefore, to reduce costs, developers must use automatic vulnerability prediction (AVP) techniques. Recent AVP research has started to employ deep learning (DL) methods. All of the suggested methods are built on feature extraction techniques that were influenced by earlier DL uses such as autonomous language processing. When developing AVP models based on traditional machine learning, code metrics were frequently employed as features. The following piece provides a method centered on deep neural networks over leveraging code metrics to detect vulnerabilities in code, bridging the difference between machine learning parameters and deep learning. Results obtained indicate that while code metrics are excellent, they weren't the best to use as DL-based AVP characteristics. © 2023 IEEE.

关键词： AVP deep learning machine learning methods Susceptibilities

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Do software Security Practices Yield Fewer Vulnerabilities? 23

Do Software Security Practices Yield Fewer Vulnerabilities?

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IEEE/ACM 45th international conference on software engineering - software engineering in Practice (ICSE-SEIP)

作者： Zahan, Nusrat Shohan, Shohanuzzaman Harris, Dan Williams, Laurie North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9798350300376

Due to the ever-increasing number of security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers in making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts. To that end, we developed five supervised machine learning models for npm and PyPI packages using the OpenSSF Scorecard security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found that four security practices (Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices influencing vulnerability count. However, we had low R-2 (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both findings indicate that additional factors may influence the package vulnerability count. Other factors, such as the scarcity of vulnerability data, time to implicate security practices vs. time to detect vulnerabilities, and the need for more adequate scripts to detect security practices, may impede the data-driven studies to indicate that a practice can aid in the reduction of externally-reported vu

关键词： machine learning Model Scorecard Tool Security Outcomes software Security Practices

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