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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2511 条记录，以下是21-30 订阅

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BORT-RD: An Improved Model for software engineering Design Feature Generation 9

BORT-RD: An Improved Model for Software Engineering Design F...

引用

9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Li, Zhenhao Lei, Hang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350306682

By studying the connections and dependencies between requirement features and design features in software development documentation, and incorporating the advantages of various Transformer-like models, a BORT-RD model applicable to generating design features is proposed in this text. this model is based on the syntactic decomposition of requirement documents and design documents to train feature phrases. the model has been improved in terms of input embedding, attention calculation and fine-turning, targeting the characteristics of software engineering documents. through experiments, the model's precision, accuracy and recall have been increased to 81.18%, 94.4%, and 83.1%, respectively, showing significant improvements compared to traditional classical Transformer models. © 2023 IEEE.

关键词： software design

来源：评论

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Hybrid Deep Learning Framework for Stock Price Prediction Incorporating Technical and Macroeconomic Indicators 9

Hybrid Deep Learning Framework for Stock Price Prediction In...

引用

9th international conference on Computer Science and engineering, UBMK 2024

作者： Turan, Ali Can Kokach, Vael Kocaçinar, Büşra Şengel, Öznur Akbulut, Fatma Patlar Istanbul Kültür University Department of Computer Engineering Istanbul Turkey Istanbul Kültür University Department of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

the complicated interaction of several elements makes accurate stock price prediction still difficult. this study suggests a hybrid deep learning model to improve stock market prediction by integrating Bidirectional Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs). Incorporating both technical and macroeconomic variables helps the model to adequately identify complex trends and interconnections inside the financial data. While the bidirectional LSTM models temporal dependencies, the CNN component shines in retrieving pertinent features from the input data. Experimental findings show that the suggested model beats conventional approaches, therefore offering traders and investors important new perspectives. © 2024 IEEE.

关键词： Prediction models

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Mutation Testing Reinvented: How Artificial Intelligence Complements Classic Methods 9

Mutation Testing Reinvented: How Artificial Intelligence Com...

引用

9th international conference on Computer Science and engineering, UBMK 2024

作者： Uzunbayir, Serhat Kurtel, Kaan Izmir University of Economics Department of Software Engineering Izmir Turkey

ISBN: (纸本)9798350365887

Mutation testing is a widely accepted method for assessing the effectiveness of software test suites. It focuses on evaluating how well a test suite can identify deliberately introduced faults, known as mutations, in the code, helping to reveal potential vulnerabilities. Traditional mutation testing approaches, however, often encounter significant issues such as high computational demands and limited fault detection range. Recently, there has been an increasing interest in integrating artificial intelligence (AI) into mutation testing to address these challenges. AI can enhance mutation testing by incorporating intelligent algorithms and automating various tasks. It can analyze the codebase, identify key program components, and strategically select mutation operators that are more likely to detect faults. this paper explores the fundamental concepts of mutation testing, relevant research, and innovations in combining AI with mutation testing, focusing on the challenges and the most effective models currently available. By integrating AI, mutation testing has seen improvements in both fault detection and computational efficiency. © 2024 IEEE.

关键词： Integration testing

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Hybrid predictive Model for Efficiency Forecasting in Garment Production: Integrating Ensemble and Time-Series Approaches 9

Hybrid Predictive Model for Efficiency Forecasting in Garmen...

引用

9th international conference on Information Technology Research

作者： Ahamed, Zamith Asanka, Ppg Dinesh Rajapakse, Chathura Univ Kelaniya Dept Ind Management Colombo Sri Lanka

Sri Lanka9;s garment industry is a critical component of the nation9;s economy, contributing significantly to GDP, export revenue, and employment, particularly for rural women. While the industry is recognized g... 详细信息

ISBN: (纸本)9798331511425;9798331511432

Sri Lanka's garment industry is a critical component of the nation's economy, contributing significantly to GDP, export revenue, and employment, particularly for rural women. While the industry is recognized globally for its ethical production and high-quality outputs, it faces challenges including dependence on imported raw materials, fluctuating global demands, and competition from low-cost manufacturing hubs like Bangladesh and Vietnam. To maintain its competitive edge, the industry must explore innovative solutions such as predictive analytics and advanced modeling techniques to optimize efficiency and sustainability. this research examines the application of predictive analytics and time series modeling to enhance production efficiency in Sri Lanka's garment sector. We explore various machine learning models, including ensemble techniques and Long Short-Term Memory (LSTM) networks, to forecast production efficiency. Using data from a major Sri Lankan garment manufacturer, spanning May 2023 to August 2024, we develop a hybrid predictive model. Data preprocessing involved steps like cleaning, handling missing values, and feature engineering, ensuring reliable inputs for the model. Our results show that the hybrid model provides a balanced approach, offering practical advantages in real-world production environments. the model demonstrates moderate prediction accuracy, reducing mean squared error (MSE) and mean absolute error (MAE), outperforming several other traditional models.

关键词： Garment Hybrid Approach Machine Learning predictive Analytics Production Efficiency

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LabSI-SAT: Low-cost CubeSat Nanosatellite Prototype Assembly Demonstration 9

LabSI-SAT: Low-cost CubeSat Nanosatellite Prototype Assembly...

引用

9th international engineering, Sciences and Technology conference, IESTEC 2024

作者： Rodriguez-Chong, Juan Caceres-Hernandez, Danilo Tecnology University of Panama Intelligent Systems Laboratory Faculty of Electrical Engineering Panama Panama Panama Panama

ISBN: (纸本)9798350377156

this document is a report on the lessons learned during the construction of a low-cost nanosatellite prototype using commercially available components. It examines the system control models and sensors that can be utilized, as well as ways to enhance the reliability and availability of both hardware and software. the project was conducted using the space race achievements of other countries as a reference, with the goal of enabling other nations seeking to develop in this field to embark on their own space endeavors. © 2024 IEEE.

关键词： Nanosatellites

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Employee Turnover Prediction on Synthetic and Real Datasets 9

Employee Turnover Prediction on Synthetic and Real Datasets

引用

9th international conference on Computer Science and engineering, UBMK 2024

作者： Murzaeva, Azhar Illik, Volkan Koc, Kadir Yunus Ibss Technology and Software R&d Department Istanbul Turkey

ISBN: (纸本)9798350365887

When an employee leaves the job, it leads to issues such as financial losses, loss of productivity, re-employment costs, the adaptation period for the new employee, and loss of time. Many studies are dedicated to predicting employee turnover to avoid these negative impacts. In this study, we train classic machine learning models to predict employee turnover. We train Logistic Regression, Decision Tree, XGBoost, Random Forest, Gradient Boosting, Ada Boosting, and Support Vector Machine methods. We use two publicly available datasets: IBM's synthetic data and an anonymous US company's real data. We compare the outcomes of models trained by collected real datasets and models trained by synthetically generated datasets. For the IBM dataset, with 92% accuracy and 0.92 f1-score, Random Forest demonstrates superior results. Additionally, Random Forest out- performed other models with 89% accuracy and 0.89 f1-score for the US company's dataset. All applied classification models demonstrated better results on the synthetic data. © 2024 IEEE.

关键词： Support vector machines

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Using LLMs for Annotation and ML Methods for Comparative Analysis of Large-Scale Turkish Sentiment Datasets 9

Using LLMs for Annotation and ML Methods for Comparative Ana...

引用

9th international conference on Computer Science and engineering, UBMK 2024

作者： Ezin, Ercan Kiziltepe, Rukiye Savran Karakus, Murat Harran University Dept. of Computer Engineering Şanliurfa Turkey Karadeniz Tech. University Dept. of Software Engineering Trabzon Turkey Ankara University Dept. of Software Engineering Ankara Turkey

ISBN: (纸本)9798350365887

With the increasing prevalence of e-commerce plat-forms, understanding customer sentiments expressed in product reviews is crucial for assessing platform and product performance. However, traditional sentiment analysis (SA) models often struggle with low-resource languages such as Turkish, particularly due to the language's agglutinative nature and morphological complexity. To address this, several public datasets are now available online. However, these datasets are often annotated using rule-based methods, resulting in poor accuracy for Machine Learning (ML) models. this study addresses these challenges by employing two large Turkish datasets, TRSAv1 and VSCR, and improving their sentiment annotations through advanced Large Language models (LLMs) such as ChatGPT4o-mini. the reannotation significantly enhanced the performance of traditional ML models-Logistic Regression, Naive Bayes, Random Forest, and Support Vector Machine-especially in the neutral and negative sentiment classes, as evidenced by increased precision, recall, and F1-scores. the findings underscore the importance of thorough data preparation and demonstrate that utilizing LLMs for sentiment annotation in Turkish can lead to more reliable SA, providing a stronger foundation for developing advanced SA tailored to low-resource languages. this study is the first to employ recent LLM for relabelling Turkish customer reviews, highlighting the potential for improved accuracy in SA for low-resource languages. © 2024 IEEE.

关键词： Decision trees

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Evaluating Machine Learning Linear Regresion Model for DVT Prediction in Rehabilitation Patients 9

Evaluating Machine Learning Linear Regresion Model for DVT P...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Malathi, K. Ruby, E. D. Kanmani Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Chennai Avadi India

ISBN: (纸本)9798350365092

the prediction of Deep Vein thrombosis (DVT) in rehabilitation inpatients is critical for preventing serious complications. this study compares the effectiveness of four machine learning models - Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Networks (DNN) - in forecasting DVT risk. Utilizing a comprehensive dataset of patient demographics, clinical indicators, and imaging findings, we aimed to identify the most accurate predictive model. Our evaluation criteria included accuracy, sensitivity, specificity, and the Area Under the Receiver Operating Characteristic curve (AUC-ROC), with a dataset split of 70% for training and 30% for testing, supplemented by cross-validation to ensure reliability. the RF model demonstrated superior performance, achieving an accuracy of 92%, with exceptional sensitivity and specificity, underscoring its potential for clinical application. While the DNN showed promise in handling complex data interactions, its computational demand and data requirements limit its immediate utility. Conversely, LR and SVM offered a balance between interpretability, efficiency, and predictive power, making them viable for varied clinical scenarios. this research highlights the pivotal role of machine learning in enhancing DVT diagnostics within rehabilitation settings, suggesting a move towards integrating such models into clinical practice to improve patient care and outcomes. Further studies are encouraged to incorporate broader data types for enriched predictive accuracy. © 2024 IEEE.

关键词： Patient rehabilitation

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Application of Machine Learning Algorithms for Efficient Disease Prediction 9

Application of Machine Learning Algorithms for Efficient Dis...

引用

9th international conference on Communication and Electronics Systems, ICCES 2024

作者： Krishna Kumar, N. Reddy, P. Venkateswarlu Ganesh, D. Nirmala, Kotari Pradeep, Modukuru Babu, J. Suresh Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ibrahimpatnam Telangana Hyderabad501506 India Andhra Pradesh Tirupati India Guru Nanak Institute of Technology Ibrahimpatnam Telangana Hyderabad501506 India Andhra Pradesh Tirupati India

ISBN: (纸本)9798350377972

this research presents the development of a System for Predicting Diseases Using Machine Learning. the system leverages machine learning algorithms to predict the likelihood of individuals developing COVID-19., heart disease, or chronic kidney disease. By inputting relevant symptoms, medical history, and other relevant factors, users can receive preliminary disease predictions without requiring immediate in-person consultation. Powered by Python and Django, the system provides a user-friendly interface for accessing these predictive models. this system aims to improve access to healthcare by facilitating early identification of potential health risks, empowering individuals to make informed decisions about their health, and potentially reducing the burden on healthcare systems. © 2024 IEEE.

关键词： Prediction models

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Business Response Prediction based on Consumer Behaviour Patterns 9

Business Response Prediction based on Consumer Behaviour Pat...

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9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Pokharkar, Vishwa Edgaonkar, Omkar Shinde, Aryajeet Nirmal, Nilesh Pune Institute of Computer Technology Electronics & Telecommunications Engineering Department Pune India

ISBN: (纸本)9798350394474

this survey paper presents a comprehensive analysis of predictive models for business response in the hospitality industry, focusing on cafes and restaurants. Leveraging data from Zomato, a popular restaurant aggregator platform, and supplemented with location data, we explore effective strategies for preprocessing and clustering based on key parameters, including location, cuisine, nearby institutions, parking, traffic, bestsellers, cost, ratings, delivery options, and establishment type (cloud kitchen/dine out). We investigate a variety of machine learning and statistical approaches tailored to distinct clusters of cafes and restaurants. Our evaluation involves training and testing models to predict business responses, considering varying customer foot traffic and preferences. the findings emphasize the importance of precise clustering and customized predictive models for improved accuracy in response forecasting. the insights derived from this survey are anticipated to significantly impact decision-making processes for businesses in the hospitality sector. © 2024 IEEE.

关键词： Forecasting

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