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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21-30 订阅

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predictive Maintenance of Aircraft Engines: Machine Learning Approaches for Remaining Useful Life Estimation 6

Predictive Maintenance of Aircraft Engines: Machine Learning...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Deepika, J. Reddy, Pappuru Manikanta Murari, Kothaluri Rahul, Boyana School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore India

ISBN: (纸本)9798331522667

In the realm of predictive maintenance for aircraft engines, ensuring precise and timely predictions of Remaining Useful Life (RUL) is paramount to enhancing safety, reducing costs, and minimizing downtime. While deep learning models dominate this field, their complexity, high computational demand, and lack of interpretability can hinder practical deployment in real-world scenarios. this study demonstrates the potential of machine learning models as an efficient and interpretable alternative for RUL prediction, leveraging the publicly available C-MAPSS dataset from NASA. Using C-MAPSS dataset, we integrated PostgreSQL for scalable data storage and Flask for interactive real-time visualization, creating a seamless pipeline from data ingestion to result interpretation. Preprocessing steps, including normalization, noise removal via box plots, and feature engineering, ensured a clean and reliable dataset. Among the models evaluated—XGBoost, Random Forest, SVM, KNN, and Linear Regression—XGBoost emerged as the best performer, achieving an RMSE of 23.8 and an R2 score of 0.67. these results nearly matches the accuracy typically associated with deep learning models while maintaining computational efficiency. Feature importance analysis through Explainable AI (XAI) further revealed critical insights into sensor contributions, enhancing interpretability and trust in the predictions. this work underscores the practicality and effectiveness of machine learning in predictive maintenance, offering a robust, transparent, and resource-efficient approach to RUL prediction. the findings highlight its potential to meet industry needs where simplicity, accuracy, and interpretability are critical for decision-making and operational efficiency. © 2025 IEEE.

关键词： predictive maintenance

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Multi-Head Self-Attention Based Arabic News Recommendation System 6

Multi-Head Self-Attention Based Arabic News Recommendation S...

引用

6th international conference on Advanced Aspects of software engineering, ICAASE 2024

作者： Sabrina, Karboua Harrag, Fouzi Meziane, Farid Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (纸本)9798331522810

this paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-Attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. the news encoder uses multi-head self-Attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-Attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models. © 2024 IEEE.

关键词： Recommender systems

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Learning Program models from Generated Inputs 23

Learning Program Models from Generated Inputs

引用

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

作者： Mammadov, Tural Saarland Univ CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (纸本)9798350322637

Recent advances in Machine Learning (ML) show that Neural Machine Translation (NMT) models can mock the program behavior when trained on input-output pairs. Such models can mock the functionality of existing programs and serve as quick-to-deploy reverse engineering tools. Still, the problem of automatically learning such predictive and reversible models from programs needs to be solved. this work introduces a generic approach for automated and reversible program behavior modeling. It achieves 94% of overall accuracy in the conversion of Markdown-to-HTML and HTML-to-Markdown markups.

关键词： software testing security testing reverse engineering deep learning

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Assessment software Reliability in Component-Based software Systems: A Comprehensive Review 6

Assessment Software Reliability in Component-Based Software ...

引用

6th international conference on Computing and Informatics, ICCI 2024

作者： Tagen, Ibrahim Ayman Wassif, Khaled T. Sayed, Sabah Moawad, Ramadan Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt

ISBN: (纸本)9798350373875

the reliability of Component-Based software Systems (CBSS) is a critical factor in the success and longevity of software applications across various domains. this comprehensive review synthesizes methodologies and findings from thirteen key studies, offering a panoramic view of the landscape of CBSS reliability assessment. We explore a spectrum of approaches, from traditional statistical models to advanced computational intelligence techniques, evaluating their effectiveness, challenges, and contexts of applicability. Our review reveals a trend towards the integration of machine learning and fuzzy logic in reliability estimation, reflecting the complexity and dynamic nature of CBSS. Moreover, we identify a significant emphasis on predictive models that cater to early detection of reliability issues, underscoring the proactive aspect of modern software reliability engineering. the findings suggest a need for more adaptable and context-sensitive methods that can accommodate the evolving nature of software components and their interactions. this review not only charts the current state of CBSS reliability assessment but also highlights future directions for research, including the integration of real-time data and the development of more robust models for uncertainty management. Our study serves as a crucial resource for researchers and practitioners seeking to enhance the reliability of CBSS, ultimately contributing to the development of more dependable software systems. © 2024 IEEE.

关键词： Machine learning

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A Comprehensive Review of Experimental and Computational Methods for Protein Structure Prediction and Classification 6

A Comprehensive Review of Experimental and Computational Met...

引用

6th international conference on Advanced Aspects of software engineering, ICAASE 2024

作者： Nabil, Bourenane Mohamed Hachem, Kermani Mohamed Zizette, Boufaida Laboratory Constantine Algeria Laboratory Constantine Algeria

ISBN: (纸本)9798331522810

Protein and genetic information are essential in personalizing treatments for individual patients. A major hurdle, however, is the in-depth comprehension of proteins, which is predominantly facilitated by identifying their structures. Accurate prediction and classification of protein structures are crucial for elucidating how proteins operate and interact within biological systems. In bioinformatics, numerous computational methods have been developed to support experimental techniques in protein structure prediction. these approaches draw on diverse data, including protein sequences, genomes, and structural characteristics, while often combining various strategies. this review evaluates the effectiveness of current methods, highlighting their advantages and drawbacks. Additionally, it explores the importance of interdisciplinary collaborations, emphasizing the integration of experimental and computational approaches to showcase how these partnerships can advance the prediction and classification of protein structures. © 2024 IEEE.

关键词： Prediction models

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Boosting software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. this work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. the objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. these indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. these algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. this approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： Data reliability

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Leveraging Pre-Trained Transformer models and Ensemble Learning for Fake News Detection: A Comparative Analysis 6

Leveraging Pre-Trained Transformer Models and Ensemble Learn...

引用

6th international conference on Advanced Aspects of software engineering, ICAASE 2024

作者： Aklouche, Billel Rahmane, Adib Takaout, Abdelhakim LIRE Laboratory University of Constantine 2-Abdelhamid Mehri Constantine Algeria NTIC Faculty TLSI Department University of Constantine 2-Abdelhamid Mehri Constantine Algeria

ISBN: (纸本)9798331522810

In recent years, the proliferation of fake news has become a major concern due to its potential to influence public opinion, cause panic, and even impact election outcomes. To address this issue, Deep Learning and Natural Language Processing (NLP) techniques have been proposed as effective solutions. In this study, we present a comprehensive evaluation of transformer-based models, particularly BERT, DistilBERT, and RoBERTa, alongside ensemble learning techniques, to enhance the performance of fake news detection tasks. Specifically, we fine-Tuned these pre-Trained models on two widely used datasets: LIAR and ISOT, which represent different levels of complexity in fake news classification. Subsequently, our approach integrates individual transformer models with ensembling methods such as Majority vote, Averaging and Highest-sum, significantly improving classification accuracy. Extensive experiments have been carried out showing that the proposed approach achieves high accuracy in detecting fake news across various standard evaluation metrics, outperforming competitive state-of-the-Art baselines. © 2024 IEEE.

关键词： Contrastive Learning

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Methodology for Developing software for Data Exchange Controllers Used in Power Electronics and Industrial Automation Systems 6

Methodology for Developing Software for Data Exchange Contro...

引用

6th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Bukin, Daniil A. Krasnoperov, Roman N. Rozhkov, Alexander N. Zhuravlev, Ivan I. National Research University "MPEI" Power Electronic Department Moscow Russia

ISBN: (纸本)9798350382891

An analysis of various approaches to selecting a hardware platform and writing software for a data exchange controller as a protocol converter for power electronics devices was carried out. Features that complicate the development of this software have been identified, and methods for eliminating or minimizing them have been proposed. Proposed and justified: choice of platform for the communication controller, operating system, programming language, software development methods, format and methods of processing configuration files to represent information models of power electronics devices, division of the developed program into threads to ensure the possibility of simultaneous data transfer from lower to upper and from upper to lower level. A method is proposed for converting protocols without support for sporadic message transmission into protocols with support for sporadic messages using a data exchange controller. © 2024 IEEE.

关键词： Electronic data interchange

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Enhancing Wind Energy Forecasting for Cost Efficiency in Hybrid Diesel and Solar Power Plants Using Artificial Neural Networks and Meteoblue Weather Data 6

Enhancing Wind Energy Forecasting for Cost Efficiency in Hyb...

引用

6th international conference on Power engineering and Renewable Energy, ICPERE 2024

作者： Fadel, Tyo Fabian Marojahan Banjarnahor, Kevin Setiadi, Fajar Hariyanto, Nanang Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia UID Kaltimra Jakarta Indonesia

ISBN: (纸本)9798331504694

this study integrates Artificial Neural Networks (ANN) with Meteoblue Weather data to improve the performance of hybrid diesel and solar power plants by accurately predicting wind energy output Focusing on the Derawan Islands in Indonesia, where traditional energy grids are challenging to establish, the research enhances energy management by adding wind power to reduce the load on diesel plants. the ANN model, trained on historical weather data, achieved high predictive accuracy with an R2 value of 0.9969 and RMSE values of 0.5772 for training and 0.5620 for testing. these results indicate a strong correlation between predicted and actual wind speeds, outperforming similar models used in other regions like Ethiopia and Chile. the findings highlight the ANN model's effectiveness in optimizing wind energy integration, making hybrid power systems more reliable and cost-effective for remote areas. © 2024 IEEE.

关键词： Windmill

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Optimised Classification of Android Malware using ML Classifiers and Stacking Ensemble Algorithm 6

Optimised Classification of Android Malware using ML Classif...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Singh, Preet Hasija, Taniya Ramkumar, K.R. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331522667

One of the biggest threats that malicious software, or malware, poses to cybersecurity is exploiting vulnerabilities to infect computer systems. this research is based on malware classification using ensemble-based and kernel-specific machine-learning classification techniques. Advanced cyber threat scenarios have a critical need for solutions that balance accuracy, precision, recall, and F1 score while providing successful malware identification. the diverse base models integrated into the proposed methodology consisted of SVM with linear, polynomial, and RBF kernels, and a stacking classifier that utilizes the base model's strengths. the results showed that, compared with various single models, a stacking classifier achieved quite a high accuracy of 99.14% and maintained high precision and recall percentages for all evaluation metrics. It draws attention to hybrid and ensemble approaches in cybersecurity and the chances to raise detection rates at the cost of reduced false classifications. Advanced ensemble strategies, new deep learning models, as well as XAI will be used for the best enhancement of performance, adaptability, and transparency against evolving malware threats. © 2025 IEEE.

关键词： Android malware

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