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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 2518 条记录，以下是211-220 订阅

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EMPIRICAL MULTI-VARIABLE TSUNAMI DAMAGE models BASED ON thE 2011 GREAT EAST JAPAN DATASET: ANALYSIS OF thE PERFORMANCES AT DIFFERENT SPATIAL SCALES 9

EMPIRICAL MULTI-VARIABLE TSUNAMI DAMAGE MODELS BASED ON THE ...

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9th ECCOMAS thematic conference on Computational Methods in Structural Dynamics and Earthquake engineering, COMPDYN 2023

作者： Di Bacco, Mario Scorzini, Anna Rita Suppasri, Anawat Department of Civil Environmental and Architectural Engineering University of L'Aquila L'Aquila Via G. Gronchi 18 L'Aquila67100 Italy Department of Civil and Environmental Engineering University of Florence Florence Via S. Marta 3 Florence50139 Italy International Research Institute of Disaster Science Tohoku University 468-1 Aoba Aramaki Aoba-ku Miyagi Sendai Japan

By implementing data-driven models for the 2011 Great East Japan earthquake and tsunami, the present study aims at investigating the effect of the level of spatial aggregation of the data on model's predictive ability and at identifying the possible existence of regional-dependent patterns affecting model's accuracy and feature importance. An extended version of the dataset compiled by the Japanese Ministry of Land, Infrastructure and Transportation (MLIT) after the 2011 event in the Tohoku region was used to generate sub datasets at different spatial scales, ranging from individual cities of different sizes to clusters at regional and multi-regional levels. the results indicate a high variance in the accuracy for the models trained on the different subsets, with relative hit rates ranging from 0.68 to 0.89 and exhibiting a positive correlation with the cardinality of the sets, as well as some regional patterns in the prediction errors. the cluster-averaged feature importance is observed to be stable for all selections and reflects the results obtained from the models trained on the whole dataset, thus allowing a more informed identification of the most significant influencing factors for tsunami damage modelling. © 2023 COMPDYN proceedings. All rights reserved

关键词： Machine learning

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Analysis of Various Hyperparameters for Predicting Hypertension using Machine Learning Algorithms 8

Analysis of Various Hyperparameters for Predicting Hypertens...

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8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Duggal, Shallu Sood, Shivani Singh, Amanpreet Singh, Harjeet Malarvel, Muthukumaran Lovely Professional University School of Computer Applications Phagwara144411 India Chitkara Institute of Engineering and Technology Chitkara University Punjab 140401 India Department of Computer Science and Engineering India

Predicting hypertension, or high blood pressure, is crucial because it9;s a risky condition that frequently doesn9;t exhibit any obvious symptoms or indicators until major problems arise. By using predictive mod... 详细信息

ISBN: (纸本)9798350367904

Predicting hypertension, or high blood pressure, is crucial because it's a risky condition that frequently doesn't exhibit any obvious symptoms or indicators until major problems arise. By using predictive models for early identification, the risk of uncontrolled hypertension-related cardiovascular events, strokes, and organ damage can be decreased to take quick action. the study aims to predict the diagnosis of hypertension using three machine learning techniques: Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine(SVM). With the focus on improving accuracy and reliability, the various hyperparameters of each individual algorithm are tuned using the cross validation and grid search approaches. After extensive testing and parameter modification, the SVM algorithm was found to be the most accurate predictor, which achieved a significant accuracy of 9 7. 0 0 %. the best hyper-parameters achieved for SVM are c value 4 and kernel as 'rbf'. the models' performance is evaluated using accuracy, specificity, sensitivity, F1-score, or AUC (Area Under the Curve). this study facilitates proactive healthcare measures by offering insightful information to determine the best algorithms for the early prediction of hypertension. © 2024 IEEE.

关键词： Prediction models

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Unveiling insights in source code defect prediction using ChatGPT: moving beyond predictive metrics 4

Unveiling insights in source code defect prediction using Ch...

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4th international conference on Signal Processing and Computer Science, SPCS 2023

作者： Pan, Cong Gu, Ai Gao, Yan Key Laboratory of General Quality Technology Application for Intelligent Manufacturing Equipment China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou511370 China

ISBN: (纸本)9781510672505

In the realm of software engineering, defect prediction stands as a vital and valuable research domain. With the emergence of powerful models like ChatGPT, the potential for enhancing software defect prediction becomes significant. Leveraging the GHPR defect prediction dataset, this study delves into the application of ChatGPT for source code-level defect prediction. Different prompt paradigms, including general-purpose prompts, prompts that emphasize minority class, prompts that include few-shot examples, prompts that include target explanations, and prompts using chain-of-thoughts (CoT), are investigated for their impact on predictive outcomes. Furthermore, a comparative analysis is conducted between specific defects predicted by ChatGPT and defects rectified by developers in the GHPR dataset. While direct application of ChatGPT in source code-level defect prediction shows results akin to random guessing, the study reveals that employing CoT prompts unveils a propensity for ChatGPT to identify a higher number of issues, proposing questions with heightened rationale. the findings emphasize the necessity to move beyond predictive metrics when utilizing ChatGPT for source code-based defect prediction, advocating for a more profound analytical approach. © 2023 SPIE.

关键词： Defects

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software Development Methodologies: Analysis and Classification 5

Software Development Methodologies: Analysis and Classificat...

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5th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2023

作者： Gurianov, Dmitrii A. Myshenkov, Konstantin S. Terekhov, Valery I. Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798350399523

this paper discusses software development life cycle models and development methodologies created on their basis. the place of methodologies and their role in the software development management process is determined, the differences and relationships between them are identified, as well as the degree of influence on the success of the project. A review of the criteria for comparing software development methodologies has been carried out. the existing methods of single-criteria and two-criteria classification of methodologies are analyzed, and their shortcomings are identified. New methods of single-criteria classification that eliminate the identified shortcomings are presented. A new multi-criteria classification based on four criteria has been developed, offering a more complete and broader hierarchical distribution of life cycle models and software development methodologies. Existing methods for selecting a software development methodology are considered, as well as an own method based on a retrospective analysis and machine learning methods presented. Improving methods of choosing a methodology is imperative to achieve the targets of Sustainable Development Goals (SDGs) Goal 9. © 2023 IEEE.

关键词： Classification (of information)

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Predicting Breast Cancer Survival: Comparative Analysis of Machine Learning and Deep Learning models 5

Predicting Breast Cancer Survival: Comparative Analysis of M...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Bathool, Saliha Alashetty, Ashvini Farquad, M.A.H. School of Engineering And Design Alliance University Bengalore India School of CS &IT. JAIN Deemed-To-Be University Bengalore India Higher Colleges of Technology Faculty of Computer Information Systems Ras Al-Khaimah United Arab Emirates

ISBN: (纸本)9798331530013

the forward-looking study is grounded in the comprehensive chronic cancer death data of Surveillance, Epidemiology, and End Results, one of the most remarkable datasets for breast cancer survival rates ever examined. the approach of this study is to divide patients' data under a particular case feature;in this case, we explore patients' survival rate for breast cancer, which is our main goal. this is done by using the T-test, log-rank test, ANOVA test, and chi-square test, as well as linear regression. In the second part, we include everything from the prediction software to building models. Logistic Regression, Decision Tree and Random Forest Support Vector Machines (S.K.), K-Nearest Neighbors, Naive Bayes, Gradient Boosting, Convolutional Neural Networks, and Long Short-Term Memory Networks models are heavily evaluated based on various key metrics such as accuracy, precision, and recall. Parameter optimization is performed for each model, and robust validation techniques are implemented to ensure the reliability and generalizability of our findings. the clustered approach is chosen to carry out a comprehensive and systematic explanation of the different factors that may influence the lifespan of women who have survived the cancer and the use of ML and DL models as a predictive tool. Survival anticipation for breast cancer is imperative in the context of personalized therapies. ML and DL models have proven to be powerful instruments in predicting the future states of patients based on their data. © 2024 IEEE.

关键词： Convolutional neural networks

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uitPETransMDS: A PE Malware Detection System Using a Hybrid Approach of Transfer Learning and Image Visualization 9

uitPETransMDS: A PE Malware Detection System Using a Hybrid ...

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9th international conference on Information Management and Technology, ICIMTech 2024

作者： Huy, Trinh Gia Nhan, Luong Nguyen thanh Cam, Nguyen Tan Faculty of Information Systems University of Information Technology Vietnam National University Faculty of Information Systems Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Faculty of Information Science and Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350390025

the rapid expansion of devices and computers, alongside technological advancements, has led to a corresponding increase in malicious attacks. Malware attacks represent a significant global threat in cyberspace. Despite the concerted efforts of security researchers and security companies to mitigate these attacks, they remain a persistent challenge. this has prompted a substantial focus on research endeavors to combat malicious software. Recent advancements in artificial intelligence have spurred various research efforts in malware detection. this paper focuses on detecting and classifying malware using transfer learning models, such as variants of VGG, ResNet, and MobileNet, on a grayscale image-based PE dataset with dimensions of 64 x 64 x 3 pixels. the proposed architecture comprises three modules. the first module is responsible for converting the PE binary file to a grayscale image. the second module aims to identify whether the input file is malicious or benign. Among the models tested, MobileNetV1 achieved the highest performance with an accuracy of 98.70% and an Fl-score of 96.46%. the third module, utilizing the ResNet101 model, is responsible for malware classification (five different types in the dataset). It achieved an accuracy of 98.78% and an Fl-score of 97.52% on the testing set. © 2024 IEEE.

关键词： Cyber attacks

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Automated classification of renal tumors on ultrasound images using deep neural network 9

Automated classification of renal tumors on ultrasound image...

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9th international conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Wu, Wei-Chieh Chiang, Te-Mei Tsai, Zhi-Ren Asia University Program in Artificial Intelligence College of Information and Electrical Engineering Taichung City Taiwan Asia University Hospital Department of Radiation Oncology Taichung City Taiwan Asia University Department of Computer Science & Information Engineering Taiwan

ISBN: (纸本)9798350363043

Kidney cancer is one of the most common cancers worldwide. the major types of malignant renal tumors are renal cell carcinoma and urothelial carcinoma. Although the majority of renal tumors are malignant, up to 20% are benign, most commonly renal cyst and angiomyolipomas. Ultrasound is the most accessible imaging tool in medical practice, but it highly depends on operator skill, which may lead to high false-negative rate in diagnosis. the purpose of this study was to develop a predictive model for the automated classification of renal tumors on ultrasound images using deep neural network. A total of 880 kidney ultrasound images were used for training and testing. Transfer learning was used to the ten Convolution neural network models. the kidney ultrasound images were classified as benign or malignant tumors. the classification performance of the model was evaluated by sensitivity and specificity. the research results show that VGG18 yielded the best performance, with a sensitivity of 79%, and a specificity of 86%. © 2024 IEEE.

关键词： Ultrasonic imaging

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Compaction of Spacecraft Operational models with Metamodeling Domain Knowledge 18

Compaction of Spacecraft Operational Models with Metamodelin...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Someya, Kazunori Aoki, Toshiaki Ishihama, Naoki Japan Aerosp Explorat Agcy Space Technol Directorate 1 Tsukuba Ibaraki Japan Japan Adv Inst Sci & Technol Sch Informat Sci Nomi Ishikawa Japan Japan Aerosp Explorat Agcy Res & Dev Directorate Tsukuba Ibaraki Japan

ISBN: (纸本)9789897586477

Due to the difficulty of performing repairs during flight, spacecraft is operated according to operational scenarios tested before launch. Operational models, a type of SysML activity diagram, can be used to depict these scenarios. Although it aids in communication between engineers and stakeholders, the activity diagram can rapidly grow rather large. Due to the extensive operational model, it is therefore challenging to review the activity diagram, which could result in serious issues. therefore, to make operational models easier to evaluate, they should be made concise. this study offers a metamodel that offers stereotypes that can succinctly characterize spacecraft operational scenarios. First, a mind map was used to depict the domain knowledge of spacecraft operations. Second, stereotype metamodel was created by extracting common knowledge from the mind map. Utilizing stereotypes, operational models' size can be decreased;however, crucial review-related data could be lost. therefore, shrinking stereotypes assures that crucial information would not be lost. Several trials were conducted and showed that the number of elements of operational models with stereotypes, as well as their size, reduced by almost half, compared with the original ones, allowing for a simplified review process and boosting trust in the accuracy of the operational scenarios.

关键词： Spacecraft Operation Metamodel Compaction Review Model-Based Systems engineering

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study 19

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Uzoma, Agomuo Kingsley Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. the framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework’s flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes. © 2025 IEEE.

关键词： Electronic health record

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Trading Runtime for Energy Efficiency Leveraging Power Caps to Save Energy across Programming Languages 17

Trading Runtime for Energy Efficiency Leveraging Power Caps ...

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17th ACM SIGPLAN international conference on software Language engineering (SLE)

作者： Cunha, Simao Silva, Luis Saraiva, Joao Fernandes, Joao Paulo Univ Minho Braga Portugal New York Univ Abu Dhabi Abu Dhabi U Arab Emirates

ISBN: (纸本)9798400711800

Energy efficiency of software is crucial in minimizing environmental impact and reducing operational costs of ICT systems. Energy efficiency is therefore a key area of contemporary software language engineering research. A recurrent discussion that excites our community is whether runtime performance is always a proxy for energy efficiency. While a generalized intuition seems to suggest this is the case, this intuition does not align with the fact that energy is the accumulation of power over time;hence, time is only one of the factors in this accumulation. We focus on the other factor, power, and the impact that capping it has on the energy efficiency of running software. We conduct an extensive investigation comparing regular and power-capped executions of 9 benchmark programs obtained from the Computer Language Benchmarks Game, across 20 distinct programming languages. Our results show that employing power caps can be used to trade running time, which is degraded, for energy efficiency, which is improved, in all the programming languages and in all benchmarks that were considered. We observe overall energy savings of almost 14% across the 20 programming languages, with notable savings of 27% in Haskell. this saving, however, comes at the cost of an overall increase of the program's execution time of 91% in average. We are also able to draw similar observations using language specific benchmarks for programming languages of different paradigms and with different execution models. this is achieved analyzing a wide range of benchmark programs from the nofib Benchmark Suite of Haskell Programs, DaCapo Benchmark Suite for Java, and the Python Performance Benchmark Suite. We observe energy savings of approximately 8% to 21% across the test suites, with execution time increases ranging from 21% to 46%. Notably, the DaCapo suite exhibits the most significant values, with 20.84% energy savings and a 45.58% increase in execution time. Our results have the potential to driv

关键词： Green software Power Cap Energy Efficiency Programming Languages Language Benchmarking

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