Titlebook: Advances in Conceptual Modeling; ER 2024 Workshops, A Motoshi Saeki,Leah Wong,José F. Reyes Román Conference proceedings 2025 The Editor(s)

有杂色

Betriebliche Datenverarbeitung als Systemon level, providing a tailored human-model interaction experience. Through increased engagement and real-world connections in UML diagrams, we aim to make models more aligned with business logic, resulting in better conceptual models and, subsequently, more effective computer programs. We report on

生锈

出处

Das Problem der Quantifizierungsubjects in the classification of a research paper’s characteristics and analysing the correctness when classifying a paper. The data analysis showed a medium level of agreement and a good level of correctness. The outcome implies that framework helps identify the type of contribution made by a rese

不妥协

seduce

Dieter Hofbauer,Ralf-Detlef Kutsche method components that align with the specific needs of AI projects. Results highlight the importance of understanding project-specific characteristics to enhance the effectiveness of design methods in AI engineering, offering practitioners actionable insights for improving quality and reliability of AI systems through tailored design approaches.

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询问

Beyond One-Fits-All: A Case Study Approach to AI System Design Methods method components that align with the specific needs of AI projects. Results highlight the importance of understanding project-specific characteristics to enhance the effectiveness of design methods in AI engineering, offering practitioners actionable insights for improving quality and reliability of AI systems through tailored design approaches.

Allowance

中子

0302-9743 which took place in Pittsburgh, PA, USA, during October 28-31, 2024. ..The 22 full papers and 1 short paper included in this book were carefully reviewed and selected from 52 submissions. They stem from the following workshops: .. .AISA’2024 - The First International Workshop on AI Services and Appl

身体萌芽

https://doi.org/10.1007/978-3-663-04858-9 and the improvement in quality of life due to AI is evaluated through two-way ANOVA, considering age and income level. The analysis reveals that both age and income level significantly impact the improvement in quality of life due to AI.