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Automatic pattern-based consistency checking in model refactoring: introducing a formal behavioral preserving method 模型重构中基于模式的自动一致性检查:引入一种形式化的行为保持方法
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-02-17 DOI: 10.1007/s11334-022-00525-8
Saeedeh Ghaedi Heidari, Shohreh Ajoudanian
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引用次数: 0
Semantic role identification for Malayalam using machine learning approaches 基于机器学习方法的马拉雅拉姆语语义角色识别
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-27 DOI: 10.1007/s11334-022-00496-w
J. P. Jayan, J. S. Kumar, T. Amudha
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引用次数: 0
An efficient Apriori algorithm for frequent pattern in human intoxication data 人类中毒数据频繁模式的一种高效Apriori算法
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-05 DOI: 10.1007/s11334-022-00523-w
M. Hassan, S. Zaman, Swarnali Mollick, M. Hassan, M. Raihan, Chetna Kaushal, Rajat Bhardwaj
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引用次数: 4
A study of efficiency measurement of Jaipur metro mass transit system using data envelopment analysis 基于数据包络分析的斋浦尔地铁轨道交通系统效率测度研究
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-05 DOI: 10.1007/s11334-022-00511-0
Pankaja Sharma, J. K. Jain, P. Kalla
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引用次数: 2
Performance analysis of supervised classification models on heart disease prediction 监督分类模型在心脏病预测中的性能分析
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-04 DOI: 10.1007/s11334-022-00524-9
E. Ogundepo, W. B. Yahya
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引用次数: 3
An enumerated analysis of NoSQL data models using statistical tools 使用统计工具对NoSQL数据模型的枚举分析
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-03 DOI: 10.1007/s11334-022-00517-8
A. Samanta, N. Chaki
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引用次数: 0
A study on hydrodynamics of rigid and emergent vegetated flows using machine learning approach 用机器学习方法研究刚性和挺水植被流的流体动力学
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-02 DOI: 10.1007/s11334-022-00519-6
Soumen Maji, Apurbalal Senapati, Arun Mondal
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引用次数: 1
Specification decomposition for reactive synthesis. 反应性合成规范分解。
IF 1.1 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-01 Epub Date: 2022-07-18 DOI: 10.1007/s11334-022-00462-6
Bernd Finkbeiner, Gideon Geier, Noemi Passing

Reactive synthesis is the task of automatically deriving a correct implementation from a specification. It is a promising technique for the development of verified programs and hardware. Despite recent advances in terms of algorithms and tools, however, reactive synthesis is still not practical when the specified systems reach a certain bound in size and complexity. In this paper, we present a sound and complete modular synthesis algorithm that automatically decomposes the specification into smaller subspecifications. For them, independent synthesis tasks are performed, significantly reducing the complexity of the individual tasks. Our decomposition algorithm guarantees that the subspecifications are independent in the sense that completely separate synthesis tasks can be performed for them. Moreover, the composition of the resulting implementations is guaranteed to satisfy the original specification. Our algorithm is a preprocessing technique that can be applied to a wide range of synthesis tools. We evaluate our approach with state-of-the-art synthesis tools on established benchmarks: the runtime decreases significantly when synthesizing implementations modularly.

反应性合成是指从规范中自动获得正确实现的任务。对于验证程序和硬件的开发来说,这是一种很有前途的技术。尽管最近在算法和工具方面取得了进展,但是,当指定的系统达到一定的规模和复杂性时,反应性合成仍然不实用。在本文中,我们提出了一种完善的模块化合成算法,可以自动将规范分解成更小的子规范。对于它们,执行独立的合成任务,显著降低了单个任务的复杂性。我们的分解算法保证子规范是独立的,即可以为它们执行完全独立的合成任务。此外,结果实现的组合保证满足原始规范。我们的算法是一种预处理技术,可以应用于广泛的合成工具。我们用最先进的合成工具在已建立的基准上评估我们的方法:模块化合成实现时,运行时显著减少。
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引用次数: 0
Guest Editorial: Intelligence for systems and software engineering. 嘉宾评论:用于系统和软件工程的智能。
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-01 DOI: 10.1007/s11334-023-00526-1
Mike Hinchey, Amit Jain, Manju Kaushik, Sanjay Misra
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引用次数: 1
A systematic method for diagnosis of hepatitis disease using machine learning. 一种利用机器学习进行肝炎疾病诊断的系统方法。
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2023-01-01 DOI: 10.1007/s11334-022-00509-8
Ravi Kumar Sachdeva, Priyanka Bathla, Pooja Rani, Vikas Solanki, Rakesh Ahuja

Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers used are support vector machine, logistic regression (LR), K-nearest neighbor, and random forest. The classifiers were employed without class balancing and in conjunction with class balancing using SMOTE strategy. Both studies, classification without class balancing and with class balancing, were compared in terms of different performance parameters. After adopting class balancing, the efficiency of classifiers improved significantly. LR with SMOTE provided the highest level of accuracy (93.18%).

肝炎是地球上最致命的疾病之一。机器学习方法可以根据一些特征来诊断肝炎疾病。在UCI数据集上,作者评估了不同分类器的性能,以制定肝炎疾病诊断的系统策略。使用的分类器有支持向量机、逻辑回归(LR)、k近邻和随机森林。分类器在没有类平衡的情况下使用,并与使用SMOTE策略的类平衡结合使用。比较了两项研究,不含类平衡的分类和有类平衡的分类在不同性能参数方面的差异。采用类平衡后,分类器的效率显著提高。带有SMOTE的LR提供了最高水平的准确度(93.18%)。
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引用次数: 8
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Innovations in Systems and Software Engineering
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