Innovations in Systems and Software Engineering最新文献_第5页

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

Innovations in Systems and Software Engineering Pub Date : 2023-01-27 DOI: 10.1007/s11334-022-00496-w

J. P. Jayan, J. S. Kumar, T. Amudha

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

Innovations in Systems and 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

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

Innovations in Systems and Software Engineering Pub Date : 2023-01-05 DOI: 10.1007/s11334-022-00511-0

Pankaja Sharma, J. K. Jain, P. Kalla

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

Innovations in Systems and Software Engineering Pub Date : 2023-01-04 DOI: 10.1007/s11334-022-00524-9

E. Ogundepo, W. B. Yahya

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

Innovations in Systems and Software Engineering Pub Date : 2023-01-03 DOI: 10.1007/s11334-022-00517-8

A. Samanta, N. Chaki

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

Innovations in Systems and Software Engineering Pub Date : 2023-01-02 DOI: 10.1007/s11334-022-00519-6

Soumen Maji, Apurbalal Senapati, Arun Mondal

引用次数: 1

Specification decomposition for reactive synthesis. 反应性合成规范分解。

IF 1.2

Innovations in Systems and 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

{"title":"Specification decomposition for reactive synthesis.","authors":"Bernd Finkbeiner, Gideon Geier, Noemi Passing","doi":"10.1007/s11334-022-00462-6","DOIUrl":"https://doi.org/10.1007/s11334-022-00462-6","url":null,"abstract":"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.","PeriodicalId":44465,"journal":{"name":"Innovations in Systems and Software Engineering","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134650152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

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

Innovations in Systems and Software Engineering Pub Date : 2023-01-01 DOI: 10.1007/s11334-023-00526-1

Mike Hinchey, Amit Jain, Manju Kaushik, Sanjay Misra

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

Innovations in Systems and 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

引用次数: 8

Automated credit assessment framework using ETL process and machine learning. 使用 ETL 流程和机器学习的自动信用评估框架。

IF 1.2

Innovations in Systems and Software Engineering Pub Date : 2022-12-31 DOI: 10.1007/s11334-022-00522-x

Neepa Biswas, Anindita Sarkar Mondal, Ari Kusumastuti, Swati Saha, Kartick Chandra Mondal

{"title":"Automated credit assessment framework using ETL process and machine learning.","authors":"Neepa Biswas, Anindita Sarkar Mondal, Ari Kusumastuti, Swati Saha, Kartick Chandra Mondal","doi":"10.1007/s11334-022-00522-x","DOIUrl":"10.1007/s11334-022-00522-x","url":null,"abstract":"In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment.","PeriodicalId":44465,"journal":{"name":"Innovations in Systems and Software Engineering","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10508957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0