Pub Date : 2026-03-13DOI: 10.1007/s40747-026-02253-z
Leo Poss, Stefan Schönig
The abstraction gap between high-frequency IoT data and high-level business process logic creates a significant bottleneck for modern enterprises. Current architectures typically rely on separate middleware for event preprocessing, which introduces significant latency due to network hops and data serialization, and increases architectural complexity, creating multiple points of failure that hinder responsive operations. This paper introduces a synergistic engine paradigm that resolves this gap by leveraging a single complex event processing engine for both event abstraction and the direct execution of declarative MP-Declare models. Through a multi-level abstraction framework, process constraints are translated into executable queries, as demonstrated by a proof-of-concept. This unified approach provides a simplified architectural foundation for building highly responsive, event-driven applications that adapt intelligently to real-time conditions, as demonstrated by a proof-of-concept and a quantitative evaluation showing sub-millisecond latency at up to 10,000 events per second.
{"title":"A synergistic engine paradigm for real-time, context-aware decision-making: integrating declarative processes and event streams","authors":"Leo Poss, Stefan Schönig","doi":"10.1007/s40747-026-02253-z","DOIUrl":"https://doi.org/10.1007/s40747-026-02253-z","url":null,"abstract":"The abstraction gap between high-frequency IoT data and high-level business process logic creates a significant bottleneck for modern enterprises. Current architectures typically rely on separate middleware for event preprocessing, which introduces significant latency due to network hops and data serialization, and increases architectural complexity, creating multiple points of failure that hinder responsive operations. This paper introduces a synergistic engine paradigm that resolves this gap by leveraging a single complex event processing engine for both event abstraction and the direct execution of declarative MP-Declare models. Through a multi-level abstraction framework, process constraints are translated into executable queries, as demonstrated by a proof-of-concept. This unified approach provides a simplified architectural foundation for building highly responsive, event-driven applications that adapt intelligently to real-time conditions, as demonstrated by a proof-of-concept and a quantitative evaluation showing sub-millisecond latency at up to 10,000 events per second.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"35 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147461836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-12DOI: 10.1007/s40747-026-02271-x
Yujing Wang, Abdul Hadi Abd Rahman, Fadilla Atyka Nor Rashid
{"title":"SCPM: monocular 3D object detection with spatiotemporal consistent pseudo-labels module","authors":"Yujing Wang, Abdul Hadi Abd Rahman, Fadilla Atyka Nor Rashid","doi":"10.1007/s40747-026-02271-x","DOIUrl":"https://doi.org/10.1007/s40747-026-02271-x","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"10 9 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147461839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-08DOI: 10.1007/s40747-026-02266-8
K. M. Kirupa Shankar, V. Santhi
The increasing need for secure and accurate analysis of sensitive healthcare data across distributed sources such as blood banks has amplified the adoption of Federated Learning (FL), which allows collaborative training without the distribution of raw data. While FL helps address data silos and ensures a degree of privacy, studies reveal that gradient updates still leak sensitive information, posing risks of data reconstruction. Although differential privacy techniques have been introduced to mitigate such threats, uniform noise injection often compromises learning performance. To overcome these limitations, this research proposes a novel privacy-adaptive framework titled Self-learning Heterogeneous-based privacy-Enhanced end-to-end Learning Design for Federated Learning (SHELD-FL). The framework integrates self-learning privacy budgeting to dynamically allocate privacy budgets based on gradient sensitivity, and heterogeneous differential privacy to vary noise levels per client according to data sensitivity, achieving a better privacy-utility balance. A gradient boosting classifier is used at the client side to enhance classification under non-IID conditions, and the Builder Optimization Algorithm (BOA) is employed at the server to optimize noise regulation during aggregation. Experimental results on electronic health records from simulated blood banks demonstrate that the proposed SHELD-FL framework achieves a high classification accuracy of 98.39% under a strong privacy setting (ε = 10), outperforming baseline approaches by 3–5%. Moreover, the framework reduces communication latency by approximately 40%, indicating its efficiency and scalability in real-world federated environments. These findings confirm that SHELD-FL offers a reliable, adaptive, and privacy-preserving solution for secure and collaborative healthcare data analysis across distributed institutions.
{"title":"Privacy-adaptive end-to-end federated learning framework with self-learning differential privacy and personalized optimization for secure healthcare intelligence","authors":"K. M. Kirupa Shankar, V. Santhi","doi":"10.1007/s40747-026-02266-8","DOIUrl":"https://doi.org/10.1007/s40747-026-02266-8","url":null,"abstract":"The increasing need for secure and accurate analysis of sensitive healthcare data across distributed sources such as blood banks has amplified the adoption of Federated Learning (FL), which allows collaborative training without the distribution of raw data. While FL helps address data silos and ensures a degree of privacy, studies reveal that gradient updates still leak sensitive information, posing risks of data reconstruction. Although differential privacy techniques have been introduced to mitigate such threats, uniform noise injection often compromises learning performance. To overcome these limitations, this research proposes a novel privacy-adaptive framework titled Self-learning Heterogeneous-based privacy-Enhanced end-to-end Learning Design for Federated Learning (SHELD-FL). The framework integrates self-learning privacy budgeting to dynamically allocate privacy budgets based on gradient sensitivity, and heterogeneous differential privacy to vary noise levels per client according to data sensitivity, achieving a better privacy-utility balance. A gradient boosting classifier is used at the client side to enhance classification under non-IID conditions, and the Builder Optimization Algorithm (BOA) is employed at the server to optimize noise regulation during aggregation. Experimental results on electronic health records from simulated blood banks demonstrate that the proposed SHELD-FL framework achieves a high classification accuracy of 98.39% under a strong privacy setting (ε = 10), outperforming baseline approaches by 3–5%. Moreover, the framework reduces communication latency by approximately 40%, indicating its efficiency and scalability in real-world federated environments. These findings confirm that SHELD-FL offers a reliable, adaptive, and privacy-preserving solution for secure and collaborative healthcare data analysis across distributed institutions.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"12 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1007/s40747-026-02261-z
Zhou Gong, Weiyu Zhou
{"title":"Ghost-free high dynamic range imaging under degradation with PINet","authors":"Zhou Gong, Weiyu Zhou","doi":"10.1007/s40747-026-02261-z","DOIUrl":"https://doi.org/10.1007/s40747-026-02261-z","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"56 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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