Pub Date : 2025-12-02DOI: 10.1007/s40747-025-02151-w
Edward B. Ssekulima, Amir H. Etemadi
{"title":"Stochastic optimization framework for capacity planning of hybrid solar PV–small hydropower systems using metaheuristic algorithms","authors":"Edward B. Ssekulima, Amir H. Etemadi","doi":"10.1007/s40747-025-02151-w","DOIUrl":"https://doi.org/10.1007/s40747-025-02151-w","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657751","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 : 2025-12-02DOI: 10.1007/s40747-025-02133-y
Younes Akbari, Faseela Abdullakutty, Somaya Al Maadeed, Ahmed Bouridane, Rifat Hamoudi
Accurate breast cancer survival prediction using multi-modal data is vital for enhancing clinical decisions. This study evaluates deep learning based fusion strategies, early, intermediate, late, and a hybrid approach, to integrate histopathology images and genomic data for one year survival prediction. We developed a robust evaluation framework, employing tailored deep learning architectures and metrics including accuracy, precision, recall, F1 score, and AUC. Model performance was validated using Kaplan–Meier curves and log-rank tests, with SHAP-based feature importance analysis enhancing interpretability. Results highlight the strengths and limitations of each fusion strategy, offering insights into optimal multi-modal learning approaches for breast cancer prognosis. Our findings underscore the importance of selecting task specific fusion methods, providing a reproducible, interpretable framework to advance survival prediction. All code and configurations are publicly available.
{"title":"Integrating histopathology and genomic data: a comparative study of fusion methods for breast cancer survival prediction","authors":"Younes Akbari, Faseela Abdullakutty, Somaya Al Maadeed, Ahmed Bouridane, Rifat Hamoudi","doi":"10.1007/s40747-025-02133-y","DOIUrl":"https://doi.org/10.1007/s40747-025-02133-y","url":null,"abstract":"Accurate breast cancer survival prediction using multi-modal data is vital for enhancing clinical decisions. This study evaluates deep learning based fusion strategies, early, intermediate, late, and a hybrid approach, to integrate histopathology images and genomic data for one year survival prediction. We developed a robust evaluation framework, employing tailored deep learning architectures and metrics including accuracy, precision, recall, F1 score, and AUC. Model performance was validated using Kaplan–Meier curves and log-rank tests, with SHAP-based feature importance analysis enhancing interpretability. Results highlight the strengths and limitations of each fusion strategy, offering insights into optimal multi-modal learning approaches for breast cancer prognosis. Our findings underscore the importance of selecting task specific fusion methods, providing a reproducible, interpretable framework to advance survival prediction. All code and configurations are publicly available.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"30 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657748","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 : 2025-12-01DOI: 10.1007/s40747-025-02185-0
Yu Lu, Bin Wang, Wen Du, Xiong Li, Botao Jiang
{"title":"Decoding digital footprints: user re-identification through mobility pattern decomposition and collaborative fusion","authors":"Yu Lu, Bin Wang, Wen Du, Xiong Li, Botao Jiang","doi":"10.1007/s40747-025-02185-0","DOIUrl":"https://doi.org/10.1007/s40747-025-02185-0","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"75 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658096","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 : 2025-12-01DOI: 10.1007/s40747-025-02172-5
A. Chandrasekar, Wen-Jer Chang, T. Radhika, S. Santhosh Kumar, Muhammad Shamrooz Aslam
This study investigates the problem of adaptive event-triggered asynchronous control (AETAC) for interval type-2 (IT2) fuzzy Markov jump systems (MJS) with uncertain parameters and deception attacks. The objective is to enhance communication efficiency by reducing the frequency of data updates and unnecessary transmissions through the utilization of AETAC. This approach optimally utilizes the limited network resources and mitigates the communication burden. An effective IT2 fuzzy closed loop system is constructed under AETAC to handle deception attacks represented as stochastic distances with uncertainties. The asynchronous behavior between the plant and the controller is characterized using a hidden Markov model (HMM). By employing augmented Lyapunov–Krasovskii functional (LKF) and recently developed integral inequalities, this study establishes sufficient conditions for system analysis in the form of linear matrix inequalities (LMIs). These conditions take into account the existence of zero equations with strictly dissipative performance. Finally, simulation studies on two numerical examples are conducted to demonstrate the effectiveness of the proposed criteria.
{"title":"Adaptive event triggering asynchronous control for interval type-2 fuzzy Markov jump systems with uncertainties and deception attacks","authors":"A. Chandrasekar, Wen-Jer Chang, T. Radhika, S. Santhosh Kumar, Muhammad Shamrooz Aslam","doi":"10.1007/s40747-025-02172-5","DOIUrl":"https://doi.org/10.1007/s40747-025-02172-5","url":null,"abstract":"This study investigates the problem of adaptive event-triggered asynchronous control (AETAC) for interval type-2 (IT2) fuzzy Markov jump systems (MJS) with uncertain parameters and deception attacks. The objective is to enhance communication efficiency by reducing the frequency of data updates and unnecessary transmissions through the utilization of AETAC. This approach optimally utilizes the limited network resources and mitigates the communication burden. An effective IT2 fuzzy closed loop system is constructed under AETAC to handle deception attacks represented as stochastic distances with uncertainties. The asynchronous behavior between the plant and the controller is characterized using a hidden Markov model (HMM). By employing augmented Lyapunov–Krasovskii functional (LKF) and recently developed integral inequalities, this study establishes sufficient conditions for system analysis in the form of linear matrix inequalities (LMIs). These conditions take into account the existence of zero equations with strictly dissipative performance. Finally, simulation studies on two numerical examples are conducted to demonstrate the effectiveness of the proposed criteria.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"44 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645288","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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