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Unified resilience model using deep learning for assessing power system performance

Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Federated learning in network traffic classification: Taxonomy of implementation, application, and impact on sixth-generation wireless networks

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engineering Applications of Artificial Intelligence 2025年 158卷

作者： Azizi Ariffin Firdaus Afifi Faiz Zaki Hazim Hanif Nor Badrul Anuar Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Research for Cyber Security and Network (CSNET) Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia Institute of Informatics & Computing in Energy Universiti Tenaga Nasional Putrajaya Kampus 43000 Kajang Selangor Malaysia

Objectives Network traffic classification (NTC) is crucial for network management. However, the surge in Internet traffic poses scalability and privacy issues. Researchers are turning to federated learning (FL) to tackle these issues. Despite the importance of FL, there is lack of literature that comprehensively reviews its implementation and application within NTC and its impact on Sixth-Generation Wireless (6G) networks. Current surveys cover the technical aspects of NTC without fully addressing the integration of FL and its broader implications. This study aims to review the technical implementation of FL in NTC and its application to various network-related areas, including 6G. Methods This study presents a taxonomy for FL implementation in NTC, considering aspects such as learning and aggregation approaches, topology, and client operations. It examines the limitations of these elements and their effects on performance, efficiency, scalability, and their impact on 6G. This study outlines a taxonomy for FL applications, focusing on privacy preservation, scalable classification, and shared security intelligence. Novelty The proposed taxonomy provides insights into research landscape and highlights its limitations. The analysis of the impact of FL-based NTC on 6G provides insight into its integration and implementation challenges. This study discusses open issues and advocates for future research directions in FL for NTC, including 6G. Findings The study identifies areas needing improvement such as privacy, addressing security, single-point-of-failure, hardware limitations, delays and heterogeneity concerns. The findings of this paper show that an optimal implementation approach is essential to cater for heterogeneity and real-time requirements of the network environment.

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Natidroid: Cross-language android permission specification

arXiv

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arXiv 2021年

作者： Li, Chaoran Chen, Xiao Sun, Ruoxi Xue, Jason Wen, Sheng Ahmed, Muhammad Ejaz Camtepe, Seyit Xiang, Yang School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Software Systems and Cybersecurity Faculty of IT Monash University ClaytonVIC3800 Australia School of Computer Science University of Adelaide North TerraceSA5005 Australia CSIRO Data61 Australia

The Android system manages access to sensitive APIs by permission enforcement. An application (app) must declare proper permissions before invoking specific Android APIs. However, there is no official documentation providing the complete list of permission-protected APIs and the corresponding permissions to date. Researchers have spent significant efforts extracting such API protection mapping from the Android API framework, which leverages static code analysis to determine if specific permissions are required before accessing an API. Nevertheless, none of them has attempted to analyze the protection mapping in the native library (i.e., code written in C and C++), an essential component of the Android framework that handles communication with the lower-level hardware, such as cameras and sensors. While the protection mapping can be utilized to detect various security vulnerabilities in Android apps, such as permission over-privilege and component hijacking, imprecise mapping will lead to false results in detecting such security vulnerabilities. To fill this gap, we thereby propose to construct the protection mapping involved in the native libraries of the Android framework to present a complete and accurate specification of Android API protection. We develop a prototype system, named NATIDROID, to facilitate the cross-language static analysis to benchmark against two state-of-the-art tools, termed AXPLORER [1] and ARCADE [2]. We evaluate NATIDROID on more than 11,000 Android apps, including system apps from custom Android ROMs and third-party apps from the Google Play. Our NATIDROID can identify up to 464 new API-permission mappings, in contrast to the worst-case results derived from both AXPLORER and ARCADE, where approximately 71% apps have at least one false positive in permission over-privilege and up to 3.6% apps have at least one false negative in component hijacking. Additionally, we identify that 24 components with at least one Native-triggered component hij

关键词： Mobile security

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A machine learning approach for risk factors analysis and survival prediction of Heart Failure patients

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Healthcare Analytics 2023年 3卷 100182-100182页

作者： Ali, Md. Mamun Al-Doori, Vian S. Mirzah, Nubogh Hemu, Asifa Afsari Mahmud, Imran Azam, Sami Al-tabatabaie, Kusay Faisal Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Software Engineering (SWE) Daffodil International University (DIU) Sukrabad Dhaka 1207 Bangladesh Medical Instrumentation Techniques Engineering Al-Rafidain University College Baghdad 10064 Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Department of Computer Science and Engineering (CSE) Daffodil International University (DIU) Sukrabad Dhaka 1207 Bangladesh College of Engineering IT and Environment Charles Darwin University Casuarina NT 0909 Australia Middle Technical University Electrical Engineering Technical College Baghdad Iraq Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Artificial Intelligence & Digital Health School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability. © 2023 The Author(s)

关键词： Feature importance Heart failure Machine learning Risk factor analysis Survival prediction

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

引用

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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Development and performance analysis of machine learning methods for predicting depression among menopausal women

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Healthcare Analytics 2023年 3卷 100202-100202页

作者： Ali, Md. Mamun Algashamy, Hussein Ali A. Alzidi, Enas Ahmed, Kawsar Bui, Francis M. Patel, Shobhit K. Azam, Sami Abdulrazak, Lway Faisal Moni, Mohammad Ali Department of Software Engineering (SWE) Daffodil International University (DIU) Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh Medical laboratory techniques Al-Rafidain University College Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Computer Engineering Marwadi University Gujarat Rajkot 360003 India College of Engineering IT and Environment Charles Darwin University Casuarina NT 0909 Australia Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya 46001 Iraq Artificial Intelligence & Digital Health School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Menopause is an obligatory phenomenon in a woman's life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features. © 2023 The Author(s)

关键词： Artificial intelligence Depression Machine learning Menopause Quality of life Random Forest

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Automatic Building of a Repository for Component-based Synthesis of Warehouse Simulation Models

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Procedia CIRP 2021年 104卷 1440-1445页

作者： Fadil Kallat Jakob Pfrommer Jan Bessai Jakob Rehof Anne Meyer Software-Engineering by Algorithms and Logic Department of Computer Science TU Dortmund University Otto-Hahn-Straße 12 44227 Dortmund Germany Production Management and Factory Planning Faculty of Mechanical Engineering TU Dortmund University Leonhard-Euler-Str. 5 44227 Dortmund Germany Digitalization in Logistics and Supply Chain Management Faculty of Mechanical Engineering TU Dortmund University Leonhard-Euler-Str. 5 44227 Dortmund Germany

Simulations are a common tool in the warehouse planning and adoption process for evaluating and comparing variants of a storage system. But simulation modeling is a complex and time-consuming task. Due to limited resources, often not all possible system variants can be modeled. A promising solution is the migration of an existing simulation model to enable component-based software synthesis. An inhabitation algorithm composes structural variants according to a synthesis goal given a repository of typed components. In this paper, we automatically generate a repository and synthesize simulation model variants using a block stacking warehouse simulation model as an example.

关键词： storage planning adoption block stacking warehouse automatic simulation model generation component-based software synthesis

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Agile Implementation-A milestone for Academics using software engineering Industry Practices

Agile Implementation-A milestone for Academics using Softwar...

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2019 Advances in science and engineering Technology International Conferences, ASET 2019

作者： Nagaria, Jumana Sadath, Lipsa Ahmed, Suhaib Computer Science Engineering Amity University Dubai United Arab Emirates Faculty Software Engineering Amity University Dubai United Arab Emirates

ISBN: (纸本)9781538682715

Agile methods are getting popular in project development. When it comes to software engineering techniques, the methodology is to be selected according to the requirements of the software being developed or the project in consideration. There is always a question as how well a technique implemented in industry can be effectively be applied in academics also to train students as if in industry. This paper aims to increase the understanding of how pair programming and scrum can be adopted in academia. A case study approach using 12 university students, supplemented with feedback form, ethical forms on pair programming in academia. The methodology for pair programming was performed in the university lab. The Scrum approach was performed in a Group projects for national level. We proposed two frameworks SIA and SEAM that uses the industry practiced models with the focus on pair programming and scrum in academics. Thus we propose two novel models named Scrum in Academia (SIA) and software engineering Agile Model (SEAM) in Academics while students engage in group projects as a team to get quality work to be submitted meeting deadline as if in industry. We argue that the approach of using pair programming and scrum methodology can be adopted in the academics. © 2019 IEEE.

关键词： Students

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