@article{khosrojerdi_leveraging_2025,

title = {Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions},

author = {F. Khosrojerdi and S. Gagnon and R. Valverde},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007798431&doi=10.3390%2fen18112960&partnerID=40&md5=2a1c721151c469168b124841a0edd4d7},

doi = {10.3390/en18112960},

issn = {19961073 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Energies},

volume = {18},

number = {11},

abstract = {In a photovoltaic (PV) system, shading caused by weather and environmental factors can significantly impact electricity production. For over a decade, artificial intelligence (AI) techniques have been applied to enhance energy production efficiency in the solar energy sector. This paper demonstrates how AI-based control systems can improve energy output in a solar power plant under shading conditions. The findings highlight that AI contributes to the sustainable development of the solar power sector. Specifically, maximum power point tracking (MPPT) control systems, utilizing metaheuristic and computer-based algorithms, enable PV arrays to mitigate the impacts of shading effectively. The effect of shading on a PV module is also simulated using MATLAB R2018b. Using actual PV data from a solar power plant, power outputs are compared in two scenarios: (I) PV systems without a control system and (II) PV arrays equipped with MPPT boards. The System Advisor Model (SAM) is employed to calculate the monthly energy output of the case study. The results confirm that PV systems using MPPT technology generate significantly more monthly energy compared to those without MPPTs. © 2025 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fareedi_semantic_2025,

title = {Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System},

author = {A. A. Fareedi and S. Gagnon and A. Ghazawneh and R. Valverde},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105009278332&doi=10.3390%2ffi17060245&partnerID=40&md5=72bd14d033887f72e7e0c8a6a1451415},

doi = {10.3390/fi17060245},

issn = {19995903 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Future Internet},

volume = {17},

number = {6},

abstract = {Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. © 2025 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fareedi_digital_2025,

title = {Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management},

author = {A. A. Fareedi and M. Ismail and S. Gagnon and A. Ghazanweh and Z. Arooj},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218906224&doi=10.3390%2fsystems13020072&partnerID=40&md5=53b218763023a1bec3d69899d4dd7a86},

doi = {10.3390/systems13020072},

issn = {20798954 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Systems},

volume = {13},

number = {2},

abstract = {The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. © 2025 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}