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Introduction to the Special Issue on Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision
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作者 Ahmad Taher Azar 《Computer Modeling in Engineering & Sciences》 2025年第7期29-36,共8页
The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems an... The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems and making decisions in many different fields.These advanced technologies have enabled significant breakthroughs in sectors including entertainment,finance,transportation,and healthcare.AI systems,which can analyze vast volumes of data,have significantly driven efficiency and innovation.With remarkable accuracy,patterns can be identified and predictions generated,improving decision-making processes and facilitating the development of more intelligent solutions.The increasing adoption of these technologies by organizations has expanded the potential for AI to change processes and improve results. 展开更多
关键词 machine learning deep learning dl analyze vast volumes datahave artificial intelligence ai machine learning ml advanced technologies solving complex problems efficiency innovationwith artificial intelligence
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Evaluating Pharmacological and Rehabilitation Strategies for Effective Management of Bipolar Disorder: A Comprehensive Clinical Study
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作者 Rocco de Filippis Abdullah Al Foysal 《Advances in Bioscience and Biotechnology》 CAS 2024年第7期406-431,共26页
Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy... Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy of pharmacological interventions and rehabilitation strategies to improve patient outcomes and quality of life. Utilizing a randomized controlled trial with multiple treatment arms, participants will receive pharmacotherapy, polypharmacotherapy, rehabilitation interventions, or combination treatments. Outcome measures will be assessed using standardized scales, including the Hamilton Depression Scale, Yale-Brown Obsessive Compulsive Scale (Y-BOCS), and Mania Scale. Preliminary data suggest improvements in symptom severity and functional outcomes with combination treatments. This research aims to inform clinical practice, guide treatment decisions, and ultimately enhance the quality of care for individuals living with bipolar disorder. Findings will be disseminated through peer-reviewed journals and scientific conferences to advance knowledge in this field. 展开更多
关键词 Bipolar Disorder (BD) Pharmacotherapy (PT) Rehabilitation Interventions (RI) Hamilton Depression Scale (HAM-D) Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Mania Scale (MS) machine learning (ML) and Artificial Intelligence (ai).
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Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains 被引量:1
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作者 Petar Radanliev David De Roure +5 位作者 Kevin Page Jason R.C.Nurse Rafael Mantilla Montalvo Omar Santos La’Treall Maddox Pete Burnap 《Cybersecurity》 CSCD 2020年第1期155-175,共21页
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r... Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks. 展开更多
关键词 Industry 4.0 Supply chain design Transformational design roadmap IIoT supply chain model Decision support for information management artificial intelligence and machine learning(ai/ML) dynamic self-adapting system cognition engine predictive cyber risk analytics
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Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains
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作者 Petar Radanliev David De Roure +5 位作者 Kevin Page Jason R.C.Nurse Rafael Mantilla Montalvo Omar Santos La’Treall Maddox Pete Burnap 《Cybersecurity》 2018年第1期767-787,共21页
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r... Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks. 展开更多
关键词 Industry 4.0 Supply chain design Transformational design roadmap IIoT supply chain model Decision support for information management artificial intelligence and machine learning(ai/ML) dynamic self-adapting system cognition engine predictive cyber risk analytics
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