Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have probl...Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have problems such as data silos,a lack of visibility in real time,fraudulent activities,and inefficiencies in tracking and traceability.Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues;it facilitates trust,security,and the sharing of data in real-time among all parties involved.Through an examination of critical technologies,methodology,and applications,this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence.The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field.As part of the process,we delved through the research on blockchain-based supply chain models,smart contracts,Decentralized Applications(DApps),and how they connect to other cutting-edge innovations like Artificial Intelligence(AI)and the Internet of Things(IoT).To quantify blockchain’s performance,the study introduces analytical models for efficiency improvement,security enhancement,and scalability,enabling computational assessment and simulation of supply chain scenarios.These models provide a structured approach to predicting system performance under varying parameters.According to the results,BT increases efficiency by automating transactions using smart contracts,increases security by using cryptographic techniques,and improves transparency in the supply chain by providing immutable records.Regulatory concerns,challenges with interoperability,and scalability all work against broad adoption.To fully automate and intelligently integrate blockchain with AI and the IoT,additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.展开更多
Objective Segmentation of medical images is a crucial process in various image analysis applications.Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical imag...Objective Segmentation of medical images is a crucial process in various image analysis applications.Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis.One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging(MRI)data.It allows for precise quantitative examination of the brain,which aids in diagnosis,identification,and classification of disorders.Consequently,the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.Methods This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans us-ing a fractional Henry horse herd gas optimization-based Shepard convolutional neural network(FrHHGO-based ShCNN).To segment the clinical brain MRI images into white matter(WM),grey matter(GM),and cerebrospinal fluid(CSF)tissues,the proposed framework was evaluated on the Lifespan Human Connectome Projects(HCP)database.The hybrid optimization algorithm,FrHHGO,integrates the fractional Henry gas optimization(FHGO)and horse herd optimization(HHO)algorithms.Training required 30 min,whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.Results Compared to the results obtained with no refinements,the Skull stripping refinement showed significant improvement.As the method included a preprocessing stage,it was flexible enough to enhance image quality,allowing for better results even with low-resolution input.Maximum precision of 93.2%,recall of 91.5%,Dice score of 91.1%,and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN,which was superior to all other approaches.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Blockchain Technology(BT)has emerged as a transformative solution for improving the efficacy,security,and transparency of supply chain intelligence.Traditional Supply Chain Management(SCM)systems frequently have problems such as data silos,a lack of visibility in real time,fraudulent activities,and inefficiencies in tracking and traceability.Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues;it facilitates trust,security,and the sharing of data in real-time among all parties involved.Through an examination of critical technologies,methodology,and applications,this paper delves deeply into computer modeling based-blockchain framework within supply chain intelligence.The effect of BT on SCM is evaluated by reviewing current research and practical applications in the field.As part of the process,we delved through the research on blockchain-based supply chain models,smart contracts,Decentralized Applications(DApps),and how they connect to other cutting-edge innovations like Artificial Intelligence(AI)and the Internet of Things(IoT).To quantify blockchain’s performance,the study introduces analytical models for efficiency improvement,security enhancement,and scalability,enabling computational assessment and simulation of supply chain scenarios.These models provide a structured approach to predicting system performance under varying parameters.According to the results,BT increases efficiency by automating transactions using smart contracts,increases security by using cryptographic techniques,and improves transparency in the supply chain by providing immutable records.Regulatory concerns,challenges with interoperability,and scalability all work against broad adoption.To fully automate and intelligently integrate blockchain with AI and the IoT,additional research is needed to address blockchain’s current limitations and realize its potential for supply chain intelligence.
文摘Objective Segmentation of medical images is a crucial process in various image analysis applications.Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis.One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging(MRI)data.It allows for precise quantitative examination of the brain,which aids in diagnosis,identification,and classification of disorders.Consequently,the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.Methods This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans us-ing a fractional Henry horse herd gas optimization-based Shepard convolutional neural network(FrHHGO-based ShCNN).To segment the clinical brain MRI images into white matter(WM),grey matter(GM),and cerebrospinal fluid(CSF)tissues,the proposed framework was evaluated on the Lifespan Human Connectome Projects(HCP)database.The hybrid optimization algorithm,FrHHGO,integrates the fractional Henry gas optimization(FHGO)and horse herd optimization(HHO)algorithms.Training required 30 min,whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.Results Compared to the results obtained with no refinements,the Skull stripping refinement showed significant improvement.As the method included a preprocessing stage,it was flexible enough to enhance image quality,allowing for better results even with low-resolution input.Maximum precision of 93.2%,recall of 91.5%,Dice score of 91.1%,and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN,which was superior to all other approaches.
