Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide ...Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.展开更多
The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems(CPS-IoT)for real-time monitoring,intelligent decision-making,and efficient resource management,particularly in intelligent tr...The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems(CPS-IoT)for real-time monitoring,intelligent decision-making,and efficient resource management,particularly in intelligent transportation and vehicular networks.Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic,data-intensive,and vehicular environments.However,challenges such as high computational overhead,uneven load distribution,and inefficient utilization of communication resources significantly hinder scalability and responsiveness.Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems.Distributed computing for selecting forwarders and analyzing threats across the IoT system enhances stability while improving energy efficiency.In addition,to achieve efficient routing decision-making,the Artificial Bee Colony algorithmis explored to enhance the effective utilization of network resources across IoT systems.Based on the simulation results,the proposed framework achieves remarkable performance in terms of throughput by 38%–41%,packet loss ratio by 30%–33%,security risk mitigation by 35%–37%,and trust level by 41%–44%as compared to existing work.展开更多
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621,Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management).
文摘Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
基金supported and funded by theDeanship of Scientific Research at ImamMohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems(CPS-IoT)for real-time monitoring,intelligent decision-making,and efficient resource management,particularly in intelligent transportation and vehicular networks.Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic,data-intensive,and vehicular environments.However,challenges such as high computational overhead,uneven load distribution,and inefficient utilization of communication resources significantly hinder scalability and responsiveness.Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems.Distributed computing for selecting forwarders and analyzing threats across the IoT system enhances stability while improving energy efficiency.In addition,to achieve efficient routing decision-making,the Artificial Bee Colony algorithmis explored to enhance the effective utilization of network resources across IoT systems.Based on the simulation results,the proposed framework achieves remarkable performance in terms of throughput by 38%–41%,packet loss ratio by 30%–33%,security risk mitigation by 35%–37%,and trust level by 41%–44%as compared to existing work.
