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Critical Patient Image Data Acquisition Strategy by Exploiting Edge Intelligence and Dynamic-Static Synergy in Smart Healthcare 认领 引用
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作者 Kiran Deep Singh Prabh Deep Singh +3 位作者 Narinder Kaur Jawad Khan Dildar Hussain Yeong Hyeon Gu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1276-1304,共29页
In smart healthcare systems,Image data of critical patients is essential in controlling and diagnosing the disease development.To acquire the medical images,traditional methods encountered the difficulty of generating... In smart healthcare systems,Image data of critical patients is essential in controlling and diagnosing the disease development.To acquire the medical images,traditional methods encountered the difficulty of generating costeffective data.This research work introduces a novel and innovative approach to collect high-quality image data from individuals with atypical clinical presentations.Initially,a new Internet of Medical Things(IoMT)image collection architecture is introduced.This design uses edge intelligence and motion-static synergy to make it easier to record both coarse-grained and fine-grained patient images.This study introduces an image acquisition technique that leverages edge intelligence and collaborative static-dynamic monitoring,exemplified in intensive care units,to improve the efficiency and data value of image acquisition in healthcare IoMT settings.This approach revolves around the three distinct steps.To begin with,an advanced YOLO-based clinical abnormality detection is implemented by the edge server to identify patients affected by abnormal physiological conditions.The images from affected patients are captured by static monitoring nodes.In the next phase,coordinate calculation methods for the localization of abnormal patients and quantification techniques for severity assessment are introduced.The final step involves the intervention of a path optimization algorithm for mobile medical assistive robots using severity metrics and principles of ant colony optimization.Ultimately,algorithmic performance evaluations at every phase indicate that acquisition efficiency and image data value surpass traditional methodologies. 展开更多
关键词 Edge intelligence image acquisition motion collaboration path optimization medical IoT patient monitoring
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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review 认领 引用 被引量：1
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作者 Syed Ijaz Ur Rahman Naveed Abbas +5 位作者 Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期1199-1231,共33页
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
关键词 Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network
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Prompt-Guided Dialogue State Tracking with GPT-2 and Graph Attention 认领 引用
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作者 Muhammad Asif Khan Dildar Hussain +5 位作者 Bhuyan Kaibalya Prasad Irfan Ullah Inayat Khan Jawad Khan Yeong Hyeon Gu Pavlos Kefalas 《Computers, Materials & Continua》 SCIE EI 2025年第12期5451-5468,共18页
Dialogue State Tracking(DST)is a critical component of task-oriented spoken dialogue systems(SDS),tasked with maintaining an accurate representation of the conversational state by predicting slots and their correspond... Dialogue State Tracking(DST)is a critical component of task-oriented spoken dialogue systems(SDS),tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values.Recent advances leverage Large Language Models(LLMs)with prompt-based tuning to improve tracking accuracy and efficiency.However,these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts,without explicitly modeling the complex dependencies between slots and values.In this work,we propose PUGG,a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer to implement a memory encoder.PUGG explicitly extracts slot values via GPT-2 and employs Graph Attention Networks(GATs)to model and reason over the intricate relationships between slots and their associated values.We evaluate PUGG on four publicly available datasets,where it achieves stateof-the-art performance across multiple evaluation metrics,highlighting its robustness and generalizability in diverse conversational scenarios.Our results indicate that the integration of GPT-2 substantially reduces model complexity and memory consumption by streamlining key processes.Moreover,prompt tuning enhances the model’s flexibility and precision in extracting relevant slot-value pairs,while the incorporation of GATs facilitates effective relational reasoning,leading to improved dialogue state representations. 展开更多
关键词 Spoken dialogue systems dialogue state tracking prompt tuning GPT-2 graph attention networks
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