Hydrocephalus is characterized by the accumulation of excess cerebrospinal fluid(CSF)in the cranium due to an imbalance between production and absorption of *** standard treatment involves the implantation of a shunt ...
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Hydrocephalus is characterized by the accumulation of excess cerebrospinal fluid(CSF)in the cranium due to an imbalance between production and absorption of *** standard treatment involves the implantation of a shunt to divert excess CSF into the peritoneal cavity,but these shunts exhibit high failure rates over *** pursuit of improved reliability and performance,this study proposes a miniaturized valve designed to mimic the natural one-way valve function of the arachnoid granulations and thereby replace the shunts.A benchtop testing setup was employed to characterize the behavior of the fabricated ***,an animal study was conducted to assess the valve’s in vivo *** involved the injection of saline into the lateral ventricle to elevate intracranial pressure(ICP),followed by the drainage of the saline through the valve inserted into the cisterna magna(CM)to reduce *** prototype features a silicone duckbill valve design combined with a silicone tube as an *** benchtop testing,the valve exhibited unidirectional flow with negligible reverse leakage,revealing that critical parameters such as the width of the fluid channel(W)and bill length(L)could be controlled to optimize valve ***,the valve configuration with W=0.8mm and L<0.5mm achieved the lowest cracking pressure(2.22±0.07 mmHg)and outflow resistance(22.00±0.70 mmHg/mL/min)within the low cracking pressure range of conventional *** observations of the in vivo test demonstrated that when untreated states,pressure differences from baseline to peak exceeded 20 mmHg due to the absence of drainage,resulting in sustained pressure ***,upon treating states by removing the clamp,pressure differences from baseline to peak remained below 5 mmHg,indicating effective drainage of injected saline through the *** promising results highlight the potential of the miniaturized duckbill valve as an alternative for ICP management in hydr
Designing motion controllers for autonomous racecars allows researchers to study the safety and computational efficiency of their algorithms under extreme conditions, while also serving as a benchmark for conventional...
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This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores c...
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Unmanned aerial systems (UAS) operating in dynamic environments must ensure safety for the duration of their flight. This paper presents an optimization method for planning safe, kinematically constrained, and time-co...
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Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1...
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Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (ScienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.
Flight delays pose significant challenges to the aviation industry, leading to increased operational costs and passenger dissatisfaction. This paper explores the use of machine learning (ML) and big data analytics to ...
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In the rapidly evolving technology landscape, smart homes are becoming increasingly common, driven by the demand for integrated management of information and services. However, despite advancements, managing electrici...
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In this manuscript, an American Zebra Optimization Algorithm (AZOA) is proposed for minimising torque-ripple in an 8/6 switched reluctance motor (SRM) drive. The major objective of the proposed technique is to improve...
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Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular puls...
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Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he
作者：
Park, Seong-JoonKwak, Hee-YoulKim, Sang-HyoKim, SunghwanKim, YongjuneNo, Jong-Seon
Institute of Artificial Intelligence Pohang37673 Korea Republic of University of Ulsan
Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of Sungkyunkwan University
Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Kyonggi University
School of Electronic Engineering Suwon16227 Korea Republic of
Department of Electrical Engineering Pohang37673 Korea Republic of Yonsei University
Institute for Convergence Research and Education in Advanced Technology Seoul03722 Korea Republic of Seoul National University
Department of Electrical and Computer Engineering Seoul08826 Korea Republic of
With the broadening applications of deep learning, neural decoders have emerged as a key research focus, specifically aimed at improving the decoding performance of conventional decoding algorithms. In particular, err...
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