检索结果-内蒙古大学图书馆

12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Fontes, Miguel Leite, Danilo Dallyson, João Cunha, António UTAD—University of Trás-os-Montes and Alto Douro Vila Real5001-801 Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal UFMA—Federal University of Maranhão São Luís Brazil

ISBN: (纸本)9783031606649

Artificial intelligence (AI) is playing a growing role today in several areas, especially in health, where understanding AI models and their predictions is extremely important for health professionals. In this context, Explainable AI (XAI) plays a crucial role in seeking to provide understandable explanations for these models. This article analyzes two different XAI approaches applied to analyzing gastric endoscopy images. The first, more conventional approach uses Grad CAM, while the second, even less explored but with great potential, is based on "similarity-based explanations". This example-based XAI technique aims to provide representative examples to support the decisions of AI models. In this study, we compare these two techniques applied to two different models: one based on the VGG16 architecture and the other based on ResNet50, designed to classify images from the KVASIR-capsule database. The results reveal that Grad-CAM provided intuitive explanations only for the VGG16 model, while the "similarity-based explanations" technique provided consistent explanations for both models. We conclude that exploring other XAI techniques can be a significant asset in improving the understanding of the various AI models. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Selective Detection in Impulsive Low-Frequency Raman Imaging Using Shaped Probe Pulses

引用

Physical Review Applied 2023年第5期19卷 054075-054075页

作者： Siddarth Shivkumar Dekel Ranann Samuel Metais Sisira Suresh Nicolas Forget Randy Bartels Dan Oron Hervé Rigneault Aix Marseille Université CNRS Centrale Marseille Institut Fresnel Marseille France Department of Physics of Complex Systems The Weizmann Institute of Science Rehovot 76100 Israel Fastlite Antibes 06600 France Departments of Electrical and Computer Engineering and Biomedical Engineering Colorado State University Fort Collins USA

Impulsive stimulated Raman scattering (ISRS) using a single short femtosecond pump pulse to excite molecular vibrations offers an elegant pump-probe approach to perform vibrational imaging below 200cm−1. One shortcoming of ISRS is its inability to offer vibrational selectivity as all the vibrational bonds whose frequencies lie within the short pump-pulse bandwidth are excited. To date, several coherent control techniques have been explored to address this issue and selectively excite a specific molecular vibration by shaping the pump pulse. There has not been any systematic work that reports an analogous shaping of the probe pulse to implement preferential detection. In this work, we focus on vibrational imaging and report vibrational selective detection by shaping the probe pulse in time. We demonstrate numerically and experimentally two pulse-shaping strategies with one functioning as a vibrational notch filter and the other functioning as a vibrational low-pass filter. This enables fast (25μs/pixel) and selective hyperspectral imaging in the low-frequency regime (<200cm−1).

关键词： Analytical chemistry Molecular spectra Optical transient phenomena Biomolecules Time-resolved light scattering spectroscopy Ultrafast femtosecond pump probe

来源：评论

学校读者我要写书评

暂无评论

RLStop: A Reinforcement Learning Stopping Method for TAR

arXiv

引用

arXiv 2024年

作者： R., Bin-Hezam M., Stevenson Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Engineering University of Sheffield Sheffield United Kingdom

We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications. RLStop is trained on example rankings using a reward function to identify the optimal point to stop examining documents. Experiments at a range of target recall levels on multiple benchmark datasets (CLEF e-Health, TREC Total Recall, and Reuters RCV1) demonstrated that RLStop substantially reduces the workload required to screen a document collection for relevance. RLStop outperforms a wide range of alternative approaches, achieving performance close to the maximum possible for the task under some circumstances. © 2024, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Early Prediction of Disease with Risk Factors Data of the Patient Using Support Vector Machines

Machine Learning based Early Prediction of Disease with Risk...

引用

International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication, MDCWC 2020

作者： Chelladurai, Usharani Pandian, Seethalakshmi Departments of Computer Science and Engineering and Electronics and Communication Engineering University College of Engineering BIT Campus Anna University Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9789811602887

Early detection of diseases plays an important role in improving the quality of healthcare and can help people to prevent dangerous health conditions. Early detection of chronic disease is a critical task in the area of health data analysis. This paper proposes a novel fuzzy logic-based SVM machine learning technique for patient-centered healthcare analytics for premature prediction of chronic diseases such as hypertension, hypothyroidism, and obesity based on individual patient risk factors data. The proposed method consists of preprocessing, feature selection, feature extraction, fuzzy SVM classification, and post-processing predicts the severity of the disease. The proposed fuzzy-based support vector machines (SVMs) are designed to classify important features by applying machine learning techniques to ensure accuracy in the prediction of chronic disease. The SVM is a margin-based classifier that maps input data onto a high-dimensional space and classifies them with a linear approximation. This technique combines outputs from different classification models and has shown the highest accuracy compared to previous techniques. None of the previous studies have integrated fuzzy with SVM classifiers for chronic disease datasets. The proposed machine learning-based disease prediction model for early diagnosis and timely treatment for non-communicable disease/chronic disease investigates the risk factors data from the patient treatment log. This model provides early risk detection, helps doctors to follow the appropriate precautions, and measures to minimize and prevent a patient from reaching critical phases of the disease. The system would significantly decrease human mortality rates and strengthen health services. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

An Artificial Intelligence Representation of Human Knowledge for Lung Nodule Classification

An Artificial Intelligence Representation of Human Knowledge...

引用

2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Gao, Yongfeng Pomeroy, Marc Cao, Weiguo Han, Fangfang Liang, Zhengrong Stony Brook University Department of Radiology Stony BrookNY11794 United States Stony Brook University Departments of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony BrookNY11794 United States

ISBN: (纸本)9781665488723

Low-dose computed tomography (LdCT), a recommended screening method for detection of early lung cancer, has high false positive (FP) rate, and lung nodule biopsy is a follow-up option to eliminate the FPs. It is challenging and meaningful to differentiate the nodule pathology by the LdCT screening data to avoid the costly interventional biopsy procedure. In this paper, we propose an artificial intelligence (AI) model to represent the human knowledge about lesion properties to differentiate the malignant nodules from benign ones. Three lesion properties in terms of heterogeneity, elasticity and growth are quantitatively represented by the proposed AI model. An augmented feature selection strategy was developed to integrate all lesion properties for the lesion classification. Experimental results show that the proposed AI model can achieve an AUC (area under the curve of receiver operating characteristics) score of 0.78 in the cases where physicians cannot determine the lesion type with AUC score around 0.5. © 2022 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multilingual Lexical Simplification via Zero-Shot Paraphrasing

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2200-2214页

作者： Kang Liu Jipeng Qiang Yun Li Yi Zhu Yunhao Yuan Kaixun Hua Xindong Wu Department of Computer Science Yangzhou University Yangzhou China Department of Industrial and Management Systems Engineering University of South Florida Tampa USA Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei China

Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings. Nonetheless, these methods necessitate distinct pretrained models tailored to diverse languages, often overlooking the imperative task of preserving a sentence’s meaning. In this paper, we propose a novel multilingual LS method via zero-shot paraphrasing (LSPG), as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. Once the input sentence is channeled into the paraphrasing, we embark on the generation of the substitutes. This endeavor is underpinned by a pioneering decoding strategy that concentrates exclusively on the lexical modifications of the complex word. To utilize the strong capabilities of large language models (LLM), we further introduce a novel approach PromLS that incorporates the results of LSPG to generate heuristic-enhanced context, enabling the LLM to generate diverse candidate substitutions. Experimental results demonstrate that LSPG surpasses BERT-based methods and zero-shot GPT3-based methods significantly in English, Spanish, and Portuguese. We also demonstrate a substantial improvement achieved by PromLS compared to the previous state-of-the-art LLM approach. LS approaches usually assume that complex words and their replacements are individual terms, concentrating on word-for-word substitutions. To tackle the more challenging task of multi-word lexical simplification, including phrase-to-phrase replacements, we extend LSPG and PromLS into MultiLSPG and MultiPromLS. MultiLSPG identifies multi-word expressions matched with their corresponding word counts in specific positions, while MultiPromLS, akin to PromLS, utilizes these candidates to generate a heuristi

关键词： Multilingual Decoding Translation Rhetoric Large language models Training Neural machine translation Encoding Speech processing Probability distribution

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Data Analytics Approach for Crisis Management in Healthcare systems

An Intelligent Data Analytics Approach for Crisis Management...

引用

International Conference on computer and Applications (ICCA)

作者： Fatma A. Madian Gamal A. Ebrahim Mostafa M. Salah Marwa A. Abdelaal Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Electrical Engineering Department Future University in Egypt Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%.

关键词： Crisis management computer viruses Simulation Decision making Time series analysis Organizations Predictive models Strategic planning Root mean square Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Incremental Nyström-based Multiple Kernel Clustering 39

Incremental Nyström-based Multiple Kernel Clustering

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Feng, Yu Liang, Weixuan Wan, Xinhang Liu, Jiyuan Liu, Suyuan Qu, Qian Guan, Renxiang Xu, Huiying Liu, Xinwang College of Computer Science and Technology National University of Defense Technology Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

ISBN: (纸本)157735897X

Existing Multiple Kernel Clustering (MKC) algorithms commonly utilize the Nyström method to handle large-scale datasets. However, most of them employ uniform sampling for kernel matrix approximation, hence failing to accurately capture the underlying data structure, leading to large approximation errors. Additionally, they often use the same landmark points for all kernel matrix approximations, reducing kernel diversity. Moreover, in scenarios where approximate kernel matrices emerge over time, these methods require storing historical kernel information and recalculating, resulting in inefficient resource utilization. To address these issues, we propose a novel MKC algorithm, termed Incremental Nyström-based Multiple Kernel Clustering (INMKC). Specifically, leverage score sampling is utilized to reduce kernel approximation errors and enhance kernel diversity. Furthermore, we employ a consensus clustering structure that aligns with the newly emerged base kernel matrix for updates, avoiding recalculating previous kernel matrices, thus saving substantial computational resources. Additionally, we tackle the challenge of aligning incremental approximate kernels with different landmark points. Extensive experiments on the proposed INMKC demonstrate its effectiveness and efficiency compared to state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

引用

Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 19-32页

作者： Linxuan Wang Jinghua Xu Shuyou Zhang Jianrong Tan Shaomei Fei Xuezhi Shi Jihong Pang Sheng Luo State Key Lab of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhouZhejiang 310058China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Zhejiang UniversityHangzhouZhejiang 310058China State Key Lab of Materials Processing and Die&Mould Technology Huazhong University of Science and TechnologyWuhanHubei 430074China School of Marine Engineering Equipment Zhejiang Ocean UniversityZhoushanZhejiang 316022China College of Business Shaoxing UniversityShaoxingZhejiang 312000China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhouZhejiang 325000China

This study presents an energy consumption(EC)forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning(FTL).To predict the EC of manufacturing products,particularly from scale-down to scale-up,a general paradigm was first developed by categorizing the overall process into three main *** operating electrical power was further formulated as a combinatorial function,based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and ***-arranged networks were constructed to investigate the impacts of fabrication variables and devices on *** the interconnections among these factors,the outputs of the neural networks were blended and fused to jointly predict the electrical *** innovatively,large artifacts can be decomposed into timedependent laser-scanning trajectories,which can be further transformed into fusionable information via neural networks,inspired by large language ***,transfer learning can deal with either scale-down or scale-up forecasting,namely,FTL with scalability within artifact *** effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed *** relative error of the average and overall EC predictions based on FTL was maintained below 0.83%.The melting fusion quality was examined using metallographic *** proposed FTL framework can forecast the EC of scaled structures,which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

关键词： Energy consumption forecasting Large metal artifacts Carbon peaking and carbon neutrality Laser powder bed fusion Fusionable transfer learning

来源：评论

学校读者我要写书评

暂无评论

Estimation of Solar Cell Circuit Model Parameters Using an Algorithm Based on Artificial Intelligence

Estimation of Solar Cell Circuit Model Parameters Using an A...

引用

Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Aydin Samani Masume Khodsuz Departments of Electrical Engineering Islamic Azad University Science and Research Branch Tehran Iran Faculty of Electrical and Computer Engineering University of Science and Technology of Mazandaran Behshahr Iran

In this article, it is intended to estimate the parameters of the equivalent circuit of the two-diode model of the PV system by using the method based on artificial intelligence. Based on this, at first, using the particle swarm algorithm as a suitable method, 7 parameters of the two-diode model were optimized. In order to check the performance of the particle swarm method, different strategies were considered for it, including PSO algorithm with random inertia coefficient, time-varying inertia coefficient, time-varying learning coefficients, contraction coefficient and average. Next, the Archimedes optimization method as a new algorithm in this field was proposed and investigated, and the parameters of the two-diode model were optimized based on it. In order to check the accuracy of the proposed methods, the results obtained from the optimization of the parameters of the two-diode PV system model were compared with some optimization methods.

关键词： Photovoltaic cells Optimization methods Estimation Mean square error methods Distributed power generation Integrated circuit modeling Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：