检索结果-内蒙古大学图书馆

IEEE International Conference on Industrial Technology (ICIT)

作者： Sajani Ranasinghe Daswin De Silva Nishan Mills Damminda Alahakoon Milos Manic Yen Lim Weranja Ranasinghe Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA Department of Urology Melbourne Australia

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Artificial Intelligence (AI) is reshaping the health-care landscape through diverse innovations, personalisations and decision-making capabilities. The human-like intelligence of Generative AI has been fundamental in driving this transformation across the sector. Despite large investments and some early successes, several studies have signalled the emergence of a productivity paradox due to inherent limitations of Generative AI that disintegrate within the complexity of healthcare systems and operations. In this study, we investigate the capabilities of Retrieval Augmented Generation (RAG) and Generative AI chatbots in addressing some of these challenges. We present the design and development of a Retrieval Augmented Generative AI Chatbot framework for consultation summaries, diagnostic insights, and emotional assessments of patients. We further demonstrate the technical value of this framework in service innovation, patient engagement and workflow efficiencies that collectively move to address the productivity paradox of AI in healthcare.

关键词： Productivity Technological innovation Image resolution Generative AI Decision making Medical services Information processing

来源：评论

学校读者我要写书评

暂无评论

An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer with XAI Techniques

引用

IEEE Access 2025年 13卷 96317-96336页

作者： Haque, Fariha Asif Hasan, Mohammad Siddique, Md. Abu Ismail Roy, Tonmoy Kanti Shaha, Tonmoy Islam, Yamina Paul, Avijit Chowdhury, Muhammad E. H. Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh Utah State University Department of Data Analytics & Information Systems Old Main Hill Logan 84322 (435) 797-1000 UT United States Chittagong University of Engineering Technology Electrical Electronic Engineering Bangladesh International Islamic University Chittagong Department of Computer Science & Engineering Chittagong 4318 Bangladesh Qatar University Department of Electrical Engineering Doha 2713 Qatar

In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that this approach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments. © 2013 IEEE.

关键词： CLAHE convolutional neural network explainable AI gradient-weighted class activation mapping Gradio Lung cancer Shapley additive explanation

来源：评论

学校读者我要写书评

暂无评论

Impact of energy efficiency on the morphology and behaviour of evolved robots 21

Impact of energy efficiency on the morphology and behaviour ...

引用

2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Rebolledo, Margarita Zeeuwe, Daan Bartz-Beielstein, Thomas Eiben, A.E. Institute for Data Science Engineering and Analytics Th Koln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

ISBN: (纸本)9781450383516

Most evolutionary robotics studies focus on evolving some targeted behavior without considering energy usage. In this paper, we extend our simulator with a battery model to take energy consumption into account in a system where robot morphologies and controllers evolve simultaneously. The results show that including the energy consumption in the fitness in a multi-objective fashion reduces the average size of robot bodies while reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set. © 2021 Owner/Author.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

AI based Stock Market Analysis and Decision Making System using Design Thinking Approach 8

AI based Stock Market Analysis and Decision Making System us...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kumar, M.Venkatesh Umamaheswari, M. Bharathi, C. Hema Maruthaveni, R. Devi, M.Nirmala Prasanna, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Dr. Sns Rajalakshmi College of Arts and Science Department of Business Administration Tamilnadu Coimbatore India Tamil Nadu Agricultural University Department of Physical Science and Information Technology Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science with Data Analytics Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Management Studies Tamilnadu Coimbatore India

ISBN: (纸本)9798350386578

Stock market is a dynamic and ever-changing environment that can be both exciting and challenging for investors. Equities, primarily referred to as stocks, are traded on stock exchanges around the world and reflect ownership in a business. The stock market is an essential tool for companies to raise capital, and investors use it to grow their wealth. Investing in stocks requires a thorough understanding of the market and the individual companies in which you are interested. The financial health of a company must be taken into account and can be analyzed using financial statements and other data. Another critical factor is the overall economic climate, as market conditions can significantly impact stock prices. It's crucial to have a long-term outlook and avoid being influenced by momentary market changes when making equity investments. A random walk is a mathematical model used to describe a sequence of steps or movements where each step is determined by chance. It is often used in various fields, including physics, finance, and statistics. Random walk theory has important implications in various fields, including the efficient market hypothesis in finance and the modeling of diffusion processes in physics. To effectively predict the future stock price using conventional and modern machine learning models. Compare the performance of different models and identify a higher accuracy prediction model using the selected important factors from disparate data sources. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning for maintenance planning of offshore vessel transfer 4

Deep reinforcement learning for maintenance planning of offs...

引用

4th International Conference on Renewable Energies Offshore, RENEW 2020

作者： Chatterjee, Joyjit Dethlefs, Nina Department of Computer Science and Technology Big Data Analytics Research Group University of Hull Hull United Kingdom

ISBN: (纸本)9780367681319

Offshore wind farm operators need to make short-term decisions on planning vessel transfers to turbines for preventive or corrective maintenance. These decisions can play a pivotal role in ensuring maintenance actions are carried out in a timely and cost-effective manner. The present optimization of offshore vessel transfer uses mathematical models rather than learning decisions from historical data. In this paper, we design a simulated environment for an offshore wind farm based on Supervisory Control & Acquisition (SCADA) data and alarm logs of historical faults in an operational turbine. Firstly, we utilise a state-of-art decision tree model to predict fault types using SCADA features, and provide explainable decisions. Next, we apply deep reinforcement learning to automatically learn maintenance priorities corresponding to different fault types for ensuring prioritized vessel transfers for critical conditions, and deciding on optimal vessel fleet size. This can lead to significant savings in maintenance costs for the offshore wind industry. © 2021 Taylor & Francis Group, London.

关键词： Offshore wind farms

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation for Innovative Tourism Expert System based on Artificial Intelligence

Design and Implementation for Innovative Tourism Expert Syst...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R. Prasanth R. Anitha T. Sarathamani M. Manoj Department of Management Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Tamilnadu India Department of Computer Science and Engineering (AI) Brainware University Kolkata India Department of Computer Science with Data Analytics KPR College of Arts Science and Research Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The tourism sector has long been looked at as a vital engine of economic expansion. The most recent developments in the tourism sector have benefitted governments, international organizations, common people and academia. For developing the tourism sector, it is important to analyze and understand tourist visitor patterns. Tourist forecasting helps maximize the tourism-based economy. The international tourist movement has grown at an unprecedented rate in recent decades, and many countries are concentrating on the tourism economy. While tourists visit a tourist place, spend a significant amount of money on their enjoyment, company, and hotel accommodations. ML algorithms are making significant contributions to forecasting system, and several researchers are working to improve the model’s accuracy. This research has proposed a machine learning approach for forecasting worldwide tourist arrivals that analyses prior quantitative information about visitors so that the governing body can arrange the necessary facilities for visitors accordingly. The proposed model is applied to a dataset compiled over a decade from all countries around the world. The detailed proposed approach and the various results of experiments are explained in this paper.

关键词： Virtual assistants Transforms Predictive models Market research Prediction algorithms Real-time systems Expert systems Forecasting Predictive analytics Meteorology

来源：评论

学校读者我要写书评

暂无评论

Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey

arXiv

引用

arXiv 2023年

作者： Ghojogh, Benyamin Ghodsi, Ali Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada

This is a tutorial paper on Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and their variants. We start with a dynamical system and backpropagation through time for RNN. Then, we discuss the problems of gradient vanishing and explosion in long-term dependencies. We explain close-to-identity weight matrix, long delays, leaky units, and echo state networks for solving this problem. Then, we introduce LSTM gates and cells, history and variants of LSTM, and Gated Recurrent Units (GRU). Finally, we introduce bidirectional RNN, bidirectional LSTM, and the Embeddings from Language Model (ELMo) network, for processing a sequence in both directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques for Autism Spectrum Disorder: current trends and future directions

Machine Learning Techniques for Autism Spectrum Disorder: cu...

引用

Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

来源：评论

学校读者我要写书评

暂无评论

Chronic Kidney Disease Classification Using Machine Learning Classifiers

Chronic Kidney Disease Classification Using Machine Learning...

引用

2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Alshakrani, Sara Taha, Ranyah Hewahi, Nabil Big Data Science and Analytics College of Science University of Bahrain Bahrain College of Information Technology University of Bahrain Department of Computer Science Bahrain

ISBN: (纸本)9781665440325

One of the important health problems is chronic kidney disease (CKD). It is increasing every day as a result of poor eating habits, insufficient water consumption, and lack of health awareness. CKD is a condition in which the kidneys are damaged over time due to a variety of causes, resulting in a progressive and irreversible loss of kidney function. Technological advancements, such as Machine Learning (ML), have a significant influence on the health sector by giving more accurate detection and successful treatment of many chronic diseases. This research paper explores a variety of ML techniques to predict kidney disease. The objective is to identify which ML classifier could be the best classifier for detecting and predicting CKD. In this work, seven ML approaches are used to predict kidney disease, namely, Gaussian Nave Bayes (GNB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Logistic Regression (LR), AdaBoost and Gradient Boosting. RF and GNB scored better than the other classifiers, with 100% accuracy. © 2021 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Extracting Biomedical Entities from Noisy Audio Transcripts 30

Extracting Biomedical Entities from Noisy Audio Transcripts

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ebadi, Nima Morgan, Kellen Tan, Adrian Linares, Billy Osborn, Sheri Majors, Emma Davis, Jeremy Rios, Anthony Department of Electrical and Computer Engineering The University of Texas San Antonio United States Department of Management Science and Statistics The University of Texas San Antonio United States Data Analytics The University of Texas San Antonio United States Department of Information Systems and Cyber Security The University of Texas San Antonio United States Department of Neurology Division of Neuropsychology UT Health San Antonio United States

ISBN: (纸本)9782493814104

Automatic Speech Recognition (ASR) technology is fundamental in transcribing spoken language into text, with considerable applications in the clinical realm, including streamlining medical transcription and integrating with Electronic Health Record (EHR) systems. Nevertheless, challenges persist, especially when transcriptions contain noise, leading to significant drops in performance when Natural Language Processing (NLP) models are applied. Named Entity Recognition (NER), an essential clinical task, is particularly affected by such noise, often termed the ASR-NLP gap. Prior works have primarily studied ASR's efficiency in clean recordings, leaving a research gap concerning the performance in noisy environments. This paper introduces a novel dataset, BioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain, focusing on extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. Our dataset offers a comprehensive collection of almost 2,000 clean and noisy recordings. In addressing the noise challenge, we present an innovative transcript-cleaning method using GPT4, investigating both zero-shot and few-shot methodologies. Our study further delves into an error analysis, shedding light on the types of errors in transcription software, corrections by GPT4, and the challenges GPT4 faces. This paper aims to foster improved understanding and potential solutions for the ASR-NLP gap, ultimately supporting enhanced healthcare documentation practices. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Error analysis

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：