检索结果-内蒙古大学图书馆

Conference on Artificial Life, ALIFE 2023

作者： Lindell, Trym A. E. Hudcová, Barbora Nichele, Stefano Artificial Intelligence Lab Department of Computer Science Faculty of Technology Art and Design Oslo Metropolitan University Oslo Norway Machine Learning Research Group Østfold University College Halden Norway Department of Algebra Faculty of Mathematics Charles University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics CTU Prague Czech Republic

Cellular Automata (CAs) have potential as powerful parallel computational systems, which has lead to the use of CAs as reservoirs in reservoir computing. However, why certain Cellular Automaton (CA) rules, sizes and input encodings are better or worse at a given task is not well understood. We present a method that enables identification and visualization of the specific information content, flow and transformations within the space-time diagram of CA. We interpret each spatio-temporal location in CA’s space-time diagram as a function of its input and call this novel notion the CA’s Canonical Computations (CCs). This allows us to analyze the available information from the space-time diagrams as partitions of the input set. The method also reveals how inputencoder-rule interactions transform the information flow by changing features like spatial and temporal location stability as well as the specific information produced. This general approach for analysing CA is discussed for the engineering of reservoir computing systems. © 2023, Massachusetts institute of technology. All rights reserved.

关键词： Sequential machines

来源：评论

学校读者我要写书评

暂无评论

Exploring Machine Learning Techniques to Identify Important Factors Leading to Injury in Curve Related Crashes

arXiv

引用

arXiv 2023年

作者： Moeinaddini, Mehdi Pourmoradnasseri, Mozhgan Hadachi, Amnir Cools, Mario ITS Lab Institute of Computer Science University of Tartu Narva mnt 18 Tartu51009 Estonia LEMA Research Group Urban & Environmental Engineering Department University of Liège Liège Belgium Department of Informatics Simulation and Modelling KULeuven Campus Brussels Brussels Belgium Faculty of Business Economics Hasselt University Diepenbeek Belgium

Different factors have effects on traffic crashes and crash-related injuries. These factors include segment characteristics, crash-level characteristics, occupant level characteristics, environment characteristics, and vehicle level characteristics. There are several studies regarding these factors’ effects on crash injuries. However, limited studies have examined the effects of pre-crash events on injuries, especially for curve-related crashes. The majority of previous studies for curve-related crashes focused on the impact of geometric features or street design factors. The current study tries to eliminate the aforementioned shortcomings by considering important pre-crash events related factors as selected variables and the number of vehicles with or without injury as the predicted variable. This research used CRSS data from the National Highway Traffic Safety Administration (NHTSA), which includes traffic crash-related data for different states in the USA. The relationships are explored using different machine learning algorithms like the random forest, C5.0, CHAID, Bayesian Network, Neural Network, C&R Tree, Quest, etc. The random forest and SHAP values are used to identify the most effective variables. The C5.0 algorithm, which has the highest accuracy rate among the other algorithms, is used to develop the final model. Analysis results revealed that the extent of the damage, critical pre-crash event, pre-impact location, the trafficway description, roadway surface condition, the month of the crash, the first harmful event, number of motor vehicles, attempted avoidance maneuver, and roadway grade affect the number of vehicles with or without injury in curve-related crashes. © 2023, CC BY.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Few-shot learning based histopathological image classification of colorectal cancer

引用

Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

来源：评论

学校读者我要写书评

暂无评论

Rice Leaf Disease Identification Using Adam Optimizer Based Modified Differential Evolution Algorithm

Rice Leaf Disease Identification Using Adam Optimizer Based ...

引用

Ambient Intelligence, Knowledge informatics and Industrial Electronics (AIKIIE), International Conference on

作者： R. Vijayarangan Nagendar Yamsani V. Thirumurugan Arthy P S Laith H. Alzubaidi RVT Research Associates Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India CSE VISTAS Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India The Islamic university Najaf Iraq

Recently, one of the grain-based crops that is farmed the most is rice, which is important to the agricultural sector. Furthermore, rice is a significant crop that is consumed by almost half of the global population. However, biotic and abiotic elements including bacteria, viruses, pests, soil fertility, and so forth have an impact on agricultural production. Early disease identification saves farmers time, increases crop yield, and guards against production loss in rice plants. The rice fields used for cultivation are the source of the rice leaf image dataset. The three disease classes that made up the data acquisition were Brown Spot, Bacterial Blight, and Leaf Blast. The ultimate goal of this proposed study is to improve rice leaf detection and classification performance. Thus, using a deep learning approach, this study presented an Adam Optimizer (AO) to carry out an efficient classification of rice leaf disease. Moreover, rice leaf disease detection and classification involve the use of Transfer Learning in conjunction with Inception V3 architecture. The Modified Differential Evolution Algorithm (MDEA) technique is used to determine the transfer learning approach's optimal learning rate. Finally, the proposed AO-MDEA shows better performance metrics in terms of accuracy (98.74%), precision (99.04%), recall (99.12%) and F1-score (99.15%) respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

Simultaneous Super-Resolution and Denoising on MRI via Condi...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenhong Liu Xingce Wang Zhongke Wu Yi-Cheng Zhu Alejandro F. Frangi School of Artificial Intelligence Beijing Normal University Beijing China Department of Neurology Peking Union Medical College Hospital Beijing China Division of Informatics Imaging and Data Sciences School of Health Sciences Christabel Pankhurst Institute Department of Computer Science School of Engineering The University of Manchester UK Department of Cardiovascular Sciences Medical Imaging Research Centre (MIRC) Department of Electrical Engineering KU Leuven Leuven Belgium lan Turing Institute London UK

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction

arXiv

引用

arXiv 2023年

作者： Sharma, Ashish Rushton, Kevin Lin, Inna Wanyin Wadden, David Lucas, Khendra G. Miner, Adam S. Nguyen, Theresa Althoff, Tim Paul G. Allen School of Computer Science & Engineering University of Washington United States Mental Health America United States Allen Institute for Artificial Intelligence United States Department of Psychiatry and Behavioral Sciences Stanford University United States Center for Biomedical Informatics Research Stanford University United States

A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful "reframed thought." Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people's access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a "high-quality" reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz

Investigation of the Impact of Height and Bandwidth on Path ...

引用

Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards living manufacturing systems 53

Towards living manufacturing systems

引用

53rd CIRP Conference on Manufacturing Systems, CMS 2020

作者： Monostori, László Váncza, József Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

The impact of the forth industrial revolution on the social and natural environment is considered significant and far-reaching, even though the interactions of the human, natural and manufactured assets are less understood, extremely complex and unpredictable. The paper discusses how present days' - so-called cyber-physical - manufacturing systems operate in the fabrics of society and the natural environment. It is underlined that they show and - to an increasing extent - will show features and capabilities reminiscent of living beings and organizations. This view helps to understand how manufacturing interacts with the ecosystem and suggests resolutions for the dilemma of competitive and sustainable manufacturing. © 2020 The Authors.

关键词： Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data

引用

computers in Biology and Medicine 2024年 179卷 108813-108813页

作者： Zhao, Chen Su, Kuan-Jui Wu, Chong Cao, Xuewei Sha, Qiuying Li, Wu Luo, Zhe Qing, Tian Qiu, Chuan Zhao, Lan Juan Liu, Anqi Jiang, Lindong Zhang, Xiao Shen, Hui Zhou, Weihua Deng, Hong-Wen Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Biostatistics University of Texas MD Anderson Pickens Academic Tower 1400 Pressler St. HoustonTX77030 United States Department of Mathematical Sciences Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems and Health Research Institute Michigan Technological University HoughtonMI49931 United States

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research. © 2024

关键词： Metabolites

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Post-Covid Economic Impact Using NLP Techniques

Unveiling the Post-Covid Economic Impact Using NLP Technique...

引用

International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Kanishk Barhanpurkar Nikita Mandlik Anand Singh Rajawat S.B. Goyal Traian Candin Mihaltan Chaman Verma Maria Simona Raboaca Thomas J. Watson College of Engineering and Applied Science Binghamton University USA School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies.

关键词：

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：