检索结果-内蒙古大学图书馆

48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Iyenghar, Padma Hu, Yuxia Kieviet, Michael Pulvermueller, Elke Wuebbelmann, Juergen Innotec GmbH Erlenweg 12 Melle49324 Germany Osnabrueck University of Applied Sciences Faculty of Engineering and Computer Science Germany University of Osnabrueck Software Engineering Research Group Germany

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Standards such as ISO 13849 and ISO 12100 enable users to model safety related control elements with safety functions, according to a specified architecture and required performance level. In this direction, a novel Artificial Intelligence (AI)-based assistant is introduced in this paper to aid in determining the required performance level parameter by indulging the user in a dialog-based conversation regarding hazard scenarios. This will help inexperienced machinery safety personnel (e.g. mechanical engineer) to get an overview of the safety engineering aspects, before consulting with safety experts for risk assessment. © 2022 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

引用

CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

来源：评论

学校读者我要写书评

暂无评论

Extended Polymorphism Semantics for fUML Models

Extended Polymorphism Semantics for fUML Models

引用

Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Maximilian Hammer Ralph Maschotta Armin Zimmermann Computer Science and Automation Department Systems and Software Engineering Group Technische Universitat Ilmenau Ilmenau Germany

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

Polymorphism is a foundational concept within the object-oriented paradigm and is a feature of any mainstream object-oriented language that supports reusability and abstraction in software designs. With fUML being a standard specification of execution semantics of UML activity diagrams, it defines functionalities to simulate polymorphic behavior within such model executions. fUML only provides a simple standard implementation for simulating dynamic polymorphism at runtime. This default implementation does not meet the criteria of polymorphism as it is well-known and established by most mainstream object-oriented programming languages over the last decades, nor does it comply with the constraints for method overriding imposed by the original UML specification. However, fUML offers extension capabilities to add user-defined behavior for semantic variation points like polymorphism handling. This paper presents an extension of fUML execution semantics, enabling refined and more sophisticated simulation of polymorphism for executable UML models to comply with UML and the general understanding and functioning principles of polymorphic behavior within the object-oriented paradigm.

关键词： Hands computer languages Runtime software design Object oriented modeling Unified modeling language Semantics Object oriented programming Standards software development management

来源：评论

学校读者我要写书评

暂无评论

Oxygen Extraction and Blood Transport in the Human Retina: A Mathematical Modelling and Analysis

Oxygen Extraction and Blood Transport in the Human Retina: A...

引用

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Kumar, Aadesh Awasthy, Neeta Singh, Bholey Deolia, Rajesh Kumar Kumar, Satendra Sengar, R.S. Gl Bajaj Group of Institutions Department of Applied Science and Humanities Mathura India Gl Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798331527495

This study aims to describe the intricate flow of blood within the retinal tissue. The main findings of this research are the introduction of a quantitative method to calculate total oxygen extraction using retinal measurements of total blood flow, providing a crucial link between blood flow dynamics and oxygen transport. To analyze the problem, the authors employ a perturbation procedure, a mathematical approach that simplifies complex systems by introducing small changes to the variables. This approach facilitates a deeper understanding of the interplay between flow mechanics and oxygen delivery in the retina. The study's findings hold potential for advancing knowledge in retinal physiology and could pave the way for improved diagnostic and therapeutic strategies for retinal vascular diseases. © 2025 IEEE.

关键词： Perturbation techniques

来源：评论

学校读者我要写书评

暂无评论

Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

引用

IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Oxygen Extraction and Blood Transport in the Human Retina: A Mathematical Modelling and Analysis

Oxygen Extraction and Blood Transport in the Human Retina: A...

引用

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Aadesh Kumar Neeta Awasthy Bholey Singh Rajesh Kumar Deolia Satendra Kumar RS Sengar Department of Applied Science and Humanities GL Bajaj Group of Institutions Mathura India Department of Computer Science and Engineering GL Bajaj Group of Institutions Mathura India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

关键词： Pathology Oxygen Perturbation methods Retina Mathematical models Blood Monitoring Blood flow Diseases Intelligent control

来源：评论

学校读者我要写书评

暂无评论

Geometric Spanning Trees Minimizing the Wiener Index 1

引用

18th International Symposium on Algorithms and Data Structures, WADS 2023

作者： Abu-Affash, A. Karim Carmi, Paz Luwisch, Ori Mitchell, Joseph S. B. Software Engineering Department Shamoon College of Engineering Beer-Sheva84100 Israel Department of Computer Science Ben-Gurion University Beer-Sheva84105 Israel Department of Applied Mathematics and Statistics Stony Brook University Stony BrookNY United States

ISBN: (数字)9783031389061

ISBN: (纸本)9783031389054

The Wiener index of a network, introduced by the chemist Harry Wiener [30], is the sum of distances between all pairs of nodes in the network. This index, originally used in chemical graph representations of the non-hydrogen atoms of a molecule, is considered to be a fundamental and useful network descriptor. We study the problem of constructing geometric networks on point sets in Euclidean space that minimize the Wiener index: given a set P of n points in Rd, the goal is to construct a network, spanning P and satisfying certain constraints, that minimizes the Wiener index among the allowable class of spanning networks. In this work, we focus mainly on spanning networks that are trees and we focus on problems in the plane (d= 2 ). We show that any spanning tree that minimizes the Wiener index has non-crossing edges in the plane. Then, we use this fact to devise an O(n4) -time algorithm that constructs a spanning tree of minimum Wiener index for points in convex position. We also prove that the problem of computing a spanning tree on P whose Wiener index is at most W, while having total (Euclidean) weight at most B, is NP-hard. Computing a tree that minimizes the Wiener index has been studied in the area of communication networks, where it is known as the optimum communication spanning tree problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Atoms

来源：评论

学校读者我要写书评

暂无评论

Forecasting land use changes in crop classification and drought using remote sensing

引用

Journal of Arid Land 2025年第5期17卷 575-589页

作者： Mashael MAASHI Nada ALZABEN Noha NEGM Venkatesan VEERAMANI Sabarunisha Sheik BEGUM Geetha PALANIAPPAN Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science Applied College at MahayilKing Khalid UniversityAbha 61421Saudi Arabia Department of Civil Engineering University College of EngineeringAnna UniversityAriyalur 621731India Department of Biotechnology P.S.R.Engineering CollegeSivakasi 626140India Department of Electronics and Communication Engineering School of Engineering and TechnologyDhanalakshmi Srinivasan UniversitySamayapuram 621112India

Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t

关键词： land use and land cover(LULC) crop attributes drought vulnerability machine learning models remote sensing

来源：评论

学校读者我要写书评

暂无评论

FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chio, KaKit Zhu, Wenhao He, Lihua Zhang, Dian Yang, Xu Luo, Wuman Macao Polytechnic University Faculty of Applied Sciences S.A.R Macao China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Shenzhen University Department of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350337020

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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：