检索结果-内蒙古大学图书馆

Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

Multimedia Tools and Applications 2025年 1-22页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans presents notable challenges. This particularly in differentiating tumors from surrounding tissues with similar intensity. This study utilizes the DeepLabV3Plus model with an Xception encoder to address these challenges. We evaluated the model on a dataset of 3064 MR images, which included meningioma, glioma, and pituitary tumors. The model achieved high segmentation performance, with an accuracy of 0.9991, Dice Similarity Coefficient (DSC) of 0.9529, and Jaccard Index (JI) of 0.9133. It also demonstrated substantial boundary precision, recording an Average Symmetric Surface Distance (ASSD) of 0.84 mm and a Hausdorff Distance (HD) of 2.3 mm. These metrics surpass those of existing state-of-the-art models. While the model performed well with well-defined tumor boundaries, it faced challenges segmenting tumors with ambiuous edges. These findings suggest significant potential for enhancing diagnostic accuracy in neuro-oncology. Future work will focus on enhancing the model’s sensitivity to subtle tumor features and expanding the dataset to include a broader range of tumor characteristics. Additionally, incorporating multimodal imaging data could further improve segmentation precision. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

引用

computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

来源：评论

学校读者我要写书评

暂无评论

Usage of Machine Learning Methods for Cause-Effect Graph Feasibility Prediction 5

Usage of Machine Learning Methods for Cause-Effect Graph Fea...

引用

5th International Conference on Machine Learning and Intelligent Systems, MLIS 2023

作者： Krupalija, Ehlimana Cogo, Emir Pozderac, Damir Ali Al Zayat, Aya Bešić, Ingmar Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo71 000 Bosnia and Herzegovina

ISBN: (纸本)9781643684468

Cause-effect graphs (CEGs) are usually applied for black-box testing of complex industrial systems. The specification process is time-consuming and can result in many errors. In this work, machine learning methods were applied for predicting the feasibility of CEG elements. All information was extracted from graphs contained in CEGSet, a dataset of CEGs. The data was converted to two different formats. The Boolean features format represents relations as separate data rows, whereas the Term-Frequency times Inverse-Document-Frequency (TF-IDF) format represents graphs as data rows. Eight machine learning models were trained on this data. The results of testing by using the 80-20 holdout method indicate that important information is lost when converting the graphs to the TF-IDF format, whereas the Boolean feature format enables 100%-accurate predictions of ensemble methods. The achieved results indicate that pre-trained models can be used as help for domain experts during the CEG specification process. © 2023 The authors and IOS Press.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

引用

30th IEEE International Symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (Department of Electronics Technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

来源：评论

学校读者我要写书评

暂无评论

Handwritten Text Recognition of Letters in Kyrgyz Language 24

Handwritten Text Recognition of Letters in Kyrgyz Language

引用

25th International Conference on computer Systems and Technologies, CompSysTech 2024

作者： Zhumaev, Ilgiz Shambetova, Burul Isaev, Ruslan Computer Science Department Ala-Too International University Bishkek Kyrgyzstan Faculty of Engineering and Informatics Ala-Too International University Bishkek Kyrgyzstan

ISBN: (纸本)9798400716843

This article deals with a new handwritten dataset, Kyrgyz-MNIST, and recognition of handwritten Kyrgyz letters through the Artificial Neural Network. There were collected 89 023 manuscript samples of 36 Kyrgyz letters. Since 33 of the 36 letters in the Kyrgyz language are similar to the Russian letters, the special letters "H", "Y", and "φ"were studied with special emphasis. The system of handwriting recognition works with Image processing methods and Machine Learning algorithms helped to recognize Kyrgyz manuscripts and convert them into computer letters. As a result, the CNN method recognized the handwriting letters more accurately. In total 36 letters were recognized with an average accuracy of 99.1%. Newly studied, the special letters in Kyrgyz Language are also recognized with more than 99% correct. © 2024 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

引用

computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

来源：评论

学校读者我要写书评

暂无评论

Behavior Trees and State Machines in Robotics Applications

引用

IEEE Transactions on Software engineering 2023年第9期49卷 4243-4267页

作者： Ghzouli, Razan Berger, Thorsten Johnsen, Einar Broch Wasowski, Andrzej Dragule, Swaib Chalmers University of Technology Department of Computer Science and Engineering Gothenburg40530 Sweden Ruhr University Bochum Faculty of Computer Science Bochum44801 Germany Chalmers University of Gothenburg Department of Computer Science and Engineering Gothenburg40530 Sweden University of Oslo Department of Informatics Oslo0316 Norway IT University of Copenhagen Software Development Group Copenhagen2300 Denmark

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the commu

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Load balancing and auto-scaling issues in container microservice cloud-based system: a review on the current trend technologies

引用

International Journal of Web engineering and Technology 2023年第4期18卷 294-318页

作者： Rabiu, Shamsuddeen Yong, Chan Huah Syed-Mohamad, Sharifah Mashita School of Computer Sciences Universiti Sains Malaysia Penang Minden11800 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia

Load balancing and auto-scaling are essential to cloud features for cloud-based container microservices as they control the number of computing resources available. Many research works have proposed load balancing and auto-scaling approaches for microservices individually;however, they are less likely to propose the two approaches in solving their problems simultaneously. The paper aimed to critically analyse the current issues related to load balancing and auto-scaling in container microservices cloud-based systems. This will open room for researchers in the field to enhance performance for better QoS to the users. We present a comprehensive literature review of the existing techniques used for load balancing and auto-scaling in cloud-based containerised microservice applications. After the in-depth review, it is found that load balancing and auto-scaling contribute as a value-added feature to the microservice applications. This can optimise the issues of Servers overloaded, service failure, traffic spikes, et cetera, during the microservices communication phase. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

GLCM Feature Extraction and PCA for Tuberculosis Detection with Neural Network

GLCM Feature Extraction and PCA for Tuberculosis Detection w...

引用

2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Alfa Farah, M. Nafis Hayyin Suristiyanti, Wiwiek Ibad, Sholihul Pramunendar, Ricardus Anggi Fajar Shidik, Guruh Dian Nuswantoro University Faculty of Computer Science Postgraduate Program in Informatics Engineering Indonesia Dian Nuswantoro University Faculty of Computer Science College Teacher in Informatics Engineering Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Automatic recognition system for medical images is quite a challenging job in the medical image processing field. X-rays, CT, and MRI all provide medical pictures and other modalities which are utilized for diagnostic purposes. As in medical sector, detecting tuberculosis (TB) is a very important stage before further treatment is carried out. Human interpretation of a vast array of X-ray pictures can result in detection mistakes, so an automatic recognition system is needed that can detect TB disease. In this study, we use a dataset with two classes and extract GLCM-based texture features from each class, and apply them to a two-layer feed-forward neural network, which gives a classification rate of 99%. © 2022 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Implementing Effective Learning with Ubiquitous Learning Technology During Coronavirus Pandemic

引用

computer Systems Science & engineering 2022年第1期40卷 389-404页

作者： Hosam F.El-Sofany Samir A.El-Seoud King Khalid University AbhaKingdom of Saudi Arabia Cairo Higher Institute for Engineering Computer Science and ManagementCairoEgypt British University in Egypt—BUE Faculty of Informatics and Computer ScienceCairoEgypt

Ubiquitous computing supports U-learning to develop and implement a new educational environment that provides effective and interactive learning to students wherever they *** study aims to present a qualitative evaluation for using U-learning instead of traditional education to avoid the spread of the Coronavirus *** authors introduce a UTAUT(Unified Theory of Acceptance and Use of Technology)model to assess the capability of the given factors that are expected to affect the learners’intention and behavior for accepting the U-learning technology for full *** research study shows a promising impact on the use of U-learning apps for implementing their learning activities in both the present and coming *** study also presents the descriptive statistics that affect the students’answers by using U-learning while implementing learning assignments including exams,projects,forums,essays,presentations,and *** research experiments demonstrate both the composite reliability(CR)and the average variance extracted(AVE)of the presented *** experimental results have shown that(1)the factor loadings≥0.75,CR≥0.9,and AVE≥0.75,which present appropriate proof for the validity and reliability of the proposed model,and(2)the total average of students that accept the U-learning is 82.99%,and who disagree is 8.13%,while who neither agree nor disagree is 6.44%.

关键词： Ubiquitous learning U-learning E-learning M-learning COVID-19 pandemic coronavirus UTAUT model

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：