检索结果-内蒙古大学图书馆

2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kumar Reddy, S.Pavan Sreevani, N. Revathi, V. Babu, Samuel Singh, Navdeep Kareem, Ali Narsapur Campus Hyderabad India Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

Automated methods for assessing bowel sounds (BS) were well developed by the early 2000s. Several teams, using diverse analytical techniques, achieved high levels of accuracy. BS has shown potential in doing non-invasive research on irritable bowel syndrome, studying gastrointestinal motility, and performing surgical procedures. This article introduces hybrid convolution and recursive neural networks that are used for BS analysis. It was among the first deep-learning approaches to be extensively studied. An experimental workflow and a novel dataset from a contact microphone device were used to assess the results. Data were obtained at night, the most neuro gastroenterological intriguing time. Previous studies solely preserved daytime information, ignoring this period. The programme accurately detects bowel noises. In addition, they acquired good diagnostic specificity. Medical professionals confirmed the data, supporting the clinical diagnosis. Medical professionals may submit patient recordings and have them analyzed online using the client-server solution. Technologies are established, but BS research needs a consistent methodology, a worldwide venue for debate, & a free platform for data interchange, hence it is not widely employed. A common BS research framework may start with the server. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Nature-Inspired AI Framework for Accurate Glaucoma Diagnosis

引用

computer Modeling in engineering & sciences 2025年第4期143卷 539-567页

作者： Jahanzaib Latif Ahsan Wajahat Alishba Tahir Anas Bilal Mohammed Zakariah Abeer Alnuaim School of Software Northwestern Polytechnical UniversityXi’an710072China Shifa College of Medicine Shifa Tameer-e-millat UniversityIslamabad44000Pakistan College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11495Saudi Arabia

Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.

关键词： Glaucoma fundus images machine learning nature-inspired algorithm segmentation classification

来源：评论

学校读者我要写书评

暂无评论

GEOMETRY-DRIVEN DESIGN OF MORPHABLE SURFACE STRUCTURES USING TOPOLOGY OPTIMIZATION AND CIRCLE PACKING

GEOMETRY-DRIVEN DESIGN OF MORPHABLE SURFACE STRUCTURES USING...

引用

ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Gao, Lingfeng Zhu, Xiaoping Tanaka, Masato Song, Yuyang Zhou, Yuqing Gu, Xianfeng David Chen, Shikui Department of Mechanical Engineering Denmark Department of Computer Science United States Department of Applied Mathematics & Statistics State University of New York Stony BrookNY11794 United States Toyota Research Institute of North America Ann ArborMI48105 United States

ISBN: (纸本)9780791888414

This paper presents a new computational framework for the co-optimization and co-control of morphable surface structures using topology optimization and circle-packing algorithms. The proposed approach integrates the design of optimized compliant components and the system-level control of the overall surface morphology. By representing the surface shape using circle packing and leveraging conformal mapping, the framework enables smooth deformation between 2D and 3D shapes while maintaining local geometry and global morphology. The morphing surface design problem is recast as designing circular compliant actuators using level-set topology optimization with displacements and stiffness objectives. The optimized component designs are then mapped back onto the circle packing representation for coordinated control of the surface morphology. This integrated approach ensures compatibility between local and global geometries and enables efficient actuation of the morphable surface. The effectiveness of the proposed framework is demonstrated through numerical examples and physical prototypes, showcasing its ability to design and control complex morphable surfaces with applications in various fields. The co-optimization and co-control capabilities of the framework are verified, highlighting its potential for realizing advanced morphable structures with optimized geometries and coordinated actuation. This integrated approach goes beyond conventional methods by considering both local component geometry and global system morphology and enabling coordinated control of the morphable surface. The general nature of our approach makes it applicable to a wide range of problems involving the design and control of morphable structures with complex, adaptive geometries. Copyright © 2024 by ASME.

关键词： Conformal mapping

来源：评论

学校读者我要写书评

暂无评论

Emotion Recognition Techniques 1

引用

1st international conference on Machine Intelligence and computer science Applications, ICMICSA 2022

作者： Knouzi, Maryam Ennaji, Fatima Zohra Hafidi, Imad Laboratory of Process Engineering Computer Science and Mathematics Department of Mathematics and Informatics National School of Applied Sciences University Sultan Moulay Slimane Beni-Mellal Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

This article evaluates techniques for obtaining facial images from convolutional neural networks to identify emotion expressions. The primary goal of the paper is to discuss the most often used approaches to analyzing and recognizing emotional expressions on the face. The works that were reviewed might be divided into two major trends: the traditional techniques and those that were especially developed using neural *** analysis revealed that using CNNs improved level of performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

引用

computer Systems science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

来源：评论

学校读者我要写书评

暂无评论

Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

引用

Journal of Bionic engineering 2022年第6期19卷 1850-1885页

作者： Gang Hu Liuxin Chen Xupeng Wang Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048People’s Republic of China School of Art and Design Xi’an University of TechnologyXi’an710054China Department of Mathematics and Computer Science University of North Carolina at PembrokePembrokeNC28372USA

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in ***,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization *** ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this *** Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local ***,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of ***,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems *** comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

关键词： Golden eagle optimizer Lévy flight Sine cosine algorithm Differential evolution strategy engineering design Bionic model

来源：评论

学校读者我要写书评

暂无评论

Automated Organ Segmentation in 3D Biomedical Images Using Dual-Path 3D Attention U-ResNet 4

Automated Organ Segmentation in 3D Biomedical Images Using D...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhavana, J. Hameed, Thushara Maruthakutti, Maheswari Sivaraman, R. Vijayanand, Sellamuthu Palanisamy Jegajothi, B. Sr University Department of Cs & Ai India College of Engineering and Technology University of Technology and Applied Sciences Nizwa Oman Dwaraka Doss Goverdhan Doss Vaishnav College Department of Mathematics Arumbakkam Tamil Nadu Chennai600 106 India Government of Engineering Department of Computer Science and Engineering Erode India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331529635

Deep learning algorithms have made considerable strides in recent years, and these advancements have the potential to significantly improve multi-organ segmentation in three-dimensional biomedical images. This would result in improved diagnostic accuracy and efficiency in medical imaging. DA-TransUNet is an innovative architecture that successfully combines information on several scales and enhances feature extraction by utilizing the DA-Block encoder and the Convolutional Block Attention Module (CBAM). The assessed propsoed model using a variety of datasets, one of which being the LiTS dataset. Finally, were able to achieve a Dice coefficient of 92.56% and a Hausdorff Distance (HD95) of 28.09 mm for liver images. DA-TransUNet surpasses existing state-of-the-art segmentation models across a variety of medical imaging datasets, as demonstrated by the results of the experiments, which reveal considerable increases in accuracy and robustness. The discoveries that have obtained contribute to the advancement of automated segmentation approaches in medical imaging, which in turn facilitates increased diagnostic capabilities. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Image Segmentation for Trash Detection Using the YOLOv7 Model

Image Segmentation for Trash Detection Using the YOLOv7 Mode...

引用

2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Yulita, Intan Nurma Wibisana, Andaru Danurdara Khairunnisa, Hanifan Ayusti Sukma, Nanda Raihan Ardiansyah, Firman Ramdhani, Muhammad Rasyid Universitas Padjadjaran Department of Computer Science Sumedang Indonesia Universitas Padjadjaran Departemen Teknik Informatika Sumedang Indonesia Akademi Digital Bandung Applied Bachelor in Digital Business Bandung Indonesia Universitas Padjadjaran Department of Electrical Engineering Sumedang Indonesia

ISBN: (纸本)9798331511470

Image segmentation is a fundamental task in computer vision and image processing, with critical applications such as environmental image analysis. Against this backdrop, the widespread success of deep learning (DL) has driven the development of new image segmentation approaches leveraging the YOLOv7 model. This study aims to make a significant contribution to waste management by providing an effective solution for identifying waste areas in images, thereby supporting more efficient environmental restoration efforts. By utilizing the YOLOv7 model and transfer learning techniques, this research seeks to enhance the effectiveness of waste area identification in images, demonstrating its potential to support more efficient and sustainable waste management practices. © 2024 IEEE.

关键词： Image segmentation

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：