检索结果-内蒙古大学图书馆

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mohammed, Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Logeshwaran, J. Jiwani, Nasmin Lewis University Department of Computer and Information System IL United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665462167

At present, various electronic devices are used to monitor human heart rates. However, its functions are to avoid predicting the problems caused by heart rate variability in advance and analyzing its implications. It makes it difficult to diagnose problems caused by heart rate variability. A human should have an average heart rate of 72. At the same time, the newborn's heart should beat between 120 and 160 beats per minute. A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time. Most heart diseases in children with Autism Spectrum Disorder (ASD) are present at birth. A significant congenital disability is a hole in the heart. Many people do not realize that having holes in the heart is a common occurrence. Before the baby is born, tiny holes form in the muscular wall that divides the heart into the right and left halves. This paper proposed Machine Learning-Based Evaluation to identify the Heart Rate Variability Response in Children with Autism Spectrum Disorder with Autism Spectrum Disorder. The reasons for this are yet to be identified. However, 70 per cent of perforations resolve spontaneously before or after birth. Exceptionally, Children with Autism Spectrum Disorder with perforations that do not close properly may require surgery or a perforator brace, depending on the location and size of the perforation. © 2023 IEEE.

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Reduce A Visual Gap Using Inpainting GAN

A Novel Approach to Reduce A Visual Gap Using Inpainting GAN

引用

2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Truong, Thanh Hien Lee, Tae-Ho Munasinghe, Viduranga Kim, Tae-Sung Kim, Jin-Sung Lee, Hyuk-Jae Seoul National University Electrical and Computer Engineering Korea Republic of Seoul National University Next-Generation Semiconductor Convergence and Open Sharing System Korea Republic of Sun Moon University Department of Electronic Engineering Korea Republic of

ISBN: (纸本)9798350344318

Image blending represents a technique employed in image composition, aimed at creating a composite image that appears as natural and realistic as possible. The objective of image blending is to ensure that the transitions between objects in the image appear seamless and without any color distortion. Recently, numerous studies investigated image blending methods adopting deep learning-based image processing algorithms and contributed to generating natural blended images. Although the previous studies show remarkable performance in many cases, they suffer from quality drop when blending incompletely cropped object. This paper introduces a novel approach that effectively reduces the unnatural edges and the color distortion in such cases. In this study, an algorithm which called edge checking (EC) is proposed to identify and manage incompletely cropped regions. To address missing or masked parts of the object image and integrate them into the image blending process, an inpainting generative adversarial model are employed. © 2023 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3157-3173页

作者： Abdelwahed Motwakel Fadwa Alrowais Khaled Tarmissi Radwa Marzouk Abdullah Mohamed Abu Sarwar Zamani Ishfaq Yaseen Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMeccaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation ***,it also provides a sequence of novel cyber-security *** IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for *** result in financial loss and data breaches for organizations and ***,it becomes crucial to secure the IoT environment from such *** this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT *** presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT *** attain this,the ECSADL-CAD model initially pre-processes the *** the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack *** last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD *** experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

关键词： Software defined networks artificial intelligence cybersecurity deep learning internet of things

来源：评论

学校读者我要写书评

暂无评论

Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal

arXiv

引用

arXiv 2025年

作者： Hu, Yuyang Lohit, Suhas Kamilov, Ulugbek S. Marks, Tim K. Department of Electrical & System Engineering Washington University St. Louis United States Departments of Computer Science & Engineering and Electrical & System Engineering Washington University St. Louis United States United States

Deep learning has achieved some success in addressing the challenge of cloud removal in optical satellite images, by fusing with synthetic aperture radar (SAR) images. Recently, diffusion models have emerged as powerful tools for cloud removal, delivering higher-quality estimation by sampling from cloud-free distributions, compared to earlier methods. However, diffusion models initiate sampling from pure Gaussian noise, which complicates the sampling trajectory and results in suboptimal performance. Also, current methods fall short in effectively fusing SAR and optical data. To address these limitations, we propose Diffusion Bridges for Cloud Removal, DB-CR, which directly bridges between the cloudy and cloud-free image distributions. In addition, we propose a novel multimodal diffusion bridge architecture with a two-branch backbone for multimodal image restoration, incorporating an efficient backbone and dedicated cross-modality fusion blocks to effectively extract and fuse features from synthetic aperture radar (SAR) and optical images. By formulating cloud removal as a diffusion-bridge problem and leveraging this tailored architecture, DB-CR achieves high-fidelity results while being computationally efficient. We evaluated DB-CR on the SEN12MS-CR cloud-removal dataset, demonstrating that it achieves state-of-the-art results. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

来源：评论

学校读者我要写书评

暂无评论

Novel Inverter Topology for Electric Vehicle Inductive Wireless Power Transfer Charging

Novel Inverter Topology for Electric Vehicle Inductive Wirel...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Hosseinzadeh, Mohammad Ali Sarebanzadeh, Maryam Kennel, Ralph Babaei, Ebrahim Garcia, Cristian Rodriguez, Jose Technical University of Munich High-Power Converters System Munich Germany University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Universidad de Talca Department of Electrical Engineering Curico Chile Universidad San Sebastian Faculty of Engineering Santiago Chile

ISBN: (纸本)9798350316445

Inductive wireless power transfer charging for electric vehicles encounters a significant challenge due to heightened common-mode current, impacting safety and electromagnetic interference (EMI) emissions. This study introduces an innovative inverter topology tailored specifically for inductive wireless power transfer charging. The novel topology aims to lower common-mode voltage while entirely eliminating common-mode current generation. By incorporating an extra capacitor and discrete diode, the proposed inverter topology maintains an equivalent number of power switches as the conventional H-bridge inverter. Utilizing theoretical analysis, simulation and experiments, the study comprehensively compares the proposed inverter with the established traditional inductive charging system. Evaluation factors encompass functionality, common-mode voltage and current behavior, power losses, and overall efficiency. The results unequivocally showcase the benefits of the proposed inverter. Notably, it successfully suppresses common-mode current, thereby enhancing safety and reducing EMI emissions. Additionally, the proposed topology demonstrates heightened efficiency compared to the traditional inverter. In sum, these findings underscore the significant advantages of the suggested inverter, highlighting its potential to mitigate common-mode current challenges and elevate operational efficiency. © 2023 IEEE.

关键词： DC-AC power converters electric vehicles inductive charger systems Wireless power transfer

来源：评论

学校读者我要写书评

暂无评论

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

引用

computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

引用

2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

A Hybrid ViT-GRU Model for Breast Cancer Detection: Addressing Class Imbalance Challenges

A Hybrid ViT-GRU Model for Breast Cancer Detection: Addressi...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Jakaria Zobair Md. Atiqur Rahman Md. Shakhawath Hossain Nusrat Khan Md Atik Asif Khan Akash Md Hasan Imam Bijoy Computer Science and Engineering East West University Dhaka Bangladesh Electronic System Aalborg University Aalborg Denmark Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

One crucial use of machine learning in medical diagnostics is the detection of breast cancer, where prompt and precise identification can greatly enhance patient outcomes. Using histopathological images from the Breast Cancer Dataset (400X, 100X, 200X, and 40X magnification levels), we present a reliable deep learning method in this work that uses Vision Transformers (ViT) and Gated Recurrent Unit (GRU) for the categorization of breast cancer. We use a weighted loss function in conjunction with random oversampling approaches to tackle the problem of dataset imbalance, guaranteeing that the model learns from underrepresented classes impartially and efficiently. Outstanding accuracy rates of 98.76% for 400X, 97.84% for 100X, 98.54% for 200X, and 98.63% for 40X photos are attained by our method. These outcomes are supported by outstanding precision, recall, and F1 scores, which show how resilient our model is at all magnification settings. Our ViT-GRU based approach continuously performs better, demonstrating its potency in feature extraction and classification. This study highlights the potential of Vision Transformers in medical image analysis as well as the significance of using creative pre-processing and loss function techniques to address class imbalance. With the potential for increased diagnostic accuracy and dependability, the findings advance automated breast cancer detection.

关键词： Deep learning computer vision Accuracy Image analysis Transfer learning Transformers Breast cancer Reliability Medical diagnosis Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Comparison of Sequential Feature Selection Performance with Various Dimensional Data to Produce Optimal Classification 8

Comparison of Sequential Feature Selection Performance with ...

引用

8th IEEE Information Technology International Seminar, ITIS 2022

作者： Rahajoe, Ani Dijah Agussalim Setyaningsih, Emy Mandyartha, Eka Prakarsa Akbar, Fawwaz Muttaqin, Faisal Universitas Pembangunan Nasional Veteran Jawa Timur Department of Computer Science Surabaya Indonesia Institut Sains & Teknologi Akprind Department of Computer System Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Feature selection is one part of preprocessing which aims to reduce data dimensions. This study aims to produce optimal performance of the best feature selection method implemented on low- and high-dimensional data. The feature selection methods used are Sequential Forward Selection (SFwS) and Sequential Backward Selection (SBwS) algorithms. Meanwhile, to test the results of the best feature selection algorithm using the classification algorithm Logistic Regression, Linear Regression, k-Nearest Neighbor (kNN). The low dimensional data test results show that the SFS algorithm with the kNN classifier and the Regression group has the best average score. The SFwS algorithm with Logistic Regression has the best average score in the high dimensional data set. The minimal number of feature selections resulted from the SFwS algorithm rather than the SBwF algorithm. Although it should have the same accuracy or number of selected features, in this study, the results were different except for SFS-Linear Regression. The highest average accuracy score for low dimensional data is the Wine dataset of 0.994 (SFS-kNN), and for high dimensional data is the Parkinson's disease dataset of 1 (SFS-LGR). The least number of feature selections obtained from the SFwS-Logistic Regression algorithm is one feature with an accuracy score of 0.8083. The Sequential Backward Selection algorithm generally has a longer running time than the Sequential Forward Selection. © 2022 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Penalized Semidefinite Programming Method for Elliptic Localization 5

Penalized Semidefinite Programming Method for Elliptic Local...

引用

5th International Conference on Information Communication and Signal Processing, ICICSP 2022

作者： Lin, Danyan Wang, Gang Wang, Ding Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Institute of Information System Engineering Pla Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781665485890

Elliptic localization has become a hot research topic and has been widely used in various fields. In this paper, we propose a penalized semidefinite programming (SDP) method for the elliptic localization problem when both the transmitter position and signal propagation speed are unknown. We first formulate a nonconvex constrained weighted least squares (CWLS) problem using the connections between the unknown variables in the measurement model. Since the nonconvex CWLS problem is difficult to solve, we relax it into a convex SDP problem by applying semidefinite relaxation (SDR). Although the SDP problem avoids local convergence or divergence, its solution cannot reach the Cramer-Rao lower bound (CRLB) accuracy owing to the relaxation. In order to address this issue, we propose to add a penalty term to tighten the above SDP problem. Furthermore, we propose an adaptive selection procedure for choosing a proper penalty factor. Simulation results validate the effectiveness and efficiency of the penalized SDP method and show that the proposed method is able to reach the CRLB. © 2022 IEEE.

关键词： Time delay

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：