检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Kuznetsov, Oleksandr Department of Theoretical and Applied Sciences eCampus University Via Isimbardi 10 CO Novedrate22060 Italy Department of Intelligent Software Systems and Technologies School of Computer Science and Artificial Intelligence V.N. Karazin Kharkiv National University 4 Svobody Sq. Kharkiv61022 Ukraine

The proliferation of Internet of Things (IoT) devices has created a pressing need for efficient security solutions, particularly against Denial of Service (DoS) attacks. While existing detection approaches demonstrate high accuracy, they often require substantial computational resources, making them impractical for IoT deployment. This paper introduces a novel lightweight approach to DoS attack detection based on Kolmogorov-Arnold Networks (KANs). By leveraging spline-based transformations instead of traditional weight matrices, our solution achieves state-of-the-art detection performance while maintaining minimal resource requirements. Experimental evaluation on the CICIDS2017 dataset demonstrates 99.0% detection accuracy with only 0.19 MB memory footprint and 2.00 ms inference time per sample. Compared to existing solutions, KAN reduces memory requirements by up to 98% while maintaining competitive detection rates. The model's linear computational complexity ensures efficient scaling with input size, making it particularly suitable for large-scale IoT deployments. We provide comprehensive performance comparisons with recent approaches and demonstrate effectiveness across various DoS attack patterns. Our solution addresses the critical challenge of implementing sophisticated attack detection on resource-constrained devices, offering a practical approach to enhancing IoT security without compromising computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Buckling analysis of laminated multiphase composite plates under in-plane loading using data-driven soft computing

引用

Engineering Analysis with Boundary Elements 2025年 176卷

作者： Topal, Umut Ly, Duy-Khuong Vu, Ho-Nam Nguyen-Thoi, T. Department of Civil Engineering Faculty of Technology Karadeniz Technical University Trabzon Turkey Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam

Laminated multiphase composite plates reinforced with carbon nanotubes (CNTs) and carbon fibers in an epoxy matrix offer excellent mechanical performance for lightweight, high-strength applications. This study focuses on their buckling behavior under in-plane loads by developing a soft computing framework that couples isogeometric analysis (IGA) with Murakami's zigzag theory for layerwise displacement representation and a deep feedforward neural network for data-driven prediction. A set of numerical simulations is conducted to generate training and validation data across various laminate thicknesses, fiber orientations, CNT/carbon fiber volume fractions, and boundary conditions. These simulation results are split into training and validation subsets, ensuring the evaluation of the machine learning model's accuracy in predicting critical buckling loads. The trained predictive models are then evaluated against reference solutions to confirm their accuracy. After verifying the models’ reliability, the framework is employed to carry out a detailed parametric investigation, assessing key parameters that affect the buckling response of laminated multiphase composites. © 2025 Elsevier Ltd

关键词： Buckling loads

来源：评论

学校读者我要写书评

暂无评论

A high precision YOLO model for surface defect detection based on PyConv and CISBA

引用

Scientific Reports 2025年第1期15卷 1-19页

作者： Ruan, Shufen Zhan, Chenmei Liu, Bo Wan, Quan Song, Kunfang The School of Mathematical and Physical Sciences Wuhan Textile University Wuhan 430200 China Research Center for Applied Mathematics and Interdisciplinary Sciences Wuhan Textile University Wuhan 430200 China The School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan 430200 China

Defect detection is vital for product quality in industrial production, yet current surface defect detection technologies struggle with diverse defect types and complex backgrounds. The challenge intensifies with multi-scale small targets, leading to significantly reduced detection performance. Therefore, this paper proposes the EPSC-YOLO algorithm to improve the efficiency and accuracy of defect detection. The algorithm first introduces multi-scale attention modules and uses two newly designed pyramid convolutions in the backbone network to better identify multi-scale defects;Secondly, Soft-NMS is introduced to replace traditional NMS, which can reduce information loss and improve multi-target detection accuracy by smoothing and suppressing the scores of overlapping boxes. In addition, a new convolutional attention module, CISBA, is designed to enhance the detection capability of small targets in complex backgrounds. In the end, we validate the effectiveness of EPSC-YOLO on NEU-DET and GC10-DET datasets. The experimental results show that, compared to YOLOv9c, increases by 2% and 2.4%, and increases by 5.1% and 2.4%, respectively. Meanwhile, EPSC-YOLO demonstrates superior accuracy and significant advantages in real-time detection of surface defects on products compared to algorithms such as YOLOv10 and MSFT-YOLO. © The Author(s) 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advanced Multimodal Prediction of Components of Livestock Feed Materials Using Knowledge Distillation

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2025年第1期3卷 272-285页

作者： Oluwabukunmi, Owoeye Babatunde Joseph, Akomolafe Ayobami Udurume, Miraculous Njoku, Judith Nkechinyere Nwakanma, Cosmas Ifeanyi Asem-Hiablie, Senorpe Mallipeddi, Rammohan Park, Tusan Uyeh, Daniel Dooyum Michigan State University Department of Biosystems and Agricultural Engineering East LansingMI48824 United States West Virginia University Department of Computer Science and Electrical Engineering MorgantownWV26505 United States Shell International Exploration and Production Inc. Shell Technology Center Biotechnology Department HoustonTX77082 United States Kyungpook National University Department of Artificial Intelligence Daegu41566 Korea Republic of Kyungpook National University Smart Agricultural Innovation Center Daegu41566 Korea Republic of Kyungpook National University Department of Applied Biosciences Daegu41566 Korea Republic of Kyungpook National University Department of Smart Bio-Industrial Mechanical Engineering Daegu41566 Korea Republic of

Accurate analysis of livestock feed quality is critical for enhancing productivity and supporting sustainable farming practices. This study explores the integration of red, green, and blue (RGB) and near-infrared (NIR) imaging modalities, leveraging their complementary strengths, RGB for physical properties and NIR for chemical compositions, to predict key nutritional metrics. A novel knowledge distillation model was developed to transfer insights from a complex teacher model to a simpler student model. The approach involved training three types of models: single-channel (RGB or NIR), double-channel (RGB and NIR), and knowledge distillation models. Key evaluation metrics, including mean squared error (MSE), mean absolute error, and root MSE, validated the model's predictive accuracy. Experimental results demonstrated that the knowledge distillation model significantly outperformed both single- and double-channel models, achieving a 91.86% reduction in the MSE compared to RGB single-channel models, an 89.68% reduction compared to NIR single-channel models, and a 63.43% improvement over double-channel models. This study provides a robust, efficient, and cost-effective solution for feed quality assessment, highlighting the transformative potential of multimodal imaging and machine learning in precision agriculture. © 2023 IEEE.

关键词： Agricultural robots

来源：评论

学校读者我要写书评

暂无评论

ICT: Iterative Clustering with Training: Preliminary Results

ICT: Iterative Clustering with Training: Preliminary Results

引用

International Conference on New Trends in Computing sciences (ICTCS)

作者： Ahmad B. Hassanat Ahmad S. Tarawneh Ahmad S. Alhasanat Mansoor Alghamdi Khalid Almohammadi Zakaria Tarawneh Dept. Computer Science Faculty of Information Technology Mutah University Karak Jordan Dept. Data Science and Artificial Intelligence Mutah University Karak Jordan School of Foundation Studies and Mathematics Oryx Universal College Doha Qatar Dept of Mathematics Al-Hussein Bin Talal University Maan Jordan Dept. of Computer Science Applied College University of Tabuk Tabuk Saudi Arabia CiTIUS Universidade de Santiago de Compostela (USC) Spain

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

We introduce Iterative Clustering with Training (ICT), a hybrid approach that combines clustering with iterative classifier-based refinement. Unlike traditional clustering methods, ICT leverages supervised learning to reassign ambiguous boundary points, improving cluster quality. The proposed approach starts with an initial clustering phase, followed by the computation of silhouette scores to separate high-confidence core points from uncertain boundary points. A classifier (e.g., Random Forest) is then trained on core points to predict labels for boundary points, refining clusters in an iterative process until convergence is achieved. To avoid trivial clusters, minimum cluster ratio constraints are implemented. Experimental results on multiple benchmark datasets show that ICT outperforms traditional clustering methods such as k-means, HDBSCAN, and spectral clustering across several evaluation metrics, such as Adjusted Rand Index, Normalized Mutual Information, and Accuracy. ICT particularly excels at handling clusters that overlap, have noise, and complex geometries, where standard methods often fail. Key advantages include adaptive core selection, iterative refinement, and classifier flexibility, making ICT suitable for applications in customer segmentation, anomaly detection, and biological data analysis.

关键词： Training Measurement Clustering methods Roads Refining Supervised learning Semisupervised learning Iterative methods Standards Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

On Metzler Positive Systems on Hypergraphs

引用

IEEE Transactions on Control of Network Systems 2025年

作者： Cui, Shaoxuan Zhang, Guofeng Jardon-Kojakhmetov, Hildeberto Cao, Ming Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands Department of Applied Mathematics The Hong Kong Polytechnic University Kowloon999077 Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen Guang Dong518057 China Engineering and Technology Institute Groningen University of Groningen Groningen9747 AG Netherlands

In graph-theoretical terms, an edge in a graph connects two vertices while a hyperedge of a hypergraph connects any more than one vertices. If the hypergraph's hyperedges further connect the same number of vertices, it is said to be uniform. In algebraic graph theory, a graph can be characterized by an adjacency matrix, and similarly, a uniform hypergraph can be characterized by an adjacency tensor. This similarity enables us to extend existing tools of matrix analysis for studying dynamical systems evolving on graphs to the study of a class of polynomial dynamical systems evolving on hypergraphs utilizing the properties of tensors. To be more precise, in this paper, we first extend the concept of a Metzler matrix to a Metzler tensor and then describe some useful properties of such tensors. Next, we focus on positive systems on hypergraphs associated with Metzler tensors. More importantly, we design control laws to stabilize the origin of this class of Metzler positive systems on hypergraphs. In the end, we apply our findings to two classic dynamical systems: a higher-order Lotka-Volterra population dynamics system and a higher-order SIS epidemic dynamic process. The corresponding novel stability results are accompanied by ample numerical examples. © 2025 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Nhan Vo, Van Quoc Long, Nguyen Dang, Viet-Hung Dac Ho, Tu Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Duy Tan University Faculty of Information Technology Da Nang550000 Viet Nam Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Information Security and Communication Technology Trondheim7034 Norway National Economics University DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology Hanoi12116 Viet Nam University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Esch-sur-Alzette4365 Luxembourg Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok10900 Thailand Khon Kaen University Applied Network Technology College of Computing Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

引用

IEEE Transactions on Network science and Engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep gradient reinforcement learning for music improvisation in cloud computing framework

引用

PeerJ computer science 2025年 11卷 e2265-e2265页

作者： Alrowais, Fadwa Arasi, Munya A. Alotaibi, Saud S. Alonazi, Mohammed Marzouk, Radwa Salama, Ahmed S. Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Applied College King Khalid University RijalAlmaa Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Mecca Saudi Arabia Department of Information Systems College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Electrical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo New Cairo Egypt

artificial intelligence (AI) in music improvisation offers promising new avenues for developing human creativity. The difficulty of writing dynamic, flexible musical compositions in real time is discussed in this article. We explore using reinforcement learning (RL) techniques to create more interactive and responsive music creation systems. Here, the musical structures train an RL agent to navigate the complex space of musical possibilities to provide improvisations. The melodic framework in the input musical data is initially identified using bi-directional gated recurrent units. The lyrical concepts such as notes, chords, and rhythms from the recognised framework are transformed into a format suitable for RL input. The deep gradient-based reinforcement learning technique used in this research formulates a reward system that directs the agent to compose aesthetically intriguing and harmonically cohesive musical improvisations. The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG), are leveraged to assess the quality of the improvised music. The proposed model obtains +0.15 of PF, -0.43 of SPD, -0.07 of ADP and 0.0041 NDG, which is a better value than other improvisation methods. © 2025 Alrowais et al.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

AI-IoT based smart agriculture pivot for plant diseases detection and treatment

引用

Scientific Reports 2025年第1期15卷 1-16页

作者： Ibrahim, Amin S. Mohsen, Saeed Selim, I.M. Alroobaea, Roobaea Alsafyani, Majed Baqasah, Abdullah M. Eassa, Mohamed Electronics and Communication Department Faculty of Engineering Ahram Canidian University (ACU) 6 October City Giza 12591 Egypt Department of Electronics and Communications Engineering Al-Madinah Higher Institute for Engineering and Technology Giza 12947 Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai El-Tor 46511 Egypt Faculty of Computer and Artificial Intelligence Sadat City University Sadat City Egypt Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University P. O. Box 11099 Taif 21944 Saudi Arabia Department of Computer Science Faculty of Information Systems and Computer Science October 6 University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases and transforming the agriculture industry into robustness and ecologically conscious. Various IoT smart agriculture techniques are escalated in this field to solve these challenges such as drop irrigation, plant diseases detection, and pest detection. Several agriculture devices were installed to perform these techniques on the agriculture field such as drones and robotics but in expense of their limitations. This paper proposes an AI-IoT smart agriculture pivot as a good candidate for the plant diseases detection and treatment without the limitations of both drones and robotics. Thus, it presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops and controlling their actuators for the plant diseases treatment. For the plant diseases detection, the paper augments a dataset of 25,940 images to classify 11-classes of plant leaves using a pre-trained ResNet50 model, which scores the testing accuracy of 99.8%, compared to other traditional works. Experimentally, the F1-score, Recall, and Precision, for ResNet50 model were 99.91%, 99.92%, and 100%, respectively. © The Author(s) 2025.

关键词： artificial intelligence (AI) Deep learning (DL) Internet of Things (IoT) Pivot Plant diseases detection Smart agriculture Unmanned aerial vehicle (UAV)

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：