检索结果-内蒙古大学图书馆

International Conference on Computing and Networking Technology (ICCNT)

作者： Kapil Rajput Ganesh Chandrasekaran Manisha Aeri Rishi Prakash Shukla Y P Ragini Hariom Gurjar Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Jaipuriya Institute of Management Jaipur India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Center of Excellence AIT Management Chandigarh University

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Intrusion detection is a critical cyber security project that entails figuring out and stopping unauthorized admission to PC systems. Conventional approaches to intrusion detection rely upon rule-based total systems and system-gaining knowledge of algorithms, which require a giant quantity of domain knowledge and won’t be appropriate for managing new and unknown assaults. On this look, we endorse a singular technique for intrusion detection: the usage of reinforcement learning (RL), a kind of artificial intelligence that learns the most advantageous rules through trial-and-blunder interactions with the surroundings. Our approach makes use of a deep reinforcement mastering agent, which is trained on a combination of community traffic statistics and recognized assault styles. The agent is accountable for evaluating the incoming community traffic and classifying it as regular or malicious. By using reinforcement studying, our method is capable of adapting to new and unknown assault patterns without the need for guide updates. Furthermore, the agent can examine its errors and improve its detection skills over the years. Our preliminary experiments display promising results, with the agent achieving high detection accuracy and minimal false alarms.

关键词： Knowledge engineering Adaptation models Accuracy Intrusion detection Reinforcement learning Multilayer perceptrons Classification algorithms computer crime

来源：评论

学校读者我要写书评

暂无评论

Protection and Communication Model of Intelligent Electronic Devices to Investigate Security Threats

Protection and Communication Model of Intelligent Electronic...

引用

Innovative Smart Grid Technologies (ISGT)

作者： Mohamed Faisal Elrawy Eleni Tekki Lenos Hadjidemetriou Christos Laoudias Maria K. Michael KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Intelligent Electronic Devices (IEDs), e.g., protective relays, have a vital role for protecting power systems and substations. In modern power systems, the performance of protection schemes by IEDs can be affected by cyber-physical, communication and cybersecurity operations and thus, a holistic modeling approach is required where all the domains are properly considered. However, commercial protective relays used in the field are mainly modeled using closed-source software. Therefore, this work proposes an integrated, realistic, and flexible model of an overcurrent protective relay compatible with IEC 61850 communication standard. This open-source model integrates the behavioural interactions between the protective relays and their physical, communication and cybersecurity operations in a digital substation. A model of transmission substation is developed in MATLAB/Simulink® for validation purposes and the effect of communication operations and cybersecurity threats on the relay's physical operations is investigated according to different case studies and attack scenarios.

关键词： Performance evaluation Substations Protocols Protective relaying Mathematical models software Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing

Periodic and Event-Triggering for Joint Capacity Maximizatio...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Christian Vitale Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering and the KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (纸本)9781665454698

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs’ gas consumption. Contrary to most of the works in the literature, the CAVs’ location uncertainty is accounted for and CAVs controls are re-optimized periodically, allowing for the optimization of system performance while creating safe trajectories. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT), which reaches a better trade-off among performance and computational and communication complexity. In a realistic simulation scenario, AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.

关键词： Vehicular and wireless technologies Uncertainty System performance Scalability Transportation Turning Trajectory

来源：评论

学校读者我要写书评

暂无评论

Federated transfer learning for distributed drought stage prediction

引用

Discover Artificial Intelligence 2025年第1期5卷 1-13页

作者： Raza, Muhammad Owais Umar, Aqsa Rasheed, Jawad Asuroglu, Tunc Alsubai, Shtwai Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Computer Science Sindh Madressatul Islam University Karachi74000 Pakistan Department of Software Engineering Istanbul Nisantasi University Istanbul34398 Turkey Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Medicine and Health Technology Tampere University Tampere33720 Finland VTT Technical Research Centre of Finland Tampere33720 Finland Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task. © The Author(s) 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

引用

computer Systems Science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

来源：评论

学校读者我要写书评

暂无评论

Boundary-Aware Contrastive Learning for Single-Source Domain Generalization in Medical Image Segmentation

Boundary-Aware Contrastive Learning for Single-Source Domain...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chenbin Zhang Zhiqiang Hu Shuyu Dai Qingyuan He Defeng Liu Kun Yan Ping Wang School of Computer Science Peking University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Department of Radiology Peking University Third Hospital Beijing China Center for Reproductive Medicine Peking University Third Hospital Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Domain shift is a prevalent issue for medical imaging in both the cross-modality and cross-site manner. As a result, the research of domain generalization meets a significant need that requires no access to any samples in novel target domains. In this paper, we propose BACON, a boundary-aware contrastive learning framework for single-source domain generalization, which enjoys the advantages of both the data-driven methods (e.g. augmentation) and inductive bias methods (e.g. normalization and whitening). BACON designs a novel data augmentation by integrating the bidirectional CutMix with the RandConv and introduces inductive bias through an innovative contrastive learning loss function. Experiments on both the cross-modality and cross-site scenario demonstrate the effectiveness, where BACON achieves a substantial improvement of 33.51% and 33.47% Dice scores over the baseline in the BraTS 2020 dataset and sets the new state of the art on the Prostate Cross-site dataset with an average Dice score of 72.51%.

关键词： Deep learning Image segmentation Adaptation models Contrastive learning Data augmentation Robustness Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Everything you wanted to know about ChatGPT: Components, capabilities, applications, and opportunities

引用

Internet Technology Letters 2024年第6期7卷

作者： Heidari, Arash Navimipour, Nima Jafari Zeadally, Sherali Chamola, Vinay Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan College of Communication and Information University of Kentucky LexingtonKY United States Pilani India

Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses. © 2024 John Wiley & Sons Ltd.

关键词： Taxonomies

来源：评论

学校读者我要写书评

暂无评论

Dual-Step Transfer Learning-Based Prediction Model for Next-Generation Intelligent Cellular Networks

Dual-Step Transfer Learning-Based Prediction Model for Next-...

引用

IEEE International Conference on Communications Workshops, ICC

作者： Waqar Ali Aziz Iacovos Ioannou Marios Lestas Vasos Vassiliou Department of Computer Science University of Cyprus Nicosia Cyprus CYENS Center of Excellence Nicosia Cyprus Department of Electrical Engineering Frederick University Nicosia Cyprus

Deep learning-enabled cellular traffic modelling and prediction is an indispensable part of next-generation intelligent mobile networks. It can help proactive resource management operations in big data-driven cellular networks. Existing approaches usually adopt a well-known prediction model for univariate time series forecasting and require a huge amount of training data. In this work, we propose a dual-step transfer learning-based prediction model for multivariate spatiotemporal cellular traffic prediction. This framework groups the base stations into different clusters based on the traffic correlation. A base model is trained on the aggregated dataset of one cluster by using a combination of Recurrent Neural Network and Bidirectional Long-Short Term Memory (RNN-BLSTM). By using parameter-based transfer learning, the base model is provided to the other clusters as a starting point where it undergoes the process of fine-tuning on an intra-cluster aggregated dataset. After that, the obtained intra-cluster prediction model is transferred to all the base stations inside the cluster, where simple training is required with few parametric adjustments. We conduct extensive experiments on a real cellular traffic dataset and results show that the proposed dual-step transfer learning-based prediction model can achieve good prediction accuracy while reducing the data, time and computation requirements.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

引用

IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1380-1393页

作者： Haitao Yuan Meng Chu Zhou Qing Liu Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21589Saudi Arabia IEEE

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent *** scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy *** factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial *** dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial *** work adopts a G/G/1 queuing system to analyze the performance of servers in *** on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all *** databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

关键词： Bees algorithm data centers distributed green cloud(DGC) energy optimization intelligent optimization simulated annealing task scheduling machine learning

来源：评论

学校读者我要写书评

暂无评论

UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

arXiv

引用

arXiv 2022年

作者： Karim, Md. Nazmul Rizve, Mamshad Nayeem Rahnavard, Nazanin Mian, Ajmal Shah, Mubarak Department of Electrical and Computer Engineering UCF United States Center for Research in Computer Vision UCF United States Department of Computer Science and Software Engineering UWA Australia

Supervised deep learning methods require a large repository of annotated data;hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism to select a possibly clean subset of data. Next, an off-the-shelf semi-supervised learning method is used for training where rejected samples are treated as unlabeled data. Our comprehensive analysis shows that current selection methods disproportionately select samples from easy (fast learnable) classes while rejecting those from relatively harder ones. This creates class imbalance in the selected clean set and in turn, deteriorates performance under high label noise. In this work, we propose UNICON, a simple yet effective sample selection method which is robust to high label noise. To address the disproportionate selection of easy and hard samples, we introduce a Jensen-Shannon divergence based uniform selection mechanism which does not require any probabilistic modeling and hyperparameter tuning. We complement our selection method with contrastive learning to further combat the memorization of noisy labels. Extensive experimentation on multiple benchmark datasets demonstrates the effectiveness of UNICON;we obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate. Our code is publicly available. © 2022, CC BY.

关键词： Deep neural networks

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：