检索结果-内蒙古大学图书馆

Automatic text summarization using deep reinforced model coupling contextualized word representation and attention mechanism

引用

Multimedia Tools and Applications 2024年第1期83卷 733-762页

作者： Aliakbarpour, Hassan Manzuri, Mohammad Taghi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

引用

computer Modeling in engineering & sciences 2025年第2期142卷 1109-1154页

作者： Moein Tosan Mohammad Reza Gharib Nasrin Fathollahzadeh Attar Ali Maroosi Department of Water Science and Engineering Faculty of AgricultureUniversity of BirjandBirjand***Iran Department of Mechanical Engineering University of Torbat HeydariehTorbat Heydarieh***Iran Department of Water Engineering Faculty of AgricultureUrmia UniversityUrmia***Iran Department of Computer Engineering University of Torbat HeydariehTorbat Heydarieh***Iran

Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water *** study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET *** findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological *** hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET *** growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional *** the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data *** research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder ***,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world *** such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these *** study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable *** integrating CNNs for automatic feature extraction and leveraging hybr

关键词： Artificial neural networks bibliometric analysis evapotranspiration hybrid models research trends systematic literature review water resources management

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Controller Assignment Method for QOS and Reliability Improvement in SDN-Smart Grid 20

An Intelligent Controller Assignment Method for QOS and Reli...

引用

20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Keramati, Marjan Mozayani, Nasser Iran University of Science and Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

Smart grid as the extended form of a traditional electricity network facilitates two-way energy supply through digital communication. In this new scheme, a lack of real-time reaction to any malfunction may disrupt the entire network. Consequently, an efficient communication network for providing the smart grid quality of service requirements and ensuring reliability is imperative. Software-defined networking improves quality of service of time-critical smart grid services by separating data and control plane. Also, proper controller placement in software-defined networking is an inevitable prerequisite for meeting quality of service requirements. So, here a quality of service-aware reinforcement learning based method is proposed for controller placement in software-defined smart grid networks. Also, improving smart grid reliability is another concern during controller placement process. Quality of service amendment of time-sensitive services in smart grid and reliability improvement are the superiorities of the paper over similar methods. © 2024 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Deep learning applications in the Internet of Things: a review, tools, and future directions

引用

Evolutionary Intelligence 2024年第5-6期17卷 3621-3654页

作者： Raoufi, Parisa Hemmati, Atefeh Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin64002 Taiwan

The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of machine learning (ML), has shown great promise in addressing the challenges of processing and analyzing such data. Considering the increasing importance of DL and data analysis, we decided to review the articles of the last few years in this field to pave the way for researchers. In this article, we used the systematic literature review (SLR) method, and in line with that, we selected and analyzed 56 articles published from 2019 to April 2024. We first discuss the DL models used in the IoT field and clarify their specific use cases. Secondly, we outline an analysis of research areas in DL-based IoT. In addition, our research extends to the tools and simulators used to evaluate studies in the DL-based IoT domain. We also examine the DL-based IoT research datasets. Finally, our review identifies future directions and open issues in DL-based IoT. We aim to contribute to an accurate understanding of the current state, challenges, and potential breakthroughs at the intersection of DL and the IoT. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Application of human activity/action recognition: a review

引用

Multimedia Tools and Applications 2025年 1-30页

作者： sedaghati, Nazanin ardebili, Sondos Ghaffari, Ali Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey Department of Computer Science Khazar University Baku Azerbaijan

Human activity recognition is a crucial domain in computer science and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, gyroscopes, etc. This field utilizes time-series signals from sensors present in smartphones and wearable devices to extract human activities. Various types of sensors, including inertial HAR sensors, physiological sensors, location sensors, cameras, and temporal sensors, are employed in diverse environments within this domain. It finds valuable applications in various areas such as smart homes, elderly care, the Internet of Things (IoT), personal care, social sciences, rehabilitation engineering, fitness, and more. With the advancement of computational power, deep learning algorithms have been recognized as effective and efficient methods for detecting and solving well-established HAR issues. In this research, a review of various deep learning algorithms is presented with a focus on distinguishing between two key aspects: activity and action. Action refers to specific, short-term movements and behaviors, while activity refers to a set of related, continuous affairs over time. The reviewed articles are categorized based on the type of algorithms and applications, specifically sensor-based and vision-based. The total number of reviewed articles in this research is 80 sources, categorized into 42 references. By offering a detailed classification of relevant articles, this comprehensive review delves into the analysis and scrutiny of the scientific community in the HAR domain using deep learning algorithms. It serves as a valuable guide for researchers and enthusiasts to gain a better understanding of the advancements and challenges within this field. © The Author(s) 2025.

关键词： Human Action Detection Human Activity Recognition (HAR) Human–computer Interaction (HCI) Sensors Smartphones

来源：评论

学校读者我要写书评

暂无评论

An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

引用

IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Mustafa, Mumtaz Begum Asemi, Adeleh University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Branch Faculty of Engineering Department of Computer Engineering Isfahan81551-39998 Iran

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domain-specific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting,"arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains - Laptops, Restaurants, Tweets, and Finance - this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2013 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

MgO-attached graphene nanosheet(MgO@GNS)reinforced magnesium matrix nanocomposite with superior mechanical,corrosion and biological performance

引用

International Journal of Minerals,Metallurgy and Materials 2024年第9期31卷 2062-2076页

作者： S.Abazari A.Shamsipur H.R.Bakhsheshi-Rad M.S.Soheilirad F.Khorashadizade S.S.Mirhosseini Department of Materials and Metallurgical Engineering Amirkabir University of TechnologyTehran ***Iran Iranian National Standards Organization of Hormozgan HormozganIran Advanced Materials Research Center Department of Materials EngineeringNajafabad BranchIslamic Azad UniversityNajafabadIran Department of Materials and Metallurgical Engineering Iran University of Science and TechnologyTehranIran

Magnesium(Mg)alloys are gaining great consideration as body implant materials due to their high biodegradability and ***,they suffer from low corrosion resistance and antibacterial *** this research,semi-powder metallurgy followed by hot extrusion was utilized to produce the magnesium oxide@graphene nanosheets/magnesium(MgO@GNS/Mg)composite to improve mechanical,corrosion and cytocompatibility *** have revealed that the incorporation of MgO@GNS nanohybrids into Mg-based composite enhanced microhardness and compressive *** vitro,osteoblast cell culture tests show that using MgO@GNS nanohybrid fillers enhances osteoblast adhesion and apatite *** presence of MgO@GNS nanoparticles in the composites decreased the opening defects,micro-cracks and micro-pores of the composites thus preventing the penetration of the corrosive solution into the *** demonstrated that the MgO@GNS/Mg composite possesses excellent antibacterial properties because of the combination of the release of MgO and physical damage to bacterium membranes caused by the sharp edges of graphene nanosheets that can effectively damage the cell wall thereby facilitating penetration into the bacterial lipid ***,the MgO@GNS/Mg composite with high mechanical strength,antibacterial activity and corrosion resistance is considered to be a promising material for load-bearing implant applications.

关键词： metal matrix composites MgO@GNS nanohybrid strengthening mechanisms antibacterial activity biocompatibility

来源：评论

学校读者我要写书评

暂无评论

A multi-objective optimization approach for overlapping dynamic community detection

引用

Soft Computing 2024年第19期28卷 11323-11342页

作者： Bahadori, Sondos Mirzaie, Mansooreh Nooraei Abadeh, Maryam Department of Computer Engineering Ilam Branch Islamic Azad University Ilam Iran Department of Electrical and Computer Engineering Golpayegan College of Engineering Isfahan University of Technology Isfahan Iran Department of Computer Engineering Abadan Branch Islamic Azad University Abadan Iran

Community detection is a valuable tool for studying the function and dynamic structure of most real-world networks. Existing techniques either concentrate on the network's topological structure or node properties without adequately addressing the dynamic aspect. As a result, in this research, we present a unique technique called Multi-Objective Optimization Overlapping Dynamic Community Detection (MOOODCD) that leverages both the topological structure and node attributes of dynamic networks. By incorporating the Dirichlet distribution to control network dynamics, we formulate dynamic community detection as a non-negative matrix factorization problem. The block coordinate ascent method is used to estimate the latent elements of the model. Our experiments on artificial and real networks indicate that MOOODCD detects overlapping communities in dynamic networks with acceptable precision and scalability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving explainable AI: a survey

引用

science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

来源：评论

学校读者我要写书评

暂无评论

Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

引用

Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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：