检索结果-内蒙古大学图书馆

Renormalization group improvement for thermally resummed effective potential

Physical Review D 2024年第7期109卷 L071901-L071901页

作者： Koichi Funakubo Eibun Senaha Department of Physics Saga University Saga 840-8502 Japan Subatomic Physics Research Group Science and Technology Advanced Institute Van Lang University Ho Chi Minh City Vietnam Faculty of Applied Technology School of Technology Van Lang University Ho Chi Minh City Vietnam

We propose a novel method for renormalization group improvement of thermally resummed effective potential. In our method, β-functions are temperature dependent as a consequence of the divergence structure in resummed perturbation theory. In contrast to the ordinary MS¯ scheme, the renormalization group invariance of the resummed finite-temperature effective potential holds order by order, which significantly mitigates a notorious renormalization scale dependence of phase transition quantities such as a critical temperature even at the one-loop order. We also devise a tractable method that enables one to incorporate temperature-dependent higher-order corrections by fully exploiting the renormalization group invariance.

关键词： Finite temperature field theory Phase transitions Renormalization group Resummation methods

来源：评论

学校读者我要写书评

暂无评论

Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

引用

International Journal of Advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and Information Organization). All rights

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

引用

computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

来源：评论

学校读者我要写书评

暂无评论

An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

引用

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

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Inter-Stream Dependencies in Time-Sensitive Networking 28

Inter-Stream Dependencies in Time-Sensitive Networking

引用

28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Chahed, Hamza Yarvis, Mark Frick, Florian Kassler, Andreas Intel Deutschland GmbH Germany Intel Corporation United States Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Institute for Control Technology of Machine Tools and Manufacturing Equipment University of Stuttgart Germany Faculty of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

ISBN: (纸本)9798331542399

Effective configuration of Time-Sensitive Networks is crucial for providing timeliness and reliability guarantees for real-time industrial applications, where many inter-dependent streams may co-exist. However, existing methods for specifying the characteristics of real-time streams to the Centralized Network Configuration element (CNC) do not allow the specification of stream dependencies. Consequently, the CNC may schedule the real-time streams in a sub-optimal way, leading to low number of real-time applications supported. To address this pressing issue, we present methods that allow to model and express such inter-stream dependencies and propose extensions to the user network interface (UNI) to convey these dependencies to the CNC, transforming it into an intent-based UNI. We define a novel metric to evaluate a specific network configuration regarding the latency budget available for scheduling the transmission of a stream. Using a set of numerical simulations, we show that when exploiting the knowledge of inter-stream dependencies, we can derive better network schedules, which results in significant increase in applications onboarding up to 2.2x and increased network utilization for real-time streams up to 3x. © 2025 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

引用

computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang Department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer Science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia *** became difficult for the user to obtain information from a large video *** process of providing an abstract of the entire video that includes the most representative frames is known as static video *** method resulted in rapid exploration,indexing,and retrieval of massive video *** propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering *** current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video *** video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster *** applying any clustering parameters,the appropriate clusters number is determined using the silhouette *** were carried out on a publicly available open video project(OVP)dataset that contained videos of different *** proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

来源：评论

学校读者我要写书评

暂无评论

Innovative Approaches to Arabic Author Identification: A Comprehensive Evaluation of Classical and Deep Learning Approaches 2

Innovative Approaches to Arabic Author Identification: A Com...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Almanaseer, Waref Alsekait, Deema Mohammed Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Abdelaziz, Amr Elsabbagh, Belal Essam, George Nader, Nour Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Riyadh11671 Saudi Arabia Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Benha University Faculty of Computers and Artificial Intelligence Benha Egypt MEU Research Unit Middle East University Amman11831 Jordan

ISBN: (纸本)9798350362633

The amount of written text on the internet has grown exponentially. When pairing that with the difficulties in sourcing the author of a text, a need emerges to be able to verify claimed author of text and attribute anonymous text to its author. These tasks fall under the umbrella of author identification, which is the process of identifying who, among a set of authors, wrote a given text. For many reasons, it is a challenging task in the field of Natural Language Processing (NLP). This paper compares the performance of different machine learning and deep learning models on the task of Arabic author identification between. The results surprisingly show that most models failed except for the feedforward neural network and Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Arabic Deep Learning Machine Learning Natural Language Processing

来源：评论

学校读者我要写书评

暂无评论

High-performance magnesium/sodium hybrid ion battery based on sodium vanadate oxide for reversible storage of Na^(+)and Mg^(2+)

引用

Journal of Energy Chemistry 2024年第9期96卷 79-88页

作者： Xiaoke Wang Titi Li Xixi Zhang Yaxin Wang Hongfei Li Hai-Feng Li Gang Zhao Cuiping Han Institute of Applied Physics and Materials Engineering University of MacaoAvenida da UniversidadeTaipaMacao 999078China School of Physics and Technology University of JinanJinan 250022ShandongChina Songshan Lake Materials Laboratory Dongguan 523808GuangdongChina Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced TechnologyShenzhen 518055GuangdongChina Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina

Magnesium ion batteries(MIBs)are a potential field for the energy storage of the future but are restricted by insufficient rate capability and rapid capacity ***-sodium hybrid ion batteries(MSHBs)are an effective way to address these ***,we report a new type of MSHBs that use layered sodium vanadate((Na,Mn)V_(8)O_(20)·5H_(2)O,Mn-NVO)cathodes coupled with an organic 3,4,9,10-perylenetetracarboxylic diimide(PTCDI)anode in Mg^(2+)/Na^(+)hybrid *** electrochemical cycling,Mg^(2+)and Na^(+)co-participate in the cathode reactions,and the introduction of Na^(+)promotes the structural stability of the Mn-NVO cathode,as cleared by several ex-situ ***,the Mn-NVO cathode presents great specific capacity(249.9 mA h g^(−1)at 300 mA g^(−1))and cycling(1500 cycles at 1500 mA g^(−1))in the Mg^(2+)/Na^(+)hybrid ***,full battery displays long lifespan with 10,000 cycles at 1000 mA g^(−1).The rate performance and cycling stability of MSHBs have been improved by an economical and scalable method,and the mechanism for these improvements is discussed.

关键词： Aqueous battery Hybrid ion battery Mg^(2+)/Na^(+)co-intercalation High-rate performance Organic-water hybrid electrolyte

来源：评论

学校读者我要写书评

暂无评论

Cumulants from short-range correlations and baryon number conservation at next-to-leading order

引用

Physical Review C 2023年第3期107卷 034914-034914页

作者： Michał Barej Adam Bzdak AGH University of Science and Technology Faculty of Physics and Applied Computer Science 30-059 Kraków Poland

We calculate the baryon number cumulants within acceptance with short-range correlations and global baryon number conservation in terms of cumulants in the whole system without baryon conservation. We extract leading and next-to-leading order terms of the large baryon number limit approximation. These approximations are checked to be very close to the exact results.

关键词： Particle correlations & fluctuations Relativistic heavy-ion collisions

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：