检索结果-内蒙古大学图书馆

IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Methods of Dynamic Sign Language Translation: A Rapid Umbrella Review 4

Deep Learning Methods of Dynamic Sign Language Translation: ...

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bopape, Lebogang Puree Tsoeu, Mohohlo Samuel Durban University of Technology Department of Electronic and Computer Engineering South Africa Durban University of Technology Department of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Countries globally are recognizing sign languages, which are used by more than 5% of national and global populations as official languages. In Africa, South Africa was the fourth country to officialize sign language in 2023. This trend combined with advances in computer vision and machine learning has resulted in an increase of scholarly literature on the translation between spoken and signed languages to bridge the language and communication divide. This has been accompanied by a high volume of published literature reviews on the subject, most of them distinguished by time and geographical contexts. Despite numerous published review papers on the subject, to date, no formal or umbrella reviews of these reviews have been performed to consolidate the similarities, contrasts, gaps, and opportunities reported in these reviews. In this paper, we conduct a first-of-its-kind, rapid systematic umbrella review of 11 literature reviews about machine translation and recognition of dynamic sign languages using machine learning. Three lenses namely, the evolution of machine learning and computer vision technologies and resulting applications to sign language translation;geographical contexts and associated applications;and the unique linguistic context of sign languages are used. We found that most reported literature has solved the sign language translation and recognition problem from a purely data-driven perspective, without due attention to specific linguistic complexities of the languages. Consequently, we found that most literature has not paid attention to proper corpora design, model design, and meaningful performance evaluation besides the generic and inherited measures of performance from spoken language processing and computer vision literature. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

引用

Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

PV/Wind Smart Grid Optimization Using Machine Learning based on Improved Spider Wasp Optimizer Algorithm to Attain SDG7 33

PV/Wind Smart Grid Optimization Using Machine Learning based...

引用

33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Zulu, Musawenkosi L.T Sarma, Rudiren Tiako, Remy Durban University of Technology Department of Electronic and Computer Engineering Durban South Africa University of KwaZulu-Natal Department of Electrical Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798331535162

The United Nations approved the Sustainable Development Goals in 2015 to help countries around the world work toward a more sustainable future. The SDG7 goal is to ensure that everyone has access to cheap, dependable, clean, renewable, and sustainable energy. In view of the shifting energy sources, modernizing the power systems is imperative to satisfy daily demands. Complex energy networks are made simpler by the power system's integration of smart grids. Furthermore, Distributed Generation (DG) is integrated with Renewable Energy Sources (RESs) such as wind and photovoltaic (PV) systems to provide converter autonomy and smart grids. However, frequency changes caused by imbalances in load and generation, together with fault outbreaks, continue to pose a challenge to smart grids. While many academics concentrate on using machine learning (ML) in steady state models, this research aims to analyze robust performance optimization and discuss potential uses of ML in the future to help with transient behavior during fault outbreaks. This research builds a power system model for a smart grid to contribute attaining SDG7. The key contribution is the tuning of the gains by the meta-heuristic optimization algorithms. The machine learning based on improved spider wasp optimizer (SWO) is used in this study, while considering the limitations of the operating system. The simulations were carried on MATLAB. Using SWO the system produced satisfactory results. © 2025 IEEE.

关键词： faults machine learning optimization renewable energy smart grid

来源：评论

学校读者我要写书评

暂无评论

Designing a skilled soccer team for RoboCup: exploring skill-set-primitives through reinforcement learning

引用

Neural Computing and Applications 2025年 1-36页

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features. © The Author(s) 2025.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 4563-4590页

作者： Sheraz, Muhammad Chuah, Teong Chee Tareen, Wajahat Ullah khan Al-Habashna, Ala'a Saeed, Sohail Imran Ahmed, Manzoor Lee, It Ee Guizani, Mohsen Multimedia University Faculty of Artificial Intelligence and Engineering Selangor Cyberjaya63100 Malaysia University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah21589 Saudi Arabia Al Hussein Technical University School of Computing and Informatics Amman Jordan Bahria University Department of Computer Engineering Karachi Pakistan School of Computer Science and Information Engineering Hubei Engineering University Xiaogan432000 China Abu Dhabi United Arab Emirates

The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G. © 2020 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Steganography Based on Optimal Multi-Threshold Block Labeling

引用

computer Systems Science & engineering 2023年第1期44卷 721-739页

作者： Shuying Xu Chin-Chen Chang Ji-Hwei Horng Department of Information Engineering and Computer Science Feng Chia UniversityTaichung407Taiwan Department of Electronic Engineering National Quemoy UniversityKinmen892Taiwan

Hiding secret data in digital images is one of the major researchfields in information ***,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud *** paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel *** uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated *** receiver can extract the secret,restore the cover image,or do both according to his/her granted *** proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current *** results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover *** level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art *** are concluded with satisfactory performance.

关键词： Reversible data hiding encryption image prediction error compression multi-threshold block labeling

来源：评论

学校读者我要写书评

暂无评论

Credit card default prediction using ML and DL techniques

引用

Internet of Things and Cyber-Physical Systems 2024年第1期4卷 293-306页

作者： Wahab, Fazal Khan, Imran Sabada, Sneha Corvit Systems Peshawar Pakistan Department of Informatics University of Sussex United Kingdom Department of Engineering and Computer Science University of Hertfordshire United Kingdom

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. © 2024 The Authors

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Prediction and Comparative Analysis of Rooftop PV Solar Energy Efficiency Considering Indoor and Outdoor Parameters under Real Climate Conditions Factors with Machine Learning Model

引用

computer Modeling in engineering & Sciences 2025年第4期143卷 1215-1248页

作者： Gokhan Sahin Ihsan Levent Gültekin Isik Wilfriedvan Sark Sabir Rustemli Copernicus Institute of Sustainable Development Utrecht UniversityPrincetonlaan 8AUtrecht3584 CBThe Netherlands Computer Engineering Department Igdir UniversityIgdir76000Turkiye Electronical and Electronic Engineering Department Engineering FacultyBitlis Eren UniversityBitlis13100Turkiye

This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy *** predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the *** this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed *** values obtained under real field conditions were used to train the models,and the results were *** Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual *** like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.

关键词： Machine learning model multi-layer perceptrons(MLP) random forest(RF) solar photovoltaic panel energy efficiency indoor and outdoor parameters forecasting

来源：评论

学校读者我要写书评

暂无评论

An Algorithm to Reduce Compression Ratio in Multimedia Applications

引用

computers, Materials & Continua 2023年第1期74卷 539-557页

作者： Dur-e-Jabeen Tahmina Khan Rumaisa Iftikhar Ali Akbar Siddique Samiya Asghar Department of Electronic Engineering Sir Syed University of Engineering&TechnologyKarachiPakistan Department of Information&Computer Science Sir Syed University of Engineering&TechnologyKarachiPakistan

In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the *** purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual *** transmission,this massive data necessitates a lot of channel *** order to overcome this problem,an effective visual compression approach is required to resize this large amount of *** work is based on lossy image compression and is offered for static color *** quantization procedure determines the compressed data quality *** images are converted from RGB to International Commission on Illumination CIE La^(∗)b^(∗);and YCbCr color spaces before being *** the transform domain,the color planes are encoded using the proposed quantization *** improve the efficiency and quality of the compressed image,the standard quantization matrix is updated with the respective image *** used seven discrete orthogonal transforms,including five variations of the Complex Hadamard Transform,Discrete Fourier Transform and Discrete Cosine Transform,as well as thresholding,quantization,de-quantization and inverse discrete orthogonal transforms with CIE La^(∗)b^(∗);and YCbCr to RGB *** to signal noise ratio,signal to noise ratio,picture similarity index and compression ratio are all used to assess the quality of compressed *** the relevant transforms,the image size and bits per pixel are also *** the(n,n)block of transform,adaptive scanning is used to acquire the best feasible compression *** of these characteristics,multimedia systems and services have a wide range of possible applications.

关键词： Color image compression color spaces discrete orthogonal transforms(DOTs) peak-to-signal noise ratio(PSNR) similarity index

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：