检索结果-内蒙古大学图书馆

Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

来源：评论

学校读者我要写书评

暂无评论

A novel One-vs-Next approach for multiclass classification

A novel One-vs-Next approach for multiclass classification

引用

IEEE Symposium on computers and Communications (ISCC)

作者： V. Hurbungs V. Bassoo T. P. Fowdur Software and Information Systems Department Faculty of Information Communication & Digital Technologies University of Mauritius Electrical and Electronic Department Faculty of Engineering University of Mauritius

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

In machine learning, multiclass classification is the process of classifying data that contains three or more classes. One-vs-Rest and One-vs-One are two popular binary transformation approaches that reduce the multiclass problem into several binary problems. One-vs-Rest is usually faster and less complex than One-vs-One and is therefore the most commonly used strategy. The main contribution of this paper is the formulation of a novel One-vs-Next method for multiclass classification. The proposed approach consists in fitting one classifier per next class pair. This technique reduces the number of binary combinations and is expected to be faster since it requires to fit less number of classifiers compared to the One-vs-Rest approach. As a proof of concept, One-vs-Next was evaluated with the improved Scalable, Efficient, and Fast classifieR (iSEFR) designed for embedded machine learning on the Edge. In addition to its computational efficiency, experiments demonstrate that the proposed approach combined with the iSEFR decision threshold gives an average increase of ~20% in accuracy for a multiclass dataset with equal number of class instances.

关键词： Performance evaluation Training computers Accuracy Microcontrollers Fitting Machine learning Computational efficiency Time complexity Testing

来源：评论

学校读者我要写书评

暂无评论

ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: an updated multi-institutional study of the academic integrity impacts of Generative Artificial Intelligence (GenAI) on assessment, teaching and learning in engineering

引用

Australasian Journal of engineering Education 2024年第2期29卷 126-153页

作者： Nikolic, Sasha Sandison, Carolyn Haque, Rezwanul Daniel, Scott Grundy, Sarah Belkina, Marina Lyden, Sarah Hassan, Ghulam M. Neal, Peter Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs Australia School of Professional Practice & Leadership University of Technology Sydney Sydney Australia School of Chemical Engineering University of New South Wales Sydney Australia College Western Sydney University Sydney Australia School of Engineering University of Tasmania Hobart Australia School of Computer Science & Software Engineering University of Western Australia Perth Australia

More than a year has passed since reports of ChatGPT-3.5’s capability to pass exams sent shockwaves through education circles. These initial concerns led to a multi-institutional and multi-disciplinary study to assess the performance of Generative Artificial Intelligence (GenAI) against assessment tasks used across 10 engineering subjects, showcasing the capability of GenAI. Assessment types included online quiz, numerical, oral, visual, programming and writing (experimentation, project, reflection and critical thinking, and research). Twelve months later, the study was repeated using new and updated tools ChatGPT-4, Copilot, Gemini, SciSpace and Wolfram. The updated study investigated the performance and capability differences, identifying the best tool for each assessment type. The findings show that increased performance and features can only heighten academic integrity concerns. While cheating concerns are central, opportunities to integrate GenAI to enhance teaching and learning are possible. While each GenAI tool had specific strengths and weaknesses, ChatGPT-4 was well-rounded. A GenAI Assessment Security and Opportunity Matrix is presented to provide the community practical guidance on managing assessment integrity risks and integration opportunities to enhance learning. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

QSroute : A QoS Aware Routing Scheme for software Defined Networking 8

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

引用

8th IEEE International Conference on software engineering and computer systems, ICSECS 2023

作者： Kamarudin, Imran Edzereiq Ameedeen, Mohamed Ariff Umar Ong, Muhamad Idaham Zabidi, Azlee Universiti Malaysia Pahang AL-Sultan Abdullah Faculty of Computing Department of Computer Systems and Networking Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

ISBN: (纸本)9798350310931

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field. © 2023 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Facial Recognition Utilizing the CLM Model and Supervised Machine Learning: A Comparative Analysis 25

Facial Recognition Utilizing the CLM Model and Supervised Ma...

引用

25th International Arab Conference on information Technology, ACIT 2024

作者： Iqtait, Musab Ababneh, Jafar Abu-Jassar, Amer Al-Refai, Mohammad Dmour, Mohammad Arabiat, Mohammad Binsaddig, Ruaa Zarqa University Faculty of Information Technology Department of Artificial Intelligence Zarqa Jordan Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Amman Arab University University College of of Computer Sciences and Information Amman Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan University of Business and Technology College of Business Administration Department of Finance Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Facial recognition is a common challenge in artificial intelligence. We extensively utilized this program in our daily life. Face recognition software was implemented on many phones to protect user identities and promptly recognize Facebook users in photographs. Despite the numerous methodologies proposed to date, the actual implementation of facial recognition continues to pose significant challenges. Distinguishing individuals by a fundamental method relies on various factors, including lighting, postural differences, and partial facial obstruction. This study aims to develop a facial recognition technique utilizing a constrained local model (CLM) and a machine learning algorithm. It is evaluated with KNN, SVR, and CCA. Furthermore, it has employed CLM and CCA to achieve a maximum recognition accuracy of 3.98 MAE. © 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

All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB·Hz^(6/7)

引用

Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Hangzhou 311100China Institute of Integrated Microelectronic Systems College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic(MWP)*** linearization schemes usually involve the fine tuning of radio-frequency(RF)power distribution,which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics *** this paper,we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is *** device consists of two carrier-depletion-based Mach-Zehnder modulators(MZMs),which are connected in series by a 1×2 thermal optical switch(OS).The OS is used to adjust the ratio between the modulation depths of the two *** a proper ratio,the complementary third-order intermodulation distortion(IMD3)of the two sub-MZMs can effectively cancel each other *** measured spurious-free dynamic ranges for IMD3 are 131,127,118,110,and 109 d B·Hz^(6∕7)at frequencies of 1,10,20,30,and 40 GHz,respectively,which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： microwave photonic systems ultrahigh SFDR all optical linearization silicon modulator active tuning rf power high linearity modulators third order intermodulation distortion integrated photonics

来源：评论

学校读者我要写书评

暂无评论

Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

引用

2023 IEEE International Conference on Computing, ICOCO 2023

作者： Nik Ahmad, Nik Azlina Lokman, Anitawati Mohd Abdullah, Munaisyah Hakim Suhaimi, Ahmad Iqbal Universiti Kuala Lumpur Malaysian Institute of Information Technology Software Engineering Section Kuala Lumpur Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Shah Alam Malaysia

ISBN: (纸本)9798350302684

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs. © 2023 IEEE.

关键词： Product design

来源：评论

学校读者我要写书评

暂无评论

QoS Prediction Model of Cloud Services Based on Deep Learning

引用

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

来源：评论

学校读者我要写书评

暂无评论

Impact of Artificial Intelligence on Decision-Making in information systems

Impact of Artificial Intelligence on Decision-Making in Info...

引用

Advanced Research in Computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models information systems

来源：评论

学校读者我要写书评

暂无评论

GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

引用

computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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：