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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Enhanced Object Detection and Classification via Multi-Method Fusion

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computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Al-Sharhanee, Kareem Ali Malalah Najim, Ali Hamza Alheeti, Khattab M. Ali Satar, Nurhizam Safie Mohd Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad10011 Iraq Department of Computer Technical Al-Diwaniyah Iraq College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the ''terrestrial' systems and the Unmanned Aerial Vehicles (UAVs)' collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing 'the efficiency and efficacy of data collection... with the assistance of Unmanned Aerial Vehicles (UAVs)' which means this work reveals practical application solutions. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding 2

Robust Spectral Efficiency Maximization in 5G Networks Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Ponnaian, Geetha Jayanthi, N. Sarasu, R. Sajitha, L.P. Ramalakshmi, R. Manimegalai, L. Department of Computer Science and Engineering Thandalam Chennai India Pallavaram Tamil Nadu Chennai600117 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India R. M. K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350371055

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios. © 2024 IEEE.

关键词： 5G networks channel impairments nonlinear stochastic precoding spectral efficiency stochastic optimization

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A Methodology for the Energy Aware Clustering and Aggregate Node Rotation with Sink Relocation in MANET 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Bhowmick, Apoorva School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India MDS University Ajmer India

ISBN: (数字)9789819919468

ISBN: (纸本)9789819919451

The word energy is frequently used in MANET in both daily life and the computer industry. Reducing energy consumption and extending node lifespan are critical components of the MANET. There are many nodes available between the path and destination as the data are transferred from the source to the destination nodes. Energy can be used by the intermediate nodes during data transfer, and some energy is lost during travel to the destination. The EAC-ASR protocol (Energy Aware Clustering Aggregate Node Rotation) with sink relocation approach is suggested to be utilised in order to get over these disadvantages. There are some processes that are present in this protocol are clustering, data aggregation, mobile node rotation by swapping algorithm and sink relocation. The theoretical and simulation analyses are done and the result will be shown in the EAC-ASR protocol reduces the energy consumption and increase the energy efficiency. Better system administration as far as conceded calls along regarded QoS imperatives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MATLAB

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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New Delay Doppler communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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Enhancing Facial Recognition and Tracking for Security Applications using Haar Cascade Classifier 2

Enhancing Facial Recognition and Tracking for Security Appli...

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2nd International Conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Jha, Meenakshi Goswami, Ankur School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India MDS University Ajmer India Department of Computer Applications Govt. Engineering College Bikaner India

ISBN: (纸本)9798350340235

Facial recognition is used in biometric technology. Facial recognition is the process of using the face to recognize or verify a person's identity. It captures, analyses, and compares patterns based on a person's facial details. The task of authenticating or identifying human faces from multimedia photos is accomplished using facial recognition technology. Viola and Jones present a method capable of accurately and quickly detecting faces in images. This technology can be used to accurately detect the facial features. Face tracking technology provides better opportunities for security and surveillance. This increases the level of protection. For example, facial tracking software helps improve surveillance strategies and serves as a basis for identifying terrorists and criminals. National borders, casinos, museums, banks or prisons are other examples where this system can be used for security. This paper proposes a face detection and eye and smile recognition system with real time images, using the Haar Cascade classifier for face, eye and smile recognition. This trace is based on the jpeg/jpg file format and can be acquired from any type of camera. It works with almost all types of image formats. Its classifier will detect facial features. In this research paper a comparison is also analyzed between face and facial detection by a camera captured image and saved images. Analysis of both types of code is also being made in this research work. The document also gives the percentage of accuracy by comparing the results. Therefore, this work aims at face processing with eye and smile detection. In this article it first detects the face using a web camera and then the captured image is processed and, on the image, it detects the face, eyes and smile. © 2023 IEEE.

关键词： Eye detection Face detection Haar cascade classifier OpenCV Smile detection Viola Jones algorithm

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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