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Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation

Soft Computing 2024年第20期28卷 11873-11894页

作者： Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan Department of Computer Science Ryerson University TorontoON Canada Department of Electrical and Computer Engineering University of Windsor WindsorON Canada Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC Australia

This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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A Customized Mobile Application for Exploring Chennai City: An Ultimate Sightseeing Companion 5th

A Customized Mobile Application for Exploring Chennai City: ...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Priya, L. Sandhiya, M. Poornimathi, K. Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Business Systems Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819788606

"TourChennai" is a customized Tourism Suggestion Android application designed to provide a seamless and user-friendly experience for exploring the city of Chennai. Developed using Android Studio, Java, and XML, the app offers comprehensive information about tour attractions, events, hotels, and other points of interest in Chennai. Its intelligent features provide real-time suggestions and personalized recommendations to enhance the user's journey. Rigorous testing with multiple users confirmed the app's robustness and user-friendliness, making it an ultimate sightseeing companion for travelers in Chennai. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tourism

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End-to-End Verifiable Decentralized Federated Learning 6

End-to-End Verifiable Decentralized Federated Learning

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6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Lee, Chaehyeon Heiss, Jonathan Tai, Stefan Hong, James Won-Ki Postech Department of Computer Science and Engineering Pohang Korea Republic of Tu Berlin Information Systems Engineering Berlin Germany

ISBN: (纸本)9798350316742

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions. © 2024 IEEE.

关键词： Federated learning

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

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computers, Materials & Continua 2024年第10期81卷 229-249页

作者： Sayat Ibrayev Batyrkhan Omarov Arman Ibrayeva Zeinel Momynkulov Joldasbekov Institute of Mechanics and Engineering Almaty050000Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050000Kazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050000Kazakhstan

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage *** study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization *** a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational *** methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the *** findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design *** bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

关键词： Multi-objective optimization genetic algorithm surrogate model deep learning walking robots

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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A Deep Learning Approach for Brain Tumor Classification

A Deep Learning Approach for Brain Tumor Classification

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2024 International Conference on computer and Applications, ICCA 2024

作者： Abdelmoniem, Aya Ebrahim, Gamal A. Abdellatif, Mohammad M. The British University in Egypt Faculty of Engineering Electrical Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350367560

The medical field is incredibly stressful for both specialists and patients, especially when dealing with life threatening cases. Brain tumor identification is one of the most challenging tasks even for experts in the field. Fortunately, the evolution of machine learning techniques and their flexibility to be integrated into many different fields, including tumor identification and classification has greatly simplified this task. For the physicians to accurately diagnose a patient, they must first have a lot of knowledge and years of experience in addition to going through many cases. Machine learning simplifies this process when it comes to learning and using this knowledge to decide without human intervention. If the data fed to the model is accurate, the decisions made will be more accurate. In this paper, an automated machine learning model is developed to identify brain tumors. Different datasets are utilized in the developed model. The accuracy achieved by the model lies between 60% and 70%. Another dataset is then used, and the model shows an accuracy of 99%, precision of 83%, F1 score of 77% and recall of 75%. © 2024 IEEE.

关键词： Federated learning

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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An Optically Transparent Near-Zero-Index Grating Metamaterial for Enhanced On-Glass Millimeter-Wave Radiation

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4092-4097页

作者： Zhou, Ziheng Zhang, Yongjian Kuang, Zhicheng Li, Yue Chen, Zhizhang David Fuzhou University College of Physics and Information Engineering Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

An optically transparent antenna (OTA) based on a grating metamaterial is proposed for launching high-gain millimeter-wave radiation on glasses. This design incorporates a glass-based dielectric image line with a grating made of a transparent conductive film (TCF), which provides an effective near-zero-index response through the precise manipulation of spatial harmonic dispersion. To effectively overcome the intrinsic loss issue of TCF and control the power leakage, the field confinement in the grating metamaterial is engineered for optimizing the purely leakage-induced and total modal attenuations at the zero phase-progress state. The prototyped grating metamaterial antenna features an average optical transmittance of 76% over the visible spectrum. The antenna is validated to generate a near-broadside directive beam, achieving a realized gain exceeding 23 dBi and a total efficiency higher than 60% from 21.1 GHz to 22.3 GHz. Through leaky mode confinement control and complex dispersion relationship engineering, this work offers a promising solution to realizing OTAs with a substantially improved gain and aperture efficiency. © 2025 IEEE.

关键词： Millimeter waves

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