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Correntropy-Based Data Selective Adaptive Filtering

IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models 25

Debiasing Classifiers by Amplifying Bias with Latent Diffusi...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Ko, Donggeun Lee, Dongjun Park, Namjun Shim, Wonkyeong Kim, Jaekwang AiM Future Seoul Korea Republic of Maum AI Gyeonggi-do Korea Republic of Department of Computer Science and Engineering Convergence Program for Social Innovation Sungkyunkwan University Suwon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of School of Convergence Convergence Program for Social Innovation Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400706295

Neural networks struggle with image classification when biases are learned and misleads correlations, affecting their generalization and performance. Previous methods require attribute labels (e.g. background, color) or utilizes Generative Adversarial Networks (GANs) to mitigate biases. We introduce DiffuBias, a novel pipeline for text-to-image generation that generates bias-conflict samples, without any training. By utilizing pretrained diffusion and image captioning models, DiffuBias generates, bias-conflict samples using the top-K losses from a biased classifier (fB) to debias the classifier. This method not only debiases effectively but also boosts classifier generalization capabilities. Our comprehensive experimental evaluations demonstrate that DiffuBias achieves state-of-the-art performance on benchmark datasets. Copyright © 2025 held by the owner/author(s).

关键词： Generative adversarial networks

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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A Comprehensive Ensemble-Based Approach for Heart Disease Prediction with Feature Selection and Model Analysis

A Comprehensive Ensemble-Based Approach for Heart Disease Pr...

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Advanced Research in Computing (ICARC), International Conference on

作者： Mst. Sazia Tahosin Ismail Hossain Pranto Mohamamd Muid Uddin Chowdhury Sonet Barua Emon Md Ashraful Alam Chonchal Khan Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science Colorado State University Colorado USA

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Heart disease is a leading global cause of mortality, emphasizing the urgent need for early and accurate diagnostic methods. However, traditional diagnostic tools often lack the scalability and precision to meet diverse healthcare demands. We propose a robust ensemble-based machine learning framework for heart disease prediction that combines advanced data preprocessing, feature selection, and model optimization techniques to address this. This study utilized a consolidated dataset of 1,888 records from five heart health databases. First, systematic preprocessing addressed missing values and standardized features. Next, a hybrid feature selection strategy, integrating Univariate Selection, Correlation Coefficient Analysis, and Mutual Information, reduced the feature set from 14 to 11 key indicators. Subsequently, we evaluated 12 machine learning models on all and reduced feature sets. Finally, our ensemble model, combining Random Forest, Decision Trees, XGBoost, CatBoost, and Extra Trees, achieved an outstanding accuracy of 98.12±1.20. Thus, our results demonstrate that integrating multiple classifiers with optimized feature selection significantly enhances predictive accuracy and model reliability. This scalable framework can transform early heart disease detection, improving outcomes across diverse healthcare settings.

关键词： Heart Analytical models Accuracy Systematics Medical services Transforms Predictive models Feature extraction Random forests Diseases

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Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection

arXiv

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arXiv 2024年

作者： Hossain, Eftekhar Sharif, Omar Hoque, Mohammed Moshiul Preum, Sarah M. Department of Computer Science and Engineering Department of Computer Science Dartmouth College United States Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Bangladesh

Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach’s effectiveness with F1-scores of 69.7% and 70.3% for the MUTE and MultiOFF datasets. The scores show approximately 2.5% and 3.2% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://***/eftekhar-hossain/Bengali-Hateful-Memes. © 2024, CC BY-NC-SA.

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Exploring the Potential of Transfer Learning for Accurate Segmentation of Pathological Structures in X-Ray Images 2

Exploring the Potential of Transfer Learning for Accurate Se...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kishore, Lalit Shaikh, M.Z. Goel, Pawan Kumar Badhoutiya, Arti SRMIST Delhi Ncr Department of Computer Application UPGhaziabad Modinagar India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

This paper explores the capability of transfer learning for the correct segmentation of pathological systems in X-ray pictures. Switch mastering is a way in gadget mastering wherein understanding received from a source challenge is transferred to an extraordinary goal task to enhance performance. This paper specializes in how transfer getting to know may be applied to segmenting pathological systems in X-ray photos. The paper discusses the proposed technique's overall performance on datasets from public medical resources gathered from healthy and diseased lungs and stomach organs. The experiments used in this paper display that with the resource of pre-educated models in transfer getting to know, the performance of the segmentation of the pathological regions in X-ray photos may be stepped forward. Eventually, the paper outlines the ability of transfer mastering for clinical picture segmentation and discusses how it can be further progressed in the future. © 2024 IEEE.

关键词： Healthy Pathological Potential And Medical Transfer

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Beamforming Design Toward Sum-Rate Maximization for Holographic Active RIS-Aided Uplink Near-Field Communications

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IEEE Internet of Things Journal 2025年第12期12卷 18582-18595页

作者： Singh, Sandeep Singh, Keshav Singh, Sandeep Kumar Shin, Hyundong Duong, Trung Q. National Sun Yat-sen University International Master’s Program in Telecommunication Engineering Kaohsiung80424 Taiwan National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Kyung Hee University Department of Electronic Engineering Gyeonggi Yongin17104 Korea Republic of Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Communication Engineering Prayagraj211004 India Memorial University Faculty of Engineering and Applied Science St. John’sA1C 5S7 Canada Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Holographically driven active reconfigurable intelligent surface (HARIS), leveraging densely packed subwavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multiuser uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of Quality-of-Service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the nonconvex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques, such as minimum-mean-square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm’s efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance. © 2014 IEEE.

关键词： Mean square error

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Automated Vehicle License Plate Recognition System: An Adaptive Approach Using Digital Image Processing 1

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3rd International Conference on Sustainable Advanced Computing, ICSAC 2021

作者： Rahman, Saidur Trisha, Tahmina Aktar Imran Hossain Imu, Md. Department of Electronics and Telecommunication Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil Institute of IT Dhaka Bangladesh

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

In the last twenty years, the number of vehicles has increased rapidly in our country. With this increase, it is gradually more difficult to track each vehicle for effective law enforcement and traffic management purposes. The growing rate of cars brings upon itself the need to rapidly check the lawfulness of the drivers. When traveling, there are several legal obligations a driver has to fulfill according to the law. In this paper, we proposed an automatic license plate recognition (ALPR) system is a digital image processing procedure that identifies the vehicle number plate without human involvement. It is a computer system that automatically detects and recognizes any digital image on the number plate. This system involves image preprocessing and edge detection using a canny edge detection algorithm, regional localization, and pytesseract used to identify the English characters in the number plate efficiently as well as with less computational complexity. This system simulates an accuracy rate of 97% (approximate) with a comparison of the average processing time of 0.75 s. Our automated system might be highly effective in real-time traffic control, security enhancement in parking lots in shopping malls, hospitals, airports, as well as in electronic toll collection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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CIMulator: Evaluating Neuromorphic Accelerators with HZO FefinFET-Based Synaptic Device 7

CIMulator: Evaluating Neuromorphic Accelerators with HZO Fef...

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Le, Hoang-Hiep Lu, Hao-Yu Baig, Md. Aftab De, Sourav Lin, Ing-Chao Lu, Darsen D. Minghsin University of Science and Technology Department of Computer Science and Information Engineering Hsinchu Taiwan National ChengKung University Intelligent Computing Industrial Doctorate Program Tainan Taiwan Institute of Microelectronics National ChengKung University Tainan Taiwan National ChengKung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350386073

In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations. © 2024 IEEE.

关键词： Multilayer neural networks

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