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Retraction Note: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

Archives of Computational Methods in Engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering

arXiv

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arXiv 2025年

作者： Ding, Guanhua Xia, Yuxuan Guan, Runwei Wu, Qinchen Huang, Tao Ding, Weiping Sun, Jinping Mao, Guoqiang School of Electronic Information Engineering Beihang University Beijing100191 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou511455 China College of Science and Engineering James Cook University SmithfieldQLD4878 Australia School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Research Laboratory of Smart Driving and Intelligent Transportation Systems Southeast University Nanjing210096 China

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional model-based trackers typically rely on random vector-based Bayesian filters within the tracking-by-detection (TBD) framework but face limitations due to heuristic data association and track management schemes. In contrast, random finite set (RFS)-based Bayesian filtering handles object birth, survival, and death in a theoretically sound manner, facilitating interpretability and parameter tuning. In this paper, we present OptiPMB, a novel RFS-based 3D MOT method that employs an optimized Poisson multi-Bernoulli (PMB) filter while incorporating several key innovative designs within the TBD framework. Specifically, we propose a measurement-driven hybrid adaptive birth model for improved track initialization, employ adaptive detection probability parameters to effectively maintain tracks for occluded objects, and optimize density pruning and track extraction modules to further enhance overall tracking performance. Extensive evaluations on nuScenes and KITTI datasets show that OptiPMB achieves superior tracking accuracy compared with state-of-the-art methods, thereby establishing a new benchmark for model-based 3D MOT and offering valuable insights for future research on RFS-based trackers in autonomous driving. Copyright © 2025, The Authors. All rights reserved.

关键词： Wiener filtering

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Modeling the effects of pressure and magnetic field on the phase change of sodium sulfate/magnesium chloride hexahydrate in nanochannels

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 26卷

作者： Ali, Ali B.M. Hussein, Rasha Abed Singh, Narinderjit Singh Sawaran Salahshour, Soheil Pirmoradian, Mostafa Mohammad Sajadi, S. Deriszadeh, Abbas Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Dentistry Al-Manara College for Medical Sciences Amarah Maysan Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Faculty of Science and Letters Piri Reis University Istanbul Tuzla Turkey Department of Mechanical Engineering Islamic Azad University Khomeinishahr Branch Khomeinishahr Iran Department of Chemistry Payam e Noor University Saqqez Branch Saqqez Kurdistan Iran Department of Chemical Engineering University of Sistan and Baluchestan Zahedan Iran

This work examines the impact of different pressure levels (1 to 5 bar) and magnetic field frequencies (0.01 to 0.05 ps⁻¹) on the thermal behavior of sodium sulfate/magnesium chloride hexahydrate as a phase change material inside iron nanochannels, using molecular dynamics simulation. The system's kinetic and potential energies converge to 39.79 eV and -7204.99 eV, indicating the stability of the nanostructures. The impact of pressure and magnetic field frequency on heat flow, maximum temperature, and charge/discharge times was examined. Increasing the pressure from 1 to 5 bar reduced the heat flux and maximum temperature to 1509 W/m² and 391.18 K, respectively. Simultaneously, the charge duration extendes to 3.99 ns, whilst the discharge duration decreases to 4.30 ns. Moreover, increasing the magnetic field frequency from 0.01 to 0.05 ps⁻¹ results in a decrease in maximum temperature and heat flux, which fell to 415.67 K and 1566 W/m², respectively. The charge time decreases to 3.87 ns and the discharge time to 4.50 ns little owing to the increase in frequency. © 2025 The Author(s)

关键词： Temperature

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SOME DENSITY THEOREMS IN NEURAL NETWORK WITH VARIABLE EXPONENT

arXiv

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arXiv 2025年

作者： Izuki, Mitsuo Noi, Takahiro Sawano, Yoshihiro Tanaka, Hirokazu Faculty of Liberal Arts and Sciences Tokyo City University 1-28-1 Tamadutsumi Setagayaku Tokyo158-8557 Japan Department of Mathematical and Data Science Otemon Gakuin University 2-1-15 Nishiai ibaraki Osaka567-8502 Japan Department of Mathematics Graduate School of Science and Engineering Chuo University 13-27 Kasuga 1-chome Bunkyoku Tokyo112-8551 Japan Faculty of Information Technology Tokyo City University 1-28-1 Tamadutsumi Setagayaku Tokyo158-8557 Japan

In this paper, we extend several approximation theorems, originally formulated in the context of the standard Lp norm, to the more general framework of variable exponent spaces. Our study is motivated by applications in neural networks, where function approximation plays a crucial role. In addition to these generalizations, we provide alternative proofs for certain well-known results concerning the universal approximation property. In particular, we highlight spaces with variable exponents as illustrative examples, demonstrating the broader applicability of our *** Codes 26A33, 42B35 Copyright © 2025, The Authors. All rights reserved.

关键词： Fourier transforms

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Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

引用

Water Resources Management 2025年

作者： Hawamdeh, Alaa Tarawneh, Anwar Sharrab, Yousef Al-Fraihat, Dimah School of Science University of Jordan Amman Jordan Faculty of Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman11622 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

Natural disasters can have devastating effects on the environment and natural resources, making flood inundation mapping and hydraulic modeling essential for forecasting critical characteristics like flood depth and water surface elevation. This study investigated key flood-influencing factors including rainstorm intensity, precipitation depth, soil type, geologic setting, and topographic features, while conducting hydraulic modeling of storm flows for 50- and 100-Year return periods, which estimated water depths in Wadi Al Wala reaching 15 m during 50-Year storms and 25 m during 100-Year storms. To enhance prediction capabilities, a Deep Neural Network (DNN) model was developed using 1980–2018 meteorological data, featuring three hidden layers (10, 20, and 30 neurons) with ReLU activation, trained on 80% of data and validated on 20% using Python’s Keras library. Using relative humidity, rainfall, wind speed, and wet bulb temperature as inputs, the model achieved 92% prediction accuracy based on MSE metrics. The integration of HEC-HMS hydrological modeling, HEC-RAS hydraulic modeling, and DNN analysis produced effective flood hazard predictions that support strategic infrastructure planning and improved disaster management in vulnerable regions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Deep neural networks

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Integrating Blockchain and Artificial Intelligence for Securing Payment processes Against Fraudulent Transactions

Integrating Blockchain and Artificial Intelligence for Secur...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： J V Naga Ramesh Nusrat Hamid Shah Malik Bader Alazzam Divya Nimma V. Selvi A Balakumar Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Faculty of Information Technology Jadara University Irbid Jordan University of Southern Mississippi Data Analyst in UMMC USA Department of Computer Science Mother Teresa Women's University Kodaikanal India Department of ECE K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In recent years, the fast growth of digital payment processes has raised large safety concerns, which include fraud and data breaches, prompting the need for robust safety mechanisms. Existing techniques, which include conventional rule-based systems, regularly fall quick in adaptability and accuracy, leading to multiplied vulnerabilities within the economic zone. This study proposes a unique integration of Blockchain technology with Artificial Intelligence (AI), specially making use of Generative Adversarial Networks (GANs), to enhance the security of monetary transactions. The technique includes growing a decentralized ledger device to make sure transparency and immutability, coupled with a GAN framework for real-time anomaly detection. Then behavior experiments the use of a complete dataset of monetary transactions to validate the effectiveness of the included technique. The consequences imply a considerable improvement in detecting fraudulent activities, accomplishing higher accuracy and decrease false positive rates in comparison to conventional methods. This research no longer most effective addresses the crucial safety gaps found in contemporary monetary transaction systems however additionally demonstrates the potential of integrating Blockchain and AI to create an extra stable monetary environment. The findings recommend that this integrated technique can serve as a scalable solution for enhancing transaction safety, providing valuable insights for both academia and industry practitioners.

关键词： Industries Economics Accuracy data breach Generative adversarial networks Real-time systems Safety Blockchains Fraud Artificial intelligence

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Automated Monitoring of Hand Hygiene Compliance Using Multi-Camera Systems in Healthcare Environments

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IEEE Internet of Things Journal 2025年

作者： Ren, Hao Lin, Guanwen Lian, Wanmin Jing, Fengshi Zou, Ya Liu, Yunting Zhang, Qingpeng Wu, Kaishun Cheng, Weibin The Affiliated Guangdong Second Provincial General Hospital Jinan University Institute for Healthcare Artificial Intelligence Application Guangzhou China City University of Macau Faculty of Data Science Macao Affiliated Guangdong Second Provincial General Hospital Jinan University Department of Healthcare-associated Infection Management Guangzhou China The Affiliated Guangdong Second Provincial General Hospital Jinan University Information Department Guangzhou China The University of Hong Kong Musketeers Foundation Institute of Data Science Hong Kong The University of Hong Kong LKS Faculty of Medicine Department of Pharmacology and Pharmacy Hong Kong Hong Kong University of Science and Technology (Guangzhou) China

This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections. © 2014 IEEE.

关键词： Computer Vision Hand Hygiene Compliance Healthcare Settings Machine Learning Multi-Camera Systems

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Enhanced machine learning models for accurate breast cancer mammogram classification

Global Transitions

引用

Global Transitions 2025年 7卷 276-295页

作者： Kiran Puttegowda Veeraprathap V H.S. Ranjan Kumar K.V. Sudheesh K. Prabhavathi Ravi Vinayakumar Kayalvily Tabianan Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India Department of Electronics and Communication Engineering ATME College of Engineering Mysuru Karnataka India Department of Artificial Intelligence and Data Science Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi Karnataka India Department of ECE B G S Institute of Technology Adichunchanagiri University B. G. Nagara Mandya Karnataka India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Faculty of Information Technology Inti International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Negeri Sembilan Malaysia

Breast cancer has now become the leading cancer type in Indian urban regions surpassing cervical cancer. The imperfect nature of current diagnostic techniques calls for more dependable assessment methods because new automated diagnostic systems do not totally eliminate imperfections. The research of Indian breast cancer datasets remains lower than international platforms owing to distinctive features of breast density, texture, lesion size and composition between these populations. The research develops automated breast cancer detection algorithms for Indian breast types that incorporate metadata to enhance system accuracy in medical diagnosis. We created machine learning algorithms for mammography imaging as part of a development process optimized for Indian breast cancer features. Our top-performing individual model achieved an AUC of 0.95 per image. When integrating four models, the AUC increased to 0.98, with independent test set results from the INbreast database showing 86.7 % sensitivity and 96.1 % specificity. These findings highlight deep learning's potential to enhance mammographic assessment by improving diagnostic accuracy, reducing false errors, and optimizing clinical practice in breast cancer detection.

关键词： Breast cancer Disease Health system Biomedical applications Mammograms Machine learning models Deep neural networks

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Survival Analysis of Kasturi Tobacco Plants in Jember District using the Stratified Cox and Extended Cox Models

引用

MethodsX 2025年

作者： Angga Iryanto Pratama Dwi Rantini Ratih Ardiati Ningrum Arip Ramadan Fazidah Othman Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

关键词： Productive Agriculture Cox Proportional Hazard Stratified Cox Extended Cox Kasturi Tobacco

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Software defect density prediction using grey system theory and fuzzy logic

引用

Soft Computing 2024年第21期28卷 12897-12916页

作者： Azzeh, Mohammad Elsheikh, Yousef Alqasrawi, Yousef Department of Data Science Princess Sumaya University for Technology Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

Defect Density (DD) is a cornerstone metric in software quality assessment, influencing decisions across quality planning, testing strategies, and resource allocation. However, inherent uncertainties within software module data significantly impede accurate prediction of DD. This paper proposes a novel predictive framework that leverages the strengths of grey system theory and fuzzy logic to address data imprecision in software defect measurement. Our approach introduces a fuzzy grey relational similarity metric that capitalizes on the benefits of both fuzzy logic membership functions and grey relational analysis for similarity assessment. We perform a rigorous evaluation comparing our model with eight established machine learning algorithms across 28 publicly available software datasets. The proposed framework demonstrates superior performance, particularly in scenarios characterized by high sparsity levels in the DD variable. We further analyze the model's efficacy across varying data sparsity levels, categorizing datasets into four groups based on sparsity ratios. Our findings reveal the model's dominance over alternative prediction methods in datasets exhibiting high and very high sparsity. Notably, traditional techniques like multi-linear regression and multi-layer perceptron exhibit limitations in handling such challenges within this specific problem domain and data landscape. Conversely, ensemble learning techniques emerge as viable alternatives for datasets with lower sparsity levels. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： System theory

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