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Modified Kelvin-Voigt fractional derivative model for viscoelasticity measurement in optical coherence elastography

Chinese Optics Letters 2025年第1期23卷 93-101页

作者： Chenming Yang Zhongliang Li Nan Nan Teng Liu Yaoli Luo Xiangzhao Wang Advanced Light Source and System R&D Center Department of Advanced Optical and Microelectronic EquipmentShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Optical coherence elastography(OCE)can quantitatively obtain the viscoelasticity of tissues using rheological models and is widely applied to the clinical diagnosis of ***,commonly used rheological models in OCE do not account for the distinctive dependence of high-frequency storage and loss moduli on frequency in tissues,which results in the rheological models failing to accurately measure the viscoelastic properties of *** this paper,a modified Kelvin-Voigt fractional derivative model is presented based on the power-law behavior of soft tissues and the dependence of high-frequency complex shear modulus on frequency in living *** the rheometer and OCE tests,the modified model can provide the prediction of the power-law relationship between the low-frequency shear viscosity and frequency;compared with the Kelvin-Voigt and Kelvin-Voigt fractional derivative models,the modified model has a higher goodness-of-fit(accuracy>96%)for the high-frequency storage moduli of gelatin ***,the proposed model can reduce the root mean square error of fit by approximately 83%for the high-frequency(1–128 kHz)storage modulus of the polydimethylsiloxane phantoms obtained from publicly available ***,the modified model accurately predicts the mechanical properties of biomimetic materials over a wide frequency range,with the potential to more accurately reflect pathological changes in tissues.

关键词： optical coherence tomography optical coherence elastography viscoelasticity rheological model

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Generating Anticancer Peptides Sequences Using Seq2Seq Modelling and Machine Learning Methods

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IEEE Access 2025年 13卷 88638-88647页

作者： Ibrahim, Muhammad Sohail Bello, Saheed Ademola Kwak, Yunsang Kim, Minseok Khan, Shujaat On-sensor AI Semiconductor Center Kumoh National Institute of Technology Gumi-si South Korea Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia School of Mechanical System Engineering Kumoh National Institute of Technology Gumi 39177 South Korea Department of Aeronautics Mechanical and Electronic Convergence Engineering Kumoh National Institute of Technology Gumi 39177 South Korea SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia

Cancer remains a major health threat with rising incidence and mortality rates. Despite the efficacy of chemotherapy, its lack of selectivity and associated severe side effects highlight the need for new, targeted anticancer therapies. Anticancer peptides (ACPs) have emerged as a promising alternative due to their biocompatibility, broad-spectrum anticancer activity, and unique mechanisms of action. This study presents a novel computational approach to design and identify ACPs using a multi-tier filtration system. Our method begins with peptide sequence generation via a recurrent neural network (RNN) trained on the acp740 dataset. The generated sequences undergo rigorous filtration: Tier-1 employs three deep learning-based classifiers (ACP-DL, ACP-MHCNN, ACP-LSE) to identify potential ACPs;Tier-2 uses a nearest centroid classifier to filter out statistically less relevant sequences;Tier-3 involves a final filtration using unsupervised nearest neighbor learning based on fused feature encoding schemes (CKSAAP, k-Mer, and BPF). Experimental results demonstrate a significant improvement in identifying viable ACP candidates, with the proposed method showing a 2.21-fold higher hit-rate compared to random sequence generation. Further analysis using t-SNE, PCA, and antimicrobial peptide (AMP) prediction tools confirms the robustness and effectiveness of the selected ACPs. Additionally, the performance comparison aided by the proposed sequence filter technique shows that the proposed technique outperformed the baseline LSTM and RNN-based sequence generation models by a margin of 8.79% and 65.93%, respectively. Further reverse analysis also showcases the robustness and efficacy of the proposed sequence generation model and the three-tier filtration method. The proposed computational approach offers a streamlined and economical alternative to traditional experimental methods, expediting the discovery of new ACPs and enhancing the accuracy of anticancer peptide predictions. The

关键词： Anticancer Peptides (ACPs) Fused Feature Encoding Multi-Tier Filtration Peptide Prediction Unsupervised Nearest Neighbor Learning

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Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction

arXiv

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arXiv 2025年

作者： Xia, Xiaobo Liu, Xiaofeng Liu, Jiale Fang, Kuai Lu, Lu Oymak, Samet Currie, William S. Liu, Tongliang School of Computer Science University of Sydney Australia Department of Statistics and Data Science Yale University United States Michigan Institute for Data and AI in Society University of Michigan United States School for Environment and Sustainability University of Michigan United States College of Information Science and Technology Penn State University United States Department of Earth System Science Stanford University United States Department of Electrical Engineering and Computer Science University of Michigan United States

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer trans-formative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

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computers in Biology and Medicine 2025年 191卷 110166-110166页

作者： Haque, Rezaul Khan, Mahbub Alam Rahman, Hamdadur Khan, Shakil Siddiqui, Md Ismail Hossain Limon, Zishad Hossain Swapno, S M Masfequier Rahman Appaji, Abhishek Department of Computer Science and Engineering East West University A 2 Jahurul Islam Ave Dhaka1212 Bangladesh Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles CA90010 United States Department of Management of Information System International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Engineering/Industrial Management Westcliff University IrvineCA92614 United States Department of Computer Science Westcliff University IrvineCA92614 United States Department Of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bangalore560019 India Maastricht University University Eye Clinic Maastricht Maastricht Netherlands

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a robust and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetB0, MobileNetV2, GoogleNet, and Multi-level CapsuleNet, using CatBoost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing robustness and interpretability. The proposed model integrates EfficientNetB0, MobileNetV2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing CatBoost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: BraTS, Msoud, Br35H, and SARTAJ. To tackle class imbalance, we applied Borderline-SMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, Vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-based diagnostic tool that enables clinicians to interact with the proposed mo

关键词： Magnetic resonance imaging

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Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chen, Zhuolong Yin, Xiaoqing Yang, Fan Li, Xiaofan Zhao, Zixuan Li, Xueying Liu, Jianghu Zhao, Yubin Xu, Cheng-Zhong Zheng, Fangfang Lin, Yong Sun Yat-Sen University School of Microelectronics Science and Technology 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Department of Psychiatry 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Pediatrics Department 0000 China Jinan University School of Intelligent System Science and Engineering 0000 China University of Macau State Key Laboratory of IoTSC Department of Computer and Information Science Macau0000 China

Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical. © 2013 IEEE.

关键词： Contrastive Learning

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

arXiv

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arXiv 2025年

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025, CC BY.

关键词： Unmanned surface vehicles

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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Medani, Mohamed Elhessewi, Ghada Moh. Samir Alqahtani, Mohammed Asklany, Somia A. Alamro, Sulaiman Albalawneh, Da’ad Alshammeri, Menwa Assiri, Mohammed Department of Information Systems Applied College at Mahayil King Khalid University Abha Saudi Arabia Department of Health Sciences College of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha 61922 Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and Arts Northern Border University Turaif Arar 91431 Saudi Arabia Department of Computer Science College of Computer Qassim University Buraydah 51452 Saudi Arabia Department of Computer Science University College in Umluj University of Tabuk Tabuk Saudi Arabia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University P.O. BOX 16273 Al-Kharj 3963 Saudi Arabia

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet;therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization Algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min–max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole‐rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad‐CAM is employed as an XAI technique to enhance

关键词： Data normalization Dementia prediction Explainability artificial intelligence Feature selection Gazelle optimization algorithm

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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HUMAN-computer DIALOGUE systemS-BASED IMAGE-TEXT VISUALIZATION, FUSION AND MULTI-INTENT MODELS' CONSTRUCTION

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Fractals 2025年

作者： Meng, Wang Obaiys, Suzan J. Karaca, Yeliz Jamaludin, Nur Amalina Binti Department of Computer System & Technology Faculty of Computer Science and Information Technology Universiti Malaya Malaysia University of Massachusetts Chan Medical School (UMASS) 55 Lake Avenue North Worcester 01655 MA United States Massachusetts Institute of Technology (MIT) 77 Massachusetts Avenue Cambridge 02139 MA United States Centre Foundation Studies Upnm (National Defense University of Malaysia) 57000 SG Besi Camp Kuala Lumpur Malaysia

Human-computer dialogue systems enable intent recognition to be a crucial aspect to determine the intentions or purposes of users during respective interactions with the system, which allows the particular system to solicit appropriate actions or responses. The recognition of user intentions has become increasingly challenging with the rapid evolution with the advent of multimedia technology and widespread use of social media platforms. Traditional unimodal approaches, especially those relying solely on either textual or visual information, may fail to fully capture the intricacies of user intentions in multimedia content. To address this limitation, the fusion of image and text modalities employing multimodal technology has emerged as a promising solution for intent recognition. Compared with single-modality data such as images and text, multimodal data can contain more information and can more accurately identify user intentions. In this paper, we propose and construct a multi-intent recognition method based on vision-language pre-training (VLP) model and cross-modality multi-head attention mechanism. The method includes two equally important stages of multimodal representation and fusion to explore the integration of image and text data to enhance the accuracy of intent recognition in multimedia content. The effectiveness of our approach for multi-intent recognition based on image and text fusion is proven by the comparative experiments with the baseline model on the public multimodal intent dataset which is used for this study is the first benchmark dataset for intent recognition in real-world multimodal scenes, including both image and text modalities. The ultimate goal aimed at attaining is to provide empowerment for making informed decisions based on interpretable models with field-specific observational and experimental aspects. © 2025 The Author(s).

关键词： BERT Computational Linguistics Deep Learning Feature Representation Human-computer Dialogue systems Image-Text Data Processing Multi-Head Attention Mechanism Multi-Intent Recognition Multimodal Fusion Multimodal Fusion Construction Model Multimodal Pre-Training Model Sequential Information VisionLanguage Pre-Training

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A Framework for Fractional Matrix Programming Problems with Applications in FBL MU-MIMO

arXiv

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arXiv 2025年

作者： Soleymani, Mohammad Jorswieck, Eduard Schober, Robert Hanzo, Lajos Signal & System Theory Group Universität Paderborn Germany Institute for Communications Technology Technische Universität Braunschweig Braunschweig38106 Germany Institute for Digital Communications Friedrich Alexander University of Erlangen-Nuremberg Erlangen91058 Germany Department of Electronics and Computer Science University of Southampton Southampton United Kingdom

An efficient framework is conceived for fractional matrix programming (FMP) optimization problems (OPs) namely for minimization and maximization. In each generic OP, either the objective or the constraints are functions of multiple arbitrary continuous-domain fractional functions (FFs). This ensures the framework’s versatility, enabling it to solve a broader range of OPs than classical FMP solvers, like Dinkelbach-based algorithms. Specifically, the generalized Dinkelbach algorithm can only solve multiple-ratio FMP problems. By contrast, our framework solves OPs associated with a sum or product of multiple FFs as the objective or constraint functions. Additionally, our framework provides a single-loop solution, while most FMP solvers require twin-loop algorithms. Many popular performance metrics of wireless communications are FFs. For instance, latency has a fractional structure, and minimizing the sum delay leads to an FMP problem. Moreover, the mean square error (MSE) and energy efficiency (EE) metrics have fractional structures. Thus, optimizing EE-related metrics such as the sum or geometric mean of EEs and enhancing the metrics related to spectral-versus-energy-efficiency tradeoff yield FMP problems. Furthermore, both the signal-to-interference-plus-noise ratio and the channel dispersion are FFs. In this paper, we also develop resource allocation schemes for multi-user multiple-input multiple-output (MU-MIMO) systems, using finite block length (FBL) coding, demonstrating attractive practical applications of FMP by optimizing the aforementioned metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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