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Deep nonparametric estimation of operators between infinite dimensional spaces

The Journal of Machine Learning Research 2024年第1期25卷 1239-1305页

作者： Hao Liu Haizhao Yang Minshuo Chen Tuo Zhao Wenjing Liao Department of Mathematics Hong Kong Baptist University Hong Kong Department of Mathematics and Department of Computer Science University of Maryland College Park Department of Electrical and Computer Engineering Princeton University School of Industrial and Systems Engineering Georgia Institute of Technology School of Mathematics Georgia Institute of Technology

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

关键词： deep neural networks nonparametric estimation operator learning generalization error analysis

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Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya Mohamed K.Nour Ahmed S.Salama Mohamed I.Eldesouki Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)*** presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

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Towards Securing Water systems Communications from Adversarial Attacks using AI Assurance

Towards Securing Water Systems Communications from Adversari...

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IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Mehmet Oguz Yardimci Feras A. Batarseh Department of Computer Science Virginia Tech Arlington Virginia USA Department of Biological Systems Engineering Virginia Tech Arlington Virginia USA

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519964

Water systems are increasingly susceptible to cyberattacks due to their reliance on networked communications for monitoring and control. This paper introduces an AI-assured approach to detect anomalies in water distribution systems communication layers using Long Short-Term Memory models, which are trained, tuned, and validated through four different scenarios targeting disruptions in communication patterns. Using the Secure Water Treatment and anomalies and malicious acts datasets, and by simulating both operational and malicious scenarios, Long Short-Term Memory model is trained on normal data and tested on both regular (idle) and attack situations. The LSTM model employs a sliding time-window technique to capture sequential dependencies in communication data; it identifies anomalies by comparing predicted and actual sequence—significant deviations that signal potential cyber threats. Our scenarios demonstrate an anomaly detection accuracy of 94%, with a false positive rate of 2.5%, indicating the model’s capacity to identify both overt and subtle attacks. Further, we evaluate the intentionality of those outliers, whether they are cyber threats or merely a human operator error. The anomalies and malicious acts dataset scenario successfully detects multiple intentional sabotage scenarios, further validating the model’s potential in real-world cyber-physical security settings. The findings suggest that integrating AI-assured, LSTM-based anomaly detection into water systems can enhance security, enabling early threat detection and reducing the overall risk to critical infrastructures communications.

关键词： Deep learning Predictive models Data models Threat assessment Critical infrastructure Security Artificial intelligence Long short term memory Anomaly detection Monitoring

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Arabic Feature-Based Text Watermarking Technique for Sensitive Detecting Tampering Attack

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computers, Materials & Continua 2021年第9期68卷 3789-3806页

作者： Fahd N.Al-Wesabi Huda G.Iskandar Saleh Alzahrani Abdelzahir Abdelmaboud Mohammed Abdul Nadhem Nemri Mohammad Medani Mohammed Y.Alghamdi Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science Faculty of Science&Arts of BaljurshiAl-Baha UniversityKSA

In this article,a high-sensitive approach for detecting tampering attacks on transmitted Arabic-text over the Internet(HFDATAI)is proposed by integrating digital watermarking and hidden Markov model as a strategy for soft *** HFDATAI solution technically integrates and senses the watermark without modifying the original *** alphanumeric mechanism order in the first stage focused on the Markov model key secret is incorporated into an automated,null-watermarking approach to enhance the proposed approach’s efficiency,accuracy,and *** first-level order and alphanumeric Markov model technique have been used as a strategy for soft computing to analyze the text of the Arabic *** addition,the features of the interrelationship among text contexts and characteristics of watermark information extraction that is used later validated for detecting any tampering of the Arabic-text *** HFDATAI strategy was introduced based on PHP with included IDE of VS *** of four separate duration datasets in random sites illustrate the fragility,efficacy,and applicability of HFDATAI by using the three common tampering attacks i.e.,insertion,reorder,and *** HFDATAI was found to be effective,applicable,and very sensitive for detecting any possible tampering on Arabic text.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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GLOW-ENV: A Dual-Data IoE-Based Approach for Integrating Glucose and Environmental Data into a Diabetes Recommendation System

GLOW-ENV: A Dual-Data IoE-Based Approach for Integrating Glu...

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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Gaitán-Guerrero, Juan F. Martínez-Cruz, Carmen López, José L. Mata-Mata, Francisco Pérez-Cordón, Luis G. Serrano, José-María Morcillo-Martínez, Juana M. Verdejo-Espinosa, Ángeles Cuevas-Martínez, Juan C. Viciana-Abad, Raquel Reche-López, Pedro J. Pérez-Lorenzo, José M. Díaz-Jiménez, David Espinilla, Macarena Department of Computer Science University of Jaén Jaén23071 Spain Department of Languages and Computer Systems University of Granada Granada18071 Spain Psycology Department Faculty of Social Work University of Jaén Jaén23071 Spain Electrical Engineering Department University of Jaén Jaén23071 Spain Telecommunication Engineering Department University of Jaén Jaén23071 Spain

This paper introduces GLOW-ENV, an intelligent Internet of Everything (IoE)-driven mobile application designed with the objective of integrating real-time glucose monitoring data and environmental metrics to enhance diabetes care and management. The proposed IoE ecosystem integrates a continuous glucose monitoring with a personalized Artificial Intelligence model designed to predict glycemic fluctuations in a near-future. Additionally, GLOW-ENV integrates a rule-based recommendation system to dynamically adapt its suggestions based on contextual glucose and environmental data. This framework advances personalized diabetes care, contributing to their progression and well-being offering valuable insights and improving decision-making. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electronic health record

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Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry

arXiv

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arXiv 2024年

作者： Hou, Wenjun Xu, Kaishuai Cheng, Yi Hu, Yan Li, Wenjie Liu, Jiang Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Nottingham Ningbo China

Comprehensively understanding surgical scenes in Surgical Visual Question Answering (Surgical VQA) requires reasoning over multiple objects. Previous approaches address this task using cross-modal fusion strategies to enhance reasoning ability. However, these methods often struggle with limited scene understanding and question comprehension, and some rely on external resources (e.g., pre-extracted object features), which can introduce errors and generalize poorly across diverse surgical environments. To address these challenges, we propose S2CAN, a simple yet effective memory-augmented framework that leverages Multimodal LLMs to improve surgical context comprehension via Self-Contained Inquiry. S2CAN operates autonomously, generating two types of memory for context augmentation: Direct Memory (DM), which provides multiple candidates (or hints) to the final answer, and Indirect Memory (IM), which consists of self-contained question-hint pairs to capture broader scene context. DM directly assists in answering the question, while IM enhances understanding of the surgical scene beyond the immediate query. Reasoning over these object-aware memories enables the model to accurately interpret images and respond to questions. Extensive experiments on three publicly available Surgical VQA datasets demonstrate that S2CAN achieves state-of-the-art performance, offering improved accuracy and robustness across various surgical scenarios. © 2024, CC BY.

关键词： Question answering

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application 13

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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13th International Electrical engineering Congress, iEECON 2025

作者： Vanna-Iampikul, Pruek Boonnateephisit, Visutr Limsoontharakul, Somphop Moonrinta, Jednipat Dailey, Matthew Ekpanyapong, Mongkol Burapha University Department of Electrical Engineering Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Prince of Songkla University Dept. Computational Science Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand School of Engineering and Technology Asian Institute of Technology Dept. of Industrial Systems Engineering Pathumthani Thailand

ISBN: (纸本)9798331543952

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments. © 2025 IEEE.

关键词： Deforestation

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Deep learning model for efficient traffic forecasting in intelligent transportation systems

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Neural Computing and Applications 2024年 1-14页

作者： Khan, Shakir Alghayadh, Faisal Yousef Ahanger, Tariq Ahamed Soni, Mukesh Viriyasitavat, Wattana Berdieva, Uguloy Byeon, Haewon Riyadh Saudi Arabia University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali140413 India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Pune India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Chulalongkorn Business School Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand The Department of Tax and Taxation Tashkent Institute of Finance Tashkent Uzbekistan Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of

In this paper, a novel intelligent transportation framework called graph convolutional neural network for distributed machine learning (GCN-DML) is presented. Its primary application is traffic forecasting in large-scale, dynamically-evolving traffic scenarios. One unique method in traffic forecasting, called GCN-DML, is that it allows user data to be processed locally on edge devices, removing the requirement to send all data to CS (CS). Designed specifically for edge computing, this distributed machine learning framework makes it easier to train neural networks on a variety of edge devices. In addition to protecting user privacy, this method reduces the difficulties related to centralised neural network training, minimises communication delays, lowers data transfer quantities, and improves overall data processing efficiency. GCN-LSTM, the composite graph convolutional networks used in this framework, combine edge-enhanced attention mechanisms with the feature transmission capabilities of graph convolutional neural networks to effectively handle the challenging task of traffic forecasting. Even as the number of cars in the network rises, they perform exceptionally well at quickly extracting and sending interaction information between vehicles, while preserving high prediction accuracy and low time complexity. The versatility of GCN-DML is extended to a range of traffic forecasting application scenarios, whereby edge devices can adjust the kind and scale of the neural network according to available compute and storage resources, all without sacrificing performance. The usefulness of GCN-DML in traffic forecasting is demonstrated by experimental evaluations on the NGSIM public dataset, where it outperforms other models with better computation time and prediction performance. It obtains noteworthy F1 scores of 0.9473, 0.9557, and 0.9391, respectively, for recall, precision, and precision. Even when there is an increase in the number of cars, the accuracy remains high,

关键词： Convolutional neural networks

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An algorithm for conditional-fault local diagnosis of multiprocessor systems under the MM⁎ model

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Theoretical computer science 2024年 987卷

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

Diagnosis is a crucial subject for maintaining the reliability of multiprocessor systems. Under the MM⁎ diagnosis model, Sengupta and Dahbura proposed a polynomial-time algorithm with time complexity O(N5) to diagnose a system with N processors. In this paper, we propose a (α,β)-trees combination S(u,X,α,β) and give an algorithm to identify the fault or fault-free status of each processor for conditional local diagnosis under the MM⁎ model. According to our results, a connected network with a (α,β)-trees combination S(u,X,α,β) for a node u is conditionally locally (α+2β−3)-diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+αβ2). As an application, we show that our algorithm can identify the status of each node of n-dimensional star graph Sn if the faulty node number does not exceed 3n−8. Compared with existing algorithms, our algorithm allows more faulty nodes in a multiprocessor system. © 2024 Elsevier B.V.

关键词： Computational complexity

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Organization-wide IOC Monitoring and Security Compliance in Endpoints using Open Source Tools 3

Organization-wide IOC Monitoring and Security Compliance in ...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Divya, P.J. George, Reenu Sara Madhusudhan, G. Padmasree, S. Jss Science and Technology University Department of Computer Science and Engineering Mysore India Indian Space Research Organization Information Technology Cell Laboratory for Electro-Optics Systems Bengaluru India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Endpoint Security is an important layer in cyber security and it is one of the first places organizations look to secure their enterprise network. Proactively and iteratively monitoring endpoints for threats and Indicators of Compromise (IOC), helps security analysts to detect malware/attacks and to respond to incidents quickly. An Indicator of Compromise (IOC) refers to data that indicates that a system may have been infiltrated by a cyber-threat. Cyber security agencies send regular cyber-alerts to various organizations nation-wide and provide IOC list in the form of hashes, IPs, URLs, domains, yara rules, etc. for monitoring the presence of IOCs in their respective network. Periodic auditing of endpoints for security compliance is also a challenging and time-consuming task for every organization. To overcome these challenges, a solution based on client-server architecture is proposed in organizational network using open-source tools named Osquery, Fleet, Filebeat and Graylog. Using these tools, endpoints can be monitored for IOCs on demand or on scheduled manner from a central server. Yara based scan can also be performed efficiently in endpoints to monitor for IOCs using this architecture. In addition to IOC monitoring, this paper explains verifying compliance of endpoints with the organization's security policy using the proposed setup. Impact on performance of the endpoints during query execution was also analyzed. This solution will contribute in strengthening the endpoint security which in turn enhances overall cyber-security posture of the organization. © 2022 IEEE.

关键词： Network architecture

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