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共 606 条记录，以下是21-30 订阅

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Deep learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. the development of red lesions in the retina is one typical sign of diabetic retinopathy. these red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. this process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. the proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. the entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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Multi-keyword Searchable Data Auditing for Cloud-Based machine learning 6th

Multi-keyword Searchable Data Auditing for Cloud-Based Mach...

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6th International conference on machine learning for Cyber Security, ML4CS 2024

作者： Yu, Haiyan Ma, Qingru Zhu, Yilu Cui, Yuxin Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

machine learning (ML) relies on large amounts of data for training, which often requires outsourcing data to cloud servers. this raises concerns about data integrity and security. Preventing data from being tampered with or lost during storage and transmission has become a critical issue in the ML process. this paper presents a novel data auditing scheme for cloud-based ML, which supports multi-keyword search functionality. It allows searching one or more keywords to ensure the integrity of data in model training. To avoid data privacy leakage, the keywords are encrypted during the auditing process. Only the encrypted keywords of the data required for model training need to be provided to the third-party auditor (TPA). Subsequently, TPA can check and ensure the integrity of the relevant data. through security analysis and performance evaluations, the results indicate that this scheme can effectively and reliably audit outsourced data while ensuring the security and privacy of model training data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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Estimating MPdist with SAX and machine learning 5th

Estimating MPdist with SAX and Machine Learning

引用

5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European conference on Advances in Databases and Information Systems, ADBIS 2024

作者： Tsoukalos, Mihalis Chronis, Pantelis Platis, Nikos Vassilakis, Costas Department of Informatics and Telecommunications University of the Peloponnese Tripolis Greece

ISBN: (纸本)9783031704208

MPdist is a distance measure which considers two time series to be similar if they share many similar subsequences. However, computing MPdist can be slow, especially for large time series. We propose a technique for the approximate computation of MPdist that uses the SAX representation of the time series to quickly estimate the Nearest Neighbor (NN) distance of each subsequence, and then applies a machine learning model to correct the accuracy loss incurred. Our method is orders of magnitude faster than the exact computation of MPdist;at the same time, our best approximation computes the NN of a time series with high accuracy. A thorough evaluation of our technique is provided. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Hyper-Parameter Optimization and Proxy Re-encryption for Federated learning 6th

Hyper-Parameter Optimization and Proxy Re-encryption for F...

引用

6th International conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Du, Wanrong Wen, Bin Ma, Mengshuai Liu, Wenlong Wei, Xiaoxun Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China

ISBN: (纸本)9789819614103

Federated learning is a distributed machine learning paradigm that allows multiple participants to collaborate on training a shared model without revealing their own data. However, federated learning faces challenges such as data heterogeneity, communication overhead, security attacks, etc., which result in low efficiency of data training. To address these issues, this paper proposes a strategy to enhance the training efficiency of federated learning data through hyperparameter optimization and proxy re-encryption. A semi-supervised model of federated learning based on generative adversarial network is designed so that each participant can automatically adjust and optimize their own training parameters according to their own data distribution and environment in order to adapt to the data heterogeneity and dynamics of federated learning. Finally, experiments demonstrate that the proposed method can significantly improve the efficiency of data training and model performance compared with traditional federated learning methods across different datasets and scenarios. Experimental results show that the proposed strategy can achieve the same or even better accuracy than centralized machine learning while safeguarding data privacy and enabling ciphertext conversion. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Scalable Deep learning: Applications in Medicine 5th

Scalable Deep Learning: Applications in Medicine

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作者： Barillaro, Luca Data Analytics Research Center Department of Medical and Surgical Sciences University "Magna Græcia" of Catanzaro Catanzaro88100 Italy

ISBN: (纸本)9783031704208

this paper aims to introduce my PhD research project, which is focused on scalable deep learning and its applications in the medical context. this project aims to design new DL algorithms or to adapt existing ones, to scalable architectures (e.g., parallel computers, GPUs), to improve the performance of typical ML tasks, such as classifications, and to experiment with them in the analysis of biomedical data, such as bioimages or molecular data. these applications are widely present in current literature thus representing a challenging opportunity. In addition to common DL and HPC approaches, the use of edge devices (i.e., Nvidia Jetson) is being explored, since this may be useful in a medical context using some key features of the edge computing paradigm (such as keeping data near the source) that are significant, i.e., for privacy reasons or legal compliance (i.e., European GPDR). My main investigated applications are on bioimages, such as Computed Tomography (CT) or functional Magnetic resonance imaging (fMRI). Research is being carried out to investigate current methodologies and how to improve them possibly. Moreover, a lot of experiments are carried out to demonstrate the impact of these approaches on traditional medical strategies. this work resulted in some experiments on the classification of medical images (CTs), medical signals (ECG), and gene expression data. My current work’s primary focus, boosted by a three-month collaboration with the University of Groningen, involves an extensive project on classifying fMRIs using machine learning techniques and concepts from graph theory tailored to exploit High-Performance computing (HPC) infrastructures. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gene expression

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Driver Distraction Prediction: A Kernel Functions Analysis for Deep Kernelized Autoencoder 6th

Driver Distraction Prediction: A Kernel Functions Analysis f...

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6th International conference on Advanced Informatics for computing Research, ICAICR 2023

作者： Chui, Kwok Tai Gupta, Brij B. Arya, Varsha Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Business Administration Asia University Taichung41354 Taiwan

ISBN: (纸本)9783031840586

During driving, the driver’s attention may be away from the road, commonly known as distracted driving. this is a key reason leading to traffic accidents. In the literature, most studies proposed machine learning algorithms for driver distraction recognition to classify the latest driving status. However, applying these algorithms cannot fully benefit the prevention of road traffic accidents and, thus, the reduction of deaths and injuries. In this paper, the driver distraction problem is formulated to predict future behaviors (distracted driving events or proper driving). the research effort is devoted to the feature extraction of drivers’ images using a deep kernelized autoencoder. Various common kernel functions are analyzed, including linear, polynomial, sigmoid, Gaussian, Laplacian kernel, radial basis function, and ANOVA radial basis function kernels. Performance evaluation of our work reveals F1-score ranges of 48.9–86.8% and 50.2–87.1% using two hidden layers and three hidden layers, respectively. Our work outperforms two existing works by an average of 3.52% in 1–5 s prediction intervals and 3.76% in 1–3 s. Various future research directions are also discussed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radial basis function networks

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Adapting Agricultural Virtual Environments in Game Engines to Improve HPC Accessibility 6th

Adapting Agricultural Virtual Environments in Game Engines ...

引用

6th Nordic e-Infrastructure Collaboration conference, NeIC 2024

作者： Baker, Dirk Norbert Bauer, Felix Schnepf, Andrea Scharr, Hanno Riedel, Morris Göbbert, Jens Henrik Hvannberg, Ebba School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany Institute of Bio- and Geosciences 3: Agrosphere Forschungszentrum Jülich GmbH Jülich Germany Institute for Advanced Simulation 8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany

ISBN: (纸本)9783031862397

E-infrastructures deliver basic supercomputing and storage capabilities but can benefit from innovative higher-level services that enable use-cases in critical domains, such as environmental and agricultural science. this work describes methods to distribute virtual scenes to the GPU nodes of a modular supercomputer for data generation. High information density virtual scenes, containing >100k geometries, typically cannot be rendered in real-time without techniques that change the information content, such as level-of-detail or culling approaches. Our work enables the concurrent and partitioned coupling to the image analysis in such a way that the data generation is dynamic and can be allocated to GPU nodes on demand, resulting in the possibility of moving through a continuous virtual scene rendered on multiple nodes. Within agricultural data analysis, the approach is especially impactful as virtual fields contain many individual geometries that coexist in one continuous system. Our work facilitates the generation of high-quality image data sets, which has the potential to solve the challenge of scarcity of well-annotated data in agricultural science. We use real-time communication standards to couple the data production with the image analysis training. We demonstrate how the use-case rendering impacts effective use of the compute nodes and furthermore develop techniques to distribute the workload to improve the data production. © the Author(s) 2025.

关键词： Virtual environments

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Research on the Application of Intelligent computing and Trustworthy machine learning in Financial Complex Systems 6

Research on the Application of Intelligent Computing and Tru...

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6th International conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Liu, Zhiyi Zhang, Kai Zheng, Yejie Xu, Shaohua Qu, Jiaxin Eastmoney Informat Co Ltd Eastmoney AI Res Inst Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350377859;9798350377842

the complexity of financial systems and the rapid increase in data volume are making the use of intelligent computing and trustworthy machine learning more important in finance. this paper discusses how intelligent computing can be applied within complex financial systems and takes a deeper look at the theory behind trustworthy machine learning and how it is used in finance. By combining the structure of complex networks with the computing power of machine learning, the paper also explores the inner workings of large neural networks and considers how to apply the theory of dynamic systems to the tuning of these networks, to improve intelligent computing in complex financial systems. Experiments based on the "scientific intelligence + machine conjecture" approach were carried out for risk assessment and market forecasting. the results show that these technologies can improve how financial institutions manage risk, help investors get more reliable information about market trends, and meet the transparency needed for regulatory compliance. the use of intelligent computing and trustworthy machine learning in complex financial systems points to a future with lots of potential for innovation and new opportunities.

关键词： Intelligent computing Trustworthy machine learning Financial Complex Systems Fifth Paradigm

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Data Mining in Mobile learning: Uncovering Insights for Enhanced Vocabulary Acquisition 6

Data Mining in Mobile Learning: Uncovering Insights for Enha...

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6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Wu, Minghai Shenyang Jianzhu Univ Sch Foreign Languages Shenyang Peoples R China

ISBN: (纸本)9798400716225

the integration of mobile learning and machine learning, driven by the proliferation of smart handheld devices and the vast amount of learning resources available on the internet, has revolutionized education. this study explores the use of mobile learning, specifically leveraging mobility and big data mining techniques, to improve college English vocabulary acquisition among university students. By addressing the challenges faced in mobile learning and applying innovative methodologies, we have developed a personalized recommendation system based on data mining algorithms. this system suggests English vocabulary words to students based on their past performance, harnessing the power of big data mining. through a comprehensive evaluation, we have demonstrated the effectiveness of our approach in enhancing vocabulary acquisition and retention compared to traditional book-based learning methods. the findings of this research not only contribute to the advancement of mobile learning but also provide valuable insights for educators and policymakers on the effective utilization of mobility and big data mining for enhanced student learning outcomes.

关键词： Data mining Mobile learning College English vocabulary acquisition machine learning

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ICCCMLA 2024 - 6th International conference on Cybernetics, Cognition and machine learning Applications

ICCCMLA 2024 - 6th International Conference on Cybernetics, ...

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6th International conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

ISBN: (纸本)9798331505790

the proceedings contain 99 papers. the topics discussed include: application of a genetic technique for selection of the best features and machine learning for malware detection on android;a survey of optimized compiler using advanced machine learning and deep learning techniques;cognitive computing in English language classrooms;a systematic analysis on security and anomaly detection using machine learning in cloud computing;creation of geospatial database farming system using machine learning;optimizing natural language understanding: a comprehensive study of Python’s top libraries;a machine learning approach to dairy farm energy disaggregation;a novel mechanism for identifying malicious and phishing URLs using machine learning classifiers;studying the effectiveness of online college education in India from student feedback;and enhanced liver disease prediction using ensemble learning.

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