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检索条件"任意字段=8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007"

共 204 条记录，以下是31-40 订阅

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Using Diffusion Models for data Augmentation on Limited Rodent OCT datasets 25th

Using Diffusion Models for Data Augmentation on Limited Rode...

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25th international conference on intelligent data engineering and automated learning

作者： Garcia-Torres, Fernando del Amor, Roefo Morales-Martinez, Sandra Barroso, Alvaro Kemper, Bjoern Schnekeriburger, Jurgen Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Centrada Ser Humano Valencia Spain Univ Munster Biomed Technol Ctr Med Fac Mendelstr 17 D-48149 Munster Germany

ISBN: (纸本)9783031777301;9783031777318

Optical coherence tomography (OCT) has transformed ophthalmology by enabling accurate diagnosis of retinal diseases. OCT in rodents is used in research studies of ophthalmic diseases and treatment evaluation. However, the scarcity of data limits the effectiveness of deep learning models for this purpose. this study explores the use of synthetic data generation to improve deep learning models for OCT image analysis from rodent retinas. To address this, the research leverages advanced class-conditional generative models to create high-quality synthetic images of ex-vivo rodent retinas embedded in two different mediums (solid resin and liquid media) with a reduced initial database. the conditional Denoising Diffusion Probabilistic Model (DDPM) proved to be the best model, as it produces more realistic images that closely mimic the characteristics of real rodent retinal scans. By augmenting the training datasets with these synthetic images, the performance of deep learning models in segmenting retinal layers improved by 7%. this approach reduces reliance on scarce biological samples and animal testing, promoting ethical research practices while improving the accuracy and efficiency of AI-driven medical diagnostics. the integration of synthetic data generation represents a significant advance in medical imaging, offering a scalable solution to data scarcity, generating reliable samples in two mediums and promoting the development of more robust and accurate diagnostic tools.

关键词： Synthetic data generation Deep learning Optical coherence tomography Image analysis Retinal diseases Denoising Diffusion Probabilistic Models Medical imaging

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Using Contrastive learning to Map Stylistic Similarities in Narrative Writers 25th

Using Contrastive Learning to Map Stylistic Similarities in ...

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25th international conference on intelligent data engineering and automated learning

作者： Valero-Redondo, Maria Huertas-Tato, Javier D'Antonio Maceiras, Sergio Martin, Alejandro Camacho, David Univ Cordoba Cordoba Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Exploring how literary authors influence each other and how intertextual connections can be found among them is a complex problem that is handled manually by experts. this research field, intertextuality, seeks to understand how writers relate to each other and how the works of one writer have reflected on the work of another. this analysis usually provides insight into the historical, cultural, biographical, and literary contexts that shape literary works. From a computational point of view, the approaches related to this problem have mainly dealt with problems of style classification, known as authorship attribution, but none have so far dealt with the problem of intertextuality. this paper proposes a novel approach based on contrastive learning to generate authorship embeddings that encapsulate the stylistic signatures of narrative writers and that can be projected into graphical representations to discern similarities between a dataset of literary writers. the embeddings generated with this approach aim to represent authorship styles are created and evaluated using a dataset of books from Project Gutemberg. During the evaluation, we perform a book and chunk-level evaluation, showing good performance in both cases. Finally, we present different graphical representations and provide a deep analysis of the relations that arise between the writers.

关键词： Contrastive learning Intertextual analysis Language Models Transformer

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Blueprint of Tomorrow: Contrasting Off-Line and On-Line Drawing Tasks for Alzheimer's Disease Screening 25th

Blueprint of Tomorrow: Contrasting Off-Line and On-Line Draw...

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25th international conference on intelligent data engineering and automated learning

作者： Hosseini-Kivanani, Nina Salobrar-Garcia, Elena Elvira-Hurtado, Lorena Salas, Mario Schommer, Christoph Leiva, Luis A. Univ Luxembourg Esch Sur Alzette Luxembourg Ramon Castroviejo Inst Ophthalmol Res Madrid Spain Univ Complutense Madrid Madrid Spain Hosp Clin San Carlos Geriatr Serv Memory Unit Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Alzheimer's disease (AD) is the leading cause of dementia. Although there is currently no cure for AD, early detection of cognitive decline can help clinicians mitigate its impact. Recently, Machine learning (ML) approaches have been developed to automatically analyze handwriting and hand-drawing tasks to support the early diagnosis of AD. In this paper, we study pentagon and clock drawing tests using both off-line (scanned image pixels) and on-line (discrete point sequences) data as input to several ML models (i.e., DensNet, ResNet, EfficientNet, RNN, LSTM, and GRU). Our study is the first to determine the most effective modality (on-line vs. off-line) and drawing tasks to distinguish healthy controls from AD patients (binary classification) as well as two stages of AD severity (multi-class classification). Our results suggest that, contrary to other domains, the off-line modality outperforms the on-line one, sometimes by a large margin: 90% vs. 60% accuracy in binary classification and 53% vs. 82% accuracy in multi-class classification. this suggests that, for drawing tasks and small-scale datasets, image-based representations may be more effective in predicting AD than those relying on more complex data representations.

关键词： Alzheimer disease Off-line handwriting On-line handwriting Deep learning data augmentation Classification

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A Survey of Industry in Cambodia and Future Prospects Industry 4.0 8

A Survey of Industry in Cambodia and Future Prospects Indust...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Chheang, Vuthy Heng, Sokha thourn, Kosorl Yahoui, Hamed Royal University of Phnom Penh Dept. of Telecommunication and Electronic Engineering Phnom Penh Cambodia Royal University of Phnom Penh Faculty of Engineering Phnom Penh Cambodia Institute of Technology of Cambodia Department of Telecommunication and Network Engineering Phnom Penh Cambodia Automation & Processing Center Dept. of Mechanical Engineering Claude Bernard University Lyon 1 Lyon France

ISBN: (纸本)9798350396546

In developed countries, industry 4.0 revolution is growing towards automation and data cloud analysis in manufacturing technologies to create a smart and high-class factory which be able to make higher production and logistics systems with few employees. this paper is based on a project funded by the ERASMUS+ Program of the European Union: ASEAN Factori 4.0, Across South East Asian Nations: From Automation and Control Training to the Overall Roll-out of Industry 4.0, 609854-EPP-1-2019-1-FREPPKA2-CBHE-JP which provided for a great survey of Cambodia industries in order to develop universities programs of automated production system. With these results of surveyed industries contributes to the Telecommunication and Electronic engineering Department of Royal University of Phnom Penh recognizing where to begin for requirement of future industry 4.0 in Cambodia, such as automated production, applied technologies, and programing skills. In short of future, Cambodia will be extended for Industry 4.0 technologies with 5G, data centers, artificial intelligent (AI), industrial Automation (IA), additive manufacturing, industrial internet of things (IIoT) and smart factories, intelligent management, cyber-security, and machine learning as similar as other developed Asian countries. © 2023 IEEE.

关键词： Industry 4.0

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Cardiovascular Disease Prediction Using Machine learning 8th

Cardiovascular Disease Prediction Using Machine Learning

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8th international conference on Information System Design and intelligent Applications, ISDIA 2024

作者： Sesha Talpa Sai, P.H.V. Chaitanya Lahari, M.L.R. Shagin, C.M. Vineeth, C.A. Vijayakumar, P. Naveen Kumar, G.S. Bhaumik, Amiya Department of Mechanical Engineering Malla Reddy College of Engineering and Technology Hyderabad India School of Electrical and Electronics Engineering Lincoln University College Petaling Jaya Malaysia Malla Reddy University Hyderabad India Lincoln University College Petaling Jaya Malaysia

ISBN: (纸本)9789819765805

Cardiovascular Diseases (CVDs) have become the leading cause of death worldwide in recent decades and are now the most dangerous illness, not just in India but throughout the world. therefore, a system that is dependable, accurate, and workable is required to identify these illnesses in time for appropriate treatment. Large-scale and sophisticated data processing has been automated by using machine learning techniques and algorithms to a variety of medical datasets. the enormous number of deaths from heart disease must be decreased, hence a rapid and effective diagnosis method must be found. Machine learning algorithms and data mining techniques are essential in this industry. the primary goal of this effort is to use machine learning algorithms to predict a patient's cardiac problems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Confluence of Machine learning and Internet of things for E-Healthcare Security 8th

Confluence of Machine Learning and Internet of Things for E-...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Pattnayak, Parthasarathi Das, Tulip Mohanty, Arpeeta Patnaik, Sanghamitra School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India Faculty of Emerging Technologies SRI SRI University Odisha Cuttack India School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India School of Law KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819784219

In the current era, the e-communication technology is developing swiftly and has made a remarkable transformation in e-healthcare sector by facilitating efficient and secure transmission of patient’s data. Internet of things (IoT) is developing drastically in the e-healthcare sector and the intelligent devices are also improving with many new innovative enhancements. the confluence of IoT and ML is manifested in many sectors but its implementation in e-healthcare sector provides real-time surveillance and monitoring of patient’s data and health status through prediction, anticipation and the capability of taking decisions intelligently. Nowadays, cryptographic and biometric systems, anomaly detection and machine learning (ML) approaches for authentication are being widely used for ensuring the security and functioning of healthcare systems. the present study aims to know how the convergence of IoT e-healthcare management system (EHMS) and ML techniques helps to design automated systems for proper monitoring and intelligent decision making. It also focuses to present an innovative architecture to safeguard the real-time healthcare from outside threats with the consumption of less possible resources of low-powered devices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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An intelligent Feature Extraction and Multiple learning-Based Classification Techniques for Diagnosis of Breast Cancer 8th

An Intelligent Feature Extraction and Multiple Learning-Base...

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8th international conference on Internet of things and Connected Technologies, ICIoTCT 2023

作者： Patro, Satyabrata Mishra, Jyotirmaya Panda, Bhavani Sankar Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam531162 India

ISBN: (纸本)9789819757855

Breast cancer is often curable if discovered in its early stages. Machine learning algorithms are used to automate disease identification. To increase the likelihood of recognizing the condition at an early stage, an effective classifier for automated diagnosis of breast cancer is needed. this research attempts to introduce a mixed machine learning model for predicting breast cancer. To outperform other strategies, it requires enormous amounts of data. Moreover, the intricate data models increase the training cost. For enhancing the efficiency of basic classifiers, ensemble learning holds great promise. A rapid discrete wavelet transforms using the wrapping approach is then used to extract the characteristics from the Region of Interest (ROI) mammography pictures. In addition to being too high to be categorized, the extracted wavelet coefficients also have incredibly high run-time complexity. the modified whale optimization method, which employs swarm intelligence, has been presented to lower time complexity and select the significant characteristics. Ensemble learning has been applied for the requisite efficiency in the suggested technique. three machine learning (ML) classifiers—the extreme gradient boost classifier (XGB), support vector machine (SVM), and enhanced granular neural network (E-GNN)—form the ensemble voting system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Breast Cancer Detection with Optimized Machine-learning Techniques 8th

Breast Cancer Detection with Optimized Machine-Learning Tech...

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8th international conference on Information and Communication Technology for intelligent Systems, ICTIS 2024

作者： Shahapurkar, Vyankatesh Dhamdhere, Vidya Ansari, Nahista G H Raisoni College of Engineering and Management Pune India

ISBN: (纸本)9789819766802

Breast cancer, ranking as the second-leading cause of cancer-related deaths in women globally, necessitates swift detection and advanced treatment strategies to mitigate its high mortality rate. this research explores the fusion of machine learning and image analysis techniques for early breast cancer detection using blood analysis data. the study introduces a novel approach by converting numerical blood analysis data into image data, significantly enhancing classification accuracy. Employing popular Convolutional Neural Network (CNN) models, namely AlexNet, ResNet50, and DenseNet201, the research achieves a superior classification accuracy of 95.33% with ResNet50. this surpasses existing studies using similar data and establishes the effectiveness of the proposed methodology. Furthermore, the conversion of numerical blood analysis images into image data represents a pioneering strategy. this research underscores the critical role of automated systems in breast cancer detection and emphasizes the potential for improved outcomes through early diagnosis. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Instance Selection Techniques for Large Volumes of data 24th

Instance Selection Techniques for Large Volumes of Data

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Cubillos, Marco Antonio Peña Ballesteros, Antonio Javier Tallón Department of Electronic Computer Systems and Automation Engineering International University of Andalusia Huelva Spain

ISBN: (纸本)9783031482311

Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that involves eliminating irrelevant and superfluous data while maintaining a high level of classification accuracy. IS, as a strategic filtering mechanism, addresses these challenges by retaining essential instances and discarding hindering elements. this refinement process optimizes classification algorithms, enabling them to excel in handling extensive datasets. In this research, IS offers a promising avenue to strengthen the effectiveness of classification in various real-world applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： data mining

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Exploring Machine learning in IoT Smart Home Automation 8

Exploring Machine Learning in IoT Smart Home Automation

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8th IEEE international conference on Software engineering and Computer Systems, ICSECS 2023

作者： Waseem, Quadri Wan Din, Wan Isni Sofiah Bin Ab Rahman, Azamuddin Nisar, Kashif Faculty of Computing Pekan Malaysia AnalytiCray Kuala Lumpur55100 Malaysia Swinburne University of Technology Sydney Professional Computing Data Science ParramattaNSW2150 Australia

ISBN: (纸本)9798350310931

the Internet of things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. these applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. 'Smart Homes' has emerged as one the latest Internet of things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. this research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail. © 2023 IEEE.

关键词： Automation

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