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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Gabriel Garcia, Jose Colomer, Adrian Naranjo, Valery Penaranda, Francisco Sales, M. A. Univ Politecn Valencia Inst Invest & Innovac Bioingn I3B Camino Vera S-N Valencia 46022 Spain Hosp Clin Univ Valencia Serv Anat Patol Valencia Spain

ISBN: (纸本)9783030034931;9783030034924

A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several colour spaces are applied to each sample in order to generate a binary mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied as a novel gland segmentation technique never before used in this type of images. 500 random gland candidates, both benign and pathological, are selected to evaluate the LCWT technique providing results of Dice coefficient of 0.85. Several shape and textural descriptors in combination with contextual features and a fractal analysis are applied, in a novel way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. the most relevant features are then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts, 3.195 benign glands and 3.000 pathological glands are obtained, from a data set of 1.468 images at 10x magnification. A careful strategy of data partition is implemented to robustly address the classification problem between artefacts and glands. Both linear and non-linear approaches are considered using machine learning techniques based on Support Vector Machines (SVM) and feedforward neural networks achieving values of sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectively.

关键词： Machine learning Multilayer perceptron Support vector machine Locally constrained watershed transform Gland unit identification Histological prostate image

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Modelling Survival by Machine learning Methods in Liver Transplantation: Application to the UNOS dataset 20th

Modelling Survival by Machine Learning Methods in Liver Tran...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Guijo-Rubio, David Villalon-Vaquero, Pedro J. Gutierrez, Pedro A. Dolores Ayllon, Maria Briceno, Javier Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci Cordoba Spain Unit Hepatobiliary Surg & Liver Transplantat Cordoba Spain

ISBN: (纸本)9783030336172;9783030336165

the aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this purpose, we consider a database from the United Network for Organ Sharing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of America from November 2004 until June 2015. the dataset contains more than a 74% of censoring, being a challenging and difficult problem. Several methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor characteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation metrics are used, being the concordance index (ipcw) the most suitable for this problem. the results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.

关键词： United Network for Organ Sharing Liver transplant Survival analysis Machine learning

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A Fuzzy Approach to the Linguistic Summarization of Time Series

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2011年第2-3期17卷 157-182页

作者： Castillo-Ortega, Rita M. Marin, Nicolas Sanchez, Daniel Univ Granada Dept Comp Sci & Artificial Intelligence Intelligent DataBases & Informat Syst Res Grp E-18071 Granada Spain

In this work we introduce a new approach to linguistic summarization of time series based on the use of a fuzzy hierarchical partition of the time dimension and the evaluation of quantified sentences. the final summary consists of a collection of quantified sentences. the use of the time hierarchy allows us to accomplish the objectives of a summary, i.e., to be general, brief, and accurate.

关键词： Fuzzy sets time series linguistic summarization intelligent information systems OLAF

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A Comprehensive Review of Recent Artificial Intelligence Techniques and IOT Applications in Dairy Farms 10

A Comprehensive Review of Recent Artificial Intelligence Tec...

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anusree, K. Rajeswari, M. Priyadharshini, C. Karunya Institute of Technology and Science Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sahrdaya College of Engineering and Technology Kerala Kodakara India

ISBN: (纸本)9798350384369

Each and every aspect of daily life has undergone a change thanks to the Internet of things technology that renders everything intelligent. the wide range of IoT applications has captivated numerous researchers, and IoT enabled dairy farming is being leveraged to undertake innovative studies integrating IoT and Artificial Intelligence (AI) technology. More prospects for intelligent dairy farming are being created by the IoT and data-driven methods. Since the global population is increasing, there is a constant appetite for milk. Developed countries use a greater amount of dairy products than developing nations do. Enhanced technological approaches are required to elevate milk production and meet the escalating demand for dairy products. It is anticipated that a farmer would be able to significantly increase milk output and conquer several traditional farming obstacles with the help of IoT and various AI solutions. this study discusses various difficulties that dairy farmers deal with on every day of their lives. the primary objective of this paper is to undertake a comprehensive review on various Io'T, machine learning(ML) and deep learning(DL) technologies used in the field of smart dairy farming. It also looks into the difficulties and issues that traditional methods encounter as well as their results. Furthermore, the development of an IoT -enabled automated system for dairy farming may benefit further from this evaluation of research. © 2024 IEEE.

关键词： Smart agriculture

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Accurate and Optimized Labelling of Fashion Products through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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10th international conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. this paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. this data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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Employing Deep Unsupervised learning Method to Identify Testing Scenarios for automated Vehicles 10

Employing Deep Unsupervised Learning Method to Identify Test...

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10th international conference on Dependable Systems and their Applications, DSA 2023

作者： Liu, Shuai Ren, Fan Yang, Guo He, Dingchang Liu, Yonggang State Key Laboratory of Vehicle Nvh and Safety Technology Chongqing401122 China Chongqing University College of Mechanical and Vehicle Engineering Chongqing40030 China Changan Automobile Intelligent Research Institute Chongqing710199 China

ISBN: (纸本)9798350304770

the significance of naturalistic driving data (NDD) lies in its application for assessing autonomous driving systems across diverse driving conditions. this paper introduces an approach to identify testing scenarios for autonomous vehicles using deep unsupervised learning techniques. Initially, a deep regularized autoencoder (DRAE) network is designed to extract feature vectors from the NDD. Next, these feature vectors are then clustered by K-Means clustering (KMC) and seven types of scenarios are obtained. Finally, a comparison is made between the proposed DRAE-KMC approach and other clustering algorithms. the outcome demonstrates that the proposed DRAE-KMC method outperforms other methods. Furthermore, Google Earth is applied to project the identified scenarios. the result illustrates that the projected scenes align with the identified scenarios, which further validates the effectiveness of proposed DRAE-KMC method. © 2023 IEEE.

关键词： Autonomous vehicles

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Comparative Analysis of Conventional and Modified Capsule Networks for Sperm Morphology Assessment 10

Comparative Analysis of Conventional and Modified Capsule Ne...

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10th international conference on Signal Processing and intelligent Systems, ICSPIS 2024

作者： Mohammadi, Hossein Serbes, Gorkem Ilhan, Hamza Osman Yildiz Technical University Department of Computer Engineering Istanbul Turkey Yildiz Technical University Department of Biomedical Engineering Istanbul Turkey

ISBN: (纸本)9798331532543

the evaluation of sperm morphology is an essential factor in the diagnosis of male-related infertility. Manual sperm morphology assessments are labor intensive and can vary significantly between observers. Although automated sperm morphology analysis approaches have shown significant success in the last two decades, there is still a need for deep learning based solutions due to the challenges faced in new data sets such as the dramatic increase seen in number of sperm samples and the diversity of the labeled abnormality types. As a solution to these challenges, this research performs a comparative analysis of the conventional capsule network (CapsNet) and its modified version (FixCaps), using a recently introduced imbalanced dataset referred to as Hi-LabSpermMorpho which contains a total of 18,456 images labeled into 18 different classes. the classes include variants of abnormalities of the head, neck, and tail of the sperm, as well as the normal class. the evaluation focused on assessing the accuracy, computational efficiency, and the effects of different optimization strategies for both models. the results have shown that FixCaps achieved higher performance than CapsNet, with its improvements such as using large-kernel convolutions and the CBAM attention mechanism, which allow the model to capture more complex features and spatial relationships of the images in the dataset. FixCaps resulted in 52.67% classification accuracy while CapsNet performed only 37.89%. © 2024 IEEE.

关键词： Image classification

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Stent graft change detection after endovascular abdominal aortic aneurysm repair

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10th international conference on intelligent data engineering and automated learning, ideal 2009

作者： Maiora, Josu García, Guillermo Tapia, Arantxa MacÍa, Iván Legarreta, Jon Haitz Paloc, Céline Graña, Manuel De Blas, Mariano Electronics and Telecomunications Department Technical University School University of the Basque Country Donostia-San Sebastián Spain Engineering Systems and Automatic Department Technical University School University of the Basque Country Donostia-San Sebastián Spain Biomedical Applications Department VICOMTech Donostia-San Sebastián Spain Computational Intelligence Group Computer Science Faculty University of the Basque Country Donostia-SanSebastián Spain Interventional Radiology Service Donostia Hospital Donostia-SanSebastián Spain

ISBN: (纸本)3642043933

the use of the endovascular prostheses in Abdominal Aortic Aneurysm (EVAR) has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long term perspective different complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present computed tomographic angiography (CTA) is the most commonly used examination for imaging surveillance for stent graft monitoring. However, endoleak complications can not always be detected by visual inspection on CTA scans. the purpose of this work was to study the application of image registration techniques to the detection of changes in the stent graft. Previously we segment the lumen using semi-automatic methods. © 2009 Springer Berlin Heidelberg.

关键词： Stents

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Segmentation of abdominal aortic aneurysms in CT images using a radial model approach

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10th international conference on intelligent data engineering and automated learning, ideal 2009

作者： MacÍa, Iván Legarreta, Jon Haitz Paloc, Céline Graña, Manuel Maiora, Josu García, Guillermo De Blas, Mariano Biomedical Applications Department Vicomtech Donostia-San Sebastián Spain Computational Intelligence Group Computer Science Faculty University of the Basque Country Donostia-SanSebastián Spain Electronics and Telecommunications Department Technical University School University of the Basque Country Donostia-San Sebastián Spain Engineering Systems and Automation Engineering Department Technical University School University of the Basque Country Donostia-San Sebastián Spain Interventional Radiology Service Donostia Hospital Donostia-SanSebastián Spain

ISBN: (纸本)3642043933

Abdominal Aortic Aneurysm (AAA) is a dangerous condition where the weakening of the aortic wall leads to its deformation and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAAs can be treated non-invasively by means of the Endovascular Aneurysm Repair technique (EVAR), which consists of placing a stent-graft inside the aorta in order to exclude the bulge from the blood circulation and usually leads to its contraction. Nevertheless, the bulge may continue to grow without any apparent leak. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel model-based approach for the semi-automatic segmentation of both the lumen and the thrombus of AAAs, using radial functions constrained by a priori knowledge and spatial coherency. © 2009 Springer Berlin Heidelberg.

关键词： Repair

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PentraFormer: learning Agents for automated Penetration Testing via Sequence Modeling 17

PentraFormer: Learning Agents for Automated Penetration Test...

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IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of things, ithings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart data, Smartdata 2024

作者： Wang, Yunfei Liu, Shixuan Wang, Wenhao Zhu, Cheng Fan, Changjun Huang, Kuihua Chen, Chao National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9798350351637

the exponential growth of computer networks has intensified the requirement for robust security measures, rendering penetration testing - an essential practice that assesses vulnerabilities by simulating attacks - critically important. automated penetration testing (APT) has evolved from rule-based approaches to intelligent decision-making, such as reinforcement learning (RL), better emulating the adaptability and decision-making process of human penetration testers. However, using RL for APT is challenged by the high cost of real-network interactions, the scarcity of security datasets, and the unique complexities of APT scenarios, which include long decision sequences and delayed rewards, complicating RL efficacy and convergence. Inspired by the Decision Transformer's proficiency in predicting action sequences for offline RL, coupled with its effectiveness in dealing with data efficiency and adaptability, we introduce a novel sequence-based methodology for designing APT agents. this approach is tailored to effectively manage the intricate aspects of data-scarce and complex APT scenarios. Our proposed model, PentraFormer, adeptly addresses these challenges prevalent in APT scenarios. the robustness of our model is attested to through extensive empirical evaluations conducted on APT-dedicated tasks. © 2024 IEEE.

关键词： Adversarial machine learning

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