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检索条件"任意字段=2019 International Conference on Applied Machine Learning and Data Science, ICAMLDS 2019"

共 11147 条记录，以下是441-450 订阅

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Multi-modal image registration and machine learning for the generation of 3D virtual histology of bone implants 15

Multi-modal image registration and machine learning for the ...

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15th SPIE conference on Developments in X-Ray Tomography

作者： Irvine, S. Lucas, C. Bootbool, M. Galli, S. Zeller-Plumhoff, B. Moosmann, J. Institute of Materials Physics Helmholtz-Zentrum Hereon Max-Planck Str. 1 Geesthacht21502 Germany Institute of Metallic Biomaterials Helmholtz-Zentrum Hereon Max-Planck Str. 1 Geesthacht21502 Germany Bruker Daltonics SPR Falkenried 88 Hamburg20251 Germany The Hebrew University of Jerusalem Jerusalem9190501 Israel University of Malmö Faculty of Odontology Department of Prosthodontics Carl Gustafs väg 34 Klerken Malmö20506 Sweden University of Rostock Faculty of Engineering and Marine Technologies Albert-Einstein-Str. 2 Rostock18059 Germany

ISBN: (纸本)9781510679641

In our correlative characterisation studies of biodegradable and permanent metal bone implants, we have performed both synchrotron-radiation microtomography (SR-µCT) and histology on the same samples. Histological staining is still the gold standard for tissue visualisation yet requires multiple time-consuming sample preparation steps (fixing, embedding, sectioning and staining) before imaging is performed on individual slices, in contrast to the non-invasive and 3D nature of tomography. In the process of correlating the corresponding data sets, we are able to combine advantages of both modalities by using machine learning methods to generate artificially stained 3D virtual histology datasets from SR-µCT datasets. For this we have developed an automated registration tool to find and fit the correct virtual tomographic plane to each histology slice. Preliminary results are promising after training a modified cycle generative adversarial network on our data, with two different histological stainings. © 2024 SPIE.

关键词： Bone

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Short Term Weather Forecasting Comparison Based on machine learning Algorithms 1

Short Term Weather Forecasting Comparison Based on Machine L...

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1st international conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Anjum Era, Chowdhury Abida Rahman, Mahmudur Alvi, Syada Tasmia Daffodil International University Dept. of CSE Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798350335569

Forecasting is the term used to describe the attempt to predict outcomes in unknown or uncertain situations. The most vital factor in many applications of weather forecasting is air temperature. The air temperature alone can't be the effecting point of forecasting weather. Moreover, with the advancement of computer technologies, forecasting models have been transformed widely. This paper approached a system that forecasts air temperature using machine learning algorithms. Several regression methods were employed to attempt to predict temperatures. This research evaluated four algorithms (Decision Tree, AdaBoost, Random Forest, and Gradient Boosting) on some meteorological data over three years (2015-2019), where 80 percent of the total data set was utilized for training and tested on 20 percent. The variables used include Wind speed, Relative Humidity, Dew point, and Air pressure. The objective was to determine which regressor achieves better outcomes for forecasting air temperature with the lowest error rate. This research concluded that the Random Forest Regressor is the most accurate in prediction. Here, MAE is used to determine the accuracy. On average, the Random forest had the lowest MAE value of 0.102, which was lower than the outcomes of the other three algorithms. © 2023 IEEE.

关键词： Weather forecasting

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Prototype-Anchored learning for learning with Imperfect Annotations 39

Prototype-Anchored Learning for Learning with Imperfect Anno...

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39th international conference on machine learning (ICML)

作者： Zhou, Xiong Liu, Xianming Zhai, Deming Jiang, Junjun Gao, Xin Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tsinghua Univ Beijing Peoples R China

The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resulting labels may be class imbalanced, noisy or human biased. It is challenging to learn unbiased classification models from imperfectly annotated datasets, on which we usually suffer from overfitting or underfitting. In this work, we thoroughly investigate the popular softmax loss and margin-based loss, and offer a feasible approach to tighten the generalization error bound by maximizing the minimal sample margin. We further derive the optimality condition for this purpose, which indicates how the class prototypes should be anchored. Motivated by theoretical analysis, we propose a simple yet effective method, namely prototype-anchored learning (PAL), which can be easily incorporated into various learning-based classification schemes to handle imperfect annotation. We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.

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Privacy-Preserving learning Models for Communication: A tutorial on Advanced Split learning 13

Privacy-Preserving Learning Models for Communication: A tuto...

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13th international conference on Information and Communication Technology Convergence, ICTC 2022

作者： Tran, Nam-Phuong Dao, Nhu-Ngoc Nguyen, The-Vi Cho, Sungrae School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of Sejong University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Along with the development of the internet, there is an explosion of data that is assembled from diverse sources such as mobile phones or sensors with unprecedented volumes. Big data paves the way for devising state-of-the-art machine learning models, which are employed for various tasks such as predicting or analysis, and bring massive impacts on people's daily lives. However, traditional learning architectures pose risks of privacy leakage. In deal with this problem, Split learning was introduced. Split learning is an emerging Distributed Collaborative machine learning approach with privacy-preserving nature. In this work, we first introduce the fundamentals of Split learning and then describe major challenges of current learning models. We then depict modern Split learning and their potential applications in variety fields. © 2022 IEEE.

关键词： Privacy-preserving techniques

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Linear and Support Vector Regression for COVID-19 Prognostication 3

Linear and Support Vector Regression for COVID-19 Prognostic...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Dumala, Anveshini Busi, Sai Tanmai Thokala, Sai Ruchitha Rani Gadamsetty, Pavithra Polisetty, Sri Hasa Vignan's Nirula Institute of Technology and Science for Women Dept. of Information Technology Guntur India

ISBN: (纸本)9798350322842

The 2019 worldwide coronavirus outbreak has had a significant impact on daily life. There is a chance that the economy and people could both suffer. Patients could receive an early identification, avoid the most severe stages of the illnesses, and the overall patient population may decrease due to ML's enhanced ability to detect illness and viral infections. Linear regression as well as support vector regression seem to be two such examples of machine learning methods used for data visualization and prediction. The number of new infections instances, the number of recovery times, and the fatality rate were all predicted. Incorporating these procedures and information, it present strategies for future forecasting. If machine learning can be used to foresee COVID-19, it could potentially reduce the death toll through early disease detection. The Mean Absolute Error is used to determine how well a Linear Regression or Support Vector Regression model performs. The promising results attest to its superior efficiency. © 2023 IEEE.

关键词： Coronavirus

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database for storing and accessing diabetes related data in a standardized way 23

Database for storing and accessing diabetes related data in ...

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23rd IEEE international Symposium on Computational Intelligence and Informatics, CINTI 2023

作者： Siket, Mate Kis, Andras Nandor Denes-Fazakas, Lehel Eigner, Gyorgy Kovacs, Levente Óbuda University Physiological Controls Research Center Hungary Institute for Computer Science and Control Kende utca 13-17 BudapestH-1111 Hungary Óbuda University Applied Informatics and Applied Mathematics Doctoral School Bécsi street 96/B BudapestH-1034 Hungary Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Óbuda University Hungary

ISBN: (纸本)9798350342949

Real-world data has a major importance in diabetes related research, especially considering the widespread applications of machine learning algorithms. There are several existing datasets of real-world data in the literature;however, they all have their specific formats, applied devices and file structures. The different charachteristics make it cumbersome to use multiple datasets for research purposes. We developed a pipeline for efficiently storing and accessing diabetes related data in a standardized way. We defined a standardized JSON format for the records and stored them in a MongoDB database. The pipeline is capable of uploading data from various sources by implementing a corresponding transformer script;also it is possible to extend it with algorithms which are called automatically for new entries in the database. The source code for constructing the pipeline is given in https://***/NeuroDiab/CINTI2023Diabdatabase. © 2023 IEEE.

关键词： Pipelines

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General Algorithm for learning from Grouped Uncoupled data and Pairwise Comparison data 29th

General Algorithm for Learning from Grouped Uncoupled Data a...

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29th international conference on Neural Information Processing

作者： Kohjima, Masahiro Nambu, Yuta Kurauchi, Yuki Yamamoto, Ryuji NTT Corp NTT Human Informat Labs Yokosuka Kanagawa Japan

ISBN: (纸本)9783031301049;9783031301056

Uncoupled regression is the problem of learning a regression model from uncoupled data that consists of a set of input values (unlabeled data) and a set of output values where the correspondence between the input and output is unknown. A recent study showed that a method using both uncoupled data and pairwise comparison data can learn the optimal model under some assumptions. However, this method cannot use grouped information and may require the implementation (almost) from scratch for some models. In this study, we extend the above existing method for handling group information and derive a general algorithm that can learn a model using the standard regression method by approximating the loss function. The effectiveness of the proposed method is confirmed by synthetic and benchmark data experiments.

关键词： Uncoupled data Regression Bregman divergence

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An Enhanced Intrusion Detection Model with FeedForward Neural Network Classifier 27

An Enhanced Intrusion Detection Model with FeedForward Neura...

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27th international conference on Advanced Communications Technology, ICACT 2025

作者： Amirjon, Asadov Gain, Mrityunjoy Oh Kim, Keon Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9791188428137

A major obstacle in the face of increasingly complex cyberattacks is network security. Proactive security measures require effective intrusion detection systems (IDS) that can precisely classify and categorize network threats. In order to improve network attack detection and classification, this paper proposes a reliable method utilizing a Feedforward Neural Network (FFNN) supplemented with Adaptive Synthetic (ADASYN) sampling. We created a model using the UNSWNB15 dataset that efficiently handles high-dimensional datasets by preprocessing data using a combination of polynomial feature transformation and one-hot encoding. The FFNN model is optimized for binary and multi-class classification tasks. It consists of layers of dense units with dropout and batch normalization. Our method’s efficacy is proven by rigorous training and validation procedures, where the model significantly increased its ability to handle class imbalances and improve classification accuracy. The synthesis of new training data by ADASYN was crucial in improving model performance, especially in underrepresented classes. Evaluation measures that highlight the potential of deep learning in network security applications are ROC-AUC scores and classification reports, which show a notable improvement in our IDS’s detection capabilities. The results show that advanced machine learning techniques can be used to enhance conventional intrusion detection systems and provide a means to build stronger network security designs. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Network intrusion

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Synthetic data generation based on grid deformation for waste recycling applications

Synthetic data generation based on grid deformation for wast...

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2023 IEEE international conference on Imaging Systems and Techniques, IST 2023

作者： Tsagarakis, Nick Antonaras, Alexandros Maniadakis, Michail Institute of Computer Science FORTH Heraklion Greece University of Nicosia School of Business Nicosia Cyprus

ISBN: (纸本)9798350330830

Today, waste recycling is supported by intelligent robots that use machine learning to identify and sort recyclables. The development of computer vision applications based on machine learning relies heavily on large datasets that are used to train deep neural network models. In recent years, methods that allow the creation of large training datasets from a limited initial set of images have been investigated. This paper describes a method in which segmented images of real recyclables (polyethylene terephthalate, PETE) are artificially deformed using mesh transformation to create new instances of the recyclable objects. The new instances are placed on real backgrounds to create synthetic images. This process allows the generation of large artificial datasets used for training neural networks. We evaluate the usability of these datasets by studying the extent to which they can improve the performance of trained models when applied in real and challenging industrial images. In particular, we consider the main metrics used to evaluate the performance of classification models, namely Accuracy, Precision and Recall. The results obtained show that including even small-scale object deformations in the artificial datasets can slightly improve the Accuracy and significantly improve the model Recall, while Precision of the model remains unchanged. © 2023 IEEE.

关键词： Computer vision

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The Development of a New System for Generating Training data of AI-Based Anomaly Detection 26

The Development of a New System for Generating Training Data...

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26th international conference on Advanced Communications Technology, ICACT 2024

作者： Truong, Thi My Choi, Won Seok Hyeon, Jeong Jang Choi, Seong Gon College of Information and Communication Engineering Chungbuk National University Chungdae-ro 1 Seowon-gu Korea Republic of Jj Solution INC Chungbuk National University Cheongju-si Chungcheongbuk-do Korea Republic of

ISBN: (纸本)9791188428120

This paper proposes a method and system for generating training data to support AI based anomaly detection. The use of AI in abnormal behavior detection systems is becoming increasingly popular, with active research on AI-based anomaly detection methods using machine learning. In general, existing research relies on open datasets provided by various laboratories like Swat, WaDI, SMAP and MSL for testing and validation purposes. Since the types of normal and malicious packets depend on the specific network to which they are applied, verifying AI-based anomaly detection methods using an open dataset may yield different results than when applied in real-world scenarios. In other words, open datasets captured from specific networks may not be suitable for applying AI-based abnormal detection methods to other networks. In addition, AI-based datasets may be insufficient for learning, leading to the use of simulated attacks. Open datasets are difficult to provide sufficient data for training and often contain malicious packets using simulated attack packets. Since malicious attacks are always transformed into new forms and developed in types, it is necessary to prepare a database for new malicious attacks and to learn about them. Therefore, one of the major challenges in developing effective anomaly detection systems is acquiring an appropriate dataset. To address this issue, we propose a system for extracting training data by collecting packets from the actual network to apply AI-based abnormal detection. Our proposed system offers the advantage of accurately reflecting the network's packet characteristics by gathering data from live networks for AI-based abnormality detection and dataset creation. Furthermore, as it incorporates a dataset for the latest malicious attacks within the network, it enables more practical anomaly detection compared to the use of existing datasets. We simulated and tested the proposed system at the laboratory level to confirm its behavior. © 20

关键词： Anomaly detection

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