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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33669 条记录，以下是4991-5000 订阅

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MosaicNet: A deep-learning-based multi-tile biomedical image stitching method

MosaicNet: A deep-learning-based multi-tile biomedical image...

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45th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Zhao, Botao Song, Ming Liu, Shengfeng Sun, Lan Jiang, Wentao Qian, Haotian Zhang, Xiao-Yong Zhang, Yu Jiang, Tianzi Zhejiang Lab Res Ctr Augmented Intelligence Hangzhou 311100 Peoples R China Chinese Acad Sci Brainnetome Ctr Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Fudan Univ Minist Educ Key Lab Computat Neurosci & Brain Inspired Intell Shanghai 200433 Peoples R China

ISBN: (纸本)9798350324471

Multi-tile image stitching aims to merge multiple natural or biomedical images into a single mosaic. This is an essential step in whole-slide imaging and large-scale pathological imaging systems. To tackle this task, a multi-step framework is usually used by first estimating the optimal transformation for each image and then fusing them into a whole image. However, the traditional approaches are usually time-consuming and require manual adjustments. Advances in deep learning techniques provide an end-to-end solution to register and fuse information of multiple tile images. In this paper, we present a deep learning model for multi-tile biomedical image stitching, namely MosaicNet, consisting of an aligning network and a fusion network. We trained the MosaicNet network on a large simulation dataset based on the VOC2012 dataset and evaluated the model on multiple types of datasets, including simulated natural images, mouse brain T2-weighted Magnetic Resonance Imaging (T2w-MRI) data, and mouse brain polarization sensitive-optical coherence tomography (PS-OCT) data. Our method outperformed traditional approaches on both natural images and brain imaging data. The proposed method is robust to different settings of hyper-parameters and shows high computational efficiency, up to approximately 32 times faster than the conventional methods.

关键词： Large dataset

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Training OOD Detectors in their Natural Habitats 39

Training OOD Detectors in their Natural Habitats

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39th international conference on machine learning (ICML)

作者： Katz-Samuels, Julian Nakhleh, Julia Nowak, Robert Li, Yixuan Univ Wisconsin Inst Fdn Data Sci Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI USA Univ Wisconsin Dept Elect & Comp Engn Madison WI USA

Out-of-distribution (OOD) detection is important for machine learning models deployed in the wild. Recent methods use auxiliary outlier data to regularize the model for improved OOD detection. However, these approaches make a strong distributional assumption that the auxiliary outlier data is completely separable from the in-distribution (ID) data. In this paper, we propose a novel framework that leverages wild mixture data-that naturally consists of both ID and OOD samples. Such wild data is abundant and arises freely upon deploying a machine learning classifier in their natural habitats. Our key idea is to formulate a constrained optimization problem and to show how to tractably solve it. Our learning objective maximizes the OOD detection rate, subject to constraints on the classification error of ID data and on the OOD error rate of ID examples. We extensively evaluate our approach on common OOD detection tasks and demonstrate superior performance. Code is available at https: //***/jkatzsam/woods_ood.

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machine learning for Job-flow Online Scheduling and Resources Allocation in Distributed Computing 6

Machine Learning for Job-flow Online Scheduling and Resource...

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6th international conference on Information Technologies in engineering Education, Inforino 2022

作者： Toporkov, Victor Yemelyanov, Dmitry Bulkhak, Artem National Research University MPEI Moscow Russia

ISBN: (纸本)9781665405775

In this work we study a promising approach for efficient online scheduling of job-flows in high performance and distributed parallel computing. The majority of job-flow optimization approaches, including backfilling and microscheduling, require apriori knowledge of a full job queue to make the optimization decisions. In a more general scenario when user jobs are submitted individually, the resources selection and allocation should be performed immediately in the online mode. In this work we consider a neural network prototype model trained to perform online optimization decisions based on a known optimal solution. For this purpose, we designed MLAK algorithm which implements 0-1 knapsack problem based on the apriori unknown utility function. In a dedicated simulation experiments with different utility functions MLAK provides resources selection efficiency comparable to a classical greedy algorithm. The interdisciplinary nature of the considered problem and its solution (including elements from the areas of optimization, scheduling algorithms, machine learning and simulation) makes it a good study example in technical disciplines. © 2022 IEEE.

关键词： Greedy algorithms Scheduling algorithms Computational modeling Scalability Prototypes machine learning Parallel processing

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Deep learning-based Secure machine-to-machine Communication in Edge-Enabled Industrial IoT 3

Deep Learning-based Secure Machine-to-Machine Communication ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Mankodiya, Harsh Jadav, Nilesh Kumar Tanwar, Sudeep Gupta, Rajesh Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India U. V. Patel College of Engineering Ganpat University Dept. of Computer Engineering Mehsana India

ISBN: (纸本)9781665462006

A still in-progress technology of the Industrial Internet of things (IIoT) involves the use of machine-to-machine (M2M) for communication. It can serve the whole purpose of automation in industries by integrating it with smart systems. However, hindrances in such a system lie in the security area. Traditional systems can be easily compromised by developing competent algorithms. The modern system with robust security sometimes fails to sustain the network traffic and load at deeper layers in the communication systems. To overcome the aforementioned issue, we propose including a trained neural network that can interpret incoming machine data streams in this study. It isolates malicious machines from normal machines by identifying attacks based on machine requests. Hence, this can prevent security breaches early. The performance of the trained neural networks is evaluated considering standard metrics such as F1-score and accuracy scores. The proposed model significantly improves the detection rate of malicious machine requests. © 2022 IEEE.

关键词： Internet of things

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Diabetes Care Survey Using Supervised and Unsupervised machine learning 3

Diabetes Care Survey Using Supervised and Unsupervised Machi...

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3rd international conference on Intelligent engineering and Management, ICIEM 2022

作者： Singh, Chandra Bhan Gupta, Anish Kumar, Dr. Rajeev Chandigarh University Computer Science and Engineering AIT Punjab Mohali India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Diabetes is brought about by undesirable ways of life, terrible eating routine, and work pressure, and it can prompt an assortment of lethal medical issues, including coronary episodes, fits, kidney disappointment, loss of feeling, etc. Diabetes can be successfully overseen assuming it is distinguished early and precisely. Approaches machine - learning are exceptionally successful in the early location and expectation of diabetes. The reason for this paper is to give a top to bottom assessment of diabetes conclusion utilizing directed and unaided machine learning calculations. This study incorporates diabetes conclusion papers from 2018 to 2021. Diabetes has been anticipated precisely utilizing choice tree-based calculations like C4.5 AdaBoost, XGBoost, and others. In huge datasets, solo machine learning procedures, for example, Principal Component Analysis and K-Mean can be utilized to choose credits and identify anomalies. As indicated by this review, as a mix of administered and unaided AI methods, K-Mean and Support Vector machine have likewise determined and assessed diabetes to have incredible exactness. © 2022 IEEE.

关键词： Decision trees

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Research and Application of Intelligent Diagnosis Technology in Permanent Magnet Generator for Stress Demagnetization Fault 3

Research and Application of Intelligent Diagnosis Technology...

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3rd international conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Shahbaz, Nadeem Chen, Yu Liang, Feng Zhao, Shouwang Zhang, Sichao Wang, Shuang Ma, Yong Zhao, Yong Deng, Wei School of Electrical Engineering Xi'An Jiaotong University Xi'an China Xi'An Thermal Power Research Institute Co. Ltd Xi'an China

ISBN: (纸本)9781665492812

Fault diagnosis before its existence and the complete shutdown is essentially critical for the whole industry. Fault diagnosis based on condition monitoring methods and artificial intelligence techniques are very potent. This paper assesses the machine-learning-based processes using air gap flux and stator current for eccentricity, magnet broken, and stator inter-turn short circuit faults in Permanent Magnet Generator (PMG). To apply machine learning, features are extracted via Discrete Wavelet Transform (DWT) technique for faulty and healthy conditions. Afterward, the classification learner toolbox in MATLAB is used to investigate various machine learning classifiers. The six fundamental classifiers comprising 23 sub-classifier algorithms are trained, whereby 16 out of 23 algorithms have achieved a perfect accuracy of (100 percent) while two have acquired an accuracy of more than 60 percent. The results indicate that air gap flux has performed better than stator current for fault diagnosis. © 2022 IEEE.

关键词： broken magnet faults eccentricity fault fault diagnosis machine learning Permanent magnet synchronous generator short circuit

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Increasing Quality Control of Ultrasonically Welded Joints Through Gaussian Process Regression 31st

Increasing Quality Control of Ultrasonically Welded Joints T...

引用

31st international conference on Flexible Automation and Intelligent Manufacturing

作者： Mongan, P. G. Hinchy, E. P. O'Dowd, N. P. McCarthy, C. T. Confirm Smart Mfg Res Ctr Limerick Ireland Univ Limerick Sch Engn Limerick V94 T9PX Ireland Univ Limerick Bernal Inst Limerick V94 T9PX Ireland

ISBN: (纸本)9783031176289;9783031176296

Due to the recent advances in digitisation of the manufacturing industry and the generation of manufacturing data, there is increasing interest to integrate machine learning on the shop floor to improve efficiency and quality control. Ultrasonic welding is an emerging joining process used in various manufacturing industries, and is an energy efficient, cost-effective method of joining similar or dissimilar materials. However, the quality of the joint achievable is heavily dependent on process input parameters. In this study, a Gaussian Process Regression (GPR) model is developed to map the relationship between process parameters and joint performance for ultrasonically welded aluminium joints, with a view to improving quality control in a manufacturing setting. Initially, a 33 full factorial design of experiments is conducted to investigate the influential parameters, then a GPR model is trained on the experimental data. In-process sensor data is also used to infer process performance. To assess the prediction performance of the model, ten unseen parameter combinations are predicted and compared to their respective experimental result. The model demonstrates a high level of accuracy producing a Pearson's correlation coefficient of 0.982 between the predicted and actual results for all data. The mean relative predictive error for unseen data is 7.35%.

关键词： Ultrasonic welding machine learning Gaussian process regression Quality control

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Research on Defect Detection in Printing Barcode Area Based on machine learning 20

Research on Defect Detection in Printing Barcode Area Based ...

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20th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Qi, Wenjing Liu, Jihong Li, Yanjing Liu, Jiawei Xi'an University of Posts &telecommunications School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350356328

The accuracy and aesthetics of information in the printing barcode area are of great significance in practical life. Flat printing defect detection is quite common, and there is currently almost no involvement in non flat printing defect detection. This work focuses on defect detection design for non flat printed barcode areas of glass bottles. After dividing the barcode area into two parts, barcode and character, we will perform defect detection on these two parts separately. For the barcode part, using support vector machines in machine learning to construct features and classify barcode defects. For the character part, a character quality evaluation method based on gray projection is adopted to output the character quality score. By combining the classification results of barcode defects with subjective judgment of quality scores, defect detection of barcode areas on non flat surfaces of glass bottles has been achieved. © 2024 IEEE.

关键词： Glass bottles

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OPTIMIZING MODELS FOR PREDICTING TORQUE ON BIT USING data FROM THE VOLVE FIELD IN NORWAY 41

OPTIMIZING MODELS FOR PREDICTING TORQUE ON BIT USING DATA FR...

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ASME 2022 41st international conference on Ocean, Offshore and Arctic engineering, OMAE 2022

作者： Höhn, Patrick Kreem Bashara, Ahmed Rahim Paz, Carlos Oppelt, Joachim Drilling Simulator Celle Clausthal University of Technology Lower Saxony Celle29221 Germany Institute of Subsurface Energy Systems Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9780791885956

Torsional oscillations can cause severe damage to downhole tools and may result in expensive fishing and sidetracking operations. The drilling industry is aware of this problem and still looking for suitable solutions to determine the drivers of the oscillations, and to quantify their effects. The mitigation of this problem requires a detailed knowledge of the parameters controlling the drilling process. Nowadays, modeling is a useful tool for describing the downhole processes using the stream of data acquired from the sensors installed in the drilling equipment. This paper focuses on torque on bit which is directly connected with torsional oscillations. The model generation is performed, either by fitting empirical models with measured data or by creating new machine learning models. Five empirical literature models are parametrized using the optimization module of the Python library SciPy. machine learning models are generated using Scikit-learn with measurement data from the Volve field in Norway. For the current testing dataset Random Forest showed the highest accuracy with a R2-score of 0.767. Other machine learning algorithms showed a comparable accuracy. However, empirical models failed to achieve reliable results. In future, the generated models can be used to optimize drilling parameters to prevent technical drilling problems. Copyright © 2022 by ASME.

关键词： Decision trees

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data Acquisition and Image Augmentation using Adversarial Networks

Data Acquisition and Image Augmentation using Adversarial Ne...

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2022 international conference on Edge Computing and Applications, ICECAA 2022

作者： Lathamaheswari, U. Jebathagam, J. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

It is possible to avoid challenges causedby overfitting, and the performance of machine learning algorithms can improve when there is a large amount of data. The improved training data diversity that is offered by data augmentation, which does not require the collection of fresh data, is beneficial to the algorithms that are used in machine learning. In this paper, we collect the data from the associated paddy leaf dataset, however, it is found that the collected data is insufficient for conducting the training and testing of a classifier. In order to increase the samples for training and testing to predict the leaf disease using a machine or a deep learning classifier, it is essential to increase the number of instances for efficient classification. In regards to this, the study uses Generative Adversarial Networks (GANs) to increase the images required for training and testing using image cropping, flipping, color transformation, rotation and noise injection on the collected datasets. The simulation is conducted in python for the generation of images from the input datasets and we store these augmented images for further processing. © 2022 IEEE.

关键词： Generative adversarial networks

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