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检索条件"任意字段=IEEE Workshop on Machine Learning for Signal Processing"

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Quantifying Intertidal Zone Species Using Semantic Segmentation

Quantifying Intertidal Zone Species Using Semantic Segmentat...

引用

ieee International Symposium on signal processing and Information Technology (ISSPIT)

作者： Shah, Mitali Dilly, Geoffrey Meeker, Kaylen Isaacs, Jason T. CSU Channel Isl Comp Sci Dept Camarillo CA 93012 USA CSU Channel Isl Biol Dept Camarillo CA 93012 USA

ISBN: (纸本)9781538675687

As anthropogenic impacts on marine ecosystems accelerates (e.g. warming, acidification, eutrophication, etc), it is essential to build robust datasets that establish biological baseline data and capture long-term trends in shifting species abundance and diversity. This data has traditionally been collected through continual revisits by skilled ecologists and taxonomists to long-term ecological monitoring sites. One novel technique developed by an intertidal ecology research group at California State University Channel Islands (CSUCI) builds 1m-wide photo-transects for the length of the tidal zone (20m from splash to low zone) at two sites on Santa Rosa Island. These photos are stitched together using software and offer high-resolution swaths of information at the island, taken twice a year. A machine learning technique, semantic segmentation, has been employed to automate the analysis of these large images, focusing first on a dominant algal species of rockweed Silvetia compressa. This automation will greatly reduce the time needed and human error involved in scoring and quantifying these transects. The study involves developing a convolutional neural network using transfer learning on a publicly available network.

关键词： semantic segmentation transfer learning convolutional neural network

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The European Space Agency Free SBAS Software Developments: learning, Practising and Accessing the EGNOS Performances in Real-Time

The European Space Agency Free SBAS Software Developments: L...

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6th European Space Agency (ESA) workshop on Satellite Navigation Technologies (NAVITEC)/ European workshop on GNSS signals and signal processing

作者： Mathur, A. R. Toran, F. Pandele, C. A. Houdek, M. Doubek, J. European Space Agcy EGNOS Project Off Toulouse France Iguassu Software Syst Prague Czech Republic

ISBN: (纸本)9781467320108;9781467320092

Since 2002, the European Space Agency (ESA) has introduced two important data access elements. First, the SISNeT (signal-In-Space over the interNET) data server, and later, in 2003, the EMS (EGNOS Message Server) data server, offering real-time and online access to the messages transmitted by the European Space Agency (ESA) EGNOS System, respectively. In addition to the wide mosaic of applications that SISNeT has opened and the potential of EMS in the context of EGNOS performance qualification and monitoring, ESA has found a remarkable potential oriented to SBAS Education behind those two services. Therefore, ESA is working, since 2002, on the development of five tools which support not only SBAS performance monitoring, but also SBAS Education. All of them are available for free download, with no additional constraints or conditions. These tools are widely used in different contexts, including post-graduate courses in European universities, R&D activities in technical centers, European SBAS-related projects, etc. These tools are mainly based on the exploitation of the potential offered by SISNeT and EMS.

关键词： EGNOS software tools SBAS education

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iR6mA-RNN: Identifying N6-Methyladenosine Sites in Eukaryotic Transcriptomes using Recurrent Neural Networks and Sequence-embedded Features 22

iR6mA-RNN: Identifying N6-Methyladenosine Sites in Eukaryoti...

引用

22nd ieee Statistical signal processing workshop (SSP)

作者： Nguyen, Binh P. Thanh-Hoang Nguyen-Vo Loc Nguyen Trinh, Quang H. Baliuag, Chalinor Do, Trang T. T. Rahardja, Susanto Victoria Univ Wellington Sch Math & Stat Wellington 6140 New Zealand Wellington Inst Technol Sch Innovat Design & Technol Lower Hutt 5012 New Zealand Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Singapore Inst Technol Infocomm Technol Cluster Singapore 138683 Singapore

ISBN: (纸本)9781665452458

As a common biological event observed in all living creatures, RNA modification is an essential post-transcriptional factor that regulates the activity, localization, and stability of RNAs. Multiple diseases are associated with RNA modification. N6-methyladenosine (6mA) modification of RNA is one of the most frequent events that affect the translational processes and structural stability of modified transcripts and control transcriptional processes in cell state maintenance and transition. To detect 6mA sites in eukaryotic transcriptomes, a number of computational models were developed as online applications to assist experimental scientists in reducing human effort and budget. However, most of those online web servers are now either outdated or inaccessible. In this study, we propose iR6mA-RNN, an effective computational framework using recurrent neural networks and sequence-embedded features, to predict possible 6mA sites in eukaryotic transcriptomes. When tested on an independent test set, the proposed model achieved an area under the receiver operating characteristic curve of 0.7972 and an area under the precision-recall curve of 0.7785. Our model also outperformed the other two existing methods. Results from another sensitivity analysis confirmed the stability of the model as well.

关键词： N6-methyladenosine RNA modification deep learning recurrent neural network sequence-embedded features

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Extendable Neural Matrix Completion

Extendable Neural Matrix Completion

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2018 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2018

作者： Nguyen, Duc Minh Tsiligianni, Evaggelia Deligiannis, Nikos Vrije Universiteit Brussel Pleinlaan 2 BrusselsB-1050 Belgium

ISBN: (纸本)9781538646588

Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image processing and data gathering to classification and recommender systems. Recently, deep neural networks have been proposed as latent factor models for matrix completion and have achieved state-of-the-art performance. Nevertheless, a major problem with existing neural-network-based models is their limited capabilities to extend to samples unavailable at the training stage. In this paper, we propose a deep two-branch neural network model for matrix completion. The proposed model not only inherits the predictive power of neural networks, but is also capable of extending to partially observed samples outside the training set, without the need of retraining or fine-tuning. Experimental studies on popular movie rating datasets prove the effectiveness of our model compared to the state of the art, in terms of both accuracy and extendability. © 2018 ieee.

关键词： Training Neural networks machine learning Computational modeling Predictive models Collaboration signal processing"

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Dynamic Bayesian Networks for NLU simulation with applications to dialog optimal strategy learning

Dynamic Bayesian Networks for NLU simulation with applicatio...

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31st ieee International Conference on Acoustics, Speech and signal processing

作者： Pietquin, Olivier Dutoit, Thierry Ecole Super Elect Superlec STS Team F-57070 Metz France

ISBN: (纸本)9781424404681

In this paper, we propose to add a model for NLU-related error generation in a modular environment for computer-based simulation of man-machine spoken dialogs. This model is jointly designed with a user model. Both of them are based on the same underlying Bayesian Network used with different parameters in such a way that it can generate a consistent user behavior, according to a goal and the interaction history, and been used as a concept classifier. The proposed simulation environment was used to train a reinforcement-leaming algorithm on a simple form-filling task and the results of this experiment show that the addition of the NLU model helps pointing out problematic situations that may occur because of misunderstandings and modifying the dialog strategy accordingly.

关键词： Bayesian networks

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AUTOMATING DETECTION OF PAPILLEDEMA IN PEDIATRIC FUNDUS IMAGES WITH EXPLAINABLE machine learning 29

AUTOMATING DETECTION OF PAPILLEDEMA IN PEDIATRIC FUNDUS IMAG...

引用

ieee International Conference on Image processing (ICIP)

作者： Avramidis, Kleanthis Rostami, Mohammad Chang, Melinda Narayanan, Shrikanth Univ Southern Calif Signal Anal & Interpretat Lab Los Angeles CA 90089 USA Univ Southern Calif USC Roski Eye Inst Los Angeles CA 90089 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema. Our approach is based on optic disc localization and detection of explainable papilledema indicators through data augmentation. Experiments on real-world clinical data demonstrate that our proposed method is effective with a diagnostic accuracy comparable to expert ophthalmologists(1).

关键词： human-centered AI model explainability papilledema pseudopapilledema multi-view learning

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machine learning Based Hospital Mortality Prediction Using Synthetic Minority Oversampling Technique 2

Machine Learning Based Hospital Mortality Prediction Using S...

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2nd ieee International Symposium on Sustainable Energy, signal processing and Cyber Security, iSSSC 2022

作者： Shah, Dhyey Jariwala, Devam Gupta, Rajeev Bharti, Santosh Pandit Deendayal Energy University Computer Science Engineering Gujarat Gandhinagar India

ISBN: (纸本)9781665490566

This paper aims to assess the efficiency, adaptiveness, and feasibility of various machine learning Algorithms for predicting mortality rates inside the hospital. The predictors and factors associated with in-hospital mortality rates in Intensive Care Units (ICUs) and other emergency sections have been poorly organized and categorized. Various Data Mining Techniques, and machine learning algorithms like Random Forest, XG-Boost, and Decision Tree, have been employed over a legitimate public dataset of various patients and their performance is measured and compared within and with existing works. © 2022 ieee.

关键词： Data mining

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GMM-HMM ACOUSTIC MODEL TRAINING BY A TWO LEVEL PROCEDURE WITH GAUSSIAN COMPONENTS DETERMINED BY AUTOMATIC MODEL SELECTION

GMM-HMM ACOUSTIC MODEL TRAINING BY A TWO LEVEL PROCEDURE WIT...

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2010 ieee International Conference on Acoustics, Speech, and signal processing

作者： Su, Dan Wu, Xihong Xu, Lei Peking Univ Minist Educ Key Lab Machine Percept Speech & Hearing Res Ctr Beijing Peoples R China

ISBN: (纸本)9781424442966

This paper investigates the Bayesian Ying-Yang (BYY) learning for speech recognition via Gaussian mixture models (GMMs) based Hidden Markov models (HMMs). A two level procedure is proposed with the hidden Markov level trained still under the maximum likelihood principle by the Baum-Welch algorithm but with the GMMs level trained under the BYY best harmony. We proposed a new batch way EM-like Ying-Yang alternation algorithm and used it as a plug-in block to the Baum-Welch algorithm. The advantage is that number of GMM components can be automatically determined during this BYY harmony learning and that the resulted model parameters become less affected than EM-ML training by the problem of overfitting and singular solution. In comparison with the standard EM-ML training and classical model selection criterions, including BIC and AIC, speech recognition experiments in a large vocabulary task on the Hub4 broadcast news database shown that the proposed algorithm provides an improved performance and also good convergence.

关键词： speech recognition model selection Bayesian Ying-Yang learning GMMs HMMs

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Ratatouille: A tool for Novel Recipe Generation 38

Ratatouille: A tool for Novel Recipe Generation

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38th ieee International Conference on Data Engineering (ICDE)

作者： Goel, Mansi Chakraborty, Pallab Ponnaganti, Vijay Khan, Minnet Tatipamala, Sritanaya Saini, Aakanksha Bagler, Ganesh Indraprastha Inst Informat Technol IIIT Delhi Ctr Computat Biol New Delhi India Indraprastha Inst Informat Technol IIIT Delhi Dept Comp Sci New Delhi India

ISBN: (纸本)9781665481045

Due to availability of a large amount of cooking recipes online, there is a growing interest in using this as data to create novel recipes. Novel Recipe Generation is a problem in the field of Natural Language processing in which our main interest is to generate realistic, novel cooking recipes. To come up with such novel recipes, we trained various Deep learning models such as LSTMs and GPT-2 with a large amount of recipe data. We present Ratatouille (https://***/ratatouille2/), a web based application to generate novel recipes.

关键词： Novel Recipe Generation Deep learning LSTM machine learning GPT2

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learning OR MODELLING? AN ANALYSIS OF SINGLE IMAGE SEGMENTATION BASED ON SCRIBBLE INFORMATION

LEARNING OR MODELLING? AN ANALYSIS OF SINGLE IMAGE SEGMENTAT...

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ieee International Conference on Image processing (ICIP)

作者： Droege, Hannah Moeller, Michael Univ Siegen Holderlinstr 3 D-57076 Siegen Germany

ISBN: (纸本)9781665441155

Single image segmentation based on scribbles is an important technique in several applications, e.g. for image editing software. In this paper, we investigate the scope of single image segmentation solely given the image and scribble information using both convolutional neural networks as well as classical model-based methods, and present three main findings: 1) Despite the success of deep learning in the semantic analysis of images, networks fail to outperform model-based approaches in the case of learning on a single image only. Even using a pretrained network for transfer learning does not yield faithful segmentations. 2) The best way to utilize an annotated data set is by exploiting a model-based approach that combines semantic features of a pretrained network with the RGB information, and 3) allowing the networks prediction to change spatially and additionally enforce this variation to be smooth via a gradient-based regularization term on the loss (double backpropagation) is the most successful strategy for pure single image learning-based segmentation.

关键词： Image Segmentation Scribbles Energy Minimization Methods machine learning

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