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RADIOMIC-BASED FRAMEWORK FOR EARLY DIAGNOSIS OF LUNG CANCER 16

RADIOMIC-BASED FRAMEWORK FOR EARLY DIAGNOSIS OF LUNG CANCER

引用

16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Shaffie, Ahmed Soliman, Ahmed Abu Khalifeh, Hadil Ghazal, Mohammed Taher, Fatma Elmaghraby, Adel Keynton, Robert El-Baz, Ayman Univ Louisville Bioengn Dept BioImaging Lab Louisville KY 40292 USA Univ Louisville Comp Engn & Comp Sci Dept Louisville KY 40292 USA Abu Dhabi Univ Chem Engn Dept Abu Dhabi U Arab Emirates Abu Dhabi Univ Dept Elect & Comp Engn Abu Dhabi U Arab Emirates Zayed Univ Coll Technol Innovat Dubai U Arab Emirates

ISBN: (纸本)9781538636411

This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF) model because of their ability to describe the spatial non-uniformity in the texture of the nodule regardless of its size. The shape features are modeled using Spherical Harmonic expansion and some basic geometric features in order to have a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 93.12%, 92.47%, and 93.60% respectively.

关键词： Computer Aided Diagnosis HOG MGRF Computer Tomography Spherical Harmonics autoencoder

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Initial Work Towards a Framework for Timely Prediction of Merchant Churn in Informal Markets in the Real World with Small Quantities of Unbalanced Data in order to Influence Behaviour

Initial Work Towards a Framework for Timely Prediction of Me...

引用

IEEE AFRICON Conference - Powering Africa s Sustainable Energy for AD Agenda - The Role ofICT and Engineering.

作者： Butgereit, Laurie Nelson Mandela Univ Sch ICT Port Elizabeth South Africa

ISBN: (纸本)9781728132891

South Africa has a a high percentage (over 80%) of pre-paid cell phone users. These users need easy access to commercial entities which can allow them to buy airtime and data vouchers. In South Africa, most major supermarkets, department stores, banks, and petrol stations sell such vouchers. To augment this, however, there is a growing population of informal traders which also sell such vouchers via apps on low cost Android point-of-sale (POS) devices. These Android POS devices connect to one of a handful of companies which aggregate vouchers from the various cell phone providers. This handful of companies is highly competitive and informal merchants often churn between the companies. This paper looks at the use of neural networks to predict whether or not a merchant is going to churn. This prediction gives the company time to offer the merchant some incentive not to migrate to the competition. Although there is a rich collection on literature on predicting merchant churn, this research is done specifically in the informal market with small quantities of data.

关键词： churn neural network autoencoder

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An Effective Model for Detect Dump Truck Plate

An Effective Model for Detect Dump Truck Plate

引用

International Symposium on Multimedia and Communication Technology (ISMAC)

作者： Kim, Jae Seoung Whangbo, Taeg Keun Gachon Univ Dept Comp Sci Seongnam Si South Korea

ISBN: (纸本)9781728119182

This paper suggests a method for enhancing vehicle license plate recognition performance and detection accuracy under unfavorable conditions through improving images by using the vehicle license plate region detection and denoising autoencoder technologies of Faster R-CNN in order to improve misrecognition in detection performance due to the slanting or breakage of license plates, or the influence of illumination, which are chronic problems in automatic license plate recognition system at construction sites. The learning of 3,000 images collected from actual construction sites were conducted for this paper, and the improvement of illumination resulted in more than 30% improvement in accuracy.

关键词： FasterR-CNN autoencoder Denoising License Plate Detection

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ComNE: Reinforcing Network Embedding with Community Learning 26th

ComNE: Reinforcing Network Embedding with Community Learning

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Fathy, Ahmed Li, Kan Beijing Inst Technol Sch Comp Sci & Technol Beijing 10081 Peoples R China

ISBN: (纸本)9783030368081;9783030368074

Learning network embedding for large-scale networks have been attracting increasing attention due to their importance in supporting numerous network analytic and data mining tasks such as node classification, clustering and visualization. In this paper, we present a novel framework for learning large-scale network embedding incorporating network topology and community structural information. Most existing network embedding methods tend to embed network topology and ignore the partially labeled community structure information that exist in real-world networks and thus are unable to efficiently learn and capture the community structure of real-world networks. Unlike existing works, our framework integrates the network topology and community structure into the learning process. We propose a deep autoencoder model to generate low-dimensional feature representations efficiently through learning network reconstruction and community classification tasks. The experimental results on several real-world networks show that our framework outperforms the state-of-the-art methods.

关键词： Large-scale network embedding Network representation learning autoencoder Community prediction

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Land Cover Classification for Synthetic Aperture Radar Imagery by Using Unsupervised 9

Land Cover Classification for Synthetic Aperture Radar Image...

引用

9th International Conference on Recent Advances in Space Technologies (RAST)

作者： Yumu, Duygu Ozkazanc, Y. ASELSAN REHIS Dept Surveillance Radar Syst Engn Ankara Turkey Hacettepe Univ Dept Elect & Elect Engn Ankara Turkey

ISBN: (纸本)9781538694480

Land cover classification is an important application in remote sensing and it plays a critical role in urban planning, land cover change monitoring and agricultural monitoring. Synthetic aperture radar (SAR) has long been recognized as an effective sensing tool for land cover monitoring, because of its ability of capturing images day and night without affected by weather conditions. On the other hand, the interpretation of SAR imagery and to get many labeled SAR images are still a challenging problem for remote sensing. Therefore, the aim of this paper is the implementation of unsupervised classification methods which require unlabeled data and comparison of their performances. In order to achieve this goal, Vertical - Vertical (VV) and Vertical - Horizontal (VII) polarization Sentinel-1 SAR images are used. The acquisition year of these images is 2018. Principal Component Analysis (PCA), Kernel PCA, Eigenface and autoencoder feature extraction methods and also user defined features are implemented for unsupervised classification and the results have been reported and discussed. The performances of these methods are compared by using the cluster validity indices which is the criterion for unsupervised classification, and also the optimum cluster number is determined. Results demonstrate that, KPCA and autoencoder methods are better for VII polarization. Also, user defined features and autoencoder are better for VV polarization.

关键词： Remote sensing SAR Land Cover Unsupervised Classification Deep Learning autoencoder PCA Eigenfaces Kernel PCA Clustering Cluster validity index K-means

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Injury Risk Prediction for Body Regions after Motor Vehicle Collisions to Guide CT Scanning Decisions

Injury Risk Prediction for Body Regions after Motor Vehicle ...

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Systems and Information Engineering Design Symposium (SIEDS)

作者： Sun, Jing You, Fang Sun, Bowei Hartka, Thomas Flower, Abigail Univ Virginia Data Sci Inst Charlottesville VA 22903 USA Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22903 USA

ISBN: (纸本)9781728109985

Full body computed tomography (CT) is a widely used clinical evaluation method to detect hidden injury for victims of motor vehicle collisions (MVCs). However, full body CT scans are time consuming and expensive for both healthcare service providers and MVC victims. Injury risk prediction models that support CT scanning decisions are therefore highly desired. Existing studies have implemented logistic regression models to predict injury risk for victims' major body regions, including head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine and lumbar spine. The work presented here involved the application of novel approaches to improve the prediction results. This study focused on examining patient information and crash data for front seat adult passengers using data from the National Automotive Sampling System - Crashworthiness Data System from 2000 to 2015. This dataset is imbued with a large amount of missingness and is highly imbalanced. Various imputation methods were employed in order to preserve the greatest amount of relevant historical data possible. The high imbalance in the data was resolved by the implementation of downsampling and synthetic minority over-sampling technique. Models that were applied in this study include logistic regression, random forests, support vector machines and gradient boosting. autoencoders were also deployed to generate features of high importance to improve prediction results. The resulting models for all seven regions yielded sensitivities and specificities of at least 96% and 30%, respectively. Overall, these models were developed not to replace physicians' decisions, but to guide their CT scanning decisions.

关键词： autoencoder healthcare injury risk prediction motor vehicle collision

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Toward an Online Network Intrusion Detection System Based on Ensemble Learning 12

Toward an Online Network Intrusion Detection System Based on...

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12th IEEE International Conference on Cloud Computing (IEEE CLOUD) held as Part of IEEE World Congress on Services (IEEE SERVICES)

作者： Hsu, Ying-Feng He, ZhenYu Tarutani, Yuya Matsuoka, Morito Osaka Univ Cybermedia Ctr Osaka Japan Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan Okayama Univ Okayama Japan

ISBN: (纸本)9781728127057

With information technology growing and rapidly increasing, ubiquitous networking technology generates a massive amount of data and is integrated into our daily life. Network intrusion detection systems (NIDS) are essential for organizations to ensure the safety and security of their communication and information. In general, there are two types of NIDS: signature-based (SNIDS) and anomaly-based (ANDIS). Most modern NIDS solutions are signature-based techniques, which require a routine signature update and cannot detect unknown types of attacks. However, ANDIS has been extensively studied and is considered a better alternative to NIDS. In this paper, we present a stacked ensemble learning based ANIDS that consists of autoencoder (AE), support vector machine (SVM), and random forest (RE) models. To show the overall applicability of our approach, we demonstrate our work through two well-known NIDS benchmark datasets: NSL-KDD and UNSW-NB15 and a real campus network log, which includes about 300 million daily records. We compare our method to three different machine learning classical models and two other reported study results. Our test result implies that our proposed method can also limit both false positive and false negative predictions.

关键词： network intrusion detection system NIDS ensemble learning deep learning autoencoder random forest support vector machine

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AUTOENCODING HRTFS FOR DNN BASED HRTF PERSONALIZATION USING ANTHROPOMETRIC FEATURES 44

AUTOENCODING HRTFS FOR DNN BASED HRTF PERSONALIZATION USING ...

引用

44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chen, Tzu-Yu Kuo, Tzu-Hsuan Chi, Tai-Shih Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781479981311

We proposed a deep neural network (DNN) based approach to synthesize the magnitude of personalized head-related transfer functions (HRTFs) using anthropometric features of the user. To mitigate the over-fitting problem when training dataset is not very large, we built an autoencoder for dimensional reduction and establishing a crucial feature set to represent the raw HRTFs. Then we combined the decoder part of the autoencoder with a smaller DNN to synthesize the magnitude HRTFs. In this way, the complexity of the neural networks was greatly reduced to prevent unstable results with large variance due to overfitting. The proposed approach was compared with a baseline DNN model with no autoencoder. The log-spectral distortion (LSD) metric was used to evaluate the performance. Experiment results show that the proposed approach can reduce LSD of estimated HRTFs with greater stability.

关键词： HRTFs Anthropometry autoencoder DNN Spatial audio

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LOOK FOR SALIENCY IN HYPERSPECTRAL IMAGES 39

LOOK FOR SALIENCY IN HYPERSPECTRAL IMAGES

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Shen, Zhiqi Luo, Xiaoyan Xue, Rui Wang, Hongyan Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Xian Satellite Control Ctr Xian Peoples R China

ISBN: (纸本)9781538691540

Image saliency detection plays an important role in many vision tasks such as object detection, visualization, and classification. Many existing methods are applicable to trichromatic or grayscale images, but most of them are failure to remote sensing hyperspectral images with hundreds of bands. In this paper, a novel purity-aware saliency method is specifically proposed for hyperspectral images. Firstly, an autoencoder model for unmixing is designed to extract the abundance. Then, an abundance fraction vector norm is calculated as pixel spectral purity. On real-world hyperspectral images, our saliency detection method achieves good performance in holding structure information and purity distribution.

关键词： hyperspectral image saliency autoencoder abundance

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Unsupervised low-rank representations for speech emotion recognition 20

Unsupervised low-rank representations for speech emotion rec...

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Interspeech Conference

作者： Paraskevopoulos, Georgios Tzinis, Efthymios Ellinas, Nikolaos Giannakopoulos, Theodoros Potamianos, Alexandros Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece Behav Signal Technol Los Angeles CA 90027 USA Univ Illinois Dept Comp Sci Champaign IL USA

We examine the use of linear and non-linear dimensionality reduction algorithms for extracting low-rank feature representations for speech emotion recognition. Two feature sets are used, one based on low-level descriptors and their aggregations (IS10) and one modeling recurrence dynamics of speech (RQA), as well as their fusion. We report speech emotion recognition (SER) results for learned representations on two databases using different classification methods. Classification with low-dimensional representations yields performance improvement in a variety of settings. This indicates that dimensionality reduction is an effective way to combat the curse of dimensionality for SER. Visualization of features in two dimensions provides insight into disriminatory abilities of reduced feature sets.

关键词： Non-linear dimensionality reduction emotion recognition speech manifold learning autoencoder

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