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检索条件"主题词=Deep Autoencoder"

共 237 条记录，以下是181-190 订阅

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A New Approach for Road Type Classification Using Multi-stage Graph Embedding Method 15th

A New Approach for Road Type Classification Using Multi-stag...

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15th Mexican Conference on Pattern Recognition (MCPR)

作者： Molefe, Mohale E. Tapamo, Jules R. Univ KwaZulu Natal Durban South Africa

ISBN: (纸本)9783031337826;9783031337833

Classifying road types using machine learning models is an important component of road network intelligent systems, as outputs from these models can provide useful traffic information to road users. This paper presents a new method for road-type classification tasks using a multi-stage graph embedding method. The first stage of the proposed method embeds high-dimensional road segment feature vectors to a smaller compact feature space using deep autoencoder. The second stage uses Graph Convolution Neural Networks to obtain an embedded vector for each road segment by aggregating information from neighbouring road segments. The proposed method outperforms the state-of-the-art Graph Convolution Neural Networks embedding method for solving a similar task based on the same dataset.

关键词： Road Networks Intelligent Systems Graph embedding methods deep autoencoder Graph Convolution Neural Networks

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Using deep Learning and Steam User Data for Better Video Game Recommendations 20

Using Deep Learning and Steam User Data for Better Video Gam...

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ACM Southeast Conference (ACMSE)

作者： Wang, Dylan Moh, Melody Moh, Teng-Sheng San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9781450371056

Recommendation systems have been used widely in many industries, including online retail, movies, and news media. Indeed, video game recommendation systems are one of the most important tools available to users and game distribution platforms today. A good recommender can help customers find games they might like faster, not only making it easier for them but also helping game distributors and developers to increase their sales and improve customer satisfaction ratings. One such platform that can greatly benefit from a recommendation system is Steam, the largest digital PC game distribution platform. Steam sees over a dozen million users login every day. It collects a considerable amount of data on each user, and this data may be used to help make better game recommendations. This paper proposes STEAMer, a solution for a new video game recommendation system for the Steam platform. STEAMer utilizes the Steam user data in conjunction with a deep autoencoder learning model to generate potential recommendations;we also apply the additional user data to an existing deep neural network-based recommendation system. Performance evaluation shows that the additional user data does indeed improve recommendation performance. Furthermore, when both systems use the additional user data, the deep autoencoder-based STEAMer still proves superior to the baseline deep neural network-based system in both mean average precision @ 10 (MAP@10) and normalized discounted cumulative gain @ 10 (NDCG@10) scores and in diversity.

关键词： deep Learning deep autoencoder deep Neural Network Recommendation System Video Game Steam

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A Semi-supervised Graph deep Neural Network for Automatic Protein Function Annotation 9th

A Semi-supervised Graph Deep Neural Network for Automatic Pr...

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9th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Sellami, Akrem Sarker, Bishnu Tabbone, Salvatore Devignes, Marie-Dominique Aridhi, Sabeur Univ Lorraine CNRS Inria LORIA Nancy France Meharry Med Coll Nashville TN USA

ISBN: (纸本)9783031078026;9783031078019

The protein function annotation based on functional properties like the Enzyme Commission (EC) numbers is a very challenging task that aims to understand life at the molecular level. Especially, the size of features for each protein is very huge and the number of labeled samples is limited, which can significantly affect the annotation accuracy. To address these issues, we propose a novel semi-supervised graph deep learning model that aims to learn better latent representations for each protein/node by taking into account the neighborhood information in order to improve the annotation. Firstly, we extract a set of features from raw protein data. Each protein is associated with a 1-D feature vector that represents its InterPro domain composition. As D, the number of possible interPro domains, is very high (>11,000), we design a deep autoencoder model (DAE) that seeks to find an efficient representation of the domain composition of proteins in a lower dimensional latent space. Then, we construct a protein graph where each node is a protein associated with its latent representation vector and each edge is weighted by the Euclidean distance between the two nodes it connects. Finally, we train a semi-supervised graph neural network (SGNN) for the automatic protein function annotation using the constructed protein graph. Experiments are conducted on four reference proteomes in UniProtKB/SwissProt, including Human, Arabidopsis Thaliana, Mouse, and Rat. Experimental results show that the proposed model is competitive for protein function annotation compared to existing methods.

关键词： deep autoencoder Feature extraction Protein function annotation Graph representation learning

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Ensembling Sparse autoencoders for Network Covert Channel Detection in IoT Ecosystems 26th

Ensembling Sparse Autoencoders for Network Covert Channel De...

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26th International Symposium on Methodologies for Intelligent Systems (ISMIS)

作者： Cassavia, Nunziato Caviglione, Luca Guarascio, Massimo Liguori, Angelica Zuppelli, Marco Inst High Performance Comp & Networking Via Pietro Bucci 8-9C I-87036 Arcavacata Di Rende Italy Inst Appl Math & Informat Technol Via Marini 6 I-16149 Genoa Italy Univ Calabria Via Pietro Bucci Arcavacata Di Rende Italy

ISBN: (纸本)9783031165641;9783031165634

Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly orchestrate botnets composed of simple devices. Therefore, we illustrate a solution based on deep Learning for the detection of covert channels targeting the TTL field of IPv4 datagrams. To this aim, we take advantage of an autoencoder ensemble to reveal anomalous traffic behaviors. An experimentation on realistic traffic traces demonstrates the effectiveness of our approach.

关键词： deep autoencoder Ensemble method Covert channel Intelligent cyber attack detection system

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Combining Satellite Images with Feature Indices for Improved Change Detection 9

Combining Satellite Images with Feature Indices for Improved...

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9th Annual IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Perez, Daniel Lu, Yan Kwan, Chiman Shen, Yuzhong Koperski, Krzysztof Li, Jiang Old Dominion Univ Dept Modeling Simulat & Visualizat Engn Norfolk VA 23529 USA Old Dominion Univ Dept Elect & Comp Engn Norfolk VA USA Appl Res LLC Rockville MD USA Digital Globe Inc Westminster CO USA

ISBN: (纸本)9781538676936

This paper presents a novel approach to detect changes in satellite images taken from the same location at different timestamps. Different change detection methods are applied to multispectral satellite images taken with the Worldview-2 (WV-2) satellite, as well as to several of their feature indices such as normalized difference vegetation index (NDVI), normalized difference soil index (NDSI), non-homogeneous feature index (NHFD) and red-blue ratio (R/B). Besides, an additional image is used to remove temporary changes like vehicles, persons etc. The combination of changes is computed with a set of pixel-wise operations, and morphological filters are applied to improve the final change map. The combination of the satellite images with their feature indices proved to produce better results than computing the changes independently. This paper summarizes the methodology and presents the results obtained.

关键词： Worldview-2 images change detection deep autoencoder remote sensing

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A Hybrid Anomaly Detection Approach for Obfuscated Malware

A Hybrid Anomaly Detection Approach for Obfuscated Malware

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4th IEEE Annual International Conference on Cyber Security and Resilience (IEEE CSR)

作者： Fuhnwi, Gerard Shu Revelle, Matthew Izurieta, Clemente Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA Pacific Northwest Natl Lab Richland WA 99352 USA Idaho Natl Lab Idaho Falls ID USA

ISBN: (纸本)9798350375367

With the rapid evolution of malicious software, cyber threats have become increasingly sophisticated, employing advanced obfuscation techniques to evade traditional detection methods. This study presents a hybrid anomaly detection approach applied to obfuscated malware. Even though there is a large body of research in this field, existing malware detection techniques have drawbacks, such as requiring large amounts of data, trustworthiness (imprecise results) of algorithms, and advanced obfuscation. There is a need to employ solid and efficient techniques for malware detection to overcome these challenges. This paper proposes a hybrid approach, combining an autoencoder with traditional machine-learning methods to create an efficient malware detection framework. We used the malware memory dataset (MalMemAnalysis-2022) to evaluate this framework. The experimental results show our proposed approach can detect obfuscated malware when a deep autoencoder used for feature learning is combined with logistic regression. It is extremely fast with an Accuracy, Detection Rate (DR), Matthew Correlation Coefficient(MCC), and Statistical Parity Difference (SPD) of 99.97%, 99.98%, 99.93%, and 0.03%, respectively.

关键词： Malware Detection Hybrid Anomaly Detection Obfuscated Malware deep autoencoder Logistic Regression

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ESDA: An Energy-Saving Data Analytics Fog Service Platform 17th

ESDA: An Energy-Saving Data Analytics Fog Service Platform

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17th International Conference on Service-Oriented Computing (ICSOC)

作者： Zhang, Tiehua Shen, Zhishu Jin, Jiong Tagami, Atsushi Zheng, Xi Yang, Yun Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia KDDI Res Inc Fujimino Japan Macquarie Univ Dept Comp Sydney NSW Australia

ISBN: (纸本)9783030337025;9783030337018

The volume of heterogeneous data collected through a variety of sensors is growing exponentially. With the increasing popularity of providing real-time data analytics services at the edge of the network, the process of harvesting and analysing sensor data is thus an inevitable part of enhancing the service experience for users. In this paper, we propose a fog-empowered data analytics service platform to overcome the frequent sensor data loss issue through a novel deep autoencoder model while keeping the minimum energy usage of the managed sensors at the same time. The platform incorporates several algorithms with the purpose of training the individual local fog model, saving the overall energy consumption, as well as operating the service process. Compared with other state-of-the-art techniques for handling missing sensor data, our platform specialises in finding the underlying relationship among temporal sensor data series and hence provides more accurate results on heterogeneous data types. Owing to the superior inference capability, the platform enables the fog nodes to perform real-time data analytics service and respond to such service request promptly. Furthermore, the effectiveness of the proposed platform is verified through the real-world indoor deployment along with extensive experiments.

关键词： Fog computing Service-oriented networking deep autoencoder Energy-saving algorithm

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Learning Typical 3D Representation from a Single 2D Correspondence Using 2D-3D Transformation Network 13th

Learning Typical 3D Representation from a Single 2D Correspo...

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13th International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ul Islam, Naeem Lee, Sukhan Sungkyunkwan Univ Coll Informat & Commun Engn Intelligent Syst Res Inst Suwon South Korea

ISBN: (纸本)9783030190637;9783030190620

Understanding 3D environment based on deep learning is the subject of interest in computer vision community due to its wide variety of applications. However, learning good 3D representation is a challenging problem due to many factors including high dimensionality of the data, modeling complexity in terms of symmetric objects, required computational costs and expected variations scale up by the order of 1.5 compared to learning 2D representations. In this paper we address the problem of typical 3D representation by transformation from its corresponding single 2D representation using deep autoencoder based Generate Adversarial Network termed as 2D-3D Transformation Network (2D-3D-TNET). The proposed model objective is based on traditional GAN loss along with the autoencoder loss, which allows the generator to effectively generate 3D objects corresponding to the input 2D images. Furthermore, instead of training the discriminator to discriminate real from generated image, we allow the discriminator to take both 2D image and 3D object as an input and learn to discriminate whether they are true correspondences or not. Thus, the discriminator learns and encodes the relationship between 3D objects and their corresponding projected 2D images. In addition, our model does not require labelled data for training and learns on unsupervised data. Experiments are conducted on ISRI_DB with real 3D daily-life objects as well as with the standard ModelNet40 dataset. The experimental results demonstrate that our model effectively transforms 2D images to their corresponding 3D representations and has the capability of learning rich relationship among them as compared to the traditional 3D Generative Adversarial Network (3D-GAN) and deep autoencoder (DAE).

关键词： deep autoencoder GAN 2D-3D Transformation Network

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Unsupervised Anomaly Detection using deep Autoencoding Mixture of Probabilistic Principal Component Analyzers 23

Unsupervised Anomaly Detection using Deep Autoencoding Mixtu...

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8th International Conference on Intelligent Information Technology (ICIIT)

作者： Tra, Viet Al-Bazzaz, Hussein Amayri, Manar Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ Canada

ISBN: (纸本)9781450399616

Although many unsupervised anomaly detection algorithms with outstanding performance have been proposed in last few decades, their performance for high-dimensional data is not guaranteed. Therefore, this paper proposes a new framework called deep autoencoding mixture of probabilistic principal component analyzers (DA-MPPCA). This framework uses deep autoencoder (DAE) as a compression network to prepare the low-dimensional representations for a subsequent estimation network NN-MPPCA. Different to conventional MPPCA that is trained by EM algorithm, NN-MPPCA is the neural network form of MPPCA which parameters can be updated via back-propagation algorithm. By jointly training DAE and NN-MPPCA in an end-to-end manner using a defined loss function, we force both dimensionality reduction and density estimation tasks into the unified framework. The experimental results on a variety of public datasets have demonstrated the superior performance of DA-MPPCA over both shallow and deep baseline models with an improvement on F1-score up to 7% over the best baseline.

关键词： Anomaly detection deep autoencoder mixture of probabilistic principal component analyzes

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NetWalk: A Flexible deep Embedding Approach for Anomaly Detection in Dynamic Networks 18

NetWalk: A Flexible Deep Embedding Approach for Anomaly Dete...

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24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Yu, Wenchao Cheng, Wei Aggarwal, Charu C. Zhang, Kai Chen, Haifeng Wang, Wei Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA NEC Labs Amer Inc Princeton NJ USA IBM Res AI San Jose CA USA Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781450355520

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors" deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NETWALK, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NETWALK has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3). flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NETWALK.

关键词： Anomaly detection dynamic network embedding deep autoencoder clique embedding

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