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International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT)

作者： Yadav, Satyajit Subramanian, Selvakumar Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781509000821

An Application Layer Distributed Denial of Service Attack (DDoS) is one of the biggest concerns for web security. Many detection methods are designed to mitigate DDoS attack based on IP and TCP layer instead of the Application layer. These methods are not suitable for detection of Application layer DDoS attack since most of the IP and TCP layer DDoS attacks are based on request flooding attack. But Application layer DDoS attacks consist of request flooding, session flooding, and asymmetric attack. The solutions available to detect Application layer DDoS attack, detect only limited number of Application layer DDoS attacks. The solutions that detect all types of Application layer DDoS attacks have huge algorithm complexity. One of the major challenges in the detection of an Application layer DDoS attack is the non-availability of features to detect such attacks. Hence it is difficult to model normal user behavior from attack behavior. In this paper, Deep learning architecture is introduced to learn deep features of Application layer DDoS attack. Deep learning architecture consist of very deep neural network, typically more than three layers. In the proposed work the concept of autoencoder is applied, which is one of the deep learning based models that learns deep useful features in the Application layer DDoS attack dataset. The stacked autoencoder deep learning architecture, is aimed to receive high level features. The performance of the proposed method was evaluated in terms of the metrics such as false positive rate and detection rate. Comparison of the proposed method with the existing methods reveals that the proposed method performs better than the existing methods.

关键词： DDoS Application Layer DDoS Attack Deep learning Feature learning autoencoder stacked autoencoder

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Printed Odia Numeral Recognition Using stacked autoencoder 9th

Printed Odia Numeral Recognition Using Stacked Autoencoder

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9th International Conference of Information and Communication Technology [ICICT]

作者： Satpathy, Subhashree Nayak, Ajit Ku. Nayak, Mamata Patnaik, Srikanta Siksha O Anusandhan Univ Dept CSE Bhubaneswar 751030 India Siksha O Anusandhan Univ Dept CSIT Bhubaneswar 751030 India

Automatic recognition of both printed and handwritten characters is the most progressive research area since last few periods. The printed character recognition rate still desires concentration of researchers because of variations in shape, size and design of characters. Odia is the native language of Odisha. Less works have been accounted in Odia script classification. Complex printed character recognition constantly demands a better feature extraction method for such diversified characters. Purpose of this manuscript is to apply autoencoder technique considering a suitable classifier for Odia printed numeral recognition. autoencoder proved to be a better method in performance for dimensionality reduction as well as classification of Odia numerals. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Odia numerals character recognition stacked autoencoder deep learning dimensionality reduction

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Deep stacked autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2022年第1期15卷 1-26页

作者： Pitchaiyan, Shanthi Savarimuthu, Nickolas Natl Inst Technol Dept Comp Applicat Tiruchirappalli India

Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local binary pattern (LBP) is known to be a popular texture feature for facial expression recognition. However, only a few approaches utilize the relationship between local neighborhood pixels itself. This paper presents a hybrid local texture descriptor (HLTD) that is derived from the logical fusion of local neighborhood XNOR patterns (LNXP) and LBP to investigate the potential of positional pixel relationship in automatic emotion recognition. The LNXP encodes texture information based on two nearest vertical and/or horizontal neighboring pixel of the current pixel whereas LBP encodes the center pixel relationship of the neighboring pixel. After logical feature fusion, the deep stacked autoencoder (DSA) is established on the CK+, MMI, and KDEF-dyn dataset, and the results show that the proposed HLTD-based approach outperforms many of the state-of-the-art methods with an average recognition rate of 97.5% for CK+, 94.1% for MMI, and 88.5% for KDEF.

关键词： Deep Neural Network Emotion Facial Expression Feature Fusion Hybrid Local Texture Descriptor stacked autoencoder

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A Home Sleep Apnea State Monitoring System using a stacked autoencoder 18

A Home Sleep Apnea State Monitoring System using a Stacked A...

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18th IEEE Sensors Conference

作者： Takao, Ikuya Nishio, Keita Kaburagi, Takashi Kumagai, Satoshi Matsumoto, Toshiyuki Kurihara, Yosuke Aoyama Gakuin Univ Dept Ind & Syst Engn Sagamihara Kanagawa Japan Int Cristian Univ Dept Nat Sci Mitaka Tokyo Japan

ISBN: (纸本)9781728116341

Ischemic heart disease is one of the most common causes of death in the world. Recent studies show that it may be caused by sleep apnea, or disordered breathing during sleep;however, sleep apnea lacks subjective symptoms. Therefore, an effective method is required to monitor sleep apnea states while a subject is at home. In this context, this study proposes a method to estimate apnea conditions using an unconstrained sensing system placed under a mattress. The system provides the probability of the user being in an apnea state for given units of time. To reduce the effects from each individual user and their sleeping position, we applied a stacked autoencoder to obtain feature vectors. These are given to a feed-forward neural network with a two-dimensional output layer. The two-dimensional output is converted to probability values by a softmax function, and labels with a higher probability are estimated as an apnea state.

关键词： apnea state estimation pressure sensor stacked autoencoder

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Bio-Mechanical Distracted Driver Recognition Based on stacked autoencoder and Convolutional Neural Network 2

Bio-Mechanical Distracted Driver Recognition Based on Stacke...

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2nd IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Assefa, Addis Abebe Tian Wenhong UESTC Sch Informat & Software Engn Chengdu Peoples R China

ISBN: (纸本)9781728151021

in this paper, we consider a problem of a biomechanical distraction of a driver;mostly it is related with hands secondary action while driving. Dealing with this type of distraction is crucial because the main causes of car accidents are due to negligent operation of in-vehicle technology, operating mobile phone while driving, and chatting with passenger. The effects of illumination conditions and driver's hands skin color make biomechanical distraction recognition very challenging. Accordingly, we propose a deep recognition model which can handle both effects, and its computational cost is relatively less when compared with the current state of the art. Our model is a sequential integration of two sub module: hand and face localizer and different types of biomechanical distraction recognizer. We conduct several simulations to investigate the effect of hand and face localization, and to measure the recognition performance of the proposed model, and its recognition performance, based on our experiment, our model achieves 98.68 % validation accuracy which is better than the current state of the art.

关键词： distracted driver stacked autoencoder biomechanical distraction

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Classification of Imbalanced Bioassay Data with Features Learned Using stacked autoencoder 15

Classification of Imbalanced Bioassay Data with Features Lea...

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15th International Conference on Machine Vision (ICMV)

作者： Shah, Jeni Joshi, Manjunath Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (纸本)9781510666184;9781510666191

Bioassay data classification is an important task in drug discovery. However, the data used in classification is highly imbalanced, leading to inaccuracies in classification for the minority class. We propose a novel approach for classification in which we train separate models by using different features that are derived by training stacked autoencoders (SAE). Experiments are performed on 7 bioassay datasets, in which each data file consists of feature descriptors for every compound along with class label of compound being active, or inactive. We first perform data cleaning using borderline synthetic minority oversampling technique (SMOTE) followed by removing the Tomek links, and then learn different features hierarchically, based on the cleaned data or feature vectors. We then train separate cost-sensitive feed-forward neural network (FNN) classifiers using the hierarchical features in order to obtain the final classification. To increase the True Positive Rate (TPR), a test sample is labeled as active if at least one classifier predicts it as active. In this paper, we demonstrate that by data cleaning and learning separate classifiers one can improve the TPR and F1 score when compared to other machine learning approaches. To the best of our knowledge, the researchers have not yet attempted the use of SAE and FNN for classifying bioassay data.

关键词： stacked autoencoder SMOTE Imbalanced data

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A stacked autoencoder for operation mode classification of complicated industrial process

A stacked autoencoder for operation mode classification of c...

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Chinese Automation Congress (CAC)

作者： Zhou, Longfei Huang, Keke Yang, Chunhua Chen, Xiaofang Xie, Yongfang Yue, Weichao Cent South Univ Sch Software Changsha Hunan Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781728113128

In this paper, we propose a novel stacked autoencoder (SAE) based operation mode classification method for the complicated industrial process. In detail, we first add the sparse and regularization constraints into SAE to learn low-dimensional nonlinear representations of high-dimensional data, then the SAE is trained in two steps: unsupervised layer-by-layer pre-training is performed first, followed by supervised fine-tuning. In order to evaluate the efficiency of the proposed method, we conduct extensive experiments on the Tennessee Eastman (TE) process and aluminum electrolysis production process in comparison with several conventional methods, the experimental results validate that the proposed method is more effective than other methods in the mode classification task.

关键词： complicated industrial process stacked autoencoder sparse and regularization constraints

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Electricity price forecast based on stacked autoencoder in spot market environment 9

Electricity price forecast based on stacked autoencoder in s...

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9th International Conference on Power and Energy Systems (ICPES)

作者： Zou, Ya Tu, Mengfu Teng, Xianliang Cao, Rongzhang Xie, Wei NARI Technol DEV CO LTD Power Grid Dispatching & Control Technol Branch C Nanjing Peoples R China NARI Technol Dev CO LTD NARI Res Inst Nanjing Peoples R China

ISBN: (纸本)9781728126586

Artificial neural network method is a common method for short-term electricity price forecasting. However, when the amount of input and output data is large, the training speed will be slow, and it is easy to fall into local extreme values or even the result is difficult to converge. In view of this, the paper proposes a deep learning model based on stacked autoencoder (SAE) to predict electricity price. This paper analyzes the factors affecting electricity price, proposes an algorithm based on stacked autoencoder model, and uses MATLAB tools to predict electricity price in PJM power market. The comparison between SAE algorithm and BP algorithm is carried out in the example. The results show that the prediction results based on SAE model are more accurate. The deep learning model has better ability to express the objective function than the shallow model, and can effectively solve the problem of traditional neural network training difficulties.

关键词： Electricity price factor Electricity price forecasting Deep learning stacked autoencoder

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Classification of Silent Speech in English and Bengali Languages Using stacked autoencoder

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SN Computer Science 2022年第5期3卷 389页

作者： Ghosh, Rajdeep Sinha, Nidul Phadikar, Souvik School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kotri Kalan 466114 India Department of Electrical Engineering NIT Silchar Assam Silchar 788010 India

The purpose of a brain–computer interface (BCI) is to enhance or support the normal functions of disabled people, and as such, BCIs have been utilized for a variety of applications, such as prostheses and identification of mental state. One such application concerned with providing a means of communication for disabled individuals is focused on the recognition of silent speech (also known as imagined speech) in an individual. Silent speech can be defined as the speech originating inside the brain of an individual that has not been vocalized by the individual. The proposed work is concerned with the classification of silent speech from the brain activity of an individual recorded using an electroencephalogram (EEG). EEG data from 45 subjects were collected while they imagined the English vowels /a/, /e/, /i/, /o/, and /u/ without vocalization. EEG data were also recorded from 22 subjects who imagined five Bengali vowels /আ/, /ই/, /উ/, /এ/ and /ও/ without vocalization. The selected Bengali vowels have a similar pronunciation to the English vowels. Various temporal and spectral features were evaluated from the EEG recordings, which were then classified using a stacked autoencoder (SAE). The SAE achieved an accuracy of 75.56% and 73.6% in classifying the silent speech from the English and Bengali languages, respectively. Moreover, it has been observed that the proposed SAE outperforms conventional methods such as common spatial pattern (CSP) and support vector machine (SVM) during classification. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Brain–computer interface Deep learning Electroencephalogram Imagined speech Silent speech stacked autoencoder

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Deep Residual Learning-based Reconstruction of stacked autoencoder Representation 25

Deep Residual Learning-based Reconstruction of Stacked Autoe...

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25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Li, Honggui Trocan, Maria Yangzhou Univ Phys Coll Sci & Technol Yangzhou Jiangsu Peoples R China Inst Super Elect Paris LISITE Res Lab Paris France

ISBN: (纸本)9781538695623

stacked autoencoder (SAE) can efficiently represent high dimensional data with low dimensional features via minimizing a reconstruction error. However, the decoder of SAE cannot achieve lossless recovery of original data. If the reestablishment performance of SAE decoder is improved, it can be employed for low-bitrate and high-quality data compression. This paper adopts deep residual learning of convolutional neural network (CNN) to promote the reconstruction capability of SAE decoder. It is demonstrated by experimental results that deep CNN is superior to SAE decoder in rebuilding the performance of image data.

关键词： stacked autoencoder deep residual learning convolutional neural network data recosntruction data compression

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