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4th International Conference on Artificial Intelligence and Signal Processing

作者： Gautam, Neeraj Kumar Mishra, Mayank Pati, Umesh C. NIT Rourkela Dept ECE Rourkela India

ISBN: (纸本)9798350350661;9798350350654

The coral reefs have been considered as an essential component of the marine ecosystem as they help ecologically as well as toward the development of medicine related to the severe diseases such as HIV infections, heart disease, etc. The Coral bleaching phenomenon has been found as the main cause that increases the worldwide dying out risk of coral reefs. The Artificial Intelligence (AI) based approach can become an essential way of early detection of coral reefs bleaching. This work has proposed a Deep Convolution autoencoder (DCAE) based model to detect the bleaching of the coral reefs. The Proposed DCAE model has been utilized to extract the deep feature image from the underwater image of coral reefs, which can be further utilize for the bleaching detection. The Kaggle repository of coral reef health dataset comprising the underwater images of healthy corals and bleached corals have been taken for the experimention. The proposed DCAE approach has achieved an accuracy of 76.60%, sensitivity of 80%, precision of 73.46%, and F1-Score of 76.59% in detecting the bleaching of coral reefs.

关键词： autoencoder Bleached Coral Reefs Classification Coral reefs Deep CNN

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CHA2: CHemistry Aware Convex Hull autoencoder Towards Inverse Molecular Design 46th

CHA2: CHemistry Aware Convex Hull Autoencoder Towards Invers...

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46th German Conference on Artificial Intelligence (KI)

作者： Ghaemi, Mohammad Sajjad Hu, Hang Hu, Anguang Ooi, Hsu Kiang Natl Res Council Canada Toronto ON Canada DRDC Suffield Res Ctr Edmonton AB Canada

ISBN: (纸本)9783031426070;9783031426087

Optimizing molecular design and discovering novel chemical structures to meet specific objectives, such as quantitative estimates of the drug-likeness score (QEDs), is NP-hard due to the vast combinatorial design space of discrete molecular structures, which makes it near impossible to explore the entire search space comprehensively to exploit de novo structures with properties of interest. To address this challenge, reducing the intractable search space into a lower-dimensional latent volume helps examine molecular candidates more feasibly via inverse design. autoencoders are suitable deep learning techniques, equipped with an encoder that reduces the discrete molecular structure into a latent space and a decoder that inverts the search space back to the molecular design. The continuous property of the latent space, which characterizes the discrete chemical structures, provides a flexible representation for inverse design to discover novel molecules. However, exploring this latent space requires particular insights to generate new structures. Therefore, we propose using a convex hull (CH) surrounding the top molecules regarding high QEDs to ensnare a tight subspace in the latent representation as an efficient way to reveal novel molecules with high QEDs. We demonstrate the effectiveness of our suggested method by using the QM9 as a training dataset along with the Self-Referencing Embedded Strings (SELFIES) representation to calibrate the autoencoder in order to carry out the inverse molecular design that leads to unfolding novel chemical structure.

关键词： Convex Hull autoencoder Inverse Molecular Design

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A Genetic Programming Encoder for Increasing autoencoder Interpretability 6th

A Genetic Programming Encoder for Increasing Autoencoder Int...

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26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar

作者： Schofield, Finn Slyfield, Luis Lensen, Andrew Victoria Univ Wellington Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand

ISBN: (纸本)9783031295720;9783031295737

autoencoders are powerful models for non-linear dimensionality reduction. However, their neural network structure makes it difficult to interpret how the high dimensional features relate to the lowdimensional embedding, which is an issue in applications where explainability is important. There have been attempts to replace both the neural network components in autoencoders with interpretable genetic programming (GP) models. However, for the purposes of interpretable dimensionality reduction, we observe that replacing only the encoder with GP is sufficient. In this work, we propose the Genetic Programming Encoder for Autoencoding (GPE-AE). GPE-AE uses a multi-tree GP individual as an encoder, while retaining the neural network decoder. We demonstrate that GPE-AE is a competitive non-linear dimensionality reduction technique compared to conventional autoencoders and a GP based method that does not use an autoencoder structure. As visualisation is a common goal for dimensionality reduction, we also evaluate the quality of visualisations produced by our method, and highlight the value of functional mappings by demonstrating insights that can be gained from interpreting the GP encoders.

关键词： Genetic programming autoencoder Dimensionality reduction Machine learning Explainable artificial intelligence

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autoencoder-Based Tongue Shape Estimation During Continuous Speech 23

Autoencoder-Based Tongue Shape Estimation During Continuous ...

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Interspeech Conference

作者： Ribeiro, Vinicius Laprie, Yves Univ Lorraine CNRS Inria LORIA F-54000 Nancy France

Vocal tract shape estimation is a necessary step for articulatory speech synthesis. However, the literature on the topic is scarce, and most current methods lack adequacy to many physical constraints related to speech production. This study proposes an alternative approach to the task to solve specific issues faced in the previous work, especially those related to critical articulators. We present an autoencoder-based method for tongue shape estimation during continuous speech. An autoencoder is trained to learn the data's encoding and serves as an auxiliary network for the principal one, which maps phonemes to the shapes. Instead of predicting the exact points in the target curve, the neural network learns how to predict the curve's main components, i.e., the autoencoder's representation. We show how this approach allows imposing critical articulators' constraints, controlling the tongue shape through the latent space, and generating a smooth output without relying on any postprocessing method.

关键词： autoencoder tongue shape estimation phonemeto-articulation

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autoencoder Model Exploration for Multi-Layer Video Compression 10

Autoencoder Model Exploration for Multi-Layer Video Compress...

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10th European Workshop on Visual Information Processing (EUVIP)

作者： Cancellier, Luiz Henrique Grellert, Mateus Guntzel, Jose Luis da Silva Cruz, Luis A. Fed Univ Santa Catarina UFSC INE PPGCC Florianopolis SC Brazil Univ Coimbra Dept Elect & Comp Engn Coimbra Portugal Inst Telecomunicacoes Coimbra Portugal

ISBN: (数字)9781665466233

ISBN: (纸本)9781665466233

The use of autoencoder models for image and video compression have been explored by a number of works published in recent years. While those works perform the original data compression in a single layer, in this work we propose the use of autoencoder models in two-layered video coding. The adoption of multi-layer encoder provides scalability and allows us for decoupling the traditional video coding implementation from the NN solutions. By restricting the use of the Neural Network (NN) solution in the enhancement layer, it becomes possible to decode the base layer bitstream without the necessity of running the decoding process with the NN. We implemented and evaluated two autoencoder models: one using a symmetric encoder/decoder architecture, and an asymmetric alternative that employs more layers on the decoder side. The models were trained to compress residues for a scenario using All Intra encoding with spatial scalability. The Asymmetric model outperformed the Symmetric one by providing better compression rates and quality results, which is confirmed by the respective BD-Rate and BD-PSNR average results of -17.06% and 0.7dB, respectively.

关键词： Video Compression Layered Video Coding Neural Networks autoencoder Intra

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autoencoder Ensemble Method for Botnets Detection on IOT Devices 21

Autoencoder Ensemble Method for Botnets Detection on IOT Dev...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Arroyo, Steven E. Ho, Shen Shyang Rowan Univ Glassboro NJ 08028 USA

ISBN: (纸本)9781665462839

Like anything else on the internet, IoT devices are very susceptible to cyber-attacks that could take out the device or install spyware. In this paper, we propose an anomaly detection solution driven by an autoencoder ensemble to detect botnets on IOT devices. In particular, the ensemble size is determined by hierarchical clustering of the features in the packet header. Moreover, one does not require an additional neural network to combine the decisions. The proposed approach is a more efficient solution for IOT problem setting and hence, overcomes the issue of lacking computational resources and memory on IOT devices, as well as run-time performance problems. Empirical results on two datasets, one from the 2016 Mirai botnet attacks on IoT devices and the other from Gafgyt malware attacks on various IOT devices, show the competitiveness and feasibility of our proposed solution.

关键词： autoencoder ensemble classifier anomaly detection IOT devices

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Innovations autoencoder and its Application in One-class Anomalous Sequence Detection

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-27页

作者： Wang, Xinyi Tong, Lang Cornell Univ Dept Elect & Comp Engn Ithaca NY 14850 USA

An innovations sequence of a time series is a sequence of independent and identically distributed random variables with which the original time series has a causal representation. The innovation at a time is statistically independent of the history of the time series. As such, it represents the new information contained at present but not in the past. Because of its simple probability structure, the innovations sequence is the most efficient signature of the original. Unlike the principle or independent component representations, an innovations sequence preserves not only the complete statistical properties but also the temporal order of the original time series. An long-standing open problem is to find a computationally tractable way to extract an innovations sequence of non-Gaussian processes. This paper presents a deep learning approach, referred to as Innovations autoencoder (IAE), that extracts innovations sequences using a causal convolutional neural network. An application of IAE to the one-class anomalous sequence detection problem with unknown anomaly and anomaly-free models is also presented.

关键词： Innovations sequence Generative adversary networks autoencoder Out-of-distribution detection Non-parametric anomaly detection

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Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of autoencoder and Deep Belief Network

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IEEE ACCESS 2022年 10卷 82156-82163页

作者： Al-Khazraji, Huthaifa Nasser, Ahmed R. Hasan, Ahmed M. Al Mhdawi, Ammar K. Al-Raweshidy, Hamed Humaidi, Amjad J. Univ Technol Baghdad Control & Syst Engn Dept Baghdad 10066 Iraq Edge Hill Univ Dept Comp Sci Ormskirk L39 4QP England Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England

Remaining Useful Life (RUL) is used to provide an early indication of failures that required performing maintenance and/or replacement of the system in advance. Accurate RUL prediction offers cost-effective operation for decision-makers in the industry. The availability of data using intelligence sensors leverages the power of data-driven methods for RUL estimation. Deep Learning is one example of a data-driven method that has a lot of applications in the industry. One of these applications is the RUL prediction where DL algorithms achieved good results. This paper presents an autoencoder-based Deep Belief Network (AE-DBN) model for Aircraft engines' RUL estimation. The AE-DBN DL model is utilized the feature extraction characteristic of AE and superiority in learning long-range dependencies of DBN. The efficiency of the proposed DL algorithm is evaluated by comparison between the proposed AE-DBRN and the state-of-the-art related method for RUL perdition for four datasets. Based on the Root Mean Square Error (RMSE) and Score indices, the outcomes reveal that the AE-DBN RUL prediction model is superior to other DL approaches.

关键词： Atmospheric modeling Aircraft Feature extraction Predictive models Aircraft propulsion Estimation Data models Artificial intelligence deep learning remaining useful life autoencoder deep belief network aircraft engine

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LS-LSTM-AE: Power load forecasting via Long-Short series features and LSTM-autoencoder

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ENERGY REPORTS 2022年 8卷 596-603页

作者： Tong, Xin Wang, Jingya Zhang, Changlin Wu, Teng Wang, Haitao Wang, Yu Peoples Publ Secur Univ China Beijing 100038 Peoples R China Henan Police Coll Zhengzhou 450046 Peoples R China Kanazawa Univ Kakuma Machi Kanazawa Ishikawa 9201164 Japan China Peoples Police Univ Langfang 065000 Peoples R China

Aiming at weak representation ability and severe loss of time series features in the traditional methods when facing large-scale and complex power load forecasting tasks, an LSTM-autoencoder model that integrates long-term and short-term features of the samples is proposed for load forecasting. The encoder part simultaneously receives long time series and short time series as input to extract time series features of different levels and generate related latent vectors. The decoder tries to reconstruct the input sequence while outputting the prediction results to ensure that the latent vectors are meaningful. In addition, the model also uses a mixture of supervised and unsupervised training methods. Experiments based on a publicly available dataset from Alberta Electric System Operator showed that the method presented in this research is superior to many existing mainstream methods, with a mean absolute error of less than 52MW between the prediction results and the actual load values. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Load forecasting Deep learning LSTM autoencoder Unsupervised learning Supervised learning

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A Semi-Supervised autoencoder-Based Approach for Protein Function Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 4957-4965页

作者： Dhanuka, Richa Tripathi, Anushree Singh, Jyoti P. Natl Inst Technol Patna Patna 800005 Bihar India

After the development of next-generation sequencing techniques, protein sequences are abundantly available. Determining the functional characteristics of these proteins is costly and time-consuming. The gap between the number of protein sequences and their corresponding functions is continuously increasing. Advanced machine-learning methods have stepped up to fill this gap. In this work, an advanced deep-learning-based approach is proposed for protein function prediction using protein sequences. A set of autoencoders is trained in a semi-supervised manner with protein sequences. Each autoencoder corresponds to a single protein function only. In particular, 932 autoencoders corresponding to 932 biological processes and 585 autoencoders corresponding to 585 molecular functions are trained separately. Reconstruction losses of each protein sample for every autoencoder are used as a feature to classify these sequences into their corresponding functions. The proposed model is tested on test protein samples and achieves promising results. This method can be easily extended to predict any number of functions having an ample amount of supporting protein sequences. All relevant codes, data and trained models are available at https://***/richadhanuka/PFP-autoencoders.

关键词： Proteins Protein engineering Predictive models Data models Protein sequence Feature extraction Biological system modeling autoencoder bioinformatics machine learning multi-label classification protein function

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