Search Results - conventional neural network-autoencoder architecture

A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer by Nayak, Deepak Ranjan, Dash, Ratnakar, Majhi, Banshidhar, Pachori, Ram Bilas, Zhang, Yudong

“… Almost all existing methods are designed using conventional machine…”
Get full text

Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities by Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde

“… This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers…”
Get full text

Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames by Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica

“… These methods promise to deliver where conventional linear techniques, such as Proper Orthogonal Decomposition (POD…”
Get full text

High-speed Optical OFDM transmission by reducing the nonlinearity of LEDs in Visible light Communication Systems by Swaminathan, S., Raajan, N. R.

“… function and network architecture, as opposed to the conventional fully computer-controlled autoencoder. Deep Recurrent Neural Network…”
Get full text

Deep learning modelling techniques: current progress, applications, advantages, and challenges by Ahmed, Shams Forruque, Alam, Md. Sakib Bin, Hassan, Maruf, Rozbu, Mahtabin Rodela, Ishtiak, Taoseef, Rafa, Nazifa, Mofijur, M., Shawkat Ali, A. B. M., Gandomi, Amir H.

“… As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited…”
Get full text

Functional autoencoder for smoothing and representation learning by Wu, Sidi, Beaulac, Cédric, Cao, Jiguo

“… representations may not be sufficient. In this study, we propose to learn the nonlinear representations of functional data using neural network autoencoders designed to process data in the form it is usually collected without the need of preprocessing…”
Get full text

Predicting Freshman Recruitment Rates: A Comparative Analysis of Metropolitan and Non-Metropolitan Universities in South Korea by Jong Na, Hyung, Shin, Ha-Young, Cho, Yongsun

“…), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, and Transformer architectures-to predict freshman enrollment outcomes…”
Get full text

再帰型オートエンコーダを用いた振動データによる工場設備の故障予測手法の提案 by 速水, 悟, 井指, 諒亮, 古川, 輝, 内藤, 孝昌, 朝日, 翔太, 松井, 彩華, 田村, 哲嗣

Get full text

Training deep neural networks for binary communication with the Whetstone method by Severa, William, Vineyard, Craig M., Dellana, Ryan, Verzi, Stephen J., Aimone, James B.

“…The computational cost of deep neural networks presents challenges to broadly deploying these algorithms…”
Get full text

Proposal of failure prediction method of factory equipment by vibration data with Recurrent Autoencoder by TAMURA, Satoshi, HAYAMIZU, Satoru, ISASHI, Ryosuke, NAITOU, Takayoshi, MATSUI, Ayaka, FURUKAWA, Akira, ASAHI, Shota

“…In this paper, we propose a method to predict the failure of factory equipment by machine learning architectures using vibration data…”
Get full text

Proposal of failure prediction method of factory equipment by vibration data with Recurrent Autoencoder by TAMURA, Satoshi, HAYAMIZU, Satoru, ISASHI, Ryosuke, NAITOU, Takayoshi, MATSUI, Ayaka, FURUKAWA, Akira, ASAHI, Shota

“…In this paper, we propose a method to predict the failure of factory equipment by machine learning architectures using vibration data…”
Get full text

A deep neural network approach to QRS detection using autoencoders by Belkadi, Mohamed Amine, Daamouche, Abdelhamid, Melgani, Farid

“…In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase…”
Get full text

Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection by Tawfik, Mohammed

“…The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats…”
Get full text

Robust reduced-order machine learning modeling of high-dimensional nonlinear processes using noisy data by Tan, Wallace Gian Yion, Xiao, Ming, Wu, Zhe

“…). To address this issue, this work develops a novel machine-learning-based reduced-order modeling method by integrating SpectralDense layers into autoencoders and incorporating them with recurrent neural networks…”
Get full text

Functional Autoencoder for Smoothing and Representation Learning by Wu, Sidi, Beaulac, Cédric, Cao, Jiguo

“… representations may not be sufficient. In this study, we propose to learn the nonlinear representations of functional data using neural network autoencoders designed to process data in the form it is usually collected without the need of preprocessing…”
Get full text

A variational U‐Net for motion retargeting by Uk Kim, Seong, Jang, Hanyoung, Kim, Jongmin

“…Motion retargeting is the process of copying motion from one character (source) to another (target) when the source and target body sizes and proportions (of…”
Get full text

A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement by Borgstrom, Bengt J., Brandstein, Michael S.

“… This paper proposes a multiscale autoencoder (MSAE) for mask-based end-to-end neural network speech enhancement…”
Get full text

A Multiscale Autoencoder (MSAE) Framework for End-to-End Neural Network Speech Enhancement by Borgstrom, Bengt J, Brandstein, Michael S

“… This paper proposes a multiscale autoencoder (MSAE) for mask-based end-to-end neural network speech enhancement…”
Get full text