Geoscience-aware deep learning: A new paradigm for remote sensing by Yong Ge, Xining Zhang, Peter M. Atkinson, Alfred Stein, Lianfa Li

“…Although DL models have powerful feature learning and representation capabilities, traditional DL has inherent problems including working as a black box and generally requiring a large number of labeled training data. …”
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Sistem Isyarat Bahasa Indonesia (SIBI) Metode Convolutional Neural Network Sequential secara Real Time by Oky Dwi Nurhayati, Dania Eridani, Muhammad Hafiz Tsalavin

“…Teknologi visi komputer deep learning convolutional neural network (CNN) digunakan untuk feature learning dan mengklasifikasi isyarat tangan pada sebuah obyek. …”
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Robust Speech Emotion Recognition Using CNN+LSTM Based on Stochastic Fractal Search Optimization Algorithm by Abdelaziz A. Abdelhamid, El-Sayed M. El-Kenawy, Bandar Alotaibi, Ghada M. Amer, Mahmoud Y. Abdelkader, Abdelhameed Ibrahim, Marwa Metwally Eid

“…This deep learning model consists of a convolutional neural network (CNN) composed of four local feature-learning blocks and a long short-term memory (LSTM) layer for learning local and long-term correlations in the log Mel-spectrogram of the input speech samples. …”
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Forest Fire Segmentation via Temporal Transformer from Aerial Images by Mohammad Shahid, Shang-Fu Chen, Yu-Ling Hsu, Yung-Yao Chen, Yi-Ling Chen, Kai-Lung Hua

“…Then, we used a transformer to perform deep temporal-feature extraction, which enhanced the feature learning of the fire pixels and made the feature extraction more robust. …”
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Brain and Nature-Inspired Learning, Computation and Recognition / by Jiao, Licheng, author 651476, Shang, Ronghua, author 651477, Liu, Fang, author 526666, Zhang, Weitong, author 651478, ScienceDirect (Online service) 7722

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Construction and improvement of English vocabulary learning model integrating spiking neural network and convolutional long short-term memory algorithm. by Yunxia Wang

“…By adding information transfer and interaction modules, the feature learning and the timing information processing are optimized to improve the vocabulary learning ability of the model in different text contents. …”
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Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions by Muhammad Tahir ul Qamar, Fatima Noor, Yi-Xiong Guo, Xi-Tong Zhu, Ling-Ling Chen

“…In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. …”
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Soft-masks guided faster region-based convolutional neural network for domain adaptation in wind turbine detection by Yang Xu, Yang Xu, Yang Xu, Xiong Luo, Xiong Luo, Xiong Luo, Manman Yuan, Manman Yuan, Manman Yuan, Bohao Huang, Jordan M. Malof

“…The predominant approaches to alleviate the domain discrepancy are adversarial feature learning strategies, which focus on feature alignment for style (e.g., color, texture, illumination, etc.) gaps without considering the content (e.g., densities, backgrounds, and layout scenes) gaps. …”
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Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning by DongHun Ryu, Jinho Kim, Daejin Lim, Hyun-Seok Min, In Young Yoo, Duck Cho, YongKeun Park

“…Our results show >99% accuracy for the binary classification of myeloids and lymphoids and >96% accuracy for the four-type classification of B and T lymphocytes, monocyte, and myelocytes. The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique. …”
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A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks by Mana Saleh Al Reshan, Samina Amin, Muhammad Ali Zeb, Adel Sulaiman, Hani Alshahrani, Asadullah Shaikh

“…The HDNN is employed to apply its feature learning capabilities and non-linear technology to capture complex patterns and relationships in HD datasets, leading to enhanced prediction accuracy. …”
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Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks by Saud S. Alotaibi, Hanan Abdullah Mengash, Noha Negm, Radwa Marzouk, Anwer Mustafa Hilal, Mohamed A. Shamseldin, Abdelwahed Motwakel, Ishfaq Yaseen, Mohammed Rizwanullah, Abu Sarwar Zamani

“…The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. …”
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A Hybrid Model for Predicting the Environment Humidity of Pigeon Sheds by Wentao Zhou, Longqin Xu, Lin Yang, Shuangyin Liu, Min He, Qingfeng Sheng, Tonglai Liu, Jianjun Guo, Dachun Feng, Shahbaz Gul Hassan, Liang Cao

“…First, the SG filter is used for data smoothing to remove abnormal noise in the data signal to enhance the feature learning ability of the prediction system. Next, the original data is selected through the PLS method to select information higher than the set threshold. …”
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LUCC Simulation Based on RF-CNN-LSTM-CA Model with High-Quality Seed Selection Iterative Algorithm by Minghao Liu, Haiyan Chen, Liai Qi, Chun Chen

“…In the traditional machine learning CA model, when using statistical methods to obtain neighborhood features, there is usually the problem that the spatio-temporal feature learning of neighborhood factors is insufficient. …”
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X3DFast model for classifying dairy cow behaviors based on a two-pathway architecture by Qiang Bai, Ronghua Gao, Rong Wang, Qifeng Li, Qinyang Yu, Chunjiang Zhao, Shuqin Li

“…To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. …”
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DMSC-Net: A deep Multi-Scale context network for 3D object detection of indoor point clouds by Zhenxin Zhang, Dixiang Xu, P. Takis Mathiopoulos, Qiang Wang, Liqiang Zhang, Zhihua Xu, Jincheng Jiang, Zhen Li

“…The proposed end-to-end network, termed as DMSC-Net, consists of an indoor point cloud feature learning backbone (FLB) unit, and three modules, namely the DMSC, a voting decision (VD) module, and an MHA module. …”
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Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas by Muhaddisa Barat Ali, Irene Yu-Hua Gu, Mitchel S. Berger, Johan Pallud, Derek Southwell, Georg Widhalm, Alexandre Roux, Tomás Gomez Vecchio, Asgeir Store Jakola

“…Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). …”
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Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods by Yaseen Ahmed Mohammed Alsumaidaee, Chong Tak Yaw, Siaw Paw Koh, Sieh Kiong Tiong, Chai Phing Chen, Talal Yusaf, Ahmed N Abdalla, Kharudin Ali, Avinash Ashwin Raj

“…Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. …”
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Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model by Andrea Sikora, Alireza Rafiei, Milad Ghiasi Rad, Kelli Keats, Susan E. Smith, John W. Devlin, David J. Murphy, Brian Murray, Rishikesan Kamaleswaran, MRC-ICU Investigator Team

“…To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. …”
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AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality by Weiquan Liu, Cheng Wang, Xuesheng Bian, Shuting Chen, Wei Li, Xiuhong Lin, Yongchuan Li, Dongdong Weng, Shang-Hong Lai, Jonathan Li

“…However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. …”
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A general-purpose organic gel computer that learns by itself by Pathik Sahoo, Pushpendra Singh, Komal Saxena, Subrata Ghosh, R P Singh, Ryad Benosman, Jonathan P Hill, Tomonobu Nakayama, Anirban Bandyopadhyay

“…Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training. …”
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