Deep Learning-based Segmentation Method for Organic Matter Identification in Oil Shale CT Images by Xin WANG, Dong YANG, Xudong HUANG

“…In order to accurately identify the organic matter in the segmented oil shale CT images, the image segmentation methods in the field of deep learning are studied, and the OM-Unet semantic segmentation network architectures describing the organic matter segmentation is built independently. …”
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Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography by Calimanut-Ionut Cira, Alberto Díaz-Álvarez, Francisco Serradilla, Miguel-Ángel Manso-Callejo

“…The approach is based on convolutional neural network architectures (VGGNet, ResNet, Xception, and DenseNet) that are modified and adapted for regression tasks with a proposed learning structure, together with an ad hoc model, specially introduced for this task. …”
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Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar by Qisong Wu, Teng Gao, Zhichao Lai, Dianze Li

“…Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. …”
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Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review by Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, Chaminda Hewage

“…The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. …”
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Seasonal and elevational changes of plant‐pollinator interaction networks in East African mountains by Fairo F. Dzekashu, Christian W. W. Pirk, Abdullahi A. Yusuf, Alice Classen, Nkoba Kiatoko, Ingolf Steffan‐Dewenter, Marcell K. Peters, H. Michael G. Lattorff

“…This study highlights changes in network architectures with elevation suggesting a potential sensitivity of plant‐bee interactions with climate warming and changes in rainfall patterns along the elevation gradients of the Eastern Afromontane Biodiversity Hotspot.…”
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Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions by Xinpeng Guo, Xinpeng Guo, Jinyu Han, Yafei Song, Zhilei Yin, Shuaichen Liu, Xuequn Shang

“…To verify the capabilities of this method, we conducted experimental analysis using the GSE28127 and GSE95496 data sets from the Gene Expression Omnibus (GEO) database, tested various neural network architectures, and used prior data for feature selection and graph embedding. …”
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Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images by Daliana Lobo Torres, Javier Noa Turnes, Pedro Juan Soto Vega, Raul Queiroz Feitosa, Daniel E. Silva, Jose Marcato Junior, Claudio Almeida

“…In recent years fully convolutional network architectures have witnessed numerous proposals adapted for the change-detection task. …”
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Car Price Quotes Driven by Data-Comprehensive Predictions Grounded in Deep Learning Techniques by Andreea Dutulescu, Andy Catruna, Stefan Ruseti, Denis Iorga, Vladimir Ghita, Laurentiu-Marian Neagu, Mihai Dascalu

“…Our study used several neural network architectures that captured complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. …”
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Self-assembly of hierarchically ordered structures in DNA nanotube systems by Martin Glaser, Jörg Schnauß, Teresa Tschirner, B U Sebastian Schmidt, Maximilian Moebius-Winkler, Josef A Käs, David M Smith

“…These show a strong dependence not only on concentration and bundling strength, but also on the underlying mechanical properties of the nanotubes. Similar network architectures to those caused by depletion forces in the low-density regime are obtained when an alternative hybridization-based bundling mechanism is employed to induce self-assembly in an isotropic network of pre-formed DNA nanotubes. …”
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Deep and Machine Learning Image Classification of Coastal Wetlands Using Unpiloted Aircraft System Multispectral Images and Lidar Datasets by Ali Gonzalez-Perez, Amr Abd-Elrahman, Benjamin Wilkinson, Daniel J. Johnson, Raymond R. Carthy

“…We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). …”
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Artificial Intelligence for Monte Carlo Simulation in Medical Physics by David Sarrut, Ane Etxebeste, Enrique Muñoz, Nils Krah, Nils Krah, Jean Michel Létang

“…In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. …”
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Regarding Solid Oxide Fuel Cells Simulation through Artificial Intelligence: A Neural Networks Application by Arianna Baldinelli, Linda Barelli, Gianni Bidini, Fabio Bonucci, Feride Cansu Iskenderoğlu

“…In this paper, several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8YSZ/LSCF anode supported planar button cells. …”
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Learning From Limited and Imbalanced Medical Images With Finer Synthetic Images From GANs by Xiaoli Qin, Francis Minhthang Bui, Ha H. Nguyen, Zhu Han

“…Altogether, to verify the robustness of using GANs to augment datasets, we compare various data augmentation approaches, when applied to different network architectures, including transfer learning, learning-from-scratch CNNs, state-of-the-art ResNet, EfficientNet, DenseNet, and the proposed multi-scale CNN. …”
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DeepDist: real-value inter-residue distance prediction with deep residual convolutional network by Tianqi Wu, Zhiye Guo, Jie Hou, Jianlin Cheng

“…Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. …”
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WeVoTe: A Weighted Voting Technique for Automatic Sentiment Annotation of Moroccan Dialect Comments by Yassir Matrane, Faouzia Benabbou, Zouheir Banou

“…The selection of these neural network architectures was underpinned by a comprehensive grid search procedure. …”
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Optimal Tilt-Wing eVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks by Shuan-Tai Yeh, Xiaosong Du

“…The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. …”
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RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures by Muhammed Zahid Karakusak, Hasan Kivrak, Hasan Fehmi Ates, Mehmet Kemal Ozdemir

“…The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. …”
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On the Problem of Restoring and Classifying a 3D Object in Creating a Simulator of a Realistic Urban Environment by Mikhail Gorodnichev, Sergey Erokhin, Ksenia Polyantseva, Marina Moseva

“…Based on the analysis, the existing neural network architectures do not solve the problems mentioned above. …”
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Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments by Lorenzo León, Cristóbal Campos, Juan Hirzel

“…By evaluating diverse network architectures and training approaches (finetuning versus feature extraction), testing various architectures, employing different training strategies, and amalgamating data, we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1, conducted in a uniform environment, accuracy ranged from 80% to 100% across all models and training strategies, with finetune mode achieving a superior performance of 94% to 99.9% compared to the feature extraction mode at 80% to 92.96%. …”
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Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning by Javier A. Cardenas, Uriel E. Carrero, Edgar C. Camacho, Juan M. Calderon

“…Five promising Neural Network architectures are developed based on a thorough literature review, incorporating LSTM, 1-D convolutional, pooling, and fully-connected layers. …”
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