Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation by Felix Nobis, Felix Fent, Johannes Betz, Markus Lienkamp

“…This paper proposes artificial neural network architectures to segment sparse radar point cloud data. …”
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Towards mechanistic models of mutational effects: Deep learning on Alzheimer’s Aβ peptide by Bo Wang, Shahab Razavi, Eric R. Gamazon

“…Among tested neural network architectures, Convolutional Neural Networks and Recurrent Neural Networks are found to be the most cost-effective models with high performance even under insufficiently-sampled DMS studies. …”
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Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States by Kul Khand, Gabriel B. Senay

“…The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. …”
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Deep-Learning-Based Classification of Bangladeshi Medicinal Plants Using Neural Ensemble Models by A. Hasib Uddin, Yen-Lin Chen, Bijly Borkatullah, Mst. Sathi Khatun, Jannatul Ferdous, Prince Mahmud, Jing Yang, Chin Soon Ku, Lip Yee Por

“…In addition to benchmarking the deep learning models, three novel neural network architectures were developed: dense-residual–dense (DRD), dense-residual–ConvLSTM-dense (DRCD), and inception-residual–ConvLSTM-dense (IRCD). …”
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Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations by Joshua Villarreal, Daniel Winklehner, Daniel Koser, Janet M Conrad

“…Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.…”
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Anatomical Landmark Detection Using a Feature-Sharing Knowledge Distillation-Based Neural Network by Di Huang, Yuzhao Wang, Yu Wang, Guishan Gu, Tian Bai

“…Existing anatomical landmark detection methods consider the performance gains under heavyweight network architectures, which lead to models tending to have poor scalability and cost-effectiveness. …”
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Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers by Xiaofan Xiong, Brian J. Smith, Stephen A. Graves, Michael M. Graham, John M. Buatti, Reinhard R. Beichel

“…In this work, we quantitatively assess the performance of two CNN-based networks (U-Net and U-Net-CBAM) and three popular Transformer-based segmentation network architectures (UNETR, TransBTS, and VT-UNet) in the context of HNC lesion segmentation in volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. …”
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A Survey on Deep Learning in COVID-19 Diagnosis by Xue Han, Zuojin Hu, Shuihua Wang, Yudong Zhang

“…Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. …”
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Pre-Trained Model-Based NFR Classification: Overcoming Limited Data Challenges by Kiramat Rahman, Anwar Ghani, Abdulrahman Alzahrani, Muhammad Usman Tariq, Arif Ur Rahman

“…In addition, each pre-trained model is paired with the four tailored neural network architectures for NFR classification including RPCNN, RPBiLSTM, RPLSTM, and RPANN. …”
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Optimal Cache Deployment for Video-On-Demand in Optical Metro Edge Nodes under Limited Storage Capacity by Omran Ayoub, Davide Andreoletti, Francesco Musumeci, Massimo Tornatore, Achille Pattavina

“…Network operators must continuously explore new network architectures to satisfy increasing traffic demand due to bandwidth-hungry services, such as video-on-demand (VoD). …”
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Combining structured and unstructured data for predictive models: a deep learning approach by Dongdong Zhang, Changchang Yin, Jucheng Zeng, Xiaohui Yuan, Ping Zhang

“…Methods In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. …”
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Animal Detection and Counting from UAV Images Using Convolutional Neural Networks by Kristina Rančić, Boško Blagojević, Atila Bezdan, Bojana Ivošević, Bojan Tubić, Milica Vranešević, Branislav Pejak, Vladimir Crnojević, Oskar Marko

“…In this paper, we present and compare the performance of several state-of-the-art network architectures, trained on a manually annotated set of images, and use it to predict the presence of objects in the rest of the dataset. …”
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Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems by Myungyon Kim, Joon Ha Jung, Jin Uk Ko, Hyeon Bae Kong, Jinwook Lee, Byeng Dong Youn

“…However, it becomes difficult to comprehensively train neural network architectures as they become deeper, due to problems in the flow of gradient information during the training phase. …”
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A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges by Nikolaos Nomikos, Panagiotis K. Gkonis, Petros S. Bithas, Panagiotis Trakadas

“…More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. …”
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Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly by Fan Liang, William Grant Hatcher, Weixian Liao, Weichao Gao, Wei Yu

“…Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. …”
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A Distributed NFV-Enabled Edge Cloud Architecture for ICN-Based Disaster Management Services by Van-Ca Nguyen, Ngoc-Thanh Dinh, Younghan Kim

“…However, present studies use a centralized networking architecture for disaster management, in which disaster information is gathered and processed at a centralized management center before incident responses are made and warning messages are sent out. …”
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Deep Learning Approach on Prediction of Soil Consolidation Characteristics by Mintae Kim, Muharrem A. Senturk, Rabia K. Tan, Ertugrul Ordu, Junyoung Ko

“…In this study, we compare four distinct deep learning-based artificial neural network architectures to evaluate their performance in predicting soil consolidation characteristics. …”
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Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest by Fatemeh Esmaeili, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth, Alan Wang

“…Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. …”
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A comparison of manual and automated neural architecture search for white matter tract segmentation by Ari Tchetchenian, Yanming Zhu, Fan Zhang, Lauren J. O’Donnell, Yang Song, Erik Meijering

“…Similar to medical image segmentation in general, a popular approach to white matter tract segmentation is to use U-Net based artificial neural network architectures. Despite many suggested improvements to the U-Net architecture in recent years, there is a lack of systematic comparison of architectural variants for white matter tract segmentation. …”
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An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification by Xiaohui Ding, Yong Li, Ji Yang, Huapeng Li, Lingjia Liu, Yangxiaoyue Liu, Ce Zhang

“…The average overall classification accuracy (OA) of PAR-ACaps with shallower architecture was measured and compared with those of the benchmarks, including random forest (RF), support vector machine (SVM), 1-dimensional convolutional neural network (1DCNN), two-dimensional convolutional neural network (CNN), three-dimensional convolutional neural network (3DCNN), Caps, and the original adaptive capsule network (ACaps) with comparable network architectures. The OA of PAR-ACaps for PU and SA datasets was 99.51% and 94.52%, respectively, which was higher than those of benchmarks. …”
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