Real-time coronary artery stenosis detection based on modern neural networks by Viacheslav V. Danilov, Kirill Yu. Klyshnikov, Olga M. Gerget, Anton G. Kutikhin, Vladimir I. Ganyukov, Alejandro F. Frangi, Evgeny A. Ovcharenko

“…To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. …”
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NASA NeMO-Net's Convolutional Neural Network: Mapping Marine Habitats with Spectrally Heterogeneous Remote Sensing Imagery by Alan S. Li, Ved Chirayath, Michal Segal-Rozenhaimer, Juan L. Torres-Perez, Jarrett van den Bergh

“…Here, we applied deep learning and neural network architectures in NASA NeMO-Net, a novel neural multimodal observation and training network for global habitat mapping of shallow benthic tropical marine systems. …”
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Detecting and Mitigating Collusive Interest Flooding Attacks in Named Data Networking by Rama A. Al-Share, Ahmed S. Shatnawi, Basheer Al-Duwairi

“…The large expansion in network services and applications seen in the last few years requires new network architectures to satisfy an increasing number of users and enhance content delivery. …”
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Modeling of Microstructure and Mechanical Properties of Heat Treated ZE41-Ca-Sr Alloys for Integrated Computing Platform by Yu Fu, Chen Liu, Yunkun Song, Hai Hao, Yongdong Xu, Zhiwen Shao, Jun Wang, Xiurong Zhu

“…The optimal ANN model is obtained by testing the performance of different network architectures. In addition, multivariate regression models have been built based on the Least Squares method. …”
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Deep Learning for Service Function Chain Provisioning in Fog Computing by Nazli Siasi, Mohammed Jasim, Adel Aldalbahi, Nasir Ghani

“…Cloud and fog computing along with network function virtualization technology have significantly shifted the development of network architectures. They yield in reduced capital and operating expenditures, while achieving network flexibility and scalability to accommodate the massive growth in data traffic volumes from user terminals requesting various services and applications. …”
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Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders by Theodoros Bermperidis, Simon Schafer, Fred H. Gage, Terrence Sejnowski, Elizabeth B. Torres, Elizabeth B. Torres, Elizabeth B. Torres

“…They give rise to different network architectures and different transitional states. …”
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Uncertainty Quantification at the Microscale: A Data-Driven Multi-Scale Approach by José Pablo Quesada-Molina, Stefano Mariani

“…Results relevant to different neural network architectures are here discussed, along with a proposal to frame the approach as a multi-fidelity, uncertainty quantification procedure.…”
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Enhanced pre-processing for deep learning in MRI whole brain segmentation using orthogonal moments by Rodrigo Dalvit Carvalho da Silva, Thomas Richard Jenkyn, Victor Alexander Carranza

“…In addition, the best image representations were used to compare different convolutional neural network architectures, including U-Net, U-Net++, and U-Net3+. …”
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RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing by Przemyslaw Flak, Roman Czyba

“…The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. …”
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<i>AutoPPI</i>: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction by Gabriela Czibula, Alexandra-Ioana Albu, Maria Iuliana Bocicor, Camelia Chira

“…Our approach, entitled <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>u</mi><mi>t</mi><mi>o</mi><mi>P</mi><mi>P</mi><mi>I</mi></mrow></semantics></math></inline-formula>, adopts a strategy based on two autoencoders, one for each type of interactions (positive and negative) and we advance three types of neural network architectures for the autoencoders. Experiments were performed on several data sets comprising proteins from four different species. …”
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An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks by Dominik Muller, Inaki Soto-Rey, Frank Kramer

“…The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. …”
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Beyond Word-Based Model Embeddings: Contextualized Representations for Enhanced Social Media Spam Detection by Sawsan Alshattnawi, Amani Shatnawi, Anas M.R. AlSobeh, Aws A. Magableh

“…They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. …”
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User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features by Walaa Aly, Saleh Aly, Sultan Almotairi

“…Feature learning using convolutional neural network architectures is applied instead of the classical hand-crafted feature extraction methods. …”
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Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks by Jian Peng, Xiaoming Mei, Wenbo Li, Liang Hong, Bingyu Sun, Haifeng Li

“…Various convolutional neural network architectures have been proposed for automatically learning features from images. …”
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Underpinning miRNA-miRNA co-functional interaction patterns in the metabolism of Oryza sativa by genome-scale network analysis by Ayushman Kumar Banerjee, Chittabrata Mal

“…Thus, for better understanding these complex biological networks, and to derive their significance and inter-dependency, a systems biology approach enables us to explore and draw vital insights into these molecular network architectures. In this study, we aimed to understand the significance of synergistic miRNA-miRNA interactions in rice by constructing and analysing metabolic networks. …”
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Infant movement classification through pressure distribution analysis by Tomas Kulvicius, Dajie Zhang, Karin Nielsen-Saines, Sven Bölte, Marc Kraft, Christa Einspieler, Luise Poustka, Florentin Wörgötter, Peter B. Marschik

“…Multiple neural network architectures were tested to distinguish the fidgety present vs. fidgety absent classes, including support vector machines, feed-forward networks, convolutional neural networks, and long short-term memory networks. …”
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Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN by Hongwen Hu, Miao Ye, Chenwei Zhao, Qiuxiang Jiang, Xingsi Xue

“…Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existing multicasting solutions. …”
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HFNet : a CNN architecture co-designed for neuromorphic hardware with a crossbar array of synapses by Gopalakrishnan, Roshan, Chua, Yansong, Sun, Pengfei, Kumar, Ashish Jith Sreejith, Basu, Arindam

“…The hardware-software co-optimization of neural network architectures is a field of research that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth and Intel Loihi. …”
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A model with contact maps at both polymer chain and network scales for tough hydrogels with chain entanglement, hidden length and unconventional network topology by You, Hao, Zheng, Shoujing, Li, Hua, Lam, Khin Yong

“…Tough hydrogels with unconventional polymer network architectures often show superior mechanical properties because of the microstructures at the polymer chain and network scales. …”
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Towards robust monocular depth estimation: a new baseline and benchmark by Xian, Ke, Cao, Zhiguo, Shen, Chunhua, Lin, Guosheng

“…Specifically, we provide valuable insights about the choices of data augmentation strategies and network architectures, which would be useful for future research in robust monocular depth estimation. …”
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