Network Anomaly Detection With Temporal Convolutional Network and U-Net Model by Anzhelika Mezina, Radim Burget, Carlos M. Travieso-Gonzalez

“…We showed, that the Temporal convolutional network in combination with Long Short-Term Memory network and U-Net model can give higher accuracy compared to other network architectures for network traffic classification. …”
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Using Convolutional Neural Networks for Cloud Detection on VEN<i>μ</i>S Images over Multiple Land-Cover Types by Ondřej Pešek, Michal Segal-Rozenhaimer, Arnon Karnieli

“…The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VEN<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula>S satellite images. …”
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A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery by L.G. Divyanth, Aanis Ahmad, Dharmendra Saraswat

“…Three semantic segmentation models were trained for each stage using SegNet, UNet, and DeepLabV3+ network architectures. Stage one used semantic segmentation to extract leaves from complex field backgrounds. …”
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Aspects of Design in Low Voltage Resilient Grids—Focus on Battery Sizing and U Level Control with P Regulation in Microgrids of Energy Communities by Mihai Sanduleac, Alexandru Sandulescu, Cristina Efremov, Constantin Ionescu, Ioan Catalin Damian, Alexandru Mandis

“…The section addresses electrical network architectures, multiple roles of storage resources related to prosumers and to islanded microgrids with high-RES penetration, and other aspects such as the impact of electrification of heating through, e.g., heat pumps. …”
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Method for Identifying Litchi Picking Position Based on YOLOv5 and PSPNet by Xiaokang Qi, Jingshi Dong, Yubin Lan, Hang Zhu

“…At the same time, this paper also compares several different network architectures for main stem detection and segmentation and selects YOLOv5 and PSPNet as the most promising models for main stem detection and segmentation tasks, respectively. …”
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Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora. by Ithalo Coelho de Sousa, Moysés Nascimento, Isabela de Castro Sant'anna, Eveline Teixeira Caixeta, Camila Ferreira Azevedo, Cosme Damião Cruz, Felipe Lopes da Silva, Emilly Ruas Alkimim, Ana Carolina Campana Nascimento, Nick Vergara Lopes Serão

“…An ANN with three hidden layers was run, each varying from 1 to 40 neurons summing 64,000 neural networks. The network architectures with the best predictive ability were selected. …”
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Differential Diagnosis of COVID-19 and Community-acquired Pneumonia Using Different Machine Learning Methods by Zhaoting KANG, Xuehui OUYANG, Jun CHAI

“…After performing artificial intelligence-based lung segmentation, the extracted abnormal CT image features were dimensionally reduced and inputted into various classical machine learning models, Three-dimensional convolutional neural network (3D CNN), and attention-Multiple-instance learning (MIL) deep neural network architectures. The diagnostic performance of the models was evaluated using metrics such as receiver operating characteristic (ROC) curves, Precision Recall (PR) curves, Area Under Curve (AUC), sensitivity, specificity, and accuracy. …”
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Generalised Kuramoto models with time-delayed phase-resetting for k-dimensional clocks by Martin Brennan, Peter Grindrod CBE

“…The Kuramoto model exploits the natural network of networks architecture of the human cortex. An array of N=1M such columns/clocks is at the 10B neuron scale of the human cortex. …”
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ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy by Sebastian Reinhard, Dominic A. Helmerich, Dominik Boras, Markus Sauer, Philip Kollmannsberger

“…We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. …”
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Unlocking the potential of neural networks in resource and data constrained environments by Gupta, Otkrist

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Multiple Simultaneous Optical Links for Space-Based Platforms by Aguilar, Alexa C.

“…New space-based network architectures, such as the broadband constellations currently being deployed by SpaceX and Telesat, among others, leverage optical intersatellite links to increase total system throughput and reduce the number of ground stations which lowers the overall system costs. …”
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Cortico-thalamic interactions for head direction coding by van der Goes, Marie-Sophie Helene

“…In the second part, I confirm that ADn is devoid of recurrent excitatory connectivity, contrary to previously proposed attractor network architectures. My results suggest that inhibition, originating in thalamic reticular nucleus, likely plays a fundamental role in the control of ADn HD. …”
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When all information is not created equal by Borade, Shashibhushan Prataprao, 1981-

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Design and analysis of optical flow switched networks by Weichenberg, Guy E. (Guy Elli), 1978-

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Enabling dedicated single-cycle connections over a shared network-on-chip by Krishna, Tushar

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Knowledge-aware representation learning for natural language processing applications by Zhang, Jiaheng

“…Answer Selection: Four distinct network architectures leveraging topic embeddings are proposed, yielding superior text representations. …”
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Cloud/edge inference for deep neural networks by Sbai, M

“…The experiments were performed on two different network architectures, MobileNetV1 and VGG16. The proposed approach makes it possible to execute part of the network on very constrained devices (e.g., microcontrollers), and under poor network conditions (e.g., LoRa) whilst retaining reasonable accuracies.…”

Homogeneous ensemble Neural Cognition for CIMB stock price prediction by Chang, Sim Vui

“…The two single neural network architectures as the building block for homogeneous ENN are used in this thesis which are FeedForward Neural Network (FFNN) and Recurrent Neural Network (RNN). …”
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Artificial Neural Network Modeling of Hydrogen-rich Syngas Production from Methane Dry Reforming over Novel Ni/CaFe2O4 Catalysts by Hossain, M. Anwar, Ayodele, Bamidele V., Cheng, C. K., Khan, Maksudur R.

“…Multi-layer perceptron (MLP) and radial basis function (RBF) neural network architectures were employed for the modeling of the experimental data obtained from methane dry reforming over novel Ni/CaFe2O3 catalysts. …”
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Task control signals in pediatric Tourette syndrome show evidence of immature and anomalous functional activity by Jessica A Church, Kristin K Wenger, Nico U F Dosenbach, Francis M Miezin, Francis M Miezin, Steven E Petersen, Steven E Petersen, Steven E Petersen, Steven E Petersen, Bradley L Schlaggar, Bradley L Schlaggar, Bradley L Schlaggar, Bradley L Schlaggar

“…Previous work has proposed a dual-networks architecture of control processing involving a task-maintenance network and an adaptive control network (Dosenbach et al., 2008). …”
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