A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays by Avantika Vardhan, Alex Makhnevich, Pravan Omprakash, David Hirschorn, Matthew Barish, Stuart L. Cohen, Theodoros P. Zanos

“…We tested three neural network architectures (ResNet-50, VGG-16, and ChexNet), three segmentation schemes (no segmentation, lung segmentation, and lateral segmentation based on spine detection), and three data balancing strategies (undersampling, double-stage sampling, and synthetic minority oversampling) using 38,079 CXR images for training, and validation with 286 images as the out-of-the-box dataset that underwent expert radiologist adjudication. …”
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Scalable deep learning for watershed model calibration by Maruti K. Mudunuru, Kyongho Son, Peishi Jiang, Glenn Hammond, Xingyuan Chen

“…Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. …”
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Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation? by Iulian Emil Tampu, Neda Haj-Hosseini, Anders Eklund

“…A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy by using context-aware mechanisms. …”
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Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures by Sheraz Naseer, Rao Faizan Ali, P.D.D Dominic, Yasir Saleem

“…We trained four representation learning models using popular deep neural network architectures to extract deep representations from ISCX 2012 traffic flows. …”
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Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images by Christopher A. Mela, Yang Liu

“…We next attempt to optimize and compare segmentation accuracy using three different neural network architectures. Results Results indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. …”
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A divide and conquer approach to maximise deep learning mammography classification accuracies. by Adam Jaamour, Craig Myles, Ashay Patel, Shuen-Jen Chen, Lewis McMillan, David Harris-Birtill

“…A divide and conquer approach is followed to analyse the effects on performance and efficiency when utilising diverse deep learning techniques such as varying network architectures (VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2), class weights, input sizes, image ratios, pre-processing techniques, transfer learning, dropout rates, and types of mammogram projections. …”
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A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction by Asha Vijayan, Asha Vijayan, Shyam Diwakar, Shyam Diwakar

“…Spiking neural networks were introduced to understand spatiotemporal information processing in neurons and have found their application in pattern encoding, data discrimination, and classification. Bioinspired network architectures are considered for event-driven tasks, and scientists have looked at different theories based on the architecture and functioning. …”
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Author's rights in the digital age: how Internet and peer-to-peer file sharing technology shape the perception of copyrights and copywrongs by Milijana Micunovic, Luka Balković

“…In the analysis special attention was given to both, the concept of authorship in the digital environment and the concept of peer-to-peer file sharing technology as not so new, but still very popular networked architecture for distributing, using and sharing digital content. …”
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Pengembangan Aplikasi Pemantauan Jaringan Berbasis Web pada Software-Defined Networking dengan Protokol SFLOW by Muhammad Ilham, Nur Rohman Rosyid

“…Abstract Software-defined networking (SDN) is one of the network architectures which programmable to ease network management using the controller application. …”
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Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey by Jia Liu, Jianjian Xiang, Yongjun Jin, Renhua Liu, Jining Yan, Lizhe Wang

“…The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.…”
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INTELLIGENT COMPUTER VISION SYSTEM FOR UNMANNED AERIAL VEHICLES FOR MONITORING TECHNOLOGICAL OBJECTS OF OIL AND GAS INDUSTRY by Ivan V. Zoev, Nikolay G. Markov, Svetlana E. Ryzhova

“…The authors developed the convolutional neural networks architectures for computer vision system from promising subclasses LeNet5 and YOLO and proposed the algorithms of error-correction data encoding/decoding for messages exchanging between these components, considering the specifics of ground and airborne components. …”
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Machine learning approaches for computer aided drug discovery by Moesser, M

“…I explore a variety of Graph Neural Network architectures in combination with PLIGs and found Graph Attention Networks to perform slightly better than other GNN architectures, performing amongst the best known protein-ligand binding affinity scoring functions.…”

Features and methods for improving large scale face recognition by Parkhi, O

“…We bring out specific details of these network architectures and their training objective functions essential to their performance and achieve state-of-art result on challenging datasets.…”

Boosting Unsupervised Dorsal Hand Vein Segmentation with U-Net Variants by Szidónia Lefkovits, Simina Emerich, László Lefkovits

“…This is the reason for building an ensemble segmentor based on majority voting by nine different network architectures (U-Net, U-Net++ and U-Net3+, all trained with BCE, Dice and focal losses). …”
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Force Profile as Surgeon-Specific Signature by Amir Baghdadi, PhD, Eddie Guo, Sanju Lama, MD, PhD, Rahul Singh, BSc, Michael Chow, MD, FRCSC, Garnette R. Sutherland, MD, FRCS, DSc. (Hon)

“…In this study, we investigate multiple neural network architectures to identify the surgeon associated with their time-series tool-tissue forces using bipolar forceps data. …”
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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

“…To this end, the main contribution of this paper is to design a robust HD prediction system using Hybrid Deep Neural Networks (HDNNs) involves combining multiple neural network architectures to extract and learn relevant features from the input data. …”
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Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network by Christos Pavlatos, Evangelos Makris, Georgios Fotis, Vasiliki Vita, Valeri Mladenov

“…The findings highlight the significance of incorporating bidirectional memory and advanced neural network architectures for precise energy consumption prediction. …”
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Can CNN‐based species classification generalise across variation in habitat within a camera trap survey? by Danielle L. Norman, Philipp H. Bischoff, Oliver R. Wearn, Robert M. Ewers, J. Marcus Rowcliffe, Benjamin Evans, Sarab Sethi, Philip M. Chapman, Robin Freeman

“…Here, we (i) compare the performance of three network architectures in identifying species in camera trap images taken from tropical forest of varying disturbance intensities; (ii) explore the impacts of training dataset configuration; (iii) use habitat disturbance categories to investigate network generalisability and (iv) test whether classification performance and generalisability improve when using images cropped to bounding boxes. …”
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Three-dimensional culture and clinical drug responses of a highly metastatic human ovarian cancer HO-8910PM cells in nanofibrous microenvironments of three hydrogel biomaterials by Hong Song, Guo-hui Cai, Jian Liang, Di-shu Ao, Huan Wang, Ze-hong Yang

“…Designer RADA16-I peptide had well-defined nanofiber networks architecture in hydrogel, which provided nanofiber cell microenvironments analogous to Matrigel and collagen I. 3D-cultured HO-8910PM cells in RADA16-I hydrogel, Matrigel, and collagen I showed viable cell proliferation, proper cell growth, and diverse cell shapes in morphology at the desired time points. …”
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An Optimistic Solver for the Mathematical Model of the Flow of Johnson Segalman Fluid on the Surface of an Infinitely Long Vertical Cylinder by Naveed Ahmad Khan, Fahad Sameer Alshammari, Carlos Andrés Tavera Romero, Muhammad Sulaiman, Seyedali Mirjalili

“…The approximate series solutions are constructed by Legendre polynomials and a Legendre polynomial-based artificial neural networks architecture (LNN) to approximate solutions for drainage problems. …”
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