Comparative analysis of classification techniques for topic-based biomedical literature categorisation by Ihor Stepanov, Ihor Stepanov, Arsentii Ivasiuk, Arsentii Ivasiuk, Oleksandr Yavorskyi, Alina Frolova, Alina Frolova

“…Development of new neural network architectures, loss functions and training procedures that bring stability to unbalanced data is a promising topic of development.…”
Get full text

ResSKNet-SSDP: Effective and Light End-To-End Architecture for Speaker Recognition by Fei Deng, Lihong Deng, Peifan Jiang, Gexiang Zhang, Qiang Yang

“…Previous research has addressed these issues by introducing deeper, wider, and more complex network architectures and aggregation methods. However, it is difficult to significantly improve the performance with these approaches because they also have trouble fully utilizing global information, channel information, and time-frequency information. …”
Get full text

Using transfer learning-based causality extraction to mine latent factors for Sjögren's syndrome from biomedical literature by Jack T. VanSchaik, Palak Jain, Anushri Rajapuri, Biju Cheriyan, Thankam P. Thyvalikakath, Sunandan Chakraborty

“…We conduct an empirical analysis of numerous neural network architectures and data transfer strategies for causal relation extraction. …”
Get full text

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China by Qianyang Wang, Yuan Liu, Qimeng Yue, Yuexin Zheng, Xiaolei Yao, Jingshan Yu

“…In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. …”
Get full text

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks by Nikita V. Artemenko, Nikita V. Artemenko, Mikhail A. Genaev, Mikhail A. Genaev, Rostislav UI. Epifanov, Evgeny G. Komyshev, Yulia V. Kruchinina, Yulia V. Kruchinina, Vasiliy S. Koval, Vasiliy S. Koval, Nikolay P. Goncharov, Dmitry A. Afonnikov, Dmitry A. Afonnikov, Dmitry A. Afonnikov

“…These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). …”
Get full text

Deep Transfer Learning for Chronic Obstructive Pulmonary Disease Detection Utilizing Electrocardiogram Signals by Inanc Moran, Deniz Turgay Altilar, Muhammed Kursad Ucar, Cahit Bilgin, Mehmet Recep Bozkurt

“…Xception, VGG-19, InceptionResNetV2, DenseNet-121, and “trained-from-scratch” convolutional neural network architectures have been investigated for the detection of COPD, and it is demonstrated that they are able to obtain high performance rates in classifying nearly 33.000 instances using diverse training strategies. …”
Get full text

Interference-Aware Intelligent Scheduling for Virtualized Private 5G Networks by Berk Akgun, Deepak Singh Mahendar Singh, Samatha Kotla, Vikas Jain, Sakshi Namdeo, Rupesh Acharya, Muruganandam Jayabalan, Abhishek Kumar, Vinay Chande, Arumugam Kannan, Jalaj Swami, Yitao Chen, John Boyd, Xiaoxia Zhang

“…These private network architectures often rely on multi-cell deployments to meet the stringent reliability and latency requirements of industrial applications. …”
Get full text

Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues by Mingqi Zhou, Ian Tamburini, Cassandra Van, Jeffrey Molendijk, Christy M Nguyen, Ivan Yao-Yi Chang, Casey Johnson, Leandro M Velez, Youngseo Cheon, Reichelle Yeo, Hosung Bae, Johnny Le, Natalie Larson, Ron Pulido, Carlos HV Nascimento-Filho, Cholsoon Jang, Ivan Marazzi, Jamie Justice, Nicholas Pannunzio, Andrea L Hevener, Lauren Sparks, Erin E Kershaw, Dequina Nicholas, Benjamin L Parker, Selma Masri, Marcus M Seldin

“…This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs.…”
Get full text

A Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique by Sajida Perveen, Abdullah Alourani, Muhammad Shahbaz, M. Usman Ashraf, Isma Hamid

“…Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). …”
Get full text

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI by Mara Pistellato, Filippo Bergamasco, Gianluca Bigaglia, Andrea Gasparetto, Andrea Albarelli, Marco Boschetti, Roberto Passerone

“…In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). …”
Get full text

Scalable Prediction of Service-Level Events in Datacenter Infrastructure Using Deep Neural Networks by Alberto Mozo, Itai Segall, Udi Margolin, Sandra Gomez-Canaval

“…To this end, we propose a generic and scalable method based on the application of deep neural network architectures for predicting service level events using only a reduced number of generic datacenter infrastructure statistics that can be monitored in a scalable way. …”
Get full text

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans by Hammam Alshazly, Christoph Linse, Mohamed Abdalla, Erhardt Barth, Thomas Martinetz

“…In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. …”
Get full text

HcLSH: A Novel Non-Linear Monotonic Activation Function for Deep Learning Methods by Heba Abdel-Nabi, Ghazi Al-Naymat, Mostafa Z. Ali, Arafat Awajan

“…An extensive set of experiments and comparisons is conducted that includes four popular image classification datasets, seven deep network architectures, and ten state-of-the-art activation functions. …”
Get full text

A deep learning knowledge distillation framework using knee MRI and arthroscopy data for meniscus tear detection by Mengjie Ying, Yufan Wang, Yufan Wang, Kai Yang, Haoyuan Wang, Xudong Liu

“…Purpose: To construct a deep learning knowledge distillation framework exploring the utilization of MRI alone or combing with distilled Arthroscopy information for meniscus tear detection.Methods: A database of 199 paired knee Arthroscopy-MRI exams was used to develop a multimodal teacher network and an MRI-based student network, which used residual neural networks architectures. A knowledge distillation framework comprising the multimodal teacher network T and the monomodal student network S was proposed. …”
Get full text

Assessing the ability of deep learning techniques to perform real-time identification of shark species in live streaming video from drones by Cormac R. Purcell, Cormac R. Purcell, Cormac R. Purcell, Cormac R. Purcell, Cormac R. Purcell, Andrew J. Walsh, Andrew J. Walsh, Andrew P. Colefax, Paul Butcher

“…We find that shallower network architectures, like MobileNet V1, tend to perform slightly worse on smaller objects, so care is needed when selecting a network to match deployment needs. …”
Get full text

A Survey of Audio Classification Using Deep Learning by Khalid Zaman, Melike Sah, Cem Direkoglu, Masashi Unoki

“…In particular, we focus on works published under five different deep neural network architectures, namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Transformers and Hybrid Models (hybrid deep learning models and hybrid deep learning models with traditional classifiers). …”
Get full text

Semantic segmentation of point cloud data using raw laser scanner measurements and deep neural networks by Risto Kaijaluoto, Antero Kukko, Aimad El Issaoui, Juha Hyyppä, Harri Kaartinen

“…The labelled points were then transformed back to 2D rasters and used for training three different neural network architectures. Further, the same georeferenced data in point cloud format was used for training the state-of-the-art point cloud semantic segmentation network RandLA-Net and the results were compared with those of our method. …”
Get full text

Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary by Kaushik Dutta, Sudipta Roy, Timothy Daniel Whitehead, Jingqin Luo, Abhinav Kumar Jha, Shunqiang Li, James Dennis Quirk, Kooresh Isaac Shoghi

“…We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. …”
Get full text

Mobile Edge-Based Information-Centric Network for Emergency Messages Dissemination in Internet of Vehicles: A Deep Learning Approach by Shahzad Rizwan, Ghassan Husnain, Farhan Aadil, Fayaz Ali, Sangsoon Lim

“…Information-Centric Networking (ICN) has emerged as a novel networking architecture that shifts the communication model from Internet protocol (IP) based host-centric to content-centric architecture. …”
Get full text

Structural Protein Effects Underpinning Cognitive Developmental Delay of the <i>PURA</i> p.Phe233del Mutation Modelled by Artificial Intelligence and the Hybrid Quantum Mechanics–M... by Juan Javier López-Rivera, Luna Rodríguez-Salazar, Alejandro Soto-Ospina, Carlos Estrada-Serrato, David Serrano, Henry Mauricio Chaparro-Solano, Olga Londoño, Paula A. Rueda, Geraldine Ardila, Andrés Villegas-Lanau, Marcela Godoy-Corredor, Mauricio Cuartas, Jorge I. Vélez, Oscar M. Vidal, Mario A. Isaza-Ruget, Mauricio Arcos-Burgos

“…We used the significant improvement in the accuracy of protein structure prediction recently implemented in AlphaFold that incorporates novel neural network architectures and training procedures based on the evolutionary, physical, and geometric constraints of protein structures. …”
Get full text