23 papers · ranked by Valyu relevance
Saddam Hussain Khan, Rashid Iqbal
Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped…
V. Carruba, S. Aljbaae, Gabriel Caritá, Rita Cassia Domingos + 1 more
'Bruno Martins'] Abstract The asteroidal main belt is crossed by a web of mean-motion and secular resonances, that occur when there is a commensurability between fundamental frequencies of the asteroids and planets. Traditionally, these objects were identified by visual inspection of the time evolution of their…
Aji Prasetya Wibawa, Agung Bella Putra Utama, Hakkun Elmunsyah, Utomo Pujianto + 2 more
'Utomo Pujianto' 'Felix Andika Dwiyanto' 'Leonel Hernandez'] CNN originates from image processing and is not commonly known as a forecasting technique in time-series analysis which depends on the quality of input data. One of the methods to improve the quality is by smoothing the data. This study introduces a novel…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Tehnan I. A. Mohamed, Olaide N. Oyelade, Absalom E. Ezugwu, Bilal Alatas
'Bilal Alatas'] Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The…
Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks…
Rhalena A. Thomas, Eddie Cai, Wolfgang Reintsch, Chanshaui Han + 9 more
Parkinson’s disease (PD) is a neurodegenerative disorder that results in the loss of dopaminergic neurons in the substantia nigra pars compacta. Despite advances in understanding PD, there is a critical need for novel therapeutics that can slow or halt its progression. Induced pluripotent stem cell (iPSC)-derived…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Danny da Costa, Rainer Goebel, Mario Senden
The distribution of retinal ganglion cells in primate visual systems portrays a densely distributed central region, with an incrementally decreasing cell density as the angle of visual eccentricity increases. This results in a non-uniform sampling of the retinal image that resembles a wheelbarrow distortion. We propose…
Andrew McNutt, Yanjing Li, Paul Francoeur, David Koes
Knowledge of the bound protein-ligand structure is critical to many drug discovery tasks. One tool for in silico bound structure elucidation is molecular docking, which samples and scores ligand binding conformations. Recent work has demonstrated that convolutional neural networks (CNNs) for protein-ligand pose scoring…
G.F. Fortino, J. C. Zamora, L. E. Tamayose, Nina S. T. Hirata + 1 more
'V. Guimarães'] An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of experiments with active target time projection chambers more efficient. The code is divided in three steps: baseline correction…
Sahan Ahmad, Gabriel Trahan, Aminul Islam
Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in computer vision tasks such as image classification, detection, segmentation, and medical image analysis. In general, an arbitrary number of epochs is used to train such neural networks. In a single epoch, the entire training data—divided…
Pinaki Saha, Minh Tho Nguyen
Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster including its chemical, optical, magnetic, mechanical and transport properties are mainly governed by the…
Yongtao Lu, Yi Huo, Zhuoyue Yang, Yibiao Niu + 3 more
'Sergei Bosiakov' 'Lei Li'] In recent years, the convolutional neural network (CNN) technique has emerged as an efficient new method for designing porous structure, but a CNN model generally contains a large number of parameters, each of which could influence the predictive ability of the CNN model. Furthermore, there…
Peng Liu, Ke Bo, Mingzhou Ding, Ruogu Fang
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion…
Jirui Fu, Helen J Huang, Yue Wen
Convolutional neural networks (CNNs) have shown promise in decoding neural drive from high-density surface electromyography (HD-sEMG) signals. However, the effects of convolutional kernel dimensionality on the generalizability and computational efficiency of CNN-based neural drive decoding remain unclear. This study…
L. M. Modafferi, R. Tenorio, D. Keitel
Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the mostly unknown depths of the pulsar. Current methods to search for such signals…
Md. Zahin Hossain George, Naimul Hossain, Md. Rafiuzzaman Bhuiyan, Abu Kaisar Mohammad Masum + 1 more
'Abu Kaisar Mohammad Masum' 'Sheikh Abujar'] Abstract—There have recently been many cases of unverified or misleading information circulating quickly over bogus web networks and news portals. This false news creates big damage to society and misleads people. For Example, in 2019 there was a rumor that the Padma Bridge…
Yue Wen, Sangjoon J. Kim, Simon Avrillon, Jackson T. Levine + 2 more
High-density electromyography (HD-EMG) decomposition algorithms are used to identify individual motor unit spike trains, which collectively constitute the neural code of movements, to predict motor intent. This approach has advanced from offline to online decomposition, from isometric to dynamic contractions, leading…
Robert Dellavalle, Torunn Sivesind, Presenjit Bhadra, Md Majurul Ahsan + 10 more
Background Convolutional neural networks (CNNs) are a type of artificial intelligence that shows promise as a diagnostic aid for skin cancer. However, the majority are trained using retrospective image data sets with varying image capture standardization. Objective The aim of our study was to use CNN models with the…
Homa Hosseinmardi, Samuel Wolken, David M. Rothschild, Duncan J. Watts
'Duncan J. Watts'] The potential for a large, diverse population to coexist peacefully is thought to depend on the existence of a public sphere in which citizens are exposed to similar facts about similar topics. A generation ago, broadcast television news was widely considered to serve this function; however, since…
Justus T. Metternich, Sujit K. Patjoshi, Tanuja Kistwal, Sebastian Kruss
Optical sensors/probes are powerful tools to identify and image (biological) molecules. Because of their optoelectronic properties, nanomaterials are often used as building blocks. Such nanosensors are assembled from an optically sensitive nanomaterial, a (biological) recognition unit, and linker chemistry that…
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…