22 papers · ranked by Valyu relevance
Huiqiang Hu, Zhenyu Xu, Yunpeng Wei, Tingting Wang + 6 more
'Huaxing Xu' 'Xiaobo Mao' 'Luqi Huang' 'Gian Carlo Tenore' 'Oscar Núñez'] Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated…
Jiaju Wu, Linggang Kong, Shijia Kang, Hongfu Zuo + 3 more
'Zheng Cheng' 'Pavlos Lazaridis'] As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly…
Paul Fergus, Carl Chalmers, Casimiro Curbelo Montañez, Denis Reilly + 2 more
'Paulo Lisböa' 'Beth L. Pineles'] Abstract—Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the wellbeing of the foetus during antenatal care. This approach has raised concerns among…
Yue Li, Zhong Ren, Chunyan Zhao, Gaoqiang Liang + 1 more
The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall…
Muhammad Arslan, Muhammad Mubeen, Muhammad Abdullah Bilal, Saadullah Farooq Abbasi
'Saadullah Farooq Abbasi'] Abstract— The demand of the Internet of Things (IoT) has witnessed exponential growth. These progresses are made possible by the technological advancements in artificial intelligence, cloud computing, and edge computing. However, these advancements exhibit multiple challenges, including cyber…
Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
This research introduces a new combined modelling approach for mapping soil salinity in the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence limits) and interpretability of two deep learning (DL) models (deep boltzmann machine-DBM) and a one dimensional convolutional neural…
Thanida Sananmuang, Denis Puthier, Kaj Chokeshaiusaha
The combination of 1DCNN and GRU networks exploits the strengths of both convolutional and recurrent architectures to effectively analyze sequential data . In this study, we employed this hybrid approach to classify FS-GCs and NFS-GCs based on their expression profiles of Model-Training DEGs. A 1DCNN processes…
Steve Thompson, Paul Fergus, Carl Chalmers, Denis Reilly
—The study in this paper presents a onedimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The system provides mechanisms in clinical practice that help diagnose patients suffering…
Habib Ben Abdallah, Christopher J. Henry, Sheela Ramanna
In addition to being extremely non-linear, modern problems require millions if not billions of parameters to solve or at least to get a good approximation of the solution, and neural networks are known to assimilate that complexity by deepening and widening their topology in order to increase the level of non-linearity…
Jie Hou, Renzhi Cao, Jianlin Cheng
Predicting the global quality and local (residual-specific) quality of a single protein structural model is important for protein structure prediction and application. In this work, we developed a deep one-dimensional convolutional neural network (1DCNN) that predicts the absolute local quality of a single protein…
Ankita Paul, Md. Abu Saleh Tajin, Anup Das, William M. Mongan + 1 more
'Kapil R. Dandekar'] Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where…
Endang Purnama Giri, Mohamad Ivan Fanany, Aniati Murni Arymurthy
In 2015, stroke was the number one cause of death in Indonesia. The majority type of stroke is ischemic. The standard tool for diagnosing stroke is CT-Scan. For developing countries like Indonesia, the availability of CT-Scan is very limited and still relatively expensive. Because of the availability, another device…
Muhammad Muneeb, Samuel F. Feng, Andreas Henschel
Converting genotype sequences into images offers advantages, such as genotype data visualization, classification, and comparison of genotype sequences. This study converted genotype sequences into images, applied two-dimensional convolutional neural networks for case/control classification, and compared the results…
Ankita Paul, Md Abu Saleh Tajin, Anup Das, William M. Mongan + 1 more
'Kapil R. Dandekar'] Abstract—Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a Deep Learning enabled wearable monitoring system for premature newborn infants…
Ilya Kuzovkin, Raul Vicente, Mathilde Petton, Jean-Philippe Lachaux + 5 more
Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We used DCNNs to investigate which frequency bands carry feature transformations of increasing complexity along the ventral…
Diego E. Galvez-Aranda, Tan Le Dinh, Utkarsh Vijay, Franco M. Zanotto + 1 more
The manufacturing process of Lithium-ion battery electrodes directly affects the practical properties of the cells, such as their performance, durability, and safety. While computational physics-based modeling has been proved as a useful method to produce insights on the manufacturing properties interdependencies as…
Xiayu Chen, Ming Zhou, Zhengxin Gong, Wei Xu + 4 more
Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks through an end-to-end deep learning strategy. Deep learning gives rise to data representations with multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs.…
Baptiste Py, Adeleke Maradesa, Francesco Ciucci
The distribution of relaxation times (DRT) method is a non-parametric approach for analyzing electrochemical impedance spectroscopy (EIS) data. However, we must be careful when using the DRT method on electrochemical systems with blocking electrodes, such as those encountered in batteries and supercapacitors. This is…
Emanuele Quattrocchi, Baptiste Py, Adeleke Maradesa, Quentin Meyer + 2 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique widely used to evaluate the properties of electrochemical systems. The distribution of relaxation times (DRT) has emerged as a model-free alternative to equivalent circuits and physical models to circumvent the inherent challenges of EIS…
Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquette + 2 more
The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale’s principle, which ensures…
Authors not listed
Quantum well defect-modified single walled carbon nanotubes are environmentally sensitive nanomaterials with wide-ranging applications in biosensing, imaging, light harvesting, quantum computing, energy storage and catalysis. The most common method for covalent functionalization of nanotubes for biosensing applications…
Niklas Laasch, Wilhelm Braun, Lisa Knoff, Jan Bielecki + 1 more
Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramount when attempting to understand the network structure of neural…