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25 papers · ranked by Valyu relevance

01
PPubMed9 Feb 2022
Deep convolutional neural networks for regular texture recognition
Ni Liu, Mitchell Rogers, Hua Cui, Weiyu Liu + 3 more
Regular textures are frequently found in man-made environments and some biological and physical images. There are a wide range of applications for recognizing and locating regular textures. In this work, we used deep convolutional neural networks (CNNs) as a general method for modelling and classifying regular and…
81relevance
02
PPubMed14 Apr 2022
Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning
Francisco Garibaldi-Márquez, Gerardo Flores, Diego A. Mercado-Ravell, Alfonso Ramírez-Pedraza + 2 more
Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a…
81relevance
03
PPubMed17 Jun 2022
Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach
Jordi-Roger Riba, Rosa Cantero, Pol Riba-Mosoll, Rita Puig + 1 more
The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating…
78relevance
04
PPubMed21 Aug 2022
Automatic Stones Classification through a CNN-Based Approach
Mauro Tropea, Giuseppe Fedele, Raffaella De Luca, Domenico Miriello + 2 more
'Floriano De Rango' 'Hsiao-Chun Wu'] This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”)…
78relevance
05
BbioRxiv4 Nov 2025
Deep Blueprint: A Literature Review and Guide to Automated Image Classification for Ecologists
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
77relevance
06
PPubMed7 Feb 2023
Fault-Level Grading of Photovoltaic Cells Employing Lightweight Deep Learning Models
Ikramullah Khosa, Abdur Rahman, Khurram Ali, Jahanzeb Akhtar + 3 more
'Ammar Armghan' 'Jehangir Arshad' 'Melkamu Deressa Amentie'] The deployment of photovoltaic (PV) cells as a renewable energy resource has been boosted recently, which enhanced the need to develop an automatic and swift fault detection system for PV cells. Prior to isolation for repair or replacement, it is critical to…
75relevance
07
AarXiv21 Jan 2022
Exploring complex pattern formation with convolutional neural networks
Christian Scholz, Sandy Scholz
Many nonequilibrium systems, such as biochemical reactions and socioeconomic interactions, can be described by reaction-diffusion equations that demonstrate a wide variety of complex spatiotemporal patterns. The diversity of the morphology of these patterns makes it difficult to classify them quantitatively and they…
75relevance
08
BbioRxiv15 Feb 2023
Classification of dog breeds using convolutional neural network models and support vector machine
Ying Cui, Bixia Tang, Gangao Wu, Lun Li + 3 more
Convolutional neural network (CNN) has been widely used for fine-grained image classification, which has proven to be an effective approach for the classification and identification of specific species. For breed classification of dog, there are several proposed methods based on dog images, however, the highest…
75relevance
09
PPubMed19 Dec 2023
An improved human activity recognition technique based on convolutional neural network
Ravi Raj, Andrzej Kos
A convolutional neural network (CNN) is an important and widely utilized part of the artificial neural network (ANN) for computer vision, mostly used in the pattern recognition system. The most important applications of CNN are medical image analysis, image classification, object recognition from videos, recommender…
72relevance
10
BbioRxiv24 Jul 2022
A benchmarking of deep neural network models for cancer subtyping using single point mutations
Pouria Parhami, Mansoor Fateh, Mohsen Rezvani, Hamid Alinejad Rokny
It is now well-known that genetic mutations contribute to development of tumors, in which at least 15% of cancer patients experience a causative genetic abnormality including De Novo somatic point mutations. This highlights the importance of identifying responsible mutations and the associated biomarkers (e.g., genes)…
71relevance
11
BbioRxiv1 Apr 2022
The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data
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…
71relevance
12
AarXiv2023
Malaria detection using Deep Convolution Neural Network
Sumit Kumar, S.Sugantha Priya, Ayush Kumar
The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlying reason is international funding has declined. Malaria, which is spread to people through the bites…
70relevance
13
AarXiv2023
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection
Muyiwa Babayomi, Oluwatosin Atinuke Olagbaju, Abdulrasheed Adedolapo Kadiri
'Abdulrasheed Adedolapo Kadiri'] Abstract Brain tumors are masses or abnormal growths of cells within the brain or the central spinal canal with symptoms such as headaches, seizures, weakness or numbness in the arms or legs, changes in personality or behaviour, nausea, vomiting, vision or hearing problems and…
70relevance
14
PPubMed20 Oct 2022
An overview of deep learning applications in precocious puberty and thyroid dysfunction
Misbah Razzaq, Frédérique Clément, Romain Yvinec
In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning applications in endocrine disorders caused by either precocious onset of adult hormone or abnormal amount of hormone production. To give access to…
69relevance
15
BbioRxiv17 Dec 2024
Plasmo3Net: A Convolutional Neural Network-Based Algorithm for Detecting Malaria Parasites in Thin Blood Smear Images
Afolabi J. Owoloye, Funmilayo C. Ligali, Ojochenemi A. Enejoh, Oluwafemi Agosile + 4 more
Early diagnosis of malaria is crucial for effective control and elimination efforts. Microscopy is a reliable field-adaptable malaria diagnostic method. However, microscopy results are only as good as the quality of slides and images obtained from thick and thin smears. In this study, we developed deep learning…
69relevance
16
AarXiv2023
Deep Learning based Multi-Label Image Classification of Protest Activities
Yingzhou Lu, Kosaku Sato, Jialu Wang
With the rise of internet technology amidst increasing urbanization rates, sharing information has never been easier, thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of usergenerated data can be used to enhance our understanding of significant societal issues…
69relevance
17
CchemRxiv2 May 2023
Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning
Jie Chen, Hengrui Zhang, Carolin Wahl, Wei Liu + 4 more
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected…
68relevance
18
AarXiv2022
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Maryam Habibpour, Hassan Gharoun, AmirReza Tajally, Hamzeh Asgharnezhad + 3 more
'Hamzeh Asgharnezhad' 'Afshar Shamsi' 'Abbas Khosravi' 'Saeid Nahavandi'] Abstract—Defects are unavoidable in casting production owing to the complexity of the casting process. While conventional human-visual inspection of casting products is slow and unproductive in mass productions, an automatic and reliable defect…
68relevance
19
BbioRxiv30 Jun 2023
Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection
Kim Bjerge, Quentin Geissmann, Jamie Alison, Hjalte M. R. Mann + 3 more
Cameras and computer vision are revolutionising the study of insects, creating new research opportunities within agriculture, epidemiology, evolution, ecology and monitoring of biodiversity. However, a major challenge is the diversity of insects and close resemblances of many species combined with computer vision are…
68relevance
20
AarXiv13 Jun 2024
Large-Scale Evaluation of Open-Set Image Classification Techniques
Halil Bişğin, Andres Palechor, Mike Suter, Manuel Günther
The goal for classification is to correctly assign labels to unseen samples. However, most methods misclassify samples with unseen labels and assign them to one of the known classes. Open-Set Classification (OSC) algorithms aim to maximize both closed and open-set recognition capabilities. Recent studies showed the…
65relevance
21
CchemRxiv5 May 2025
Obstacle detection during Navigation using Convolutional Neural Networks with LSTM
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…
64relevance
22
CchemRxiv19 Sep 2023
Electron density mapping of boron clusters via convolutional neural networks to augment structure prediction algorithms
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…
63relevance
23
CchemRxiv31 Jan 2025
Deep Learning-based Image Caption Generator for Real-time Monitoring and Predictive Control of Concentration of Polluting gases
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…
62relevance
24
CchemRxiv5 Feb 2024
Time-dependent deep learning manufacturing process model for battery electrode microstructure prediction
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…
61relevance
25
CchemRxiv29 Mar 2024
Condensing Molecular Docking CNNs via Knowledge Distillation
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…
58relevance

Results for CNN classification

Deep convolutional neural networks for regular texture recognition

Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach

Automatic Stones Classification through a CNN-Based Approach

Deep Blueprint: A Literature Review and Guide to Automated Image Classification for Ecologists

Fault-Level Grading of Photovoltaic Cells Employing Lightweight Deep Learning Models

Exploring complex pattern formation with convolutional neural networks

Classification of dog breeds using convolutional neural network models and support vector machine

An improved human activity recognition technique based on convolutional neural network

A benchmarking of deep neural network models for cancer subtyping using single point mutations

The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data

Malaria detection using Deep Convolution Neural Network

Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection

An overview of deep learning applications in precocious puberty and thyroid dysfunction

Plasmo3Net: A Convolutional Neural Network-Based Algorithm for Detecting Malaria Parasites in Thin Blood Smear Images

Deep Learning based Multi-Label Image Classification of Protest Activities

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning

An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products

Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

Large-Scale Evaluation of Open-Set Image Classification Techniques

Obstacle detection during Navigation using Convolutional Neural Networks with LSTM

Electron density mapping of boron clusters via convolutional neural networks to augment structure prediction algorithms

Deep Learning-based Image Caption Generator for Real-time Monitoring and Predictive Control of Concentration of Polluting gases

Time-dependent deep learning manufacturing process model for battery electrode microstructure prediction

Condensing Molecular Docking CNNs via Knowledge Distillation

Deep convolutional neural networks for regular texture recognition

Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach

Automatic Stones Classification through a CNN-Based Approach

Deep Blueprint: A Literature Review and Guide to Automated Image Classification for Ecologists

Fault-Level Grading of Photovoltaic Cells Employing Lightweight Deep Learning Models

Exploring complex pattern formation with convolutional neural networks

Classification of dog breeds using convolutional neural network models and support vector machine

An improved human activity recognition technique based on convolutional neural network

A benchmarking of deep neural network models for cancer subtyping using single point mutations

The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data

Malaria detection using Deep Convolution Neural Network

Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection

An overview of deep learning applications in precocious puberty and thyroid dysfunction

Plasmo3Net: A Convolutional Neural Network-Based Algorithm for Detecting Malaria Parasites in Thin Blood Smear Images

Deep Learning based Multi-Label Image Classification of Protest Activities

Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning

An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products

Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

Large-Scale Evaluation of Open-Set Image Classification Techniques

Obstacle detection during Navigation using Convolutional Neural Networks with LSTM

Electron density mapping of boron clusters via convolutional neural networks to augment structure prediction algorithms

Deep Learning-based Image Caption Generator for Real-time Monitoring and Predictive Control of Concentration of Polluting gases

Time-dependent deep learning manufacturing process model for battery electrode microstructure prediction

Condensing Molecular Docking CNNs via Knowledge Distillation