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A Review on Deep Learning for Plant Species Classification using Leaf Vein

Neethu Mariam Sunny, KTU
2020 International Journal of Engineering Research and  
For the easy identification of plants it is important to identify and classify them accurately.  ...  Plants have a wide range of application in agriculture and medical purpose, and is especially significant to the bio diversity research. Plants are essential for the environmental protection.  ...  Convolutional Neural Network Convolutional Neural Networks are usually used as deep learmning method. It is used to extract deep, semantic capabilities of an object.  ... 
doi:10.17577/ijertv9is060653 fatcat:cgott574i5a4xgp5345j5vkdpa

Artificial Intelligence-Based Drug Design and Discovery [chapter]

Yu-Chen Lo, Gui Ren, Hiroshi Honda, Kara L. Davis
2019 Cheminformatics and its Applications [Working Title]  
The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure-Activity  ...  The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse  ...  a deep neural network [72] .  ... 
doi:10.5772/intechopen.89012 fatcat:327njwv46rc2hi32nwx3nbkqkq

A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation

Yuchai Wan, Zhongshu Zheng, Ran Liu, Zheng Zhu, Hongen Zhou, Xun Zhang, Said Boumaraf
2021 Life  
The explanation and visualization make the decision-making process of the deep neural network transparent for the clinicians.  ...  We further explore the explanation of the diagnosis decision of the deep neural network through visualizing the areas of interest of the network.  ...  of deep neural networks.  ... 
doi:10.3390/life11060582 fatcat:dq5czs5mtbdtpfunckk7jgxlyq

A New Channel Boosted Convolutional Neural Network using Transfer Learning [article]

Asifullah Khan, Anabia Sohail, Amna Ali
2020 arXiv   pre-print
We present a novel architectural enhancement of Channel Boosting in a deep convolutional neural network (CNN).  ...  In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its original channel.  ...  Acknowledgements We thank Higher Education Commission of Pakistan for granting funds under NRPU research program (NRPU: 3408); and Pattern Recognition lab at DCIS, PIEAS, for providing us computational  ... 
arXiv:1804.08528v5 fatcat:gnvdsyibk5g4pnblhc6qi2mulq

Attention, please! A survey of Neural Attention Models in Deep Learning [article]

Alana de Santana Correia, Esther Luna Colombini
2021 arXiv   pre-print
For the last six years, this property has been widely explored in deep neural networks.  ...  Currently, the state-of-the-art in Deep Learning is represented by neural attention models in several application domains.  ...  A deep recurrent neural network, at each step, processes a multi-resolution crop of the input image, called a glimpse.  ... 
arXiv:2103.16775v1 fatcat:lwkw42lrircorkymykpgdmlbwq

Learning with Capsules: A Survey [article]

Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah
2022 arXiv   pre-print
Unlike CNNs, capsule networks are designed to explicitly model part-whole hierarchical relationships by using groups of neurons to encode visual entities, and learn the relationships between those entities  ...  Additionally, we provide a detailed explanation of how capsule networks relate to the popular attention mechanism in Transformers, and highlight non-trivial conceptual similarities between them in the  ...  ACKNOWLEDGMENTS The authors would like to thank all reviewers, and especially Professor Chris Williams from the School of Informatics of the University of Edinburgh, who provided constructive feedback  ... 
arXiv:2206.02664v1 fatcat:auiy6oo5tbfghkppfyxysjiyty

Consensual and Hierarchical Classification of Remotely Sensed Multispectral Images

J. Lee, O. Ersoy
2006 2006 IEEE International Symposium on Geoscience and Remote Sensing  
NEURAL NETWORKS FOR CLASSIFICATION Neural Networks Neural network is a mathematical or computational model for information processing.  ...  Several methods which create members of neural network ensemble making different errors have been developed.  ... 
doi:10.1109/igarss.2006.1004 fatcat:t3v2dej6qfgefadu7vh6yftc3q

Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion [article]

Anirud Thyagharajan, Benjamin Ummenhofer, Prashant Laddha, Om J Omer, Sreenivas Subramoney
2021 arXiv   pre-print
Segment-Fusion can be flexibly employed with any network architecture for semantic/instance segmentation.  ...  This paper presents Segment-Fusion, a novel attention-based method for hierarchical fusion of semantic and instance information to address the part misclassifications.  ...  Recent advancements in 3D scene understanding have been greatly influenced by deep neural networks achieving Part misclassification with baseline +Segment-Fusion Figure 1 .  ... 
arXiv:2111.08434v1 fatcat:7vncs24zvvaofptxoozxko43ja

Machine learning in chemoinformatics and drug discovery

Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman
2018 Drug Discovery Today  
To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR  ...  With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound  ...  Acknowledgments We thank all members of the Helix group at Stanford University for their helpful feedback and suggestions.  ... 
doi:10.1016/j.drudis.2018.05.010 pmid:29750902 pmcid:PMC6078794 fatcat:ckxznjxuujajle6iqycgi74d7i

A Comprehensive Review of Speech Emotion Recognition Systems

Taiba Majid Wani, Teddy Surya Gunawan, Syed Asif Ahmad Qadri, Mira Kartiwi, Eliathamby Ambikairajah
2021 IEEE Access  
A portion of these algorithms' benefit is that there is no requirement for feature extraction and selection steps. 8) DEEP NEURAL NETWORKS Deep Neural Networks (DNN) is a neural network with multiple  ...  In recent times, deep learning classifiers have become common such as Deep Belief Networks, Deep Neural Network, Deep Boltzmann Machine, Convolution Neural Network, Recurrent Neural Network, and Long Short-Term  ... 
doi:10.1109/access.2021.3068045 fatcat:otlyazg5mzg3rpjqv56jecmjfq

Text Classification Algorithms: A Survey

Kowsari, Jafari Meimandi, Heidarysafa, Mendu, Barnes, Brown
2019 Information  
Many machine learning approaches have achieved surpassingresults in natural language processing.  ...  In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts  ...  The funding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  ... 
doi:10.3390/info10040150 fatcat:qfmjtzsaoreahdwdwlfhymjtru

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Mohamed A. Kassem, Khalid M. Hosny, Robertas Damaševičius, Mohamed Meselhy Eltoukhy
2021 Diagnostics  
It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods.  ...  This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/diagnostics11081390 fatcat:r4gyqfwberfofhcbx2xsn7vpf4

Artificial Neural Network for Folk Music Style Classification

Qinliang Ning, Junyan Shi, Hasan Ali Khattak
2022 Mobile Information Systems  
In this paper, we use artificial neural networks to classify folk music styles and transform audio signals into a sound spectrum.  ...  In this paper, we use artificial neural networks to classify folk music styles and transform audio signals into a sound spectrum to avoid the problem of manually selecting features.  ...  Acknowledgments is project was supported by the Key Scientific Research Project of the Hunan Provincial Department of Education, Project no. 19a104.  ... 
doi:10.1155/2022/9203420 fatcat:arxti5o43vf33he4h4pllcw6nm

How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking [article]

Nicola De Cao, Michael Schlichtkrull, Wilker Aziz, Ivan Titov
2021 arXiv   pre-print
First, this makes the approach efficient because we predict rather than search. Second, as with probing classifiers, this reveals what the network 'knows' at the corresponding layers.  ...  The decision to include or disregard an input token is made with a simple model based on intermediate hidden layers of the analyzed model.  ...  Pathologies of neural models make interpretations difficult. EMNLP.Yoav Goldberg. 2017. Neural network methods for natural language processing.  ... 
arXiv:2004.14992v3 fatcat:cg6eqxkn5rh47o7ngdtvndfxje

Cell image classification: a comparative overview [article]

Mohammad Shifat-E-Rabbi, Xuwang Yin, Cailey Elizabeth Fitzgerald, Gustavo K. Rohde
2022 arXiv   pre-print
We review three different approaches for cell image classification: numerical feature extraction, end to end classification with neural networks, and transport-based morphometry.  ...  of cancer from images acquired using cytological and histological techniques.  ...  Acknowledgements This work was supported in part by National Institutes of Health awards GM130825 and GM090033.  ... 
arXiv:1906.03316v2 fatcat:45icigrv5zhgxa3afs62hyjrki
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