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Coconut trees detection and segmentation in aerial imagery using mask region‐based convolution neural network

Muhammad Shakaib Iqbal, Hazrat Ali, Son N. Tran, Talha Iqbal
2021 IET Computer Vision  
Maked Region-based Convolutional Neural Network approach was used identification and segmentation of coconut trees.  ...  For the segmentation task, Mask R-CNN model with ResNet50 and ResNet1010 based architectures was used.  ...  The region-based convolutional neural network (R-CNN) has proved to be very successful for segmentation tasks [19, 20] .  ... 
doi:10.1049/cvi2.12028 fatcat:qjrq72dq2zf23gogy2q4bwmpta

Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

Fatima Es-sabery, Khadija Es-sabery, Hamid Garmani, Abdellatif Hair, S. Krit
2021 E3S Web of Conferences  
This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS).  ...  In summary, our method integrates the advantages of CNN and C4.5 techniques and overcomes the shortcomings of ambiguous data in the tweets using FRBS, which is consists of three-phase: fuzzification phase  ...  In [12] , the authors offered a novel convolutional neural-fuzziness network that combines fuzzy logic theory and a convolutional neural network.  ... 
doi:10.1051/e3sconf/202129701052 fatcat:gs5ztgmb5bdh7okuj2itwaipne

Neuro-Symbolic Learning: Principles and Applications in Ophthalmology [article]

Muhammad Hassan, Haifei Guan, Aikaterini Melliou, Yuqi Wang, Qianhui Sun, Sen Zeng, Wen Liang, Yiwei Zhang, Ziheng Zhang, Qiuyue Hu, Yang Liu, Shunkai Shi (+15 others)
2022 arXiv   pre-print
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.  ...  This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly  ...  In the first step, the optic disc was localized and extracted from retinal fundus images using a convolutional neural network.  ... 
arXiv:2208.00374v1 fatcat:pktmnomj3bbwpjyj7lmu37rl7i

Deep Learning for Photonic Design and Analysis: Principles and Applications

Bing Duan, Bei Wu, Jin-hui Chen, Huanyang Chen, Da-Quan Yang
2022 Frontiers in Materials  
In addition, the optical neural networks with high parallelism and low energy consuming are also highlighted as novel computing architectures.  ...  In this paper, we review the recent advances of deep learning for the photonic structure design and optical data analysis, which is based on the two major learning paradigms of supervised learning and  ...  Design and Analysis: Principles and Applications.  ... 
doi:10.3389/fmats.2021.791296 fatcat:6vkopuvmcvg3vdroqfktxy3une

Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues [article]

Nan Li, Lianbo Ma, Guo Yu, Bing Xue, Mengjie Zhang, Yaochu Jin
2022 arXiv   pre-print
Finally, key applications, open issues and potentially promising lines of future research are suggested.  ...  ., what and how to evolve/optimize), and focus on the discussions of solution representation and search paradigm in handling the optimization problem by EC.  ...  Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues • 23  ... 
arXiv:2208.10658v1 fatcat:6bi4bujbmfbyre65eeslilzmjm

Three-dimensional direct laser writing of biomimetic neuron interfaces in the era of artificial intelligence: principles, materials, and applications

Haoyi Yu, Qiming Zhang, Xi Chen, Haitao Luan, Min Gu
2022 Advanced Photonics  
The study of BNI holds the key for curing neuron disorder diseases and creating innovative artificial neural networks (ANNs).  ...  BNIs are two-dimensional or three-dimensional (3D) artificial interfaces mimicking the geometrical and functional characteristics of biological neural networks to rebuild, understand, and improve neuronal  ...  Acknowledgments We would like to acknowledge the support from the Science and Technology Commission of Shanghai Municipality (Grant No. 21DZ1100500), the Shanghai Municipal Science and Technology Major  ... 
doi:10.1117/1.ap.4.3.034002 fatcat:zjvb2m2qpvbgrct76m2ae4u7wi

Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

2018 Remote Sensing  
These methods were designed based on different principles and strategies, and therefore show different strengths and limitations.  ...  This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets.  ...  The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  ... 
doi:10.3390/rs10040527 fatcat:lplwrowbfvhehb3qjjsaejjo4a

Detection of Brain Tumor in MRI Image through Fuzzy-Based Approach [chapter]

Neha Mathur, Yogesh Kumar Meena, Shruti Mathur, Divya Mathur
2018 High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications  
images, enhancing specific image features, and reducing data on both resolution and brightness.  ...  Accurate detection is very important and critical for the generation of correct diagnosis. The major problem that comes across while analyzing MRI images of a brain is inaccurate data.  ...  The working of an artificial computer neural network and the expert systems is analogous to the above process.  ... 
doi:10.5772/intechopen.71485 fatcat:rmblfmnmlvbypdvrwdqepfnle4

Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation

Junhong Shen, Lin F. Yang
Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating deep neural networks into RL frameworks.  ...  To mitigate these issues, we propose a theoretically principled nearest neighbor (NN) function approximator that can replace the value networks in deep RL methods.  ...  Thus, realworld application of tabular theories remains a challenge. In this paper, we bridge the gap between RL theory and practice by a theoretically principled deep RL acceleration technique.  ... 
doi:10.1609/aaai.v35i11.17151 fatcat:muoyk5vlxbe5xgahirq2qkfccq

A review of computer micro-vision-based precision motion measurement: principles, characteristics and applications

Sheng Yao, Hai Li, Shuiquan Pang, Benliang Zhu, Xianmin Zhang, Sergej Fatikow
2021 IEEE Transactions on Instrumentation and Measurement  
Recent advances of applications empowered by the developed computer microvision-based methods are also presented.  ...  Working principles of microvision systems are first introduced and described, where the hardware configuration, model calibration, and motion measurement algorithms are systematically summarized.  ...  tree algorithm.  ... 
doi:10.1109/tim.2021.3065436 fatcat:nyqncgluvbfjbhx4ib3pmr6oba

The Social Public Issues Analysis Model Based on Deep Learning

Yanqiong Gu, Jianyong Shi, Man Fai Leung
2022 Scientific Programming  
prediction model based on improved single-channel CNN network and decision tree is constructed.  ...  To improve the governance effect of public communities and protect community security, combined with the basic principles and network structure of CNN network in deep learning, a community security risk  ...  Case Prediction Model Based on Convolutional Neural Network Construction of Convolutional Neural Network Model.  ... 
doi:10.1155/2022/8676124 fatcat:xjqd7d4onfczbi6y2h3zhfigla

Research on road extraction of remote sensing image based on convolutional neural network

Yuantao Jiang
2019 EURASIP Journal on Image and Video Processing  
To solve this problem, a road extraction method based on convolutional neural network is proposed in this paper.  ...  Finally, because of the influence of natural scene factors such as house and tree shadow, the non-road noise still exists in the road results extracted by the optimized convolutional neural network method  ...  Acknowledgements The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.  ... 
doi:10.1186/s13640-019-0426-7 fatcat:lrirteba4fgujp57v6olji2xoq

Image Classification in HTP Test Based on Convolutional Neural Network Model

Lin Liu, Bai Yuan Ding
2021 Computational Intelligence and Neuroscience  
Compared with traditional neural networks, deep learning networks have deeper and more network layers and can learn more complex processing functions.  ...  Convolutional neural network is a mature technology in deep learning. The traditional HTP assessment process relies on the experience of researchers to extract painting features and classification.  ...  Convolutional Neural Network 4.1. e Architecture of Convolutional Neural Networks.  ... 
doi:10.1155/2021/6370509 pmid:34659394 pmcid:PMC8519680 fatcat:fbvc633thndinhpagtc6bojbqu

Fruits, Vegetable and Plants Category Recognition Systems Using Convolutional Neural Networks : A Review

Srivalli Devi S, A. Geetha
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
trend in computer vision applications and are broadly employed in agricultural domains for identification of plants and its parts, soil type classification, water resources, harvesting prediction and  ...  in fertilizer and pest management.  ...  Neural networks, also known as artificial neural network, are based on how the neurons work in human brain, in fact it's a simulation of the working principle of human brain's neurons.  ... 
doi:10.32628/cseit1953114 fatcat:julxi5avujefvhehdk37iof7ke

Application of the transfer learning to the medical images texture classification task

M Privalov, M Stupina, V. Breskich, A. Zheltenkov, Y. Dreizis
2020 E3S Web of Conferences  
Presented work investigates different approaches to solving image classification task with neural networks, specifically, using pre-processing for feature extraction and end-to-end application of convolutional  ...  neural networks (CNN).  ...  Convolutional neural networks are even more effective in such task because of the principle of their operation.  ... 
doi:10.1051/e3sconf/202022401020 fatcat:kkztebtz7rbyvbamkgq5hjn6eu
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