Multiclass Pest Detection and Classification Based on Deep Learning using Convolution Neural Networks

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2020.6240 ◽

2020 ◽

Vol 8 (6) ◽

pp. 1469-1473

Author(s):

Mohamed Asif M

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolution Neural Networks ◽

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Sub-scene Target Detection and Recognition Using Deep Learning Convolution Neural Networks

Lecture Notes in Electrical Engineering - ICDSMLA 2019 ◽

10.1007/978-981-15-1420-3_119 ◽

2020 ◽

pp. 1082-1101

Author(s):

Suresh Merugu ◽

Kamal Jain ◽

Anshul Mittal ◽

Balasubramanian Raman

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Target Detection ◽

Convolution Neural Networks ◽

Detection And Recognition

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Integrating compositional pattern-producing networks and optimized convolution neural networks using deep learning techniques for detecting brain abnormalities

Multimedia Tools and Applications ◽

10.1007/s11042-019-7174-7 ◽

2019 ◽

Vol 79 (15-16) ◽

pp. 10489-10503

Author(s):

B. L. Velammal

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Brain Abnormalities ◽

Convolution Neural Networks ◽

Learning Techniques ◽

Compositional Pattern

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Efficient Hyperparameter Optimization for Convolution Neural Networks in Deep Learning: A Distributed Particle Swarm Optimization Approach

Cybernetics & Systems ◽

10.1080/01969722.2020.1827797 ◽

2020 ◽

pp. 1-22

Author(s):

Yu Guo ◽

Jian-Yu Li ◽

Zhi-Hui Zhan

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Particle Swarm ◽

Optimization Approach ◽

Hyperparameter Optimization ◽

Swarm Optimization ◽

Convolution Neural Networks

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Semantic Segmentation Using Deep Learning for Brain Tumor MRI via Fully Convolution Neural Networks

Information and Communication Technology for Intelligent Systems - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-13-1742-2_2 ◽

2018 ◽

pp. 11-19 ◽

Author(s):

Sanjay Kumar ◽

Ashish Negi ◽

J. N. Singh

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Brain Tumor ◽

Semantic Segmentation ◽

Convolution Neural Networks ◽

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Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning

2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) ◽

10.1109/rteict42901.2018.9012507 ◽

2018 ◽

Author(s):

Manjunath Jogin ◽

Mohana ◽

M S Madhulika ◽

G D Divya ◽

R K Meghana ◽

...

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Deep Learning ◽

Convolution Neural Networks

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Detecting Defects in PCB using Deep Learning via Convolution Neural Networks

2018 13th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT) ◽

10.1109/impact.2018.8625828 ◽

2018 ◽

Author(s):

Venkat Anil Adibhatla ◽

Jiann-Shing Shieh ◽

Maysam F. Abbod ◽

Huan-Chuang Chih ◽

Chi- Chang Hsu ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolution Neural Networks

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Improvising the performance of image-based recommendation system using convolution neural networks and deep learning

Soft Computing ◽

10.1007/s00500-020-04803-0 ◽

2020 ◽

Vol 24 (19) ◽

pp. 14531-14544 ◽

Author(s):

A. Razia Sulthana ◽

Maulika Gupta ◽

Shruthi Subramanian ◽

Sakina Mirza

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Recommendation System ◽

Convolution Neural Networks

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Multi-Representational Music Emotion Recognition using Deep Convolution Neural Networks

10.36227/techrxiv.17054549 ◽

2021 ◽

Author(s):

Tharindu Bathigama ◽

Sarasi Madushika

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Emotion Recognition ◽

Experimental Approach ◽

Convolution Neural Networks

This research attempts to study the effects of different representations of audio in relation to music emotion recognition. We have used an experimental approach to this problem and have used deep learning to create models that can recognise the emotion.

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Multi-Representational Music Emotion Recognition using Deep Convolution Neural Networks

10.36227/techrxiv.17054549.v1 ◽

2021 ◽

Author(s):

Tharindu Bathigama ◽

Sarasi Madushika

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Emotion Recognition ◽

Experimental Approach ◽

Convolution Neural Networks

This research attempts to study the effects of different representations of audio in relation to music emotion recognition. We have used an experimental approach to this problem and have used deep learning to create models that can recognise the emotion.

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Early Detection of Disease in Rice Paddy: A Deep Learning based Convolution Neural Networks Approach

10.1109/icccnt51525.2021.9580072 ◽

2021 ◽

Author(s):

Anghsuman Chakraborty ◽

Soumik Layek ◽

Ravi Sankar ◽

Sangit Saha ◽

Alokesh Ghosh ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Early Detection ◽

Early Detection Of Disease ◽

Convolution Neural Networks

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