[PDF] Top 20 Disease Detection of Plants using Deep Learning and Convolutional Neural Networks
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Disease Detection of Plants using Deep Learning and Convolutional Neural Networks
... First convolutional layer dressings the information picture utilizing 32 kernels of size 3x3 then max-pooling is applied to get yield, it is utilised as a contribution for the second convolutional layer of ... See full document
5
UAV based slope failure detection using deep learning convolutional neural networks
... The detection of slope failures that occur along road networks remains a challenging task due to the complexity of possible triggering factors and the many shapes and sizes that the associated mass ... See full document
24
Evaluation of different machine learning methods and deep learning convolutional neural networks for landslide detection
... use deep-learning methods and CNNs for landslide ...high detection accuracy for identifying landslide ...landslide detection from GF-1 images with four spectral bands and 8 m spatial ... See full document
21
Spam detection in im images using convolutional neural networks
... precisely what this paper intends tom illustrate. As mentioned earlier in this paper, classifying emails has been a standard classification problem for ears. But we are no longer limited to emails anymore. Spammers have ... See full document
6
Empirical Assessment of Transfer Learning Techniques for Surgical Tools Classification
... Machine learning conventional techniques[6] used handcrafted features and different classification methods such as Support Vector Machine (SVM),Neural Networks(NN) for images related ... See full document
6
Convolutional Neural Networks and Hash Learning for Feature Extraction and of Fast Retrieval of Pulmonary Nodules
... studies, deep learning has been widely applied to CBIR ...few deep learning methods to explore CBMIR ...lung disease (ILD) based on convolutional neural network is ... See full document
16
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
... towards an automated detection and localisation of key building defects, e.g., mould, deterioration, 18.. and stain, from images.[r] ... See full document
23
Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks
... Sarcasm is a well known, commonly used and well-studied topic in linguistics. In spite of being so widely used and being part of our speech, it’s inherently very challenging not only for machines but for humans also to ... See full document
10
A deep learning method for pathological voice detection using convolutional deep belief networks
... voice detection is introduced in this work. Convolutional neural network is shown to effectively extract features from spectrograms of voice recordings and diagnose voice ...disorders. ... See full document
5
Integrated Management System For Sugarcane Disease Using Deep Learning Techniques-A Review
... for deep learning in a top - down and bottom-up and the plant ...[7], deep multiple instance learning (DMIL-WDDS) framework for the wheat disease diagnosis it aims to deal with the ... See full document
5
Applying deep matching networks to Chinese medical question answering: a study and a dataset
... MV-LSTM without CSCR. It can be attributed to the CWS failure in the medical domain. There is no sig- nificant difference between these two input units with multi-CNN, which is opposite to the conclusion from Zhang et ... See full document
10
Vision based human action recognition using machine learning techniques
... Inspired by the dense sampling in image classification, the concept of dense trajectories for action recognition from videos was introduced [68]. The authors sampled the dense points from each image frame and tracked ... See full document
173
Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... Some results with eyeglasses removal are shown in Fig.4. The training samples of artificially synthesized eyeglasses on face images have removed completely by the proposed approach. Then, our approach was applied to ... See full document
8
Blind Navigation System using Artificial Intelligence
... In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is calculated by a non-linear function of the sum of its inputs. Artificial ... See full document
5
Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks
... VGG16 convolutional layers, which were loaded with the Imagenet-trained VGG16 weights available in Keras ...their convolutional equivalents as per the FCN-8s ...non-VGG16 convolutional layers were ... See full document
12
Deep Learning: Approaches and Challenges
... popular deep learning tools and libraries that are available to construct and execute efficiently deep learning ...Environment, deep learning toolk- its provide a development ... See full document
8
Classification of Age and Gender using Deep Learning
... Deep learning is a class of machine learning algorithms that cause a precipitation of voluminous layers of nonlinear processing units for feature extraction and ...profound neural system (DNN) ... See full document
6
Collaborative Edge Computing in Mobile Internet of Things
... machine learning models for AD diagnosis using MRI ...patients using T1 weighted MRI ...model using random forest classifier for AD diagnosis from MRI and PET ...AD detection including ... See full document
137
YNUWB at SemEval 2019 Task 6: K max pooling CNN with average meta embedding for identifying offensive language
... • Convolutional layer: In this layer, the ob- tained word vectors are subjected to convo- lution operations to obtain multiple feature ...the convolutional layer, the size of which is N ∗ d, and N is the ... See full document
5
Deep Learning as a Frontier of Machine Learning: A Review
... machine learning which is because of advancement and introduction of deep ...of learning and a higher level of abstraction, deep learning models have an advantage over conventional ... See full document
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