A Comparative Study on the Ship Classification Performance of the Deep Learning Model According to Dataset Difference

2019 ◽  
Author(s):  
SungWon Moon ◽  
YoonHyung Kim ◽  
Dowon Nam ◽  
Wonyoung Yoo ◽  
Changick Kim
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Model ◽  
Classification Performance ◽  
Ship Classification ◽  
Deep Learning Model

scholarly journals ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19

2021 ◽  
Vol 7 ◽  
pp. e551
Author(s):  
Nihad Karim Chowdhury ◽  
Muhammad Ashad Kabir ◽  
Md. Muhtadir Rahman ◽  
Noortaz Rezoana
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Learning Model ◽  
Classification Performance ◽  
Data Sets ◽  
X Rays ◽  
Proposed Model ◽  
Highly Effective ◽  
Chest X Ray ◽  
Deep Learning Model

The goal of this research is to develop and implement a highly effective deep learning model for detecting COVID-19. To achieve this goal, in this paper, we propose an ensemble of Convolutional Neural Network (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 from chest X-rays. To make the proposed model more robust, we have used one of the largest open-access chest X-ray data sets named COVIDx containing three classes—COVID-19, normal, and pneumonia. For feature extraction, we have applied an effective CNN structure, namely EfficientNet, with ImageNet pre-training weights. The generated features are transferred into custom fine-tuned top layers followed by a set of model snapshots. The predictions of the model snapshots (which are created during a single training) are consolidated through two ensemble strategies, i.e., hard ensemble and soft ensemble, to enhance classification performance. In addition, a visualization technique is incorporated to highlight areas that distinguish classes, thereby enhancing the understanding of primal components related to COVID-19. The results of our empirical evaluations show that the proposed ECOVNet model outperforms the state-of-the-art approaches and significantly improves detection performance with 100% recall for COVID-19 and overall accuracy of 96.07%. We believe that ECOVNet can enhance the detection of COVID-19 disease, and thus, underpin a fully automated and efficacious COVID-19 detection system.

A Comparative Study of Different Deep Learning Model for Recognition of Handwriting Digits

2021 ◽  
Author(s):  
Asif Karim ◽  
Pronab Ghosh ◽  
Atqiya Abida Anjum ◽  
Masum Shah Junayed ◽  
Zahid Hasan Md ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Study

2021 ◽  
pp. 159-172
Author(s):  
Francesco Grussu ◽  
Marco Battiston ◽  
Marco Palombo ◽  
Torben Schneider ◽  
Claudia A. M. Gandini Wheeler-Kingshott ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Model Fitting ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer

2019 ◽  
Vol 26 (2) ◽  
pp. 945-962 ◽  
Author(s):  
Okyaz Eminaga ◽  
Omran Al-Hamad ◽  
Martin Boegemann ◽  
Bernhard Breil ◽  
Axel Semjonow
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Gleason Score ◽  
Clinical Decision ◽  
Learning Model ◽  
Classification Performance ◽  
Study Data ◽  
Learning Approaches ◽  
Tumor Stages ◽  
Deep Learning Model

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient’s subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.

scholarly journals Weed Identification Methodology by using Transfer Learning

2021 ◽  
Vol 5 (2) ◽  
pp. 28-39
Author(s):  
Bushra Idrees
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Learning Model ◽  
Classification Performance ◽  
Environmental Harm ◽  
Weed Species ◽  
Weed Detection ◽  
Clear Identification ◽  
Deep Learning Model

From recent past years, Weed identification remained a hot topic for researchers. Majority of work focused on the detection of weed but we are trying to identify the weed via weed name. The unrivaled successes of deep learning make the researchers able to evaluate different weed species in the complex rangeland climate. Nowadays, with an increasing population, farming productivity needs to be increased a lot to meet the demand for accurate weed detection. Increased demand for an increase in the use of herbicides, resulting in environmental harm. In this research work, the picture of weed helps to detect and differentiate as per area, and its name. The main aim of this research is the identification of weed so that fewer herbicides can use. This research work will contribute toreducing the higher use of herbicides by helping clear identification of weed names through its features. We use transfer learning in machine learning. The deep Weeds dataset is used for the evaluation. For this, we use the deep learning model ResNet50 to get better results. The Deep Weeds dataset contains 17,509 images that are label and eight nationally recognized species of weed belonged to 8 across northern Australia locations. This paper declares a baseline for classification performance on the dataset of weed while utilizing the deep learning model ResNet-50 and it is a benchmark too. Deep learning model ResNet-50 attained an average accuracy classification of 96.16. The findings are high enough to make effective use of weed control methods in Pakistan for futurefield implementation. The results confirm that our System offers more effective Weed recognition than many other systems.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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