scholarly journals A Novel Approach of Ensembling the Transfer Learning Methods for Rice Plant Disease Detection and Classification

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 439-448
Author(s):  
Parameswar Kanuparthi ◽  
Vaibhav Bejgam ◽  
V. Madhu Viswanatham
Keyword(s):  
Transfer Learning ◽  
Rice Plant ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Brown Spot ◽  
Generalization Error ◽  
Learning Models ◽  
The People ◽  
Novel Approach ◽  
Crop Type

Agriculture, the primary sector of Indian economy. It contributes around 18 percent of overall GDP (Gross Domestic Product). More than fifty percent of Indians belong to an agricultural background. There is a necessary to rapidly increase the agriculture production in India due to the vast increasing of population. The significant crop type for most of the people in India is rice but it was one of the crops that has been mostly affected by the cause of diseases in majority of the cases. This results in reduced yield that lead to loss for farmers. The major challenges faced while cultivating the rice crops is getting infected by the diseases due to the various effects that include environmental conditions, pesticides used and natural disasters. Early detection of rice diseases will eventually help farmers to get out from disasters and help in better yield. In this paper, we are proposing a new method of ensembling the transfer learning models to detect the rice plant and classify the diseases using images. Using this model, the three most common rice crop diseases are detected such as Brown spot, Leaf smut and Bacterial leaf blight. Generally, transfer learning uses pre-trained models and gives better accuracy for the image datasets. Also, ensembling of machine learning algorithms (combining two or more ML algorithms) will help in reducing the generalization error and also makes the model more robust. Ensemble learning is becoming trendier as it reduces generalization error as well as makes the model more robust. The ensembling technique that was used in the paper is majority voting. Here we are proposing a novel model that ensembles three transfer learning models which are InceptionV3, MobileNetV2 and DenseNet121 with an accuracy of 96.42%.

scholarly journals COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

2021 ◽  
Vol 11 (23) ◽  
pp. 11423
Author(s):  
Chandrakanta Mahanty ◽  
Raghvendra Kumar ◽  
Panagiotis G. Asteris ◽  
Amir H. Gandomi
Keyword(s):  
Transfer Learning ◽  
Ensemble Methods ◽  
Cost Effective ◽  
Majority Voting ◽  
Learning Models ◽  
Medical Practitioners ◽  
Healthcare Facilities ◽  
Ensemble Techniques ◽  
Effective Manner ◽  
Detection Strategies

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

scholarly journals Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques

2020 ◽  
Vol 9 (12) ◽  
pp. 19-22
Keyword(s):  
Machine Learning ◽  
Soil Type ◽  
Soil Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
The People ◽  
Learning Techniques ◽  
Crop Type

The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type.

scholarly journals Classification of MR Brain tumors with Deep Plain and Residual Feed forward CNNs through Transfer learning

2019 ◽  
Vol 8 (6) ◽  
pp. 1758-1763
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Complex Structure ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Feed Forward ◽  
Sensitivity Specificity ◽  
Fully Connected ◽  
Mr Brain

Medical imaging plays an important role in the diagnosis of some critical diseases and further treatment process of patients. Brain is a central and most complex structure in the human body that works with billions of cells, which controls all other organ functioning. Brain tumours observed as uncontrolled abnormal cell growth in brain tissues. Classification of such cells in a early stage will increase the survival rate of the patient. Machine learning algorithms have contributed much in automation of such tasks. Further improvement in prediction rate is possible through deep learning models. In this paper presents experiments by deep transfer learning models on publicly available dataset for Brain tumour classification. Pre-trained plain and residual feed forward models such as Alexnet, VGG19, ResNet50, ResNet101 and GoogleNet are used for the purpose of feature extraction, Fully connected layers and softmax layer for classification is used commonly. The evaluation metrics Accuracy, Sensitivity, Specificity and F1-Score were computed.

scholarly journals Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 417 ◽  
Author(s):  
Mohammad Farukh Hashmi ◽  
Satyarth Katiyar ◽  
Avinash G Keskar ◽  
Neeraj Dhanraj Bokde ◽  
Zong Woo Geem
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Medical Center ◽  
Training Dataset ◽  
Test Accuracy ◽  
X Rays ◽  
Learning Models ◽  
Novel Approach ◽  
Unseen Data

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

Deep learning based affective computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saurabh Kumar
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Transfer Learning ◽  
Affective Computing ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Text And Image ◽  
Content Type

PurposeDecision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.Design/methodology/approachThe present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.FindingsThe result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.Originality/valueThe study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

Adaptive Model Scheduling for Resource-efficient Data Labeling

2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Mu Yuan ◽  
Lan Zhang ◽  
Xiang-Yang Li ◽  
Lin-Zhuo Yang ◽  
Hui Xiong
Keyword(s):  
Deep Learning ◽  
Heuristic Algorithms ◽  
Data Item ◽  
Adaptive Model ◽  
Learning Models ◽  
The People ◽  
Novel Approach ◽  
Machine Learning Model ◽  
Efficient Data ◽  
Model Execution

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.

scholarly journals Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Seyed Masoud Rezaeijo ◽  
Mohammadreza Ghorvei ◽  
Razzagh Abedi-Firouzjah ◽  
Hesam Mojtahedi ◽  
Hossein Entezari Zarch
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Learning Algorithms ◽  
Chest Ct ◽  
Machine Learning Algorithms ◽  
Limited Resources ◽  
Learning Models ◽  
Laboratory Equipment ◽  
Middle Income ◽  
Middle Income Countries

Abstract Background This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. Results The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. Conclusions The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

scholarly journals Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases

2019 ◽  
Vol 9 (1) ◽  
pp. 5365-5368
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Rice Plant ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Brown Spot ◽  
Human Beings ◽  
Data Set

Rice is one of the most important foods on earth for human beings. India and China are two countries in the world mostly depend on rice. The output of this crop depends on the many parameters such as soil, water supply, pesticides used, time duration, and infected diseases. Rice Plant Disease (RPD) is one of the important factors that decrease the quantity and quality of rice. Identifying the type of rice plant disease and taking corrective action against the disease in time is always challenging for the farmers. Although the rice plant is affected by many diseases, Bacterial Leaf Blight (BLB), Brown Spot (BS), and Leaf Smut (LS) are major diseases. Identification of this disease is really challenging because the infected leaf has to be processed by the human eye. So in this paper, we focused on machine learning techniques to identify and classify the RPD. We have collected infected rice plant data from the UCI Machine Learning repository. The data set consists of 120 images of infected rice plants in which 40 images are BLB, 40 are BS, and 40 are LS. Experiments are conducted using Decision tree-based machine learning algorithms such as RandomForest, REPTree, and J48. In order to extract the numerical features from the infected images, we have used ColourLayoutFilter supported by WEKA. Experimental analysis is done using 65% data for training and 35% data for testing. The experiments unfold that the Random Forest algorithm is exceptional in predicting RPD.

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