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.

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.

scholarly journals Cross-evaluation of social mining for classification of depressed online personas

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alina Trifan ◽  
José Luis Oliveira
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Post Partum ◽  
Evaluation Study ◽  
Continuous Increase ◽  
Digital Phenotyping ◽  
Public Datasets ◽  
The Impact ◽  
Social Mining

Abstract With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.

The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models

2020 ◽  
pp. 219-226
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Learning Models ◽  
Machine Learning Models

Feed Forward Neural Network-Based Diagnostic Tool for Gas Turbine Power Plant

2002 ◽  
Author(s):  
Lorenzo Dambrosio ◽  
Marco Bomba ◽  
Sergio M. Camporeale ◽  
Bernardo Fortunato
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Diagnostic Tool ◽  
Power Plants ◽  
Early Stage ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Single Fault ◽  
Fully Connected ◽  
Gas Turbine Power Plant

A diagnostic tool able to detect faults that may occur in a gas turbine power plant at an early stage of their emergence is of a great importance for power production. In the present paper, a diagnostic tool, based on Feed Forward Neural Networks (FFNN), has been proposed for gas turbine power plants with a condition monitoring approach. The main aim of the proposed diagnostic tool is to reliably detect not only every considered single fault, but also two or more faults that may occur contemporarily. Two different FFNNs compose the proposed diagnostic tool. The first network, that is not-fully connected, operates a fault pre-processing in order to evaluate the influence of the single fault variable on the single fault condition. The second FFNN detects the fault conditions by means of an iterative process. Such a diagnostic tool has been applied to a mathematical model of a single shaft gas turbine for power generation, resulting able to detect the 100% of single faults and the 80% of combined faults.

scholarly journals Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Laith Alzubaidi ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Jinglan Zhang ◽  
Ye Duan
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Transfer Learning ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Learning Models ◽  
Sickle Cells ◽  
Microscopy Images

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

scholarly journals The classification of skateboarding tricks via transfer learning pipelines

2021 ◽  
Vol 7 ◽  
pp. e680
Author(s):  
Muhammad Amirul Abdullah ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Aizzat Zakaria ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Accuracy ◽  
Learning Models ◽  
Test Dataset ◽  
Flat Ground

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

scholarly journals Deep learning models for classification of gases detected by sensor arrays of artificial nose

2019 ◽  
Author(s):  
Ismael Araujo ◽  
Juan Gamboa ◽  
Adenilton Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Classification Problems ◽  
Artificial Nose ◽  
Learning Techniques

To recognize patterns that are usually imperceptible by human beings has been one of the main advantages of using machine learning algorithms The use of Deep Learning techniques has been promising to the classification problems, especially the ones related to image classification. The classification of gases detected by an artificial nose is one other area where Deep Learning techniques can be used to seek classification improvements. Succeeding in a classification task can result in many advantages to quality control, as well as to preventing accidents. In this work, it is presented some Deep Learning models specifically created to the task of gas classification.

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 Supervised Learning Based Classification of Cardiovascular Diseases

2021 ◽  
Vol 20 ◽  
pp. 24-34
Author(s):  
Arif Hussain ◽  
Hassaan Malik ◽  
Muhammad Umar Chaudhry
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Matthews Correlation Coefficient ◽  
Early Stage ◽  
Support Vector ◽  
Logistics Regression ◽  
Efficiency And Effectiveness ◽  
Sensitivity Specificity ◽  
Better Than

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

scholarly journals Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

2020 ◽  
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Fruits And Vegetables ◽  
Early Stage ◽  
Economic Loss ◽  
Disease Diagnosis ◽  
Data Set ◽  
Plant Leaf ◽  
Leaf Disease

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.

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