Mandelbug Classification Engine: Transfer Learning and NLP Approach

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

Voice disorders are among the most prevalent problems affecting a huge proportion of the population, with the majority of them resulting in communication issues and a deterioration in quality of life. Early detection leads to more effective treatment. Thus, better approaches for accurately diagnosing vocal abnormalities are essential. Machine learning techniques such as Convolutional Neural Networks (CNNs), Random Forests, and transfer learning approaches have all proven useful in this discipline. CNNs offer incredible audio signal processing capabilities, since they can convert raw voice data into spectrograms and automatically extract key properties. CNNs can detect minor patterns associated with numerous voice disorders, improving classification accuracy. Random Forest is a technique used in ensemble learning to enhance robustness and accuracy in classification tasks. The utility of this approach stems from its capacity to overcome existing data processing issues, making it a substantial contribution to the area. Thus, we propose the system with CNN and Random Forest as the classification models and obtained the 97.31% accuracy for transfer learning model.

Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity. This provides a better interpretability of the model’s understanding on the test domains. With these insights, our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach. This Progressive Learning for TL (PL-TL) is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species, 5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms, though it is not a benchmark dataset. To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms, Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes. Also, the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3, and Vision Transformer a non-CNN model. It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%, 97.5%, 80.1% on Cassava, PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy, precision, f1-score is also studied.

Data-driven or machine learning (ML) approaches already achieved significant success in many engineering areas even fatigue assessment of industrial parts and structures. Machine learning approaches work well under the common assumption that the training data covers the relevant feature space of the application. Rebuilding new models or establish new databases for similar feature spaces needs a high effort. In such cases, knowledge transfer or transfer learning can be used. In this study, transfer learning approach is used to determine the fatigue life of welded steel joints (target task) with a ML-algorithm that is trained in non-welded steel specimen. Twenty-two fatigue test data series were used. The results of the transfer learning approach were compared with a conventional machine learning approach that was trained also on data of welded joints. Furthermore, the results were compared to an advanced analytical approach (IBESS) for the fracture mechanic-based fatigue life assessment of welded joints and fatigue strength values from recommendations.

BERT is a pre-trained language model that achieves state-of-the-art performance on natural language processing (NLP) tasks. Once it was published, it quickly became one of the most popular models in the NLP field. The official recommended method for applying BERT to downstream tasks is fine-tuning. However, we argue that transfer learning is also a very practical approach to applying BERT, and this approach has its own advantages compared to fine-tuning. In this paper, we explore the advantages of the transfer learning approach through the method that uses transfer learning and fine-tuning approaches separately to apply BERT to the same eight GLUE benchmark tasks and compare the training time spent and model performance scores, such as accuracy or F1 score, of the two approaches. Finally, this paper finds that among all eight GLUE tasks and on small training sets, transfer learning saves 30% to 50% of the time compared to a fine-tuning approach to train the models on the data of downstream tasks and can achieve very similar performance. Besides, for the same amount of time, transfer learning can obtain higher performance scores than fine-tuning on larger training sets. In conclusion, on small training sets, transfer learning has a huge advantage in time consumption under the premise of approximate performance, and it also performs better on large training sets under the same length of time, compared to fine-tuning.

Today breast cancer is the leading type of cancer among women undergoing cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible convolutions. Histopathological images from the biopsy examination present a large amount of structural information that can signi cantly improve the prognosis for breast cancer. The pathological analysis, which involves the microscopic examination of the histopathological slides, is a challenging task. An automated computer-aided detection (CAD) procedure is inevitable, as it may decrease the pathologist examination time and help detect the disease at an early stage. Lately, deep learning methods using arti cial neural networks are consistently in use to improve the performance of CAD methods. A common practice among recent studies is to use the transfer learning approach of training deep neural network architectures. Transfer Learning is an established learning approach that facilitates a deep neural network to train quickly on a speci c dataset and resolve an interdisciplinary problem. Deep Learning methods employing the transfer learning approach have provided highly competitive results on the datasets consisting of the whole slide images, which are captured generally at high resolutions. However, the performance is not remarkably appreciable on the small and low-resolution image datasets, in particular the datasets that include patch samples. In this direction, the present study proposes a novel domain-speci c learning strategy, Breast Histo-Fusion, which aims to detect breast cancer even from images of low resolution and small size. Further, four state-of-the-art deep CNNs (AlexNet, VGG19, ResNet, and DenseNet) are trained using both learning approaches: Transfer Learning and Histo-Fusion on the IDC dataset. The proposed Histo-Fusion learning approach has improved the discriminating abilities and performance of each deep CNN by providing better results of (AlexNet -95.75%, VGG19-95.96%, ResNet34-96.17%, ResNet50-96.67%, and DenseNet121-97.49%) compared to (AlexNet -90.41, VGG19-90.51%, ResNet34-90.83%, ResNet50-92.27%, and DenseNet121-93.05%) using the transfer learning strategy. As a result, the procedure can help expert pathologists to perform accurate diagnoses and reduce false-positive rates.

Purpose Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers. Design/methodology/approach In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection. Findings By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively. Originality/value In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Manual inspection of fruit surface defects is time consuming, involves labour cost, prone to human error and possess inconsistent classification standards in fruit sorting application. To solve this issue, an automatic fruit sorting algorithm using deep learning technique was proposed to identify surface defects namely splitting, pitting, and stem-end rot found in mandarin fruits. The fruit sorting algorithm consists of K-Medoids segmentation and Convolutional Neural Network (CNN) classification model. The grayscale images of mandarin fruit surface were captured from an image acquisition system built with Near Infrared (NIR) camera. A preprocessing median filter was applied to remove random noise. After preprocessing, segmentation was carried out using K-Medoids clustering to crop the fruit surface image from the background region. Different CNN models namely VGG-16, InceptionV3 and MobileNet were trained and tested with and without transfer learning approach using the cropped image dataset. After training, the cropped fruit surface image was given to CNN model for defect classification. The classification results of the above models improved significantly after implementing transfer learning method. The VGG-16 model achieved a maximum overall classification accuracy of 90% without transfer learning and 99.53% with transfer learning approach when compared with the InceptionV3 and MobileNet. Overall accuracy of MobileNet improved from 57% to 98% after transfer learning and also it takes minimum time for inference. Considering both the overall accuracy and inference time parameters with the transfer learning approach, the MobileNet is found to be the best model for mandarin fruit sorting application.

Transfer learning has been considered a key solution for the problem of learning when there is a lack of knowledge in some target domains. Its idea is to benefit from the learning on different (but related in some way) domains that have adequate knowledge and transfer what can improve the learning in the target domains. Although incompleteness is one of the main causes of knowledge shortage in many machine learning real-world tasks, it has received a little effort to be addressed by transfer learning. In particular, to the best of our knowledge, there is no single study to utilize transfer learning for the symbolic regression task when the underlying data are incomplete. The current work addresses this point by presenting a transfer learning method for symbolic regression on data with high ratios of missing values. A multi-tree genetic programming algorithm based feature-based transformation is proposed for transferring data from a complete source domain to a different, incomplete target domain. The experimental work has been conducted on real-world data sets considering different transfer learning scenarios each is determined based on three factors: missingness ratio, domain difference, and task similarity. In most cases, the proposed method achieved positive transductive transfer learning in both homogeneous and heterogeneous domains. Moreover, even with less significant success, the obtained results show the applicability of the proposed approach for inductive transfer learning.

Plant diseases have a huge impact on agricultural productivity and global food security. Rapid Identification and timely control of these diseases are crucial in order to reduce yield loss. During recent years deep learning techniques have demonstrated very promising results with regard to automating the detection of plants diseases from leaf images. Transfer learning, one of the popular approaches of deep learning, has been extensively employed to leverage large scale pre trained models and adapt them to new tasks with limited data. This paper presents a study on the application of transfer learning in the detection of plant leaf conditions diseases. A transfer learning approach is proposed where a pre trained convolutional neural. The purpose of this is fine-tuning a CNN model on plant-leaf images for the classification of various diseases.

Traditional machine learning approaches usually assume that the training data and the test data follow the same distribution. When the training data and the test data are not drawn from the same distribution, most traditional machine learning approaches cannot work well. Transfer learning approaches allow the distributions of the training data and the test data to be different. Transfer learning approaches mainly focus on how to reduce the difference between the training data and the test data, but mostly ignore the potential semantic information. This paper proposes a transductive transfer learning approach based on manifold learning (TTL-ML). TTL-ML designs a new feature representation space, where the semantic information hidden in the dataset structure is preserved, and the distribution difference between the source domain and the target domain is reduced. Experimental results on the common transfer learning datasets demonstrate that the proposed approach outperforms the several state-of-the-art approaches on image classification.

The concept of transfer learning has received a great deal of concern and interest throughout the last decade. Selecting an ideal representational framework for instances of various domains to minimize the divergence among source and target domains is a fundamental research challenge in representative transfer learning. The domain adaptation approach is designed to learn more robust or higher-level features, required in transfer learning. This paper presents a novel transfer learning framework that employs a marginal probability-based domain adaptation methodology followed by a deep autoencoder. The proposed frame adapts the source and target domain by plummeting distribution deviation between the features of both domains. Further, we adopt the deep neural network process to transfer learning and suggest a supervised learning algorithm based on encoding and decoding layer architecture. Moreover, we have proposed two different variants of the transfer learning techniques for classification, which are termed as (i) Domain adapted transfer learning with deep autoencoder-1 (D-TLDA-1) using the linear regression and (ii) Domain adapted transfer learning with deep autoencoder-2 (D-TLDA-2) using softmax regression. Simulations have been conducted with two popular real-world datasets: ImageNet datasets for image classification problem and 20_Newsgroups datasets for text classification problem. Experimental findings have established and the resulting improvements in accuracy measure of classification shows the supremacy of the proposed D-TLDA framework over prominent state-of-the-art machine learning and transfer learning approaches.

One of the major limitations of brain-computer interface (BCI) is its long calibration time. Typically, a big amount of training data needs to be collected at the beginning of each session in order to tune the parameters of the system for the target user due to between sessions/subjects non-stationarity. To mitigate this limitation, transfer learning can be potentially one useful solution. Transfer learning extracts information from different domains (raw data, features, or classification domain) to compensate the lack of labelled data from the test subject. Within this chapter transfer learning definitions and techniques are fully explained.After that, some of the available transfer learning applications in BCI are explored. Then, a brief discussion about applying transfer learning in the different domains is included. The discussion shows that despite some advances, a successful transfer learning framework for BCI still needs to be developed. Finally, future research directions in this topic are suggested in order to successfully and reliably reduce the calibration time for new subjects and increase the accuracy of the system.

Crop type mapping by using transfer learning

<p>Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.</p>