Adaptive and User-Friendly Framework for Image Classification with Transfer Learning Models

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models’ performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

This paper proposes an English-Chinese machine translation research method based on transfer learning. First, it expounds the theory of neural machine translation and transfer learning and related technologies. Neural machine translation is discussed, the advantages and disadvantages of various models are introduced, and the transformer neural machine translation model framework is selected. For low-resource Chinese-English parallel corpus and Tibetan-Chinese parallel corpus, 30 million Chinese-English parallel corpora, 100,000 Chinese-English low-resource parallel corpora, and 100,000 Tibetan-Chinese parallel corpora were used to pretrain the transformer machine translation architecture. The decoders are all composed of 6 identical hidden layers, the initialization of the model parameters is done by the transformer uniform distribution, and the model training uses Adam as the optimizer. In the model transfer part, the parameters with the better effect of the pretrained model are transferred to the low-resource Chinese-English and Tibetan-Chinese machine translation model training, so as to achieve the purpose of knowledge transfer. The results show that the model transfer learning of low-resource Chinese-English parallel corpus improves the translation system's translation by 3.97 BLEU values compared with the translation system without transfer learning at 0.34 BLEU values. Model transfer learning on low-resource Tibetan-Chinese parallel corpus increases the BLEU value by 2.64 BLEU compared to the translation system without transfer learning. The neural machine translation system that uses BPE technology for preprocessing plus model transfer learning is compared to the translation system that only performs transfer learning and shows an improved 0.26 BLEU value.It is verified that the transfer learning method proposed in this paper has a certain improvement in the effect of low-resource Chinese-English and Tibetan-Chinese neural machine translation models.

Dynamic source domain selection: An adaptive EEG transfer learning framework for mitigating negative transfer.

Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs. Approach. We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine. Main results. Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively. Significance. By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.

The central problem of our work is to study the transfer of learning: how the previously acquired experience in solving problems affects the acquisition of experience in solving new problems. In this article, we have carried out a literary review of the main approaches to the definition of the «transfer of learning» concept in the psychological and pedagogical literature. Each approach is described from the point of view of the paradigm and methodology that underlies and reflects the idea of the nature of the learning process. For each approach, an assessment of the possibilities and limitations of its use in adaptive learning is given. Examples are given of how in the researches on adaptive learning, using digital technologies, certain tasks and measurements related to learning and learning transfer are implemented. The main conclusion from the literature review is that the transfer cannot claim to be an explanatory construct, this concept describes a phenomenon and the task of investigating the influence of previously acquired experience on a new one. The investigation of the transfer of learning is reduced to the investigation of learning in the situation of transfer. We propose the own model of learning transfer and requirements for possible measurable digital traces are put forward. The novelty of the model is that the transfer of learning is observed within the framework of the systemic approach and the paradigm of enactivism, in terms of individual experience organization, without classification of types of tasks or thinking and reasoning processes from the perspective of an external observer-expert as it happens in the most studies. Based on the proposed model, we put forward the methodological requirements in the framework of adaptive learning and the problem of learning transfer for what digital traces to measure and from what methodological view to investigate them.

This study aimed to compare the axial cranial ultrasound images of normal and open neural tube defect (NTD) fetuses using a deep learning (DL) model and to assess its predictive accuracy in identifying open NTD.It was a prospective case-control study. Axial trans-thalamic fetal ultrasound images of participants with open fetal NTD and normal controls between 14 and 28 weeks of gestation were taken after consent. The images were divided into training, testing, and validation datasets randomly in the ratio of 70:15:15. The images were further processed and classified using DL convolutional neural network (CNN) transfer learning (TL) models. The TL models were trained for 50 epochs. The data was analyzed in terms of Cohen kappa score, accuracy score, area under receiver operating curve (AUROC) score, F1 score validity, sensitivity, and specificity of the test.A total of 59 cases and 116 controls were fully followed. Efficient net B0, Visual Geometry Group (VGG), and Inception V3 TL models were used. Both Efficient net B0 and VGG16 models gave similar high training and validation accuracy (100 and 95.83%, respectively). Using inception V3, the training and validation accuracy was 98.28 and 95.83%, respectively. The sensitivity and specificity of Efficient NetB0 was 100 and 89%, respectively, and was the best.The analysis of the changes in axial images of the fetal cranium using the DL model, Efficient Net B0 proved to be an effective model to be used in clinical application for the identification of open NTD. · Open spina bifida is often missed due to the nonrecognition of the lemon sign on ultrasound.. · Image classification using DL identified open spina bifida with excellent accuracy.. · The research is clinically relevant in low- and middle-income countries..

The classification process is also used in artificial intelligence (AI), which is intelligence created by a computer, so that it can mimic actions like humans in general and can capture events that occur in the surrounding environment. Seeing the very high development of the international coffee trade, it can be concluded that if there is a type of coffee that has the best quality, it will be sought after by coffee importing countries. Batik is cloth that is painted using canting and liquid wax to form motifs or patterns that have high artistic value. Batik has a variety of unique and distinctive patterns that reflect the region where the batik motif originates. One area that has batik motifs in Indonesia is batik in the North Coastal Region of Java Island. In this area, various batiks are produced according to the characteristics of the area itself. Regarding information related to the introduction of types of batik motifs, perhaps it comes from people who have batik skills and batik craftsmen who best understand batik as a whole, while the general public does not really know about batik motifs. Because batik has different motifs and batik motifs in some areas have motifs that are almost uniform but not the same. One of the technologies that can be used is deep learning. The purpose of this study is to propose a Convolutional Neural Network (CNN) and Transfer Learning (TL) model to be implemented in an intelligent system for the process of image classification of coffee bean types. The method used in this study is the PSOCNN transfer learning model VGG16 From the results of tests carried out on 2 models, namely the CNN model, the PSOCNN transfer learning model VGG16. it was found that the highest accuracy was obtained when classifying batik motives images using the PSOCNN-transfer learning model VGG16, which was 83%. The level of accuracy that is increased when compared to the usual CNN model indicates that the use of transfer learning has a good effect on the level of accuracy obtained. Although an increase of 6% is significant, but with this increase it opens up opportunities to increase even higher by using other transfer learning models

The application of transfer learning to T1ρ MRI tibiofemoral cartilage segmentation.

In this paper we propose a new adaptive learning framework that classifies learner's based on individual preferences in terms of understanding and processing information. This framework generates learner's learning style based on Felder Silverman learning style model and suggest learning content based on learning style. The paper outlines how the system allows instructors to monitor learner's learning style and suggest learning content based on the inferred learning style. The framework supports both static and automatic student modelling approach. In this sense, Adaptive learning framework resolves the limitation of traditional learning management system thus improving learner's learning process.

This work presents the initial results of the ALF (Adaptive Learning Framework) project, a development framework to facilitate the auditing, management and maintenance of interactive and multimedia didactic materials that can be adapted to suit the user”s requirements. ALF aims to build software components that are reusable in educational environments, providing for: a parameterizable system (writing of contents, definition of pedagogical strategies and management and subsequent maintenance), a personalized display of educational modules (adaptation of contents to the student”s needs) and the automatic formulation and solution of exercises. The first prototype built with ALF is described, which is an interactive exercise for teaching mathematics.

Transfer learning is a subfield of machine learning that leverages proficiency in one or more prediction tasks to improve proficiency in a related task. For chemical property prediction, transfer learning models represent a promising approach for addressing the data scarcity limitations of many properties by utilizing potentially abundant data from one or more adjacent applications. Transfer learning models typically utilize a latent variable that is common to several prediction tasks and provides a mechanism for information exchange between tasks. For chemical applications, it is still largely unknown how correlation between the prediction tasks affects performance, the limitations on the number of tasks that can be simultaneously trained in these models before incurring performance degradation, and if transfer learning positively or negatively affects ancillary model properties. Here we investigate these questions using an autoencoder latent space as a latent variable for transfer learning models for predicting properties from the QM9 data set that have been supplemented with semiempirical quantum chemistry calculations. We demonstrate that property prediction can be counterintuitively improved by utilizing a simpler linear predictor model, which has the effect of forcing the latent space to organize linearly with respect to each property. In data scarce prediction tasks, the transfer learning improvement is dramatic, whereas in data rich prediction tasks, there appears to be little adverse impact of transfer learning on prediction performance. The transfer learning approach demonstrated here thus represents a highly advantageous supplement to property prediction models with no downside in implementation.

Lung Disease is one of the most common healthcare issues in the entire world in all age groups of humans due to the cause of smoking, infection and contaminated air. Pneumonia, Coronavirus disease 2019 (COVID-19) and Tuberculosis are the common types of lung disease. To reduce the diagnosis of the Lung Disease, several research works have been done with the help of Artificial Intelligence based-technologies. Prediction and classification of Lung Disease are the common tasks using Chest X-ray images, CT scan images, and MRI images with the help of machine learning, deep learning, and transfer learning models. The main idea of this paper is to show the comparative analysis of ResNet-50, MobileNet-V2, VGG-19 and DenseNet201 CNN-based Transfer Learning techniques and suggesting which image classification algorithm is more recommended to predicting and classifying Lung Disease classification problem. Those four algorithms have been trained on the X-ray images and evaluated using performance metrics techniques. The results have been shown 94.64% accuracy obtained using ResNet50, 97.43% accuracy obtained using VGG19, 98.49% accuracy obtained using MobileNetV2, and 98.05% accuracy obtained using DenseNet201 pre-trained model. Based on the experimental results MobileNet model has been outperformed as compared to other models. Training time per epoch and ROC-AUC values of MobileNet was better than other models. Based on our studies, MobileNet transfer learning is recommended pre-trained model for Lung Disease prediction and classification problem.

Betta fish, known as freshwater fighters, are in demand because of their beauty and characteristics. These betta fish such as Crowntail, Halfmoon, Doubletail, Spadetail, Plakat, Veiltail, Paradise, and Rosetail are hard to recognize without knowledge about them. Therefore, transfer learning of Convolutional Neural Network models was proposed to classify the betta fish from the image. The transfer learning process used a pre-trained model from ImageNet of VGG16, MobileNet, and InceptionV3 and fine-tuned the models on the betta fish dataset. The models were trained on 461 images, validated with 154 images, and tested on 156 images. The result shows that the InceptionV3 model excels with 0.94 accuracies compared to VGG16 and MobileNet which acquire 0.93 and 0.92 accuracy respectively. With good accuracy, the trained model can be used in betta fish recognition applications to help people easily identify betta fish from the image.

Breast cancer (BC) is the second leading cause of cancer-related death among women in the United States. Previous studies have shown that African American (AA) women have disproportionately high mortality rate in BC compared to European American (EA) women in the United States. Existing studies have suggested that transfer learning could reduce health disparities in BC by transferring information from a majority group (e.g., EA women) to a minority group (e.g., AA women). However, their performance may be decreased due to limited training patient samples and using single-omics data only. We recently presented a transfer learning method by integrating two multi-omics data for reducing cancer disparities. However, the integration model was not optimized, and its performance in reducing disparities was not robust. To address these concerns, we propose a novel weighted multi-modal transfer learning model that can optimize the integration of multi-omics data to alleviate racial disparities in BC. Specifically, we first calculated the patient-patient similarity by using the Pearson Correlation Coefficient (PCC), based on which we performed weighted integration of multi-omics data. Then, we performed a nested grid search method to find the optimal weights for each type of omics data, which were subsequently fed into a transfer learning model. To demonstrate the effectiveness of our model, we performed the tasks of predicting Disease-Specific Survival (DSS) prognosis for the 1098 female BC patient samples from The Cancer Genome Atlas (TCGA) database. Results suggested that our model with optimized weighted integration of three multi-omics (including mRNA, miRNA and methylation) data significantly outperformed previous multi-omics transfer learning models in terms of AUC. In addition, our multi-modal transfer learning model performed remarkably better than single-omics and two-omics-integration transfer learning models, suggesting that the inclusion of more diverse omics data contributes to a better reduction of racial disparities in BC. Furthermore, the performance of our transfer learning model was also superior to that of conventional mixed models and independent models. In summary, our proposed weighted multi-modal transfer learning model achieves far better performance than existing state-of-the-art methods for reducing health disparities in BC. We expect that our proposed model will provide a new way to reduce health disparities in BC diagnosis, prognosis, and treatment, and it can be extensible to alleviate health disparities in other types of cancer. Citation Format: Min-jeong Baek, Lusheng Li, Jieqiong Wang, Vimla Band, Shibiao Wan. A weighted multi-modal transfer learning model for alleviating racial disparities in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3620.

Image classification and prediction using transfer learning in colab notebook