Transfer Learning-based Approach Research Articles

Transfer Learning-Based Approach for Thickness Estimation on Optical Coherence Tomography of Varicose Veins.

In-depth mechanical characterization of veins is required for promising innovations of venous substitutes and for better understanding of venous diseases. Two important physical parameters of veins are shape and thickness, which are quite challenging in soft tissues. Here, we propose the method TREE (TransfeR learning-based approach for thicknEss Estimation) to predict both the segmentation map and thickness value of the veins. This model incorporates one encoder and two decoders which are trained in a special manner to facilitate transfer learning. First, an encoder-decoder pair is trained to predict segmentation maps, then this pre-trained encoder with frozen weights is paired with a second decoder that is specifically trained to predict thickness maps. This leverages the global information gained from the segmentation model to facilitate the precise learning of the thickness model. Additionally, to improve the performance we introduce a sensitive pattern detector (SPD) module which further guides the network by extracting semantic details. The swept-source optical coherence tomography (SS-OCT) is the imaging modality for saphenous varicose vein extracted from the diseased patients. To demonstrate the performance of the model, we calculated the segmentation accuracy-0.993, mean square error in thickness (pixels) estimation-2.409 and both these metrics stand out when compared with the state-of-art methods.

Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach.

Lung cancer is the second most common cancer and the leading cause of cancer death globally. Low dose computed tomography (LDCT) is the recommended imaging screening tool for the early detection of lung cancer. A fully automated computer-aided detection method for LDCT will greatly improve the existing clinical workflow. Most of the existing methods for lung detection are designed for high-dose CTs (HDCTs), and those methods cannot be directly applied to LDCTs due to domain shifts and inferior quality of LDCT images. In this work, we describe a semi-automated transfer learning-based approach for the early detection of lung nodules using LDCTs. In this work, we developed an algorithm based on the object detection model, you only look once (YOLO) to detect lung nodules. The YOLO model was first trained on CTs, and the pre-trained weights were used as initial weights during the retraining of the model on LDCTs using a medical-to-medical transfer learning approach. The dataset for this study was from a screening trial consisting of LDCTs acquired from 50 biopsy-confirmed lung cancer patients obtained over 3 consecutive years (T1, T2, and T3). About 60 lung cancer patients' HDCTs were obtained from a public dataset. The developed model was evaluated using a hold-out test set comprising 15 patient cases (93 slices with cancerous nodules) using precision, specificity, recall, and F1-score. The evaluation metrics were reported patient-wise on a per-year basis and averaged for 3 years. For comparative analysis, the proposed detection model was trained using pre-trained weights from the COCO dataset as the initial weights. A paired t-test and chi-squared test with an alpha value of 0.05 were used for statistical significance testing. The results were reported by comparing the proposed model developed using HDCT pre-trained weights with COCO pre-trained weights. The former approach versus the latter approach obtained a precision of 0.982 versus 0.93 in detecting cancerous nodules, specificity of 0.923 versus 0.849 in identifying slices with no cancerous nodules, recall of 0.87 versus 0.886, and F1-score of 0.924 versus 0.903. As the nodule progressed, the former approach achieved a precision of 1, specificity of 0.92, and sensitivity of 0.930. The statistical analysis performed in the comparative study resulted in a -value of 0.0054 for precision and a -value of 0.00034 for specificity. In this study, a semi-automated method was developed to detect lung nodules in LDCTs using HDCT pre-trained weights as the initial weights and retraining the model. Further, the results were compared by replacing HDCT pre-trained weights in the above approach with COCO pre-trained weights. The proposed method may identify early lung nodules during the screening program, reduce overdiagnosis and follow-ups due to misdiagnosis in LDCTs, start treatment options in the affected patients, and lower the mortality rate.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.

Rapid prediction of the cure-induced distortion of newly developed composite parts via transfer learning

Accurately predicting Cure-Induced Distortion (CID) is paramount to ensuring manufacturing precision. Existing prediction models heavily rely on material parameters associated with the CID, which presents challenges for newly developed composites due to the time-consuming, expensive, and limited accuracy of parameter characterization processes. With recognizing the shared curing mechanism across various composites, this paper introduces a transfer learning-based approach that leverages a small dataset of practical CID data from the newly developed composite and historical knowledge from existing composites to achieve rapid and accurate CID predictions. This approach was validated for two typical CID prediction cases by implementing a transfer learning framework, and various sampling methods were explored to assess the stability and repeatability of prediction results. The results indicate that the proposed method can predict the CID within a 5% margin of error. This paper offers a promising approach for predicting CID in the design and manufacturing processes.

ResNet50-1D-CNN: A new lightweight resNet50-One-dimensional convolution neural network transfer learning-based approach for improved intrusion detection in cyber-physical systems

The cyber-physical system (CPS) plays a crucial role in supporting critical infrastructure like water treatment facilities, gas stations, air conditioning components, and smart grids, which are essential to society. However, these systems are facing a growing susceptibility to a wide range of emerging attacks. Cyber-attacks against CPS have the potential to cause disruptions in the accurate sensing and actuation processes, resulting in significant harm to physical entities and posing concerns for the overall safety of society. Unlike common security measures like firewalls and encryption, which often aren't enough to deal with the unique problems that CPS architectures present, deploying machine learning-based intrusion detection systems (IDS) that are specifically made for CPS has become an important way to make them safer. The application of machine learning algorithms has been suggested as a means of mitigating cyber-attacks on CPS. However, the limited availability of labelled data pertaining to emerging attack techniques poses a significant challenge to the accurate detection of such attacks. In the given scenario, transfer learning emerges as a promising methodology for the detection of cyber-attacks, as it involves the implicit modelling of the system. In this research, we propose a new lightweight transfer learning method via ResNet50-CNN1D for intrusion detection in CPS. The Adaptive Gradient (Adagrad) optimizer was applied in the proposed model to minimize the loss function through the adjustment of network weight. We tested how well the suggested ResNet50-1D-CNN model worked using the UNSW-NB15 dataset and a control system dataset called HAI. The HAI dataset was taken from the testbed and based on a planned physical attack scenario. By calculating the coefficient scores for the top ten (10) features in the HAI and UNSW-NB15 data, it was possible to determine the relevance of a feature. The rationale behind employing transfer learning was to mitigate the complexity associated with the classification of cyber-attacks and runtime. The utilization of transfer learning resulted in notable reductions in both the training and testing times required for the detection of attacks. On the HAI data, the results showed an accuracy of 97.32 %, recall of 98.41 %, F1-score of 96.32 %, and precision of 97.09 %. On the UNSW-NB15 data, the results showed an accuracy of 99.89 %, recall of 99.09 %, F1-score of 98.01 %, and precision of 98.70 %.

Model predictive control of nonlinear processes using transfer learning-based recurrent neural networks

Artificial neural networks (ANNs), one of the deep learning techniques that has sparked a lot of attention recently for its exceptional modeling capabilities of nonlinear systems, are an essential candidate for model-based control systems. In particular, recurrent neural networks (RNN) have shown remarkable capacity to forecast the dynamic behavior of nonlinear processes using time-series data. In order to regulate the rate at which chemical species are produced in intricate processes, RNNs have successfully served as the predictive model in model-based controllers. However, the necessity for massive amounts of data to capture complex processes is a drawback of typical RNN models. With the goal of efficient utilization of the available data, a different, transfer learning-based training approach for RNNs is presented in this work. The transfer learning strategy is taken into consideration to overcome the challenges stemming from lack of data for the construction of RNN models. In particular, weight-sharing RNNs are developed using a priori physical knowledge. Next, a comprehensive analysis of a complex chemical process on a large scale is conducted to showcase the benefits of the suggested approach across various data sizes and its effectiveness when combined with MPC in contrast to conventional RNNs.

Heart failure survival prediction using novel transfer learning based probabilistic features.

Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

Modified ResNet152v2: Binary Classification and Hybrid Segmentation of Brain Stroke Using Transfer Learning-Based Approach

Abstract Introduction: The brain is harmed by a medical condition known as a stroke when the blood vessels in the brain burst. Symptoms may appear when the brain’s flow of blood and other nutrients is disrupted. The World Health Organization (WHO) claims that stroke is the leading cause of disability and death worldwide. A stroke can be made less severe by detecting its different warning symptoms early. A brain stroke can be quickly diagnosed using computed tomography (CT) images. Time is passing quickly, although experts are studying every brain CT scan. This situation can cause therapy to be delayed and mistakes to be made. As a result, we focused on using an effective transfer learning approach for stroke detection. Material and methods: To improve the detection accuracy, the stroke-affected region of the brain is segmented using the Red Fox optimization algorithm (RFOA). The processed area is then further processed using the Advanced Dragonfly Algorithm. The segmented image extracts include morphological, wavelet features, and grey-level co-occurrence matrix (GLCM). Modified ResNet152V2 is then used to classify the images of Normal and Stroke. We use the Brain Stroke CT Image Dataset to conduct tests using Python for implementation. Results: Per the performance analysis, the proposed approach outperformed the other deep learning algorithms, achieving the best accuracy of 99.25%, sensitivity of 99.65%, F1-score of 99.06%, precision of 99.63%, and specificity of 99.56%. Conclusions: The proposed deep learning-based classification system returns the best possible solution among all input predictive models considering performance criteria and improves the system’s efficacy; hence, it can assist doctors and radiologists in a better way to diagnose Brain Stroke patients.

Tchebichef Transform Domain-Based Deep Learning Architecture for Image Super-Resolution.

Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low- frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.

A Transfer Learning-Based Approach to Estimating Missing Pairs of On/Off Ramp Flows

A Transfer Learning-Based Approach to Estimating Missing Pairs of On/Off Ramp Flows

Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy

Locally advanced breast cancer (LABC) is a severe type of cancer with a poor prognosis, despite advancements in therapy. As the disease is often inoperable, current guidelines suggest upfront aggressive neoadjuvant chemotherapy (NAC). Complete pathological response to chemotherapy is linked to improved survival, but conventional clinical assessments like physical exams, mammography, and imaging are limited in detecting early response. Early detection of tissue response can improve complete pathological response and patient survival while reducing exposure to ineffective and potentially harmful treatments. A rapid, cost-effective modality without the need for exogenous contrast agents would be valuable for evaluating neoadjuvant therapy response. Conventional ultrasound provides information about tissue echogenicity, but image comparisons are difficult due to instrument-dependent settings and imaging parameters. Quantitative ultrasound (QUS) overcomes this by using normalized power spectra to calculate quantitative metrics. This study used a novel transfer learning-based approach to predict LABC response to neoadjuvant chemotherapy using QUS imaging at pre-treatment. Using data from 174 patients, QUS parametric images of breast tumors with margins were generated. The ground truth response to therapy for each patient was based on standard clinical and pathological criteria. The Residual Network (ResNet) deep learning architecture was used to extract features from the parametric QUS maps. This was followed by SelectKBest and Synthetic Minority Oversampling (SMOTE) techniques for feature selection and data balancing, respectively. The Support Vector Machine (SVM) algorithm was employed to classify patients into two distinct categories: nonresponders (NR) and responders (RR). Evaluation results on an unseen test set demonstrate that the transfer learning-based approach using spectral slope parametric maps had the best performance in the identification of nonresponders with precision, recall, F1-score, and balanced accuracy of 100, 71, 83, and 86%, respectively. The transfer learning-based approach has many advantages over conventional deep learning methods since it reduces the need for large image datasets for training and shortens the training time. The results of this study demonstrate the potential of transfer learning in predicting LABC response to neoadjuvant chemotherapy before the start of treatment using quantitative ultrasound imaging. Prediction of NAC response before treatment can aid clinicians in customizing ineffectual treatment regimens for individual patients.

Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment

Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment

Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces.

Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples. To address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection. The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <½ of the training samples needed by previous studies. Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.

Detection and classification of brain tumor using hybrid deep learning models

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively.

Contrastive and transfer learning-based visual small component inspection in assembly

Deep learning-based visual quality inspection methods have been increasingly adopted in product assembly process. However, the inspection for small component remains a challenging issue due to the small size and difficulty in feature extraction. Meanwhile, due to the large number of assembly scenarios of small components, the dependency on abundant manually labeled training dataset limits the further application of these inspection methods. To mitigate the situation, this paper designs a contrastive and transfer learning-based visual inspection approach to inspect the assembly state of small component. An attention-guided lightweight feature extraction and reconstruction encoder is designed to extract features of small component, of which the deconvolution operation is leveraged to recover the resolution of the feature map, and the bidirectional feature pyramid network algorithm is introduced to effectively fuse multi-scale feature information. Moreover, an unsupervised contrastive pre-training method for the designed encoder is proposed to effectively learn the visual representation of small component. With the use of a little annotated data, the pre-trained encoder can be flexibly fine-tuned and transferred to different small component assembly scenarios. The comparative experiments demonstrate the effectiveness of our method.

Covid-19 detection using modified xception transfer learning approach from computed tomography images

The significance of efficient and accurate diagnosis amidst the unique challenges posed by the COVID-19 pandemic underscores the urgency for innovative approaches. In response to these challenges, we propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database. While many approaches propose an intensive data preprocessing and/or complex model architecture, our method focuses on offering an efficient solution with minimal manual engineering. Specifically, we investigate the suitability of a modified Xception model for COVID-19 detection. The method involves adapting a pre-trained Xception model, incorporating both the architecture and pre-trained weights from ImageNet. The output of the model was designed to make the final diagnosis decisions. The training utilized 128 batch sizes and 224x224 input image dimensions, downsized from standard 512x512. No further da processing was performed on the input data. Evaluation is conducted on the 'COV19-CT-DB' CT image dataset, containing labeled COVID-19 and non-COVID-19 cases. Results reveal the method's superiority in accuracy, precision, recall, and macro F1 score on the validation subset, outperforming the VGG-16 transfer model and thus offering enhanced precision with fewer parameters. Furthermore, compared to alternative methods for the COV19-CT-DB dataset, our approach exceeds the baseline approach and other alternatives on the same dataset. Finally, the adaptability of the modified Xception transfer learning-based model to the unique features of the COV19-CT-DB dataset showcases its potential as a robust tool for enhanced COVID-19 diagnosis from CT images.

A transfer learning-based machine learning approach to predict mechanical properties of different material types fabricated by selective laser melting process

The necessity for a massive dataset has limited the desirability of the machine learning approaches for industrial applications, especially in the metal additive manufacturing processes, where, collecting a large dataset is expensive and virtually ineffective. Concerning this restriction, an effective machine learning technique should be developed to bridge the gap between the academia and the industry. Hence, in this research, a transfer learning-based artificial neural network (TL-ANN) model was developed to predict the mechanical properties of different metallic specimens fabricated by selective laser melting (SLM) process. This model was integrated with a Bayesian hyperparameters optimization algorithm to select the optimum training parameters of the model. The proposed model consists of a target network and a source network. The source network was trained based on the mechanical properties that were obtained experimentally for various materials, including pure and alloyed copper, steel, titanium, nickel, etc. The overall regression correlation coefficient ( R) of the TL-ANN model was about 0.99, with the mean square error of testing, validation, and training of datasets of about 2.031, 1.423, and 1.068, respectively, representing the successful execution of the source network in prediction of the mechanical properties of the SLMed parts. Using the achieved knowledge of the source network, the target network was trained to predict the mechanical properties of the target material (here SLMed pure and alloyed aluminum specimens). The obtained results revealed that with the help of the transfer learning, the hybrid neural network could predict the mechanical properties of SLM-fabricated aluminum parts with a high accuracy level, even with the small number of training dataset of the target material. To demonstrate the influence of transfer learning in the accuracy of the model, a separate network was developed from scratch, i.e. with random initial weights of the neurons. The R-values of the test dataset of the individual model for the output parameters of ultimate tensile strength, relative density, and yield strength of the fabricated aluminum samples were 0.787, 0.742, and 0.817, respectively, as compared with that of TL-ANN model of 0.966, 0.903, and 0.971, respectively, representing an average of 21% enhancement in the accuracy of the predictivity of the model by application of transfer learning algorithm.

Efficiency in Orchid Species Classification: A Transfer Learning-Based Approach

Orchid is a type of plant that grows on land. It is highly valued for its beauty and is cherished by many because of its graceful flower shape, delicate fragrance, vibrant colors, and noble symbolism. Although there are various types of orchids, some of them look similar in appearance and color, making it challenging for people to distinguish them quickly and accurately. The existing methods for classifying orchid species face issues with accuracy due to the similarities between different species and the differences within the same species. This affects their practical use. To address these challenges, this paper introduces an efficient method for classifying orchid species using transfer learning. The main achievement of this study is the successful utilization of transfer learning to achieve accurate orchid species classification. This approach reduces the need for large datasets, minimizes overfitting, cuts down on training time and costs, and enhances classification accuracy. Specifically, the proposed approach involves four phases. First, we gathered a collection of 12 orchid image sets, totaling 12,227 images, through a combination of network sources and field photography. Next, we analyzed the distinctive features present in the collected orchid image sets. We identified certain connections between the acquired orchid datasets and other datasets. Finally, we employed transfer learning technology to create an efficient classification function for orchid species based on these relationships. As a result, our proposed method effectively addresses the challenges highlighted. Experimental results demonstrate that our classification algorithm, which utilizes transfer learning, achieves a classification accuracy rate of 96.16% compared to not using the transfer learning method. This substantial improvement in accuracy greatly enhances the efficiency of orchid classification.

An ensemble approach for enhancing generalization and extendibility of deep learning facilitated by transfer learning: principle and application in curing monitoring

Machine learning (ML) and deep learning (DL) have exhibited significant advantages compared to conventional data analysis methods. However, the limitations of poor generalization and extendibility impede the broader application of these methods beyond specific learning tasks. To address this challenge, this study proposes a transfer learning-based ensemble approach called SMART. This approach incorporates synthetic minority oversampling technique, average reinforced interpolation, series data imaging, and fine-tuning. To validate the effectiveness of SMART, we conduct experiments on curing monitoring of polymeric composites and construct a hybrid dataset with highly heterogeneous features. We compare the performance of SMART with exemplary ML algorithms using conventional evaluation indicators, including Accuracy, Precision, Recall, and F1-score. The experimental results demonstrate that the SMART approach exhibits superior generalization capacity and extendibility, achieving indicator scores above 0.9900 in new scenarios. These findings suggest that the proposed SMART approach has the potential to break through the limitations of conventional ML and DL models, enabling wider applications in the industrial sectors.

A novel transfer learning-based approach for plant species prediction using leaf images

A novel transfer learning-based approach for plant species prediction using leaf images