Transfer Learning Mechanism Research Articles

Facial emotion recognition using deep quantum and advanced transfer learning mechanism.

Facial expressions have become a common way for interaction among humans. People cannot comprehend and predict the emotions or expressions of individuals through simple vision. Thus, in psychology, detecting facial expressions or emotion analysis demands an assessment and evaluation of decisions for identifying the emotions of a person or any group during communication. With the recent evolution of technology, AI (Artificial Intelligence) has gained significant usage, wherein DL (Deep Learning) based algorithms are employed for detecting facial expressions. The study proposes a system design that detects facial expressions by extracting relevant features using a Modified ResNet model. The proposed system stacks building-blocks with residual connections and employs an advanced extraction method with quantum computing, which significantly reduces computation time compared to conventional methods. The backbone stem utilizes a quantum convolutional layer comprised of several parameterized quantum-filters. Additionally, the research integrates residual connections in the ResNet-18 model with the Modified up Sampled Bottle Neck Process (MuS-BNP), retaining computational efficacy while benefiting from residual connections. The proposed model demonstrates superior performance by overcoming the issue of maximum similarity within varied facial expressions. The system's ability to accurately detect and differentiate between expressions is measured using performance metrics such as accuracy, F1-score, recall, and precision. This performance analysis confirms the efficacy of the proposed system, highlighting the advantages of quantum computing in feature extraction and the integration of residual connections. The model achieves quantum superiority, providing faster and more accurate computations compared to existing methodologies. The results suggest that the proposed approach offers a promising solution for facial expression recognition tasks, significantly improving both speed and accuracy.

MobileNet-CAL: Tomato pest and disease classification method based on transfer learning and attention mechanism

Based on the MobileNetV3 model, this paper introduces the Coordinate attention (CA) attention mechanism and transfer learning to propose the MobileNet-CAL model to solve the problems of insufficient attention to model channel information, long classification training time, high training cost, and weak generalization ability. The model combines data enhancement, attention mechanism and transfer learning technology to improve the classification effect of the model, and updates the fully connected layer at the same time. Finally, the model is optimized by combining the Auxiliary loss function and the ASGD optimizer to improve the classification accuracy of the model, speed up the collection speed, and enhance the robustness of the model. Finally, the model is evaluated from multiple angles such as Accuracy, F1-score, Precision, Recall, and Specificity in the tomato pest and disease leaf dataset. Its Accuracy reaches 98.59% an accuracy of, which is improved by, and by, 4.47%respectively, compared with the original model. Experiments show that MobileNet-CAL can improve the classification accuracy to a certain extent, verifying that the model proposed 4.05%、3.43%、16.57% in 4.13%this paper is effective in the classification task of tomato pest and disease leaves.

Review Paper on Detection of Diabetic Retinopathy through Quantum Transfer Learning

Diabetic Retinopathy (DR) is a common complication among diabetes patients that can cause vision impairment owing to lesions on the retina. Late-stage discovery of DR often leads to irreversible blindness. The usual approach of diagnosing DR through retinal fundus imaging by ophthalmologists is both time-consuming and costly. Although classical transfer learning methods have been extensively employed for computer-aided DR detection, their high maintenance costs can restrict their performance. In contrast, Quantum Transfer Learning is projected to provide a more effective solution to this difficulty, acting on heuristic principles and being highly optimized for the task. Our suggested method will exploit this hybrid quantum transfer learning mechanism to detect DR. We propose to utilize the Blindness Detection dataset from Kaggle to develop our model, leveraging pre-trained classical neural networks for initial feature extraction. For the classification stage, we will utilize a Variational Quantum Classifier. This future effort seeks to prove that quantum computing, coupled with quantum machine learning, can do tasks with a level of power and efficiency unattainable by classical computers alone. By harnessing these new technologies, we intend to greatly enhance the identification and diagnosis of Diabetic Retinopathy, perhaps saving many from the risk of blindness

Co-evolution genetic programming-based hyper-heuristics for the stochastic project scheduling problem with resource transfer and idle costs

In this paper, we study the stochastic resource-constrained project scheduling problem with transfer and idle costs (SRCPSP-TIC) under uncertain environments, where the resource transfer and idle take time and costs. Priority rule (PR) based heuristics are the most commonly used approaches for project scheduling under uncertain environments due to their simplicity and efficiency. For PR-based heuristics of the SRCPSP-TIC, activity priority rules (APRs) and transfer priority rules (TPRs) are necessary to decide the activity sequence and resource transfer. Traditionally, APRs and TPRs need to be manually designed, which is time-consuming and difficult to adapt to different scheduling scenarios. Therefore, based on two individual representation methods, we propose two co-evolution genetic programming (CGP) based hyper-heuristics to evolve APRs and TPRs automatically. Furthermore, a fitness function surrogate-assisted method and a transfer learning mechanism are designed to improve the efficiency and solution quality of the CGP. Based on the instances with different stochastic activity duration distributions, we test the performance of different CGP-based hyper-heuristics and compare the evolved PRs with the classical PRs to demonstrate the effectiveness of evolved PRs. Experimental results show that the proposed algorithms can automatically evolve efficient PRs for the SRCPSP-TIC.

Towards the generalization of time series classification: A feature-level style transfer and multi-source transfer learning perspective

Transfer learning-based methods hold promise for enhancing classification task performance. However, a transfer learning mechanism for hard-to-classify time series classification tasks caused by limited samples in training set is still to be accomplished. Drawing inspiration from domain adaptation and style transfer methods in computer vision, we aim to address sample scarcity and enhance classifier generalization by leveraging abundant unrelated time series datasets. In this paper, a transfer learning mechanism called feature-level style transfer, where the feature alignment is conducted in a domain adaptation-inspired manner, has been proposed to target initially hard-to-classify time series tasks with limited training samples. The proposed mechanism eliminates constraints on source dataset selection, enabling the utilization of weakly-related or unrelated datasets to enhance classifier performance. Furthermore, a voting mechanism has been formulated to achieve more accurate predictions by taking transferable information learnt from different source domains into comprehensive consideration.

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

Facial micro-expression recognition using stochastic graph convolutional network and dual transferred learning

Micro-expression recognition (MER) has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. However, the best recognition accuracy on recent public dataset is still low compared to the accuracy of macro-expression recognition. In this paper, we propose a novel graph convolution network (GCN) for MER achieving state-of-the-art accuracy. Different to existing GCN with fixed graph structure, we define a stochastic graph structure in which some neighbors are selected randomly. As shown by numerical examples, randomness enables better feature characterization while reducing computational complexity. The whole network consists of two branches, one is the spatial branch taking micro-expression images as input, the other is the temporal branch taking optical flow images as input. Because the micro-expression dataset does not have enough images for training the GCN, we employ the transfer learning mechanism. That is, different stochastic GCNs (SGCN) have been trained by the macro-expression dataset in the source network. Then the well-trained SGCNs are transferred to the target network. It is shown that our proposed method achieves the state-of-art performance on all four well-known datasets. This paper explores stochastic GCN and transfer learning with this random structure in the MER task, which is of great importance to improve the recognition performance.

Technology and Disease Prediction: Addressing the Challenges in Retinal Disorder Prediction Using Transfer Learning Mechanism

Technology and Disease Prediction: Addressing the Challenges in Retinal Disorder Prediction Using Transfer Learning Mechanism

An Interpretable Approach to the Solutions of High-Dimensional Partial Differential Equations

In recent years, machine learning algorithms, especially deep learning, have shown promising prospects in solving Partial Differential Equations (PDEs). However, as the dimension increases, the relationship and interaction between variables become more complex, and existing methods are difficult to provide fast and interpretable solutions for high-dimensional PDEs. To address this issue, we propose a genetic programming symbolic regression algorithm based on transfer learning and automatic differentiation to solve PDEs. This method uses genetic programming to search for a mathematically understandable expression and combines automatic differentiation to determine whether the search result satisfies the PDE and boundary conditions to be solved. To overcome the problem of slow solution speed caused by large search space, we propose a transfer learning mechanism that transfers the structure of one-dimensional PDE analytical solution to the form of high-dimensional PDE solution. We tested three representative types of PDEs, and the results showed that our proposed method can obtain reliable and human-understandable real solutions or algebraic equivalent solutions of PDEs, and the convergence speed is better than the compared methods. Code of this project is at https://github.com/grassdeerdeer/HD-TLGP.

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.

Multigrade brain tumor classification in MRI images using Fine tuned efficientnet

Medical imaging plays a vital role in detecting and treating brain tumors. Malignant or non-malignant brain tissue’s abnormal growth causes long-term brain damage. It is crucial to detect and properly categorize the kind of brain tumor. Specialists normally use MRI to create high-contrast grayscale brain images to segment them. Convolutional neural networks (CNN) driven by deep learning (DL) have transformed computer-assisted testing systems by producing good results in a wide range of medical imaging analytics applications, including tumor diagnosis in the brain. The paper introduces a lightweight fine-tuned Convolutional Neural Network EfficientNet ’ECNN’ to detect brain tumors. In this study, we provide a transfer learning-based measurement strategy for grouping cerebrum growths in three distinct datasets with different classifications, such as meningioma, glioma, and pituitary growth, using fine-tuned EfficientNets. The findings of this research rely on Efficient Nets to classify brain tumors in three different types of datasets utilizing a fine-tuned transfer learning mechanism. With EfficientNetV2S as the system’s foundation, our proposed way of fine-tunned pre-trained EfficientNetV2S model outperformed for all datasets over state of the art methods. The effectiveness of the suggested model has been assessed using performance metrics, and outcomes were compared to those produced using state-of-the-art approaches. The average test accuracy, recall, precision, and sensitivity score are 98.48%, 98%, 98.5%, and 98.71%, respectively.

AN EPILEPSY DIAGNOSIS METHOD BASED ON A TRANSFERRED ALEXNET MODEL AND EEG SIGNALS

Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches.

Lie Detection Technology of Bimodal Feature Fusion Based on Domain Adversarial Neural Networks

In the domain of lie detection, a common challenge arises from the dissimilar distributions of training and testing datasets. This causes a model mismatch, leading to a performance decline of the pretrained deep learning model. To solve this problem, we propose a lie detection technique based on a domain adversarial neural network employing a dual-mode state feature. First, a deep learning neural network was used as a feature extractor to isolate speech and facial expression features exhibited by the liars. The data distributions of the source and target domain signals must be aligned. Second, a domain-antagonistic transfer-learning mechanism is introduced to build a neural network. The objective is to facilitate feature migration from the training to the testing domain, that is, the migration of lie-related features from the source to the target domain. This method results in improved lie detection accuracy. Simulations conducted on two professional lying databases with different distributions show the superiority of the detection rate of the proposed method compared to an unimodal feature detection algorithm. The maximum improvement in detection rate was 23.3% compared to the traditional neural network-based detection method. Therefore, the proposed method can learn features unrelated to domain categories, effectively mitigating the problem posed by different distributions in the training and testing of lying data.

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model.

Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the Bidirectional Encoder Representations from Transformer (BERT) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.

Pine wood nematode disease area identification based on multi-temporal multi-source remote sensing images and BIT model

Pine wood nematode (PWN) disease is a severe contagious disease in forests which endangers the health of forests, as it can rapidly kill trees. The detection of regions impacted by this disease plays a crucial role in forest management, remediation efforts and ensuring the overall ecological security of forests. With the advancements in remote sensing technology, remote sensing images offer distinct advantages such as extensive coverage, timely data acquisition and high spatial resolution, making them highly suitable for accurately and efficiently identifying PWN disease areas. This article introduces a deep learning approach for the identification of PWN disease areas using multi-temporal and multi-source remote sensing images. Specifically, a specialized model called the bitemporal image transformer (BIT) model is developed and trained using multi-temporal Beijing-2 and Beijing-3 images acquired at different stages of PWN disease. BIT is a twin network for semantic segmentation with transformer structure and its backbone utilizes the Resnet structure. The input bitemporal image can be represented by several semantic tokens and encoded using a transformer encoder. The contextual information learned from the tokens is subsequently applied back to the pixel space. Finally, transformer decoder is utilized to refine the original features. To evaluate the model’s generalization ability, the best-trained model for different regions affected by pine nematode disease is identified using a transfer learning mechanism. The experimental results demonstrate a significant enhancement in classification accuracy using the proposed method, with a noteworthy 15.23% improvement in F1-score, 9.34% in accuracy, 11.7% in precision, 12.88% in recall, compared to traditional methods. Moreover, the model exhibits impressive generalization ability across various forest regions. In summary, this research introduces a promising approach that utilizes remote sensing images and deep learning techniques as a powerful tool for precisely identifying and managing areas affected by PWN disease.

A Trajectory Prediction Method of Drogue in Aerial Refueling Based on Transfer Learning and Attention Mechanism

A Trajectory Prediction Method of Drogue in Aerial Refueling Based on Transfer Learning and Attention Mechanism

Understanding the black-box: towards interpretable and reliable deep learning models

Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for predictions is unknown. Consequently, the reliability of the model becomes questionable in many circumstances. Explainable AI (XAI) plays an important role in improving the transparency and interpretability of the model thereby making it more reliable for real-time deployment. To investigate the reliability and truthfulness of DL models, this research develops image classification models using transfer learning mechanism and validates the results using XAI technique. Thus, the contribution of this research is twofold, we employ three pre-trained models VGG16, MobileNetV2 and ResNet50 using multiple transfer learning techniques for a fruit classification task consisting of 131 classes. Next, we inspect the reliability of models, based on these pre-trained networks, by utilizing Local Interpretable Model-Agnostic Explanations, the LIME, a popular XAI technique that generates explanations for the predictions. Experimental results reveal that transfer learning provides optimized results of around 98% accuracy. The classification of the models is validated on different instances using LIME and it was observed that each model predictions are interpretable and understandable as they are based on pertinent image features that are relevant to particular classes. We believe that this research gives an insight for determining how an interpretation can be drawn from a complex AI model such that its accountability and trustworthiness can be increased.

Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.

Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and attention mechanisms to enhance model interpretability and robustness, emphasizing localized features and enabling accurate discrimination of complex cases. Our method involves transfer learning with deep CNN models-Xception, VGG16, ResNet50, MobileNet, and DenseNet121-augmented with the convolutional block attention module (CBAM). The pre-trained models are finetuned, and the two CBAM models are incorporated at the end of the pre-trained models. The models are compared to state-of-the-art breast cancer diagnosis approaches and tested for accuracy, precision, recall, and F1 score. The confusion matrices are used to evaluate and visualize the results of the compared models. They help in assessing the models' performance. The test accuracy rates for the attention mechanism (AM) using the Xception model on the "BreakHis" breast cancer dataset are encouraging at 99.2% and 99.5%. The test accuracy for DenseNet121 with AMs is 99.6%. The proposed approaches also performed better than previous approaches examined in the related studies.

Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism

Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism

Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism.

Cholecystokinin (CCK) can make the human body feel full and has neurotrophic and anti-inflammatory effects. It is beneficial in treating obesity, Parkinson's disease, pancreatic cancer, and cholangiocarcinoma. Traditional biological experiments are costly and time-consuming when it comes to finding and identifying novel CCK-secretory peptides, and there is an urgent need to develop a new computational method to predict new CCK-secretory peptides. This study combines the transfer learning method with the SMILES enumeration data augmentation strategy to solve the data scarcity problem. It establishes a fusion model of the hierarchical attention network (HAN) and bidirectional long short-term memory (BiLSTM), which fully extracts peptide chain features to predict CCK-secretory peptides efficiently. The average accuracy of the proposed method in this study is 95.99%, with an AUC of 98.07%. The experimental results show that the proposed method is significantly superior to other comparative methods in accuracy and robustness. Therefore, this method is expected to be applied to the preliminary screening of CCK-secretory peptides.