Deep Transfer Learning Techniques Research Articles

Voice recognition by deep transfer learning and vision transformers to secure voice authentication

Speech recognition is crucial for ensuring the security of personal devices and financial transactions. Attaining high accuracy and robustness in voice authentication is challenging due to the presence of voice and environmental variability. Recent advancements in the field of deep learning, particularly in transfer learning and visual transformers, have the potential to enhance voice recognition systems. This study employs advanced deep transfer learning techniques, including Vision Transformers (ViT), VGG16, and a customized Convolutional Neural Network (CNN), to enhance the accuracy and security of speech authentication. The objective is to evaluate and contrast various solutions' voice recognition and authentication accuracy. The experiment included 3000 voice samples, with an equal distribution of 1500 samples from male participants and 1500 from female participants. The dataset was used to train Vision Transformers, VGG16 with transfer learning, and a custom CNN. The models were assessed based on their accuracy in identifying and authenticating voice samples. The VGG16 model achieved the highest level of accuracy in speech recognition, with a precision rate of 95%. The Vision Transformer and custom CNN exhibited satisfactory performance. However, VGG16 demonstrated higher accuracy. The most accurate voice authentication model studied is the VGG16 model based on transfer learning. This study suggests that the security and reliability of voice recognition systems can be enhanced through the use of deep learning techniques.

A Novel Optimized Colonic adenocarcinoma Detection using Deep Transfer Learning Approach with XceptionTS Model

Colonic adenocarcinoma is a major contributor to global mortality, highlighting the crucial need for efficient detection and classification techniques. This research presents a new method called XceptionTS for classifying and detecting colon cancer using colonoscopy pictures. The XceptionTS method utilizes deep transfer learning techniques by leveraging the Xception model architecture. Nonlinear Mean Filtering (NMF) is used as a noise reduction method in image processing to improve the quality of colonoscopy pictures. We combine the MobileNetV2 and ResNet-50 models for healthcare image segmentation and feature extraction, respectively. The XceptionTS classifier efficiently gives accurate class labels to medical photos by combining Tabu Search Optimization with the strong Xception architecture. The assessment of the effectiveness of XceptionTS model is done using a dataset of 1560 colonoscopy images. An extensive comparison study is undertaken by analyzing the efficacy of our suggested approach with existing research. The XceptionTS system outperforms previous methodologies in colon cancer classification and detection tasks, showing higher accuracy and robustness according to experimental results. Our findings indicate that the XceptionTS technique shows potential as an advanced tool to increase the effectiveness of Colonic adenocarcinoma diagnosis, which could lead to better patient outcomes and healthcare management.

Underwater Rescue Target Detection Based on Acoustic Images.

In order to effectively respond to floods and water emergencies that result in the drowning of missing persons, timely and effective search and rescue is a very critical step in underwater rescue. Due to the complex underwater environment and low visibility, unmanned underwater vehicles (UUVs) with sonar are more efficient than traditional manual search and rescue methods to conduct active searches using deep learning algorithms. In this paper, we constructed a sound-based rescue target dataset that encompasses both the source and target domains using deep transfer learning techniques. For the underwater acoustic rescue target detection of small targets, which lack image feature accuracy, this paper proposes a two-branch convolution module and improves the YOLOv5s algorithm model to design an acoustic rescue small target detection algorithm model. For an underwater rescue target dataset based on acoustic images with a small sample acoustic dataset, a direct fine-tuning using optical image pre-training lacks cross-domain adaptability due to the different statistical properties of optical and acoustic images. This paper therefore proposes a heterogeneous information hierarchical migration learning method. For the false detection of acoustic rescue targets in a complex underwater background, the network layer is frozen during the hierarchical migration of heterogeneous information to improve the detection accuracy. In addition, in order to be more applicable to the embedded devices carried by underwater UAVs, an underwater acoustic rescue target detection algorithm based on ShuffleNetv2 is proposed to improve the two-branch convolutional module and the backbone network of YOLOv5s algorithm, and to create a lightweight model based on hierarchical migration of heterogeneous information. Through extensive comparative experiments conducted on various acoustic images, we have thoroughly validated the feasibility and effectiveness of our method. Our approach has demonstrated state-of-the-art performance in underwater search and rescue target detection tasks.

A new approach for heart disease detection using Motif transform-based CWT's time-frequency images with DenseNet deep transfer learning methods.

Electrocardiogram (ECG) signals are extensively utilized in the identification and assessment of diverse cardiac conditions, including congestive heart failure (CHF) and cardiac arrhythmias (ARR), which present potential hazards to human health. With the aim of facilitating disease diagnosis and assessment, advanced computer-aided systems are being developed to analyze ECG signals. This study proposes a state-of-the-art ECG data pattern recognition algorithm based on Continuous Wavelet Transform (CWT) as a novel signal preprocessing model. The Motif Transformation (MT) method was devised to diminish the drawbacks and limitations inherent in the CWT, such as the issue of boundary effects, limited localization in time and frequency, and overfitting conditions. This transformation technique facilitates the formation of diverse patterns (motifs) within the signals. The patterns (motifs) are constructed by comparing the amplitudes of each individual sample value in the ECG signals in terms of their largeness and smallness. In the subsequent stage, the obtained one-dimensional signals from the MT transformation were subjected to CWT to obtain scalogram images. In the last stage, the obtained scalogram images were subjected to classification using DenseNET deep transfer learning techniques. The combined approach of MT+CWT+DenseNET yielded an impressive success rate of 99.31 %.

Categorization of Dehydrated Food through Hybrid Deep Transfer Learning Techniques

The essentiality of categorizing dry foods plays a crucial role in maintaining quality control and ensuring food safety for human consumption. The effectiveness and precision of classification methods are vital for enhanced evaluation of food quality and streamlined logistics. To achieve this, we gathered a dataset of 11,500 samples from Mendeley and proceeded to employ various transfer learning models, including VGG16 and ResNet50. Additionally, we introduce a novel hybrid model, VGG16-ResNet, which combines the strengths of both architectures. Transfer learning involves utilizing knowledge acquired from one task or domain to enhance learning and performance in another. By fusing multiple Deep Learning techniques and transfer learning strategies, such as VGG16-ResNet50, we developed a robust model capable of accurately classifying a wide array of dry foods. The integration of Deep Learning (DL) and transfer learning techniques in the context of dry food classification signifies a drive towards automation and increased efficiency within the food industry. Notably, our approach achieved remarkable results, achieving a classification accuracy of 99.78% for various dry food images, even when dealing with limited training data for VGG16-ResNet50.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

Defending against Misinformation: Evaluating Transformer Architectures for Quick Misinformation Detection on Social Media

Prior to the technological revolution, reliance on traditional sources such as newspapers and other mass communication channels was the predominant method of consuming news. With the pervasive influence of social media, traditional news sources have undergone significant displacement, yielding predominance to platforms rooted in social media networks. Informal news propagation through social media has been observed to be fast in nature. With the information propagating fast, the misinformation also propagates rapidly on social media. If the misinformation is not detected early, it can cause serious problems. Thus the problem of identifying misinformation at the onset needs much attention. This paper explores various architectures for detecting misinformation, employing deep transfer learning techniques. The study utilizes transformer architectures, namely MPNet, SentenceT5, and Generalizable T5-based dense Retrievers, to generate embeddings. These architectures have undergone pre-training and fine-tuning on diverse tasks. The system’s performance is assessed using a benchmark dataset of misinformation from Reddit, referred to as ‘Fakeddit’. When tested on unseen data, the models perform well, and outperform previously reported baselines and other models on the given dataset with textual modality. The results indicate that transfer learning with zero-shot or few-shot learning can prove very cost-effective and efficient for the problem of misinformation detection.

A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy

Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting which patients will respond to CRT remains a challenge. This study explores the application of deep transfer learning techniques to train a predictive model for CRT response. Methods: In this study, the short-time Fourier transform (STFT) technique was employed to transform ECG signals into two-dimensional images. A transfer learning approach was then applied to the MIT-BIT ECG database to pre-train a convolutional neural network (CNN) model. The model was fine-tuned to extract relevant features from the ECG images and then tested on our dataset of CRT patients to predict their response. Results: Seventy-one CRT patients were enrolled in this study. The transfer learning model achieved an accuracy of 72% in distinguishing responders from non-responders in the local dataset. Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in identifying CRT responders. The performance of our model outperformed clinic guidelines and traditional machine learning approaches. Conclusion: The utilization of ECG images as input and leveraging the power of transfer learning allows for improved accuracy in identifying CRT responders. This approach offers potential for enhancing patient selection and improving the outcomes of CRT.

Deep Transfer Learning for Detecting Covid-19, Pneumonia and Tuberculosis Using Cxr Images – A Review

Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia, and tuberculosis in chest X-ray (CXR) images. It provides an overview of current state-of the-art CXR image classification techniques and discusses the challenges and opportunities in applying transfer learning to this domain. The paper provides a thorough examination of recent research studies that used deep transfer learning algorithms for COVID-19, pneumonia, and tuberculosis detection, highlighting the advantages and disadvantages of these approaches. Finally, the review paper discusses future research directions in the field of deep transfer learning for CXR image classification, as well as the potential for these techniques to aid in the diagnosis and treatment of lung diseases.

Determination and classification of fetal sex on ultrasound images with deep learning

Today, various prenatal diagnostic methods are used to determine the sex of the fetus. All of these medical methods require intervention by a specialist. The sensitivity of fetal ultrasonography (USG) scanning, which is the most commonly used diagnostic method, is variable and depends on the experience of the sonographer. In this study, an automatic, objective and reliable determination of fetal sex was aimed at using deep transfer learning techniques on USG images. For the study, a dataset containing 4400 fetal USG images, of which sexes were labeled by a gynecologist expert in the field, was created. In the first step, images were classified with fine-tuned convolutional neural networks. Following this classification, the fine-tuned DenseNet201 (ft-DenseNet201) network, which gave the most successful result with an accuracy of 0.9627, was used as the feature extractor network in the second step. Obtained features were classified by Logistic Regression (LR), Linear Support Vector Machine (LSVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest and AdaBoost algorithms. Among the 10 different classifiers used in the application, ft-DenseNet201 + LSVM (0.9782), ft-DenseNet201 + KNN (0.9727) and ft-DenseNet201 + LR (0.9718) algorithms gave very high accuracy values. This study can be evaluated as an automatic, objective, reliable and new medical method in determination of fetus sex; and can be used as an auxiliary system for specialists and patients by being integrated with USG devices.

Advanced deep learning techniques for early disease prediction in cauliflower plants

Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers’ incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers.

Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines

In recent years, deep transfer learning techniques have been successfully applied to solve RUL prediction across different working conditions. However, for RUL prediction across different machines in which the data distribution and fault evolution characteristics vary largely, the extraction and transition of prognostic knowledge become more challenging. Even if fault mode information can assist in the knowledge transfer, model bias will inevitably exist on the target machine with mixed or unknown faults. To address this issue from a transferability perspective, this paper proposes a novel selective transfer learning approach for RUL prediction across machines. First, the paper utilizes the tensor representation to construct the meta-degradation trend of each fault mode and evaluates the transferability of source domain data from fault mode and degradation characteristics through a new cross-machine transfer degree indicator (M-TDI). Second, a Long Short-Term Memory (LSTM)-based selective transfer strategy is proposed using the M-TDIs. The paper designs a training algorithm with an alternating optimization scheme to seek the optimal tensor decomposition and knowledge transfer effect. Theoretical analysis proves that the proposed approach significantly reduces the upper bound of prediction error. Furthermore, experimental results on three benchmark datasets prove the effectiveness of the proposed approach.

Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques

This paper presents a comprehensive analysis of deep transfer learning methods, supervised methods, and semi-supervised methods in the context of protein fitness prediction, with a focus on small datasets. The analysis includes the exploration of the combination of different data sources to enhance the performance of the models. While deep learning and deep transfer learning methods have shown remarkable performance in situations with abundant data, this study aims to address the more realistic scenario faced by wet lab researchers, where labeled data is often limited.The novelty of this work lies in its examination of deep transfer learning in the context of small datasets and its consideration of semi-supervised methods and multi-view strategies. While previous research has extensively explored deep transfer learning in large dataset scenarios, little attention has been given to its efficacy in small dataset settings or its comparison with semi-supervised approaches.Our findings suggest that deep transfer learning, exemplified by ProteinBERT, shows promising performance in this context compared to the rest of the methods across various evaluation metrics, not only in small dataset contexts but also in large dataset scenarios. This highlights the robustness and versatility of deep transfer learning in protein fitness prediction tasks, even with limited labeled data.The results of this study shed light on the potential of deep transfer learning as a state-of-the-art approach in the field of protein fitness prediction. By leveraging pre-trained models and fine-tuning them on small datasets, researchers can achieve competitive performance surpassing traditional supervised and semi-supervised methods. These findings provide valuable insights for wet lab researchers who face the challenge of limited labeled data, enabling them to make informed decisions when selecting the most effective methodology for their specific protein fitness prediction tasks.Additionally, the study investigated the combination of two different sources of information (encodings) through our enhanced semi-supervised methods, yielding noteworthy results improving their base model and providing valuable insights for further research.The presented analysis contributes to a better understanding of the capabilities and limitations of different learning approaches in small dataset scenarios, ultimately aiding in the development of improved protein fitness prediction methods.

New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques

New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.

An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries

Safe and reliable operations of lithium-ion batteries in electric vehicles (EVs), etc., highly depend on the accurate state of charge (SOC) estimated by the battery management system (BMS). However, due to the battery's nonlinear operating conditions and complex electrochemistry, accurately estimating SOC is a major challenge. In this paper, an adaptive strong tracking square-root extended Kalman filter (ASTSEKF) with nonlinear condition adaptability is proposed for the recursive correction, denoising, and op4timization of the SOC estimation of lithium-ion batteries. The proposed ASTSEKF optimizer introduces an adaptive fading factor, a weight adjustor, and a strong tracking filter to recursively update the posteriori error covariance matrix using a Cholesky decomposition and corrects the uncertainties of the EKF method. The effectiveness of the ASTSEKF method is demonstrated by utilizing it to refine and enhance the estimations of a closed-loop nonlinear autoregressive model with exogenous input (NARX) and a deep feed-forward neural network (DFFNN) utilizing deep transfer learning techniques. The Levenberg-Marquardt and adaptive moment estimation approaches are employed to address gradient issues and stabilize the networks. Battery tests are carried out at different charge-discharge rates, temperatures, complex working conditions, and aging levels. The comparative SOC results show that the proposed ASTSEKF optimizer ensures an overall maximum mean absolute error, mean square error, and root mean square error improvements of 89.82%, 91.67%, and 90.76%, respectively. Additionally, it denoises, stabilizes, optimizes, and quickly converges the final SOC with a good balance between optimal accuracy and computational complexity. In comparison to other existing methods, the proposed ASTSEKF optimizer can overcome nonlinearities encountered by the BMS under various operating conditions to provide accurate SOC estimation in EVs.

Outdoor mobility aid for people with visual impairment: Obstacle detection and responsive framework for the scene perception during the outdoor mobility of people with visual impairment

Outdoor mobility of individuals with visual impairment is challenging particularly where collision with obstacles can have significant impact on both physical and mental health. A variety of technological mobility aids for visually impaired people (VIP) have been studied and proposed in the literature which mainly utilise machine intelligence and deep learning (DL) approaches for object detection. However, object detection via the existing approaches suffers from reliability challenge due to real-time dynamics or the lack of available domain knowledge for specific obstacles identified by the VIP as potential hazards. In the present study, an object detection model (ObDtM) based on deep transfer learning techniques was developed for a custom-built dataset comprising of specific obstacles identified by the VIP as potential hazards. A custom dataset was compiled and manually annotated from various publicly available sources to train the ObDtM. Experiments were conducted to evaluate the proposed ObDtM for unseen obstacles kept as the test set. Results showed that ObDtM outperformed the state-of-the-art with 97% mean Average Precision (mAP), indicating a robust and generalizable DL approach. The compiled dataset and the ObDtM is useful for several potential use cases, particularly highlighting the use of DL in IoT and smart city applications. Additionally, a smart synergetic outdoor mobility framework was proposed for VIP (SOMAVIP) allowing comprehensive and accurate semantic representation of the surroundings by utilising the proposed ObDtM, cloud services, internet of things (IoT), and digital environment in the context of emerging smart city infrastructure. The proposed SOMAVIP can be highly impactful for improving VIPs’ quality of life mainly for safer, cost-effective, and reliable independent outdoor mobility enriched with real-time perception and interpretations of the surroundings.

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.

Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains

High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to move the distribution of features gleaned from unlabeled data in the source domain. However, traditional deep transfer learning techniques do not take into account the relationships between subdomains within the same class of different domains, resulting in suboptimal transfer learning performance and limiting the use of intelligent fault diagnosis for wheel bearings under various conditions. In order to tackle this problem, we have developed the Deep Subdomain Transfer Learning Network (DSTLN). This innovative approach transfers the distribution of features by harmonizing the subdomain distributions of layer activations specific to each domain through the implementation of the Local Maximum Mean Discrepancy (LMMD) method. The DSTLN consists of three modules: a feature extractor, fault category recognition, and domain adaptation. The feature extractor is constructed using a newly proposed SA-ConvLSTM model and CNNs, which aim to automatically learn features. The fault category recognition module is a classifier that categorizes the samples based on the extracted features. The domain adaptation module includes an adversarial domain classifier and subdomain distribution discrepancy metrics, making the learned features domain-invariant across both the global domain and subdomains. Through 210 transfer fault diagnosis experiments with wheel bearing data under 15 different operating conditions, the proposed method demonstrates its effectiveness.