Application Of Deep Learning Algorithms Research Articles

Satellite remote sensing image classification based on multiple deep learning algorithms

This paper explores the use of satellite remote sensing technology to observe and measure the earth's surface and classify remote sensing images using various deep learning algorithms. By importing AlexNet, VggNet, GoogleNet and MobileNet models, 90% of the data are randomly selected for training and 10% for testing, and the changes in the loss and accuracy of the validation set during training are recorded, as well as the accuracy of the best round of epochs for training.During the training process, the validation set's loss gradually decreased and converged, and the accuracy of the validation set gradually increased and stabilised. The results show that all four models are able to classify remote sensing images well, among which the highest accuracy is MobileNet model, which reaches 99.8%, and Googlenet model is the second one, which reaches 97.9%.AlexNet and VggNet models also have higher accuracy, which are 93.3% and 95.4%, respectively. Overall, the results of this paper provide an effective solution for satellite remote sensing image classification and provide a reference for the application of deep learning algorithms in remote sensing.

Enhancing green sea turtle (Chelonia mydas) conservation for tourists at Little Liuqiu island, Taiwan: Application of deep learning algorithms

Observing marine life has emerged as a pivotal catalyst for the growth of the Blue Economy. Yet, overzealous and recurrent observations may exert undue stress on marine creatures, thereby complicating the sustainable management of marine assets. After the easing of Covid-19 pandemic restrictions, a notable influx of tourists to Little Liuqiu Island, Taiwan puts considerable stress on its green sea turtle population. This surge intensified incidents of illegal harassment, triggering concerns from both the local community and administrative bodies. The prevailing challenge is to ensure tourists observe these turtles with respect, refraining from behaviors such as touching or pursuing them. Addressing this, our study harnesses deep learning algorithms to equip ecotourism operators and tourists with tools to detect green sea turtles across diverse coastal terrains, reinforcing conservation efforts. Our analysis scrutinized object detection AI models, namely YOLOv3, YOLOv5s, and YOLOv5l. Fieldwork was undertaken on the island to gather ample training images, capturing elusive green sea turtles in various settings, from coastline strolls to drone imagery. Supplemental images sourced from local social media platforms were later added. Contrary to expectations, we found that merely expanding the training dataset did not guarantee improved outcomes. Instead, the variance in image content, considering distances, angles, and turtle appearances, played a pivotal role in enhancing model precision. Through our experimentation, the streamlined YOLOv5s model consistently eclipsed its more complex counterparts in performance. An AI service, underpinned by the YOLOv5s model, has been launched to distinguish between green sea turtle types for tourist-focused conservation initiatives. Future iterations will incorporate user feedback to refine accuracy. Our research breaks new ground, spotlighting the intricacies of gathering natural environment data, pinpointing optimal AI models, and evaluating their practical implications for green sea turtle conservation.

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.

Application of deep learning algorithms in predicting the exchange rate of Chinese yuan against the US dollar

This paper mainly studies the feasibility of using deep learning algorithms to predict the exchange rate between the Chinese yuan and the US dollar. Firstly, this study chooses the Long Short Term Memory Network (LSTM) as the main algorithm, which is a deep learning model suitable for processing time series data. Then, the study collects a large amount of data, including historical exchange rate data, macroeconomic indicators, and political events that may affect exchange rates. These data provide sufficient information for the model to learn and predict exchange rate fluctuations. The study found that the deep learning model can accurately predict the volatility of the RMB US dollar exchange rate by contrasting the calculated results of the pattern with effective exchange rate record, and its predicted results have high stability. Deep learning has great potential in the field of exchange rate forecasting, providing more accurate predictive information for financial transactions.

Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN

The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection. At present, there are few amplification methods for Side-scan sonar images, and the amplification image quality is not ideal, which is not suitable for the characteristics of Side-scan sonar images. Addressing the current shortage of sample augmentation methods for Side-scan sonar, this paper proposes a method for augmenting single underwater target images using the CBL-sinGAN network. Firstly, considering the low resolution and monochromatic nature of Side-scan sonar images while balancing training efficiency and image diversity, a sinGAN network is introduced and designed as an eight-layer pyramid structure. Secondly, the Convolutional Block Attention Module (CBAM) is integrated into the network generator to enhance target learning in images while reducing information diffusion. Finally, an L1 loss function is introduced in the network discriminator to ensure training stability and improve the realism of generated images. Experimental results show that the accuracy of shipwreck target detection increased by 4.9% after training with the Side-scan sonar sample dataset augmented by the proposed network. This method effectively retains the style of the images while achieving diversity augmentation of small-sample underwater target images, providing a new approach to improving the construction of underwater target detection models.

Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and Molecular Subtype.

Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.

Classification of hazelnut varieties based on bigtransfer deep learning model

Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 × 1, BiT-M R101 × 3 and BiT-M R152 × 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 × 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country.

Research on the application of deep learning algorithms to PCB defect detection

In today's electronics industry, Printed Circuit Boards play a crucial role in providing the layout for circuit components and conductive traces in nearly all electronic devices. The quality of components soldered onto Printed Circuit Boards directly impacts product performance. To ensure the performance of electronic devices, Printed Circuit Boards defect detection based on deep learning algorithms has become a pivotal technology in the defect inspection process within the electronics industry. However, the application of deep learning algorithms in this context faces several challenges. These challenges include difficulties in acquiring Printed Circuit Boards defect datasets, limited generalization capability in Printed Circuit Boards defect detection, and slow and low-quality Printed Circuit Boards image stitching processes. To enhance researchers' understanding of deep learning-based Printed Circuit Boards defect detection, this paper analyzes the challenges associated with deep learning in the Printed Circuit Boards defect detection process and proposes several viable solutions. In conclusion, this paper provides insights into the future of deep learning-based Printed Circuit Boards defect detection.

[Multi-modal physiological time-frequency feature extraction network for accurate sleep stage classification].

Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen's Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.

Deep Learning Enhanced Multisensor Data Fusion for Building Assessment Using Multispectral Voxels and Self-Organizing Maps

Efforts in the domain of building studies involve the use of a diverse array of geomatic sensors, some providing invaluable information in the form of three-dimensional point clouds and associated registered properties. However, managing the vast amounts of data generated by these sensors presents significant challenges. To ensure the effective use of multisensor data in the context of cultural heritage preservation, it is imperative that multisensor data fusion methods be designed in such a way as to facilitate informed decision-making by curators and stakeholders. We propose a novel approach to multisensor data fusion using multispectral voxels, which enable the application of deep learning algorithms as the self-organizing maps to identify and exploit the relationships between the different sensor data. Our results indicate that this approach provides a comprehensive view of the building structure and its potential pathologies, and holds great promise for revolutionizing the study of historical buildings and their potential applications in the field of cultural heritage preservation.

Survey on Utilizing Deep Learning Algorithms to Improve Face Detection in Videos with Challenging Conditions

Face detection in videos is a fundamental task with significant practical applications in various domains, including security, surveillance, and human-computer interaction. However, it becomes increasingly challenging when dealing with videos that contain various adverse conditions, such as low lighting, occlusions, pose variations, and scale changes. Traditional face detection methods often struggle to provide accurate and reliable results under these challenging conditions. This research explores the application of deep learning algorithms to enhance the performance of face detection in videos under challenging conditions. To validate the effectivenessof our approach, we conduct extensive experiments on benchmark datasets with various challenging conditions. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques in terms of accuracy, robustness, and computational efficiency. Additionally, we provide an in-depth analysis of the model’s performance under different challenging scenarios, highlighting its ability to handle occlusions, pose variations, and low- resolution frames effectively. Keywords: Face Detection , Deep Learning , Video Analysis, Challenging Conditions , Low lighting , Occlusion .

Deep Multi-task Learning for Animal Chest Circumference Estimation from Monocular Images

AbstractThe applications of deep learning algorithms with images to various scenarios have attracted significant research attention. However, application scenarios in animal breeding managements are still limited. In this paper we propose a new deep learning framework to estimate the chest circumference of domestic animals from images. This parameter is a key metric for breeding and monitoring the quality of animal in animal husbandry. We design a set of feature extraction methods based on a multi-task learning framework to address the challenging issues in the main estimation task. The multiple tasks in our proposed framework include object segmentation, keypoint estimation, and depth estimation of cow from monocular images. The domain-specific features extracted from these tasks improve upon our main estimation task. In addition, we also attempt to reduce unnecessary computations during the framework design to reduce the cost of subsequent practical implementation of the developed system. Our proposed framework is tested on our own collected dataset to evaluate its performance.

Enhancing Electrical Power Demand Prediction Using LSTM-Based Deep Learning Models for Local Energy Communities

The pursuit of accurate electrical power demand forecasting has led to the application of deep learning algorithms, notably demonstrating promising outcomes despite the prerequisite of substantial training data. This study pioneers a new learning paradigm, employing sophisticated deep learning models specifically, Long Short-Term Memory networks and recurrent neural networks (RNNs). Leveraging historical load data, temperature, wind speed, and day-ahead predicted spot prices, this approach follows a structured flow involving data preprocessing, sequence generation, model training, and subsequent prediction of future load demand using LSTM-based RNN variants. The study’s paramount findings underscore the substantial advancement achieved by this proposed methodology over prevailing techniques. The method significantly improves prediction accuracy by over 11%, demonstrating the efficacy of deep learning models and a significant leap forward in forecasting precision. Beyond its superior predictive capabilities, this novel strategy serves as a catalyst for enhancing energy distribution management in local energy communities. Its effectiveness lies not only in its precision but also in enabling the optimization and cost-effective control of energy distribution, vital for sustainable energy management in these communities. Ultimately, this pioneering approach presents a robust solution poised to revolutionize the landscape of electrical power demand forecasting and its practical application in local energy systems.

Revolutionizing Mushroom Identification: Improving efficiency with ultrasound-assisted frozen sample analysis and deep learning techniques

The aim of this research is to explore the potential applications of deep learning algorithms in categorizing ultrasound-assisted frozen mushrooms based on their quality attributes. To achieve this, an object detection model was trained on a dataset of images grouped by control (mushrooms frozen by immersion method without ultrasonication), pulse (mushrooms frozen by immersion method in combination with pulsed mode ultrasonication), continues (mushrooms frozen by immersion method in combination with continuous mode ultrasonication), and raw data (mushrooms before freezing. The accuracy of the developed model was 100 % for continues and control groups, while the model achieved accuracies of 92 % and 95 % for raw and pulse ones, respectively. Specifically, the model achieved a total accuracy rate of 96 % for identifying mushrooms. The model's excellent performance in accurately classifying images shows its ability to recognize subtle differences in image quality and clarity. The findings could provide valuable insights into using deep learning algorithms for analyzing mushroom quality data and developing intelligent mushroom processing systems.

N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms

N2 injection to enhance coal seam gas drainage is an effective technology for coalbed methane recovery. Nevertheless, there is a limited body of research addressing the application of ECBM technology for gas management in low permeability coal seams within underground coal mines, and the effect is uncertain. Additionally, numerical simulators have been challenged to accurately predict methane emissions during gas injection and replacement. In this study, firstly, an underground field trials of N2 injection replacement in a coal mine with low-permeability coal seams in China were conducted, and a comparative analysis of “N2 injection to enhance gas drainage” and “conventional drainage” technique was performed. The result show that injecting pressurized N2 into a low-permeability coal seam could increase the pressure gradient to promote the directional flow of free gas from the coal seam cracks to the gas-production boreholes. After N2 injection, the mixed flow rate and CH4 flow rate significantly increased. Additionally, the gas content in the coal seam in the experimental area significantly decreased. Secondly, the lag-effect was discussed. Upon discontinuation of N2 injection, the gas-producing mixed flow initially increased slightly, then gradually decreased, and eventually settled at a high level. Despite the attenuation of N2 injection volume, the CH4 flow rate continued to increase during intermittent injection and remained high after gas injection discontinuation. Finally, three deep learning algorithms (BP, LSTM, and TCN) were employed to predict the dynamic change of gas drainage in coal seams displaced by N2 injection, based on the results of the field test. The results indicated that deep learning algorithms exhibited superior prediction performance. The TCN model demonstrated the lowest approximation error rate, displaying optimal performance in predicting the CH4 flow rate during nitrogen injection for enhanced gas drainage. Additionally, it exhibited good applicability to other similar field test results.

Deep Learning Based Recognition of Lepidoptera Insects

Abstract The successful application of cutting-edge computer vision technology to automatic insect classification has long been a focus of research in insect taxonomy. The results of this research have a wide range of applications in areas such as environmental monitoring, pest diagnosis and epidemiology. However, there is still a gap between the current techniques used in automatic insect classification and the latest computer vision techniques. The research in this paper is conducted on Lepidoptera, a class of insects that are widely infested, including butterflies and moths. The study focuses on the application of deep learning algorithms in image processing of Lepidoptera insects. In order to improve the recognition rate for Lepidoptera insect recognition, this paper uses a detection model based on deep neural networks to realize the recognition of Lepidoptera insects in complex environments. Specifically, the yolov7 algorithm is adopted as the basic model for this experiment, and the reasons for using this model are explained in terms of the splicing of network modules, loss function, positive sample allocation strategy, and the merging of convolution and normalization, respectively. Through experiments, it is proved that the algorithm can effectively improve the gesture recognition rate, the recognition accuracy reaches 79.5%, and the recognition speed is as high as 33.08it/s.

Unveiling the Predictive Power: Comparative Analysis of Cutting-Edge Deep Learning Models for Stock Price Forecasting

Accurate stock price prediction plays a fundamental role in informing government financial regulations and facilitating effective arbitrage strategies for investors. With the application of deep learning algorithms in finance, significant progress has been made to improve the accuracy of stock price prediction. In this paper, first, we collected stock price data from four listed companies from different sectors. Then, we used four competitive methods for prediction, namely LSTM, GRU-LSTM, Attention-LSTM and Transformer-LSTM. The validity of the study is supported by multiple sets of comparative experiments. Our experimental results show that LSTM shows superiority in predicting stock prices, while Transformer-LSTM model has better generalization ability.

A Review of Research on Spectrum Sensing Based on Deep Learning

In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions.

[Review on ultrasonographic diagnosis of thyroid diseases based on deep learning].

In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.