Neural Network Structure Research Articles

A study on hybrid-architecture deep learning model for predicting pressure distribution in 2D airfoils

This study introduces a novel deep learning-based technique for predicting pressure distribution images, aimed at application in image-based approximate optimal design. The proposed approach integrates both unsupervised and supervised learning paradigms, employing autoencoders (AE) for the unsupervised component and fully connected neural networks (FNN) for the supervised component. A surrogate model based on 2D image data was developed, enabling a comparative analysis of three distinct methods: the conventional AE, the convolutional autoencoder (CAE), and a hybrid CAE, which combines the CAE with a conventional AE. Extensive experiments demonstrated that the CAE method achieved the highest learning capability and restoration rate for pressure distribution images of 2D airfoils. The compressed latent image data were utilized as inputs for the FNN, which was trained to predict latent features. These features were decoded to forecast the corresponding pressure distribution images. The results showed excellent concordance with those derived from computational fluid dynamics (CFD) simulations, achieving a match rate exceeding 99.99%. This methodology significantly simplifies and accelerates image prediction, rendering it feasible without requiring specialized CFD knowledge. Moreover, it enhances accuracy while streamlining the neural network structure. Consequently, it provides foundational technology for image data-based optimization, establishing a platform for future AI-driven design and optimization advancements.

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties.

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments. The model demonstrates superior performance across various prediction tasks of materials' properties, achieving particularly notable results in properties sensitive to many-body interactions. For band gap prediction, EOSnet achieves a mean absolute error of 0.163 eV, surpassing previous state-of-the-art models. The model also excels in predicting mechanical properties and classifying materials, with 97.7% accuracy in metal/nonmetal classification. These results demonstrate that embedding GOM fingerprints into node features enhances the ability of GNNs to capture complex atomic interactions, making EOSnet a powerful tool for materials' discovery and property prediction.

Simulation Research on Data Acquisition and Supervisory Control System of Distribution Network Based on Vector Regression Model

With the gradual maturity of deep learning and the Internet of Things, a smart grid is urgently needed to be established. For the intellectualization of the power grid, non-intrusive load monitoring (NILM) technology is the key step of intellectualization. The traditional load monitoring method is an invasive scheme, in which monitoring sensors are usually installed at the output end of each grid load. This method requires a lot of manpower and material resources in terms of equipment installation and maintenance, which is difficult to sustain. Therefore, monitoring electrical appliances are installed at the entrance of the distribution network to decompose the classification and operation status of the individual electrical appliances in the grid. Aiming at the problem of long-term multi-state electrical apparatus monitoring, this paper mainly optimizes the two optimal deep learning models Seq2Seq and WindowGRU based on activation function optimization, regular function optimization and neural network structure optimization. PReLU, Leaky-ReLU and ELU are used and tested. For the optimization based on regular functions, the value of F1-score is 0.9700, which is 0.1534 more than the optimization algorithm Seq2SeqLR in this paper. Meanwhile, it can also reach 0.8100 for other devices, which is 0.2373 more than the nilmTCN designed in this paper. The Recall value of the identification is improved to 0.6673, which is significantly higher than that of other models. It is proved that the research contents of this paper can improve the effective analysis of load energy consumption data, and can minimize unnecessary energy consumption to achieve the purpose of saving electricity.

Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework

This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model’s prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field.

Series-connected data-based model to estimate effluent chemical oxygen demand in industrial wastewater treatment process.

Continuous monitoring of chemical oxygen demand (COD) is essential to ensure efficient and sustainable wastewater treatment and regulatory compliance. However, traditional hardware measurements are laborious, infrequent and costly. In this research, a cost-effective real-time alternative is presented. ARMAX, ARX, Neural Network and PLSR model structures were identified and tested for finding data-based model for real-time estimation of effluent COD in a full-scale industrial WWTP. To aim for estimating effluent COD without physically measuring it, a novel chain of two estimators was created by connecting in series the identified influent and effluent COD models. A comprehensive and systematic model identification was carried out to find the model inputs, delays, parameters and training windows using an exhaustive search algorithm. The results showed that using solely linear model structure it is possible to identify sufficiently accurate (R: 0.67, MAPE: 7.33%) and practical (interpretable and implementable) data-based estimation model which has predictive ability even up to 20h horizon. As the series-connected model structure reaches the required margin of error it has potential for real-world industrial usage alongside or even replacing the hardware online sensor. Estimation model enhances resiliency and provides real-time insights into effluent quality in varying operating conditions and during unexpected disturbances.

Temporal logic inference for interpretable fault diagnosis of bearings via sparse and structured neural attention.

This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation. STLN retains the advantageous properties of traditional neural networks while facilitating formal interpretation through temporal logic descriptions. Empirical validation on experimental datasets shows that STLN not only performs robustly in fault diagnosis tasks, but also provides interpretable explanations of the decision-making process, thus enabling interpretable fault diagnosis.

Learning‐Based Progression Detection of Alzheimer’s Disease Using 3D MRI Images

Alzheimer’s disease (AD) is an irreversible brain disease. In addition to the functional deterioration of memory and cognition, patients with severe conditions lose their self‐care ability. Patients exhibiting symptoms are often attributed to aging and thus lack proper medical care. If it can be diagnosed early, the doctor can provide adequate treatments to mitigate the symptoms. Magnetic resonance imaging (MRI) can reflect the characteristics of different human tissues and organs, and is a common tool implemented in clinical examinations. In this study, we tested learning‐based approaches to detect disease progression in AD patients using MRI. Specifically, each patient is categorized as one of the following four classes: cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and AD. To extract 3D information in MRI, we proposed a 3D convolutional neural network structure based on ResNet3D‐18. We designed various multiclass classification frameworks. Moreover, we implemented ensemble classification combining these frameworks. Experiments demonstrated great potential for learning‐based approaches on the Alzheimer’s Disease Neuroimaging Initiative dataset. The ensemble approach performed the best with an accuracy of 0.950, which is competitive with neurologists in diagnosing AD progression in clinical practice. With precise detection, patients can understand their conditions early and seek proper treatments.

APPLICATION ARTIFICIAL NETWORK FROM DATABASE PREDICTED NORMALIZED TEMPERATURE AND HUMIDITY IN ROOM

The Artificial neural network is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. A neural network is a machine that is designed to model the way in which the brain performs a particular task or function of interest. Basic building block of every artificial neural network is neuron. Such a model has three simple sets of multiplication, summation and activation. The purpose of this network is to examine neural network and their emerging applications in the field of engineering focusing on control. The network is implemented by using electronic components and is simulated in software on a digital computer. In this work examined the application of neural network for predicted normalized temperature and humidity in room and the learning process. A neural network derives its computing through its massively parallel distributed structure and its ability to learn and generalize. Generalization refers to the neural network’s production of reasonable outputs for inputs not encountered during training or learning. The function of which is to modify the synaptic weights of the network in an orderly fashion to attain a desired design objective. The needs for neural networks, training of neural networks and important algorithms have been discussed. Artificial Neuron is sum function that sums all weighted inputs and bias. At the exit of artificial neuron the sum of previously weighted inputs and bias is passing trough activation function that is also called transfer function. It concluded by identifying limitations, recent advances and promising future research directions.

KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT Scans

This study presents KidneyNet, an innovative computer-aided diagnosis (CAD) system designed to identify chronic kidney diseases (CKDs), such as kidney stones, cysts, and tumors, in CT scans. KidneyNet utilizes a convolutional neural network (CNN) structure consisting of eight convolutional layers, three pooling layers, a flattening layer, and two fully connected layers. Small filters enhance computational efficiency by reducing the number of parameters and minimizing the risk of overfitting compared to larger filters. The model captures more complex and abstract features as data move through the layers. The initial layers identify basic patterns, while the deeper layers focus on more intricate representations. KidneyNet aims to enhance the efficiency and accuracy of kidney disease diagnosis. Additionally, the model incorporates the gradient-weighted class activation mapping (Grad-CAM) algorithm, which helps to pinpoint affected areas in the scans. This feature improves interpretability, allowing clinicians to identify which regions the model deemed significant for detecting abnormalities such as tumors, cysts, or stones. Through extensive testing on a CT kidney dataset, KidneyNet demonstrated impressive performance metrics, with 99.88% accuracy, 99.92% specificity, 99.76% sensitivity, 99.58% precision, and an F1 score of 99.67%, outperforming existing models. This approach alleviates the diagnostic burden on radiologists and promotes early detection, potentially saving lives. This study highlights the critical role of advanced imaging analysis in addressing kidney conditions and emphasizes KidneyNet’s capability to deliver precise and cost-effective diagnoses.

Based on the N2N-SAMP for sparse underwater acoustic channel estimation

IntroductionOrthogonal Frequency Division Multiplexing (OFDM) is widely recognized for its high efficiency in modulation techniques and has been extensively applied in underwater acoustic communication. However, the unique sparsity and noise interference characteristics of the underwater channel pose significant challenges to the performance of traditional channel estimation methods.MethodsTo address these challenges, we propose a sparse underwater channel estimation method that combines the Noise2Noise (N2N) algorithm with the Sparsity Adaptive Matching Pursuit (SAMP) algorithm. This novel approach integrates the N2N technique from image denoising theory with the SAMP algorithm, utilizing a constant iteration termination threshold that does not require prior information. The method leverages the U-net neural network structure to denoise noisy pilot signals, thereby restoring channel sparsity and enhancing the accuracy of channel estimation.ResultsSimulation results indicate that our proposed method demonstrates commendable channel estimation performance across various signal-to-noise ratio (SNR) conditions. Notably, in low SNR environments, the N2N-SAMP algorithm significantly outperforms the traditional SAMP algorithm in terms of Mean Squared Error (MSE) and Bit Error Rate (BER). Specifically, at SNR levels of 0 dB, 10 dB, and 20 dB, the MSE of channel estimation is reduced by 58.95%, 76.08%, and 19.42%, respectively, compared to the SAMP algorithm that selects the optimal threshold based on noise power. Furthermore, the system’s BER is decreased by 12.35%, 26.41%, and 29.62%, respectively.DiscussionThe findings suggest that the integration of N2N and SAMP algorithms offers a promising solution for improving channel estimation in underwater communication channels, especially under low SNR conditions. The significant reduction in MSE and BER highlights the effectiveness of our proposed method in enhancing the reliability and accuracy of underwater communication systems.

Interaction of Asymmetric Adaptive Network Structures and Parameter Balance in Image Feature Extraction and Recognition

To better process irregular sample images for their image feature extraction and recognition, this essay proposes asymmetric adaptive neural network (AACNN) structures, including dual structures of an adaptive image feature extraction network (AT-CNN) and adaptive image recognition network (AT-ACNN). They both comprise an Adaptive Transform (AT) module and a deep learning network, but the ACNN comprises pixel-adaptive convolutional (PAC) kernels that CNN does not have, reflecting the asymmetry of these network structures. Structural analysis and comparative testing experiments indicated that the proposed method is more appropriate and effective for dealing with irregular sample images with different sizes and views, mainly focusing on their feature extraction accuracy and image recognition efficiency. The proposed method constructs the interaction between asymmetric dual network structures, essential in improving model performance and efficiency. It specifically manifests that the PAC kernels in an ACNN resolves the problem of content-agnostic convolution in image recognition by learning image features from a pre-trained CNN. On the other hand, it improves image recognition efficiency by using feature maps extracted from the pre-trained CNN to train the classifiers in the ACNN. We also found that parameter balance was essential in adaptive neural network structure for better performance in further testing experiments. When setting the Dropout layer parameter at 0.5 and the iteration number was 32, the proposed model achieved adequate recognition accuracy and efficiency. Smaller parameters affect model performance, but more extensive parameters significantly increase computational burden and loss. Comparative testing experiments fully validated its superiority compared with traditional methods based on CNNs. Using traditional carving patterns from Anhui Province as an example, we conducted image recognition and feature graphic application under ideal parameter balance conditions and thereby demonstrated the practicality and value of the proposed method.

Deep reinforcement learning for collision avoidance of autonomous ships in inland rivers

Due to the characteristics of large inertia, large time delay and non-linearity in ship motion, the manoeuvrability of ships is an important issue related to the safety of ship navigation. The manipulation of ships significantly affects the safety of ship navigation, and its importance will gradually increase with the development of autonomous ships (ASs). Due to the complexity of inland waterways and the density of ships, collisions are common. In this work, an autonomous learning framework with deep reinforcement learning (DRL) was constructed for ASs. The state space, action space, reward function and neural network structure of DRL were designed based on the AS's manoeuvring characteristics and control requirements. A deep deterministic policy gradient (DDPG) algorithm was then used to implement the controller. Finally, some representative route segments of an inland waterway were selected for simulation research based on a virtual simulation environment. The designed DRL controller was able to quickly converge from the training and learning process to meet the control requirements. The effectiveness of the DDPG algorithm was verified by comparisons with the experimental results. This study provides a reference for future research on collision-avoidance technology for ASs in inland rivers.

ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs

In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is introduced to convert the compound uncertain element, comprising uncertain dynamics and time-varying ocean disturbances, into a linear parametric form with just one unknown parameter. Then, to improve the tracking performance of the UUVs trajectory tracking closed-loop control system, an actor-critic neural network structure based on ADHDP technology is introduced to adaptively adjust the weights of the action-critic network, optimizing the performance index function. Finally, an ADHDP-based robust self-learning control scheme is constructed, which makes the UUVs closed-loop system have good robustness and control performance. The theoretical analysis demonstrates that all signals in the UUVs trajectory tracking closed-loop control system are bounded. The simulation results for the UUVs validate the effectiveness of the proposed control scheme.

Identifying neural network structures explained by personality traits: combining unsupervised and supervised machine learning techniques in translational validity assessment

AbstractThere have been studies previously the neurobiological underpinnings of personality traits in various paradigms such as psychobiological theory and Eysenck’s model as well as five-factor model. However, there are limited results in terms of co-clustering of the functional connectivity as measured by functional MRI, and personality profiles. In the present study, we have analyzed resting-state connectivity networks and character type with the Lowen bioenergetic test in 66 healthy subjects. There have been identified direct correspondences between network metrics such as eigenvector centrality (EC), clustering coefficient (CC), node strength (NS) and specific personality characteristics. Specifically, N Acc L and OFCmed were associated with oral and masochistic traits in terms of EC and CC, while Insula R is associated with oral traits in terms of NS and EC. It is noteworthy that we observed significant correlations between individual items and node measures in specific regions, suggesting a more targeted relationship. However, the more relevant finding is the correlation between metrics (NS, CC, and EC) and overall traits. A hierarchical clustering algorithm (agglomerative clustering, an unsupervised machine learning technique) and principal component analysis were applied, where we identified three prominent principal components that cumulatively explain 76% of the psychometric data. Furthermore, we managed to cluster the network metrics (by unsupervised clustering) to explore whether neural connectivity patterns could be grouped based on combined average network metrics and psychometric data (global and local efficiencies, node strength, eigenvector centrality, and node strength). We identified three principal components, where the cumulative amount of explained data reaches 99%. The correspondence between network measures (CC and NS) and predictors (responses to Lowen’s items) is 62% predicted with a precision of 90%.

Harnessing Defects in SnSe Film via Photo-Induced Doping for Fully Light-Controlled Artificial Synapse.

2D-layered materials are recognized as up-and-coming candidates to overcome the intrinsic physical limitation of silicon-based devices. Herein, the coexistence of positive persistent photoconductivity (PPPC) and negative persistent photoconductivity (NPPC) in SnSe thin films prepared by pulsed laser deposition provides an excellent avenue for engineering novel devices. It is determined that surface oxygen is co-regulated by physisorption and chemisorption, and the NPPC is attributed to the photo-controllable oxygen desorption behavior. The dominant behavior of chemisorption induces high stability, while physisorption provides room for adjusting NPPC. A simple fully light-modulated artificial synaptic device based on SnSe film is constructed to operate various synaptic plasticity and reversible modulation of conductance by applying 430 and 255nm illuminations. A three-layer artificial neural network structure with a high accuracy of 95.33% to recognize handwritten digital images is implemented based on the device. Furthermore, the pressure-related cognition response of humans while climbing and the foraging and recognition behaviors of anemonefish are mimicked. This work demonstrates the potential of 2D-layered materials for developing neuromorphic computing and simulating biological behaviors without additional treatment. Furthermore, the one-step method for preparation is highly adaptable and expected to realize large-area growth and integration of SnSe-based devices.

Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor.

Three-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation, and noise. These factors make it challenging to apply existing point cloud classification algorithms in real industrial scenarios. Currently, most studies enhance model robustness from the perspective of neural network structure. However, researchers have found that simply adjusting the neural network structure has proven insufficient in addressing the decline in accuracy caused by data corruption. In this article, we use local feature descriptors as a preprocessing method to extract features from point cloud data and propose a new neural network architecture aligned with these local features, effectively enhancing performance even in extreme cases of data corruption. In addition, we conducted data augmentation to the 10 intentionally selected categories in ModelNet40. Finally, we conducted multiple experiments, including testing the robustness of the model to occlusion and coordinate transformation and then comparing the model with existing SOTA models. Furthermore, in actual scene experiments, we used depth cameras to capture objects and input the obtained data into the established model. The experimental results show that our model outperforms existing popular algorithms when dealing with corrupted point cloud data. Even when the input point cloud data are affected by occlusion or coordinate transformation, our proposed model can maintain high accuracy. This suggests that our method can alleviate the problem of decreased model accuracy caused by the aforementioned factors.

Partially precise instrument measurements-aided deep learning for industrial quality prediction

Material composition is a kind of important quality index in the process industry. Even though instruments for online measuring these compositions have been widely applied, the precision of material composition measurements is suspicious due to corrosion, scaling and other factors. Laboratory values are more convinced, while these instruments are largely idle in real applications. Nevertheless, despite suspicious precision, partially precise trends exist in these measurements, which are also useful for indicating the variation in quality. This means that a wealth of information directly related to quality variables can provide positive guidance for quality prediction. Enlightened by the requirement of information utilization, a long short-term memory network with embedded trend consistency criteria (TCC-LSTM) is proposed for industrial quality prediction through extremely efficient utilization of partially precise quality instrument data. Specifically, based on the property that the trends of the measured values for quality variable are similar to that of the corresponding laboratory values over time, six trend consistency criteria are designed to evaluate the reliability of instrument data, so as to determine the contribution weights of these data in deep learning-based quality prediction. Moreover, in the neural network structure, the space-wise and time-wise attention mechanisms are designed for capturing important variables and time information. Extensive experiments on an actual alumina digestion process demonstrate the efficiency of TCC-LSTM, whose correlation coefficient is averagely improved by 0.2247 and mean absolute error is as low as 0.008079.

Application Research of 3D Virtual Interactive Technology in Interactive Teaching of Arts and Crafts

In the virtual reality teaching of arts and crafts, the uploaded image data often contains a lot of noise, which significantly reduces the quality of uploaded images and seriously affects the teaching effect of interactive teaching of arts and crafts based on virtual reality. For this reason, the research proposes an image denoising model based on convolutional neural network and wavelet transform, which adopts residual neural network structure and batch normalization algorithm, aiming at gradient explosion and gradient disappearance that may be caused by convolutional neural network, And the problem of low training efficiency has been optimized. The results show that when the noise standard deviation is 50, model 1 can achieve the target accuracy with only 82 iterations, while model 2 requires as many as 56 iterations. For images of different types and noise intensities, the average PSNR values of model 1 are 29.3dB, 30.5dB and 28.9dB, respectively. In addition, by applying Model 1 to VR teaching of arts and crafts, the average score of class 1 is 9.3 points higher than that of class 2. The proposed model effectively optimizes the virtual reality teaching process of arts and crafts, and provides a useful contribution to the application of virtual reality in the field of education.

Data Driven Approach for Predicting Pore Pressure of Oil and Gas Wells, Case Study of Iraq Southern Oilfields

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables are vertical depth, bulk density, and acoustic compressional wave velocity, with the activation function of tangent sigmoid. The average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient (R2) were applied for evaluation. The results revealed that the best artificial neural network structure was (3-8-1), with average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient R2 of -0.52, 1.01, 3994, 63.2, and 0.995, respectively. A C++ computer program is provided with a calculation sample to simplify the implementation of the proposed artificial neural network. The dependency degree of pore pressure on each input parameter is investigated, revealing the highest impact of depth on pore pressure prediction. Furthermore, to check the validity of the artificial neural network against the different datasets, the artificial neural network performance was compared with 84 new data points and showed an advantage over the existing models. The very good performance of artificial neural network for different types of oil reservoirs and formations reveals an insignificant effect of lithology on the prediction of pore pressure.

Deep learning in fluorescence imaging and analysis

AbstractFluorescence imaging (FI) has been instrumental in advancing biological research and enhancing biomedical diagnostics. Despite its widespread applications, FI faces challenges such as efficiently acquiring high signal‐to‐noise ratio (SNR) images, improving spatiotemporal resolution, and conducting precise quantitative analysis. Deep learning (DL), which emulates the neural network structure of the human brain, excels at learning from complex data patterns, extracting subtle features, and enhancing the SNR and spatiotemporal resolution of fluorescence images. These advancements significantly elevate the quality and usability of imaging data. Additionally, DL technology is adept at handling large datasets efficiently, which is crucial for improving the accuracy and efficiency of image analysis. This article reviews the latest advances in the application of DL to FI methodologies and their subsequent impact on biology and biomedicine. It also explores the future possibilities for DL in FI research, and providing insights and prospects could shape the field's trajectory.