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As a fundamental and profound task in remote sensing, change detection from very-high-resolution (VHR) images plays a vital role in a wide range of applications and attracts considerable attention. Current methods generally focus on the research of simultaneously modeling and discriminating the changed and unchanged features. In practice, for bi-temporal VHR optical remote sensing images, the temporal spectral variability tends to exist in all bands throughout the entire paired images, making it difficult to distinguish none-changes and changes with a single model. In this paper, motivated by this observation, we propose a novel hybrid end-to-end framework named dual learning-based Siamese framework (DLSF) for change detection. The framework comprises two parallel streams which are dual learning-based domain transfer and Siamese-based change decision. The former stream is aimed at reducing the domain differences of two paired images and retaining the intrinsic information by translating them into each other’s domain. While the latter stream is aimed at learning a decision strategy to decide the changes in two domains, respectively. By training our proposed framework with certain change map references, this method learns a cross-domain translation in order to suppress the differences of unchanged regions and highlight the differences of changed regions in two domains, respectively, then focus on the detection of changed regions. To the best of our knowledge, the idea of incorporating dual learning framework and Siamese network for change detection is novel. The experimental results on two datasets and the comparison with other state-of-the-art methods verify the efficiency and superiority of our proposed DLSF.

This research paper delves into contemporary methodologies for eradicating clouds and nebulae from space images utilizing advanced deep learning technologies such as conditional generative adversarial networks (conditional GAN), cyclic generative adversarial networks (CycleGAN), and space-attention generative adversarial networks (space-attention GAN). Cloud cover presents a significant obstacle in remote sensing, impeding accurate data analysis across various domains including environmental monitoring and natural resource management. The proposed techniques offer novel solutions by leveraging spatial attention mechanisms to identify and subsequently eliminate clouds from images, thus uncovering previously concealed information and enhancing the quality of space data. The study emphasizes the necessity for further research aimed at refining cloud removal algorithms to accommodate diverse detection conditions and enhancing the overall efficiency of deep learning in satellite image processing. By highlighting potential benefits and advocating for ongoing exploration, the paper underscores the importance of advancing cloud removal techniques to improve data quality and unlock new applications in Earth remote sensing. In conclusion, the proposed approaches hold promise in addressing the persistent challenge of cloud cover in space imagery, paving the way for more accurate data analysis and future advancements in remote sensing technologies.

The aging of society is a global concern nowadays. Falls and fall-related injuries can influence the elderly’s daily living, including physical damage, psychological effects, and financial problems. A reliable fall detection system can trigger an alert immediately when a fall event happens to reduce the adverse effects of falls. Notably, the wrist-based fall detection system provides the most acceptable placement for the elderly; however, the performance is the worst due to the complicated hand movement modeling. Many works recently implemented deep learning technology on wrist-based fall detection systems to address the worst, but class imbalance and data scarcity issues occur. In this study, we analyze different data augmentation methodologies to enhance the performance of wrist-based fall detection systems using deep learning technology. Based on the results, the conditional diffusion model is an ideal data augmentation approach, which improves the F1 score by 6.58% when trained with only 25% of the actual data, and the synthetic data maintains a high quality.

With the development of the new energy industry, a large number of silicon wafers need to be tested for production quality through the automation industry. The development of deep learning technology has brought huge technological improvements to the industrial quality inspection industry. Through the image segmentation technology based on deep learning, it can accurately divide the defects existing in the silicon wafer. In this paper, the UNet deep learning network is used to segment the hidden cracks in the silicon wafer. The network can extract the shallow semantic features in the silicon wafer well. It uses 5,000 samples collected on the industrial site as the training set,1,000 pieces the sample is used as the test set, and the segmentation accuracy IOU can reach 58.7%.

As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived walkability in detail and analyze the differences to prepare alternatives for improving the neighborhood’s walking environment. The study area is Jeonju City, one of the medium-sized cities in Korea. For the evaluation of perceived walkability, 196,624 street view images were crawled and 127,317 pairs of training datasets were constructed. After developing a convolutional neural network model, the scores of perceived walkability are predicted. For the evaluation of physical walkability, eight indicators are selected, and the score of overall physical walkability is calculated by combining the scores of the eight indicators. After that, the scores of perceived and physical walkability are visualized, and the difference between them is analyzed. This study is novel in three aspects. First, we develop a deep learning model that can improve the accuracy of perceived walkability using street view images, even in small and medium-sized cities. Second, in analyzing the characteristics of street view images, the possibilities and limitations of the semantic segmentation technique are confirmed. Third, the differences between perceived and physical walkability are analyzed in detail, and how the results of our study can be used to prepare alternatives for improving the walking environment is presented.

In order to improve the accuracy and efficiency of sports training data analysis, this paper proposes an optimized analysis model by combining Iterative Dichotomiser 3 (ID3) decision tree algorithm and deep learning model. As an important scientific tool, sports training data analysis aims to provide decision support for athletes and coaches, optimize training programs and improve sports performance through accurate data mining and model prediction. Traditional analysis methods have shortcomings in dealing with complex and multidimensional data, while analysis methods based on artificial intelligence can significantly improve the ability of feature extraction and prediction. Based on this background, this paper comprehensively evaluates the performance of each model in different dimensions by comparing six key indicators: mean square error (MSE), mean absolute error (MAE), information gain, feature importance, sports performance improvement rate and training target achievement rate. The experimental results show that the optimized model has the best MSE, and its MSE is only 1.05 under the information gain. It is significantly better than Extreme Gradient Boosting (XGBoost) of 1.48 and Capsule Networks (CapsNets) of 1.25. In terms of MAE, the minimum error of the optimized model is 0.65, while the maximum error of XGBoost is 1.11. In terms of information gain and feature importance, the optimization model is also outstanding, with the highest information gain of 1.02 and the feature importance maintained at a high level of 0.94 in many dimensions. Meanwhile, the optimized model is superior to other models in sports performance improvement rate (up to 6.71) and training target achievement rate (up to 78.32%). Therefore, this paper has certain reference significance to the field of sports training data analysis.

The main clinical manifestations of stroke are motor, language, sensory, and mental disorders. After treatment, in addition to being conscious, other symptoms will still remain in varying degrees. This is the sequelae of stroke, including numbness, facial paralysis, central paralysis, and central paralysis. If the sequelae of stroke are not treated effectively, they can easily develop into permanent sequelae. Most of the affected people have sequelae, and most of them have symptoms of upper limb paralysis. Therefore, it is of great significance to study how to carry out effective rehabilitation training for stroke patients to reduce the disease and even restore their motor function. Based on this background, this research aims to use deep learning technology to design a stroke rehabilitation model based on electroencephalography (EEG) signals. First, the patient’s EEG signal will be preprocessed. Then, an improved deep neural network model (IDNN) is used to get the EEG classification results. The traditional DNN model construction process is simple and suitable for scenarios where there is no special requirement for the data format, but the generalization of a single DNN model is usually poor. Large margin support vector machine (LM_SVM) is an extension method of support vector machine (SVM), suitable for any occasion. By optimizing the edge distribution, better generalization performance can be obtained. Taking into account the advantages of DNN and LM_SVM and the high aliasing characteristics of stroke data, an improved DNN model is proposed. Finally, based on the EEG recognition result of the model, the rehabilitation equipment is controlled to assist the patient in rehabilitation treatment. The experimental results verify the superiority of the EEG classification model used, and further prove that this research has good practical value.

In the current machine learning methods, deep learning is the focus of attention. Deep learning technology has achieved rapid development in various related fields, especially in the field of face recognition applications. Deep learning is to imitate the mechanism of the human neural perception system by layer-by-layer autonomous learning to obtain high-level abstract features, which can solve the distribution of facial changes, with fast learning speed and high recognition accuracy. This article introduces the advantages and core technologies of face deep learning, and studies and analyzes the application of face deep learning technology in university management.

Polyphonic choral singing can not only cultivate musical imagination and improve musical literacy but also allow singers to feel the harmonious and beautiful musical rhythm during polyphonic choral singing. To improve the accuracy of polyphonic singing, the study designed a music source separation structure based on recurrent neural networks using deep learning technology. And, combined with ResNet and CBAM, a joint neural network based on Res-CBAM was designed for optimization. After that, the main melody of the human voice was extracted using the polyphonic music melody extraction algorithm that was created in this paper. The listening training was then done in three areas: pitch, rhythmic rhythm, and vocal balance. The trained community choir members showed significant improvements in singing ability, breath control, pitch, rhythm, polyphonic choral ability, and expressiveness (p<0.05). It indicates that the auditory discrimination training based on the polyphonic music melody extraction algorithm has a facilitative effect on the accuracy of polyphonic singing in the choir.

This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'Article HighlightsArtificial intelligence and deep learning are changing drug discovery, making target identification quicker and more effective. For instance, these technologies can analyze vast amounts of biological data to identify potential drug targets in a fraction of the time it would take a human researcher.Generative models, a key element in the generation of new drug compounds, underscore the significance of interdisciplinary partnerships in advancing drug discovery.While significant progress has been achieved in AI-driven drug discovery, the existence of challenges underscores the ongoing need for innovation and problem-solving in this dynamic field.