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Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical implications in emotional health care, human-computer interaction, and so on. This paper provides a comprehensive study of different methods for extracting electroencephalography (EEG) features for emotion recognition from four different perspectives, including time domain features, frequency domain features, time-frequency features, and nonlinear features. We summarize the current pattern recognition methods adopted in most related works, and with the rapid development of deep learning (DL) attracting the attention of researchers in this field, we pay more attention to deep learning-based studies and analyse the characteristics, advantages, disadvantages, and applicable scenarios. Finally, the current challenges and future development directions in this field were summarized. This paper can help novice researchers in this field gain a systematic understanding of the current status of emotion recognition research based on EEG signals and provide ideas for subsequent related research.

With the aging of the population, the quality of life and happiness of the elderly are increasingly becoming concerns of society. Smart senior care (SSC) products are an important tool to improve the quality of life of the elderly, and their application has been widely discussed. However, due to differences in cognitive characteristics and habits of the elderly, they may face difficulties when using smart products. Therefore, how to design a suitable SSC product APP interface for the elderly has become an urgent problem to be solved. The rapid development of deep learning (DL) provides a new way to deal with this problem. This paper aims to optimize the APP interface design of SSC products suitable for the elderly by using DL technology. Specifically, this paper designs a model based on a deep-Q-network (DQN) algorithm, which can better adapt to the usage habits and needs of the elderly through training and optimization. At the same time, the proposed model is evaluated comprehensively to verify its performance and superiority. To achieve the above goals, this paper first summarizes the existing SSC technology and the design suitable for the elderly. On this basis, a model based on the DQN algorithm is designed, and four datasets are used for training and testing. In the design of the model, this paper pays special attention to how to make the interface more friendly and easy to operate to meet the specific demands and preferences of the elderly. After a comprehensive evaluation, it is found that the proposed DQN algorithm model has achieved remarkable improvement in performance. This model has an average return of about 8.8 and 3.9 compared to other models, which have an average return of about 0.9 and 0.2, respectively. This shows that the model designed in this paper performs well in terms of accuracy and average return and performs better than other algorithms. The results of this paper reveal that optimizing the age-appropriate design of the SSC product APP interface through DL technology can notably enhance the user experience and satisfaction of the elderly. This not only helps the elderly to make better use of smart products and improve their quality of life but also provides useful inspiration and guidance for research and practice in related fields.

Public opinion will significantly affect investor decision-making and stock prices, which ultimately has an impact on the long-term development of the new energy industry. This paper mainly aims to delve in the impact of public opinion on the efficacy of financial risk early warning effect and try to establish an enhanced financial risk early warning model for the new energy list companies. To achieve this, we collect the financial data and public evaluation texts of 185 new energy listed companies, converting the text into emotional indicators which are combined with financial indicators to build a financial risk early warning model for new energy listed companies. The contributions of this paper are as follows: (1) The experiment validation demonstrates that the combination of 7 deep learning models and Bagging algorithm highly improves the accuracy of the sentiment analysis model, achieving an accuracy of 84.09%. (2) The accuracy of financial early warning models is generally enhanced after adding sentiment indicators, among which the accuracy of the BP neural network model reached 95.78%. (3) Through clustering analysis, the evaluation models can productively divide the warning intervals, thereby bolstering the interpretability and applicability of early warning results. Therefore, we suggest that when establishing the financial early warning system, it's necessary to take public opinions into consideration. Aside from improving the early warning effect, it also can be used as a separate indicator for daily monitoring.

Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.

To enhance the integration of deep learning into tennis education and instigate reforms in sports programs, this paper employs deep learning techniques to analyze tennis tactics. The experiments initially introduce the concepts of sports science and backpropagation neural networks. Subsequently, these theories are applied to formulate a comprehensive system of tennis tactical diagnostic indicators, encompassing construction principles, basic requirements, diagnostic indicator content, and evaluation indicator design. Simultaneously, a Back Propagation Neural Network (BPNN) is utilized to construct a tennis tactical diagnostic model. The paper concludes with a series of experiments conducted to validate the effectiveness of the constructed indicator system and diagnostic model. The results indicate the excellent performance of the neural network model when trained on tennis match data, with a mean squared error of 0.00037146 on the validation set and 0.0104 on the training set. This demonstrates the outstanding predictive capability of the model. Additionally, the system proves capable of providing detailed tactical application analysis when employing the tennis tactical diagnostic indicator system for real-time athlete diagnosis. This functionality offers robust support for effective training and coaching during matches. In summary, this paper aims to evaluate athletes' performance by constructing a diagnostic system, providing a solid reference for optimizing tennis training and education. The insights offered by this paper have the potential to drive reforms in sports programs, particularly in the realm of tennis education.

Background: Agar plate analysis is vital for microbiological testing in industries like food, pharmaceuticals, and biotechnology. Manual inspection is slow, laborious, and error-prone, while existing automated systems struggle with the complexity of real-world agar plates. A shortage of diverse datasets hinders the development and evaluation of robust automated systems. Methods: In this paper, two new annotated datasets and a novel methodology for synthetic agar plate generation are presented. The datasets comprise 854 images of cultivated agar plates and 1,588 images of empty agar plates, encompassing various agar plate types and microorganisms. These datasets are an extension of the publicly available BRUKERCOLONY dataset, collectively forming one of the largest publicly available annotated datasets for research. The methodology is based on an efficient image generation pipeline that also simulates cultivation-related phenomena such as haemolysis or chromogenic reactions. Results: The augmentations significantly improved the Dice coefficient of trained U-Net models, increasing it from 0.671 to 0.721. Furthermore, training the U-Net model with a combination of real and 150% synthetic data demonstrated its efficacy, yielding a remarkable Dice coefficient of 0.729, a substantial improvement from the baseline of 0.518. UNet3+ exhibited the highest performance among the U-Net and Attention U-Net architectures, achieving a Dice coefficient of 0.767. Conclusions: Our experiments showed the methodology's applicability to real-world scenarios, even with highly variable agar plates. Our paper contributes to automating agar plate analysis by presenting a new dataset and effective methodology, potentially enhancing fully automated microbiological testing.

Objective: This study trains a U-shaped fully convolutional neural network (U-Net) model based on peripheral contour measures to achieve rapid, accurate, automated identification and segmentation of periprostatic adipose tissue (PPAT). Methods: Currently, no studies are using deep learning methods to discriminate and segment periprostatic adipose tissue. This paper proposes a novel and modified, U-shaped convolutional neural network contour control points on a small number of datasets of MRI T2W images of PPAT combined with its gradient images as a feature learning method to reduce feature ambiguity caused by the differences in PPAT contours of different patients. This paper adopts a supervised learning method on the labeled dataset, combining the probability and spatial distribution of control points, and proposes a weighted loss function to optimize the neural network's convergence speed and detection performance. Based on high-precision detection of control points, this paper uses a convex curve fitting to obtain the final PPAT contour. The imaging segmentation results were compared with those of a fully convolutional network (FCN), U-Net, and semantic segmentation convolutional network (SegNet) on three evaluation metrics: Dice similarity coefficient (DSC), Hausdorff distance (HD), and intersection over union ratio (IoU). Results: Cropped images with a 270 × 270-pixel matrix had DSC, HD, and IoU values of 70.1%, 27 mm, and 56.1%, respectively; downscaled images with a 256 × 256-pixel matrix had 68.7%, 26.7 mm, and 54.1%. A U-Net network based on peripheral contour characteristics predicted the complete periprostatic adipose tissue contours on T2W images at different levels. FCN, U-Net, and SegNet could not completely predict them. Conclusion: This U-Net convolutional neural network based on peripheral contour features can identify and segment periprostatic adipose tissue quite well. Cropped images with a 270 × 270-pixel matrix are more appropriate for use with the U-Net convolutional neural network based on contour features; reducing the resolution of the original image will lower the accuracy of the U-Net convolutional neural network. FCN and SegNet are not appropriate for identifying PPAT on T2 sequence MR images. Our method can automatically segment PPAT rapidly and accurately, laying a foundation for PPAT image analysis.

Objective: A Colon Cleanliness Rating Algorithm (CCRA) based on colonoscopy image analysis is proposed in this paper, in order to solve the problem that the results of Colon Cleanliness (or Bowel Preparation Quality) rating caused by manual inspection are inconsistent. Methods: Firstly, CCRA intercepts images from the colonoscopy video. Secondly, each colonoscopy image's stool area is segmented by U-Net to obtain the 2-classification segmentation results. Finally, the colon cleanliness is obtained by comparing the average area of the stool area with the standard proportion. Results: After testing, the pixel accuracy of the U-Net model is 97.02 %, IoU is 83.67 %, accuracy is 92.17 %, recall is 90.21 %, F1-Score is 90.95 %. The accuracy of CCRA is 92.45 %–99.275 % Conclusion: The experimental results show that the CCRA proposed in this paper can quickly and accurately output the colon cleanliness rating of patients without manpower.

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different frequencies of the landscape acoustic attribute and the relationship of said sounds with ecosystem health metrics and indicators (e.g., species richness, acoustic biodiversity, vectors of structural change, gradients of vegetation cover, landscape connectivity, and temporal and spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use of ARUs and their capacity to record hours of audio for months at a time have created a need for automatic processing methods to reduce time consumption, correlate variables implicit in the recordings, extract features, and characterize sound patterns related to landscape attributes. Consequently, traditional machine learning methods have been commonly used to process data on different characteristics of soundscapes, mainly the presence–absence of species. In addition, it has been employed for call segmentation, species identification, and sound source clustering. However, some authors highlight the importance of the new approaches that use unsupervised deep learning methods to improve the results and diversify the assessed attributes. In this paper, we present a systematic review of machine learning methods used in the field of ecoacoustics for data processing. It includes recent trends, such as semi-supervised and unsupervised deep learning methods. Moreover, it maintains the format found in the reviewed papers. First, we describe the ARUs employed in the papers analyzed, their configuration, and the study sites where the datasets were collected. Then, we provide an ecological justification that relates acoustic monitoring to landscape features. Subsequently, we explain the machine learning methods followed to assess various landscape attributes. The results show a trend towards label-free methods that can process the large volumes of data gathered in recent years. Finally, we discuss the need to adopt methods with a machine learning approach in other biological dimensions of landscapes.

Deep learning practices in the agriculture sector can address many challenges faced by the farmers such as disease detection, yield estimation, soil profile estimation, etc. In this paper, disease classification for the sugarcane plant and the experimentation involved thereby is thoroughly discussed. Experimental results include the performances of the well-known existing transfer learning techniques and proposed ensemble deep learning based architecture that incorporates stack ensemble of two networks with one having level-wise spatial attention helping to provide better generalization. A Self-created database of sugarcane leaf diseases is introduced to the research community through this paper. It involves 5 categories with a total of 2569 images. Here, it is observed that best performing transfer learning method, MobileNet-V2 shows an accuracy of around 84% with the lowest number of parameters whereas ensemble model reaching to 86.53% with less epochs and with acceptable number of parameters.