Search Results (82)

Chronic cough is associated with several respiratory diseases and is a significant burden on physical, social, and psychological health. Non-invasive, real-time, continuous, and quantitative monitoring tools are highly desired to assess cough severity, the effectiveness of treatment, and monitor disease progression in clinical practice and research. There are currently limited tools to quantitatively measure spontaneous coughs in daily living settings in clinical trials and in clinical practice. In this study, we developed a machine learning model for the detection and classification of cough sounds. Mel spectrograms are utilized as a key feature representation to capture the temporal and spectral characteristics of coughs. We applied this approach to automate cough analysis using 300 h of audio recordings from cough challenge clinical studies conducted in a clinical lab setting. A number of machine learning algorithms were studied and compared, including decision tree, support vector machine, k-nearest neighbors, logistic regression, random forest, and neural network. We identified that for this dataset, the CRNN approach is the most effective method, reaching 98% accuracy in identifying individual coughs from the audio data. These findings provide insights into the strengths and limitations of various algorithms, highlighting the potential of CRNNs in analyzing complex cough patterns. This research demonstrates the potential of neural network models in fully automated cough monitoring. The approach requires validation in detecting spontaneous coughs in patients with refractory chronic cough in a real-life setting. Full article

Construction workplaces often face unforeseen collision hazards due to a decline in auditory situational awareness among on-foot workers, leading to severe injuries and fatalities. Previous studies that used auditory signals to prevent collision hazards focused on employing a classical beamforming approach to determine equipment sounds’ Direction of Arrival (DOA). No existing frameworks implement a neural network-based approach for both equipment sound classification and localization. This paper presents an innovative framework for sound classification and localization using multichannel sound datasets artificially synthesized in a virtual three-dimensional space. The simulation synthesized 10,000 multi-channel datasets using just fourteen single sound source audiotapes. This training includes a two-staged convolutional recurrent neural network (CRNN), where the first stage learns multi-label sound event classes followed by the second stage to estimate their DOA. The proposed framework achieves a low average DOA error of 30 degrees and a high F-score of 0.98, demonstrating accurate localization and classification of equipment near workers’ positions on the site. Full article

The prevalence of squamous cell carcinoma is increasing, and efforts that aid in an early and accurate diagnosis are crucial to improve clinical outcomes for patients. Cornulin, a squamous epithelium-specific protein, has recently garnered attention due to its implications in the progression of squamous cell carcinoma developed in several tissues. As an epidermal differentiation marker, it is involved in skin anchoring, regulating cellular proliferation, and is a putative tumor suppressor. The physiologically healthy squamous epithelium displays a considerable level of Cornulin, whereas squamous cell carcinomas have marked downregulation, suggesting that Cornulin expression levels can be utilized for the early detection and follow-up on the progression of these types of cancer. Cornulin’s expression patterns in cervical cancer have been examined, and findings support the stepwise downregulation of Cornulin levels that accompanies the progression to neoplasia in the cervix. Additional studies documented a similar trend in expression in other types of cancer, such as cutaneous, esophageal, and oropharyngeal squamous cell carcinomas. The consistent and predictable pattern of Cornulin expression across several squamous cell carcinomas and its correlation with key clinicopathological parameters make it a reliable biomarker for assessing the transformation and progression events in the squamous epithelium, thus potentially contributing to the early detection, definitive diagnosis, and more favorable prognosis for these cancer patients. Full article

In recent years, EEG-based emotion recognition technology has made progress, but there are still problems of low model efficiency and loss of emotional information, and there is still room for improvement in recognition accuracy. To fully utilize EEG’s emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification and 97.69% and 98.05% in ternary classification, and on the SEED dataset, we achieved an accuracy of 98.63%, surpassing relevant algorithms. Additionally, the model’s efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption. Full article

Text-based CAPTCHAs remain the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs), are the mainstream approach for text CAPTCHA recognition and are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment; they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive CAPTCHA with a CNN combined with an RNN (CRNN) module and trainable Adaptive Fusion Filtering Networks (AFFN), which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexities show that, compared with the baseline model Deep CAPTCHA, the number of parameters of our proposed model is reduced by about 70%, and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed. Compared with several of the latest models, the model proposed by the study also has better comprehensive performance. Full article

CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-target activity. However, there is still room for improvement. Here, we proposed a hybrid neural network named CrnnCrispr, which integrates a convolutional neural network and a recurrent neural network for on-target activity prediction. We performed unbiased experiments with four mainstream methods on nine public datasets with varying sample sizes. Additionally, we incorporated a transfer learning strategy to boost the prediction power on small-scale datasets. Our results showed that CrnnCrispr outperformed existing methods in terms of accuracy and generalizability. Finally, we applied a visualization approach to investigate the generalizable nucleotide-position-dependent patterns of sgRNAs for on-target activity, which shows potential in terms of model interpretability and further helps in understanding the principles of sgRNA design. Full article

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education. Full article

With the increase in the scale of breeding at modern pastures, the management of dairy cows has become much more challenging, and individual recognition is the key to the implementation of precision farming. Based on the need for low-cost and accurate herd management and for non-stressful and non-invasive individual recognition, we propose a vision-based automatic recognition method for dairy cow ear tags. Firstly, for the detection of cow ear tags, the lightweight Small-YOLOV5s is proposed, and then a differentiable binarization network (DBNet) combined with a convolutional recurrent neural network (CRNN) is used to achieve the recognition of the numbers on ear tags. The experimental results demonstrated notable improvements: Compared to those of YOLOV5s, Small-YOLOV5s enhanced recall by 1.5%, increased the mean average precision by 0.9%, reduced the number of model parameters by 5,447,802, and enhanced the average prediction speed for a single image by 0.5 ms. The final accuracy of the ear tag number recognition was an impressive 92.1%. Moreover, this study introduces two standardized experimental datasets specifically designed for the ear tag detection and recognition of dairy cows. These datasets will be made freely available to researchers in the global dairy cattle community with the intention of fostering intelligent advancements in the breeding industry. Full article

Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model. Full article

In the realm of multilingual, AI-powered, real-time optical character recognition systems, this research explores the creation of an optimal, vocabulary-based training dataset. This comprehensive endeavor seeks to encompass a range of criteria: comprehensive language representation, high-quality and diverse data, balanced datasets, contextual understanding, domain-specific adaptation, robustness and noise tolerance, and scalability and extensibility. The approach aims to leverage techniques like convolutional neural networks, recurrent neural networks, convolutional recurrent neural networks, and single visual models for scene text recognition. While focusing on English, Hungarian, and Japanese as representative languages, the proposed methodology can be extended to any existing or even synthesized languages. The development of accurate, efficient, and versatile OCR systems is at the core of this research, offering societal benefits by bridging global communication gaps, ensuring reliability in diverse environments, and demonstrating the adaptability of AI to evolving needs. This work not only mirrors the state of the art in the field but also paves new paths for future innovation, accentuating the importance of sustained research in advancing AI’s potential to shape societal development. Full article

The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver’s physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver’s physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis. Full article

One of a map’s fundamental elements is its annotations, and extracting these annotations is an important step in enabling machine intelligence to understand scanned map data. Due to the complexity of the characters and lines, extracting annotations from scanned Chinese maps is difficult, and there is currently little research in this area. A deep-learning-based framework for extracting annotations from scanned Chinese maps is presented in the paper. Improved the EAST annotation detection model and CRNN annotation recognition model based on transfer learning make up the two primary parts of this framework. Several sets of the comparative tests for annotation detection and recognition were created in order to assess the efficacy of this method for extracting annotations from scanned Chinese maps. The experimental findings show the following: (i) The suggested annotation detection approach in this study revealed precision, recall, and h-mean values of 0.8990, 0.8389, and 0.8635, respectively. These measures demonstrate improvements over the currently popular models of −0.0354 to 0.0907, 0.0131 to 0.2735, and 0.0467 to 0.1919, respectively. (ii) The proposed annotation recognition method in this study revealed precision, recall, and h-mean values of 0.9320, 0.8956, and 0.9134, respectively. These measurements demonstrate improvements over the currently popular models of 0.0294 to 0.1049, 0.0498 to 0.1975, and 0.0402 to 0.1582, respectively. Full article

The quality of railroad wheelsets is an important guarantee for the safe operation of wagons, and mastering the production information of wheelsets plays a vital role in vehicle scheduling and railroad transportation safety. However, when using objection detection methods to detect the production information of wheelsets, there are situations that affect detection such as character tilting and unfixed position. Therefore, this paper proposes a deep learning-based method for accurately detecting and recognizing tilted character information on railroad wagon wheelsets. It covers three parts. Firstly, we construct a tilted character detection network based on Faster RCNN for generating a wheelset’s character candidate regions. Secondly, we design a tilted character correction network to classify and correct the orientation of flipped characters. Finally, a character recognition network is constructed based on convolutional recurrent neural network (CRNN) to realize the task of recognizing a wheelset’s characters. The result shows that the method can quickly and effectively detect and identify the information of tilted characters on wheelsets in images. Full article

Type II diabetes mellitus (T2DM) accounts for approximately 90% of all diabetes mellitus cases in the world. Glucagon-like peptide-1 receptor (GLP-1R) agonists have established an increased capability to target directly or indirectly six core defects associated with T2DM, while the underlying molecular mechanisms of these pharmacological effects are not fully known. This exploratory study was conducted to analyze the effect of treatment with GLP-1R agonists on the urinary peptidome of T2DM patients. Urine samples of thirty-two T2DM patients from the PROVALID study (“A Prospective Cohort Study in Patients with T2DM for Validation of Biomarkers”) collected pre- and post-treatment with GLP-1R agonist drugs were analyzed by CE-MS. In total, 70 urinary peptides were significantly affected by GLP-1R agonist treatment, generated from 26 different proteins. The downregulation of MMP proteases, based on the concordant downregulation of urinary collagen peptides, was highlighted. Treatment also resulted in the downregulation of peptides from SERPINA1, APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF, PIGR, and TTR, many of which were previously found to be associated with increased insulin resistance and inflammation. The findings indicate potential molecular mechanisms of GLP-1R agonists in the context of the management of T2DM and the prevention or delaying of the progression of its associated diseases. Full article

Uyghur text recognition faces several challenges in the field due to the scarcity of publicly available datasets and the intricate nature of the script characterized by strong ligatures and unique attributes. In this study, we propose a unified three-stage model for Uyghur language recognition. The model is developed using a self-constructed Uyghur text dataset, enabling evaluation of previous Uyghur text recognition modules as well as exploration of novel module combinations previously unapplied to Uyghur text recognition, including Convolutional Recurrent Neural Networks (CRNNs), Gated Recurrent Convolutional Neural Networks (GRCNNs), ConvNeXt, and attention mechanisms. Through a comprehensive analysis of the accuracy, time, normalized edit distance, and memory requirements of different module combinations on a consistent training and evaluation dataset, we identify the most suitable text recognition structure for Uyghur text. Subsequently, utilizing the proposed approach, we train the model weights and achieve optimal recognition of Uyghur text using the ConvNeXt+Bidirectional LSTM+attention mechanism structure, achieving a notable accuracy of 90.21%. These findings demonstrate the strong generalization and high precision exhibited by Uyghur text recognition based on the proposed model, thus establishing its potential practical applications in Uyghur text recognition. Full article