Convolutional Recurrent Neural Network Research Articles

Automatic multi-label diagnosis of single-lead ECG using novel hybrid residual recurrent convolutional neural networks

Arrhythmia is a prevalent cardiovascular disease that can unveil various heart health issues. In recent times, the rise of wearable devices has garnered attention towards the advantages of portable single-lead ECG devices in heart health monitoring. Achieving end-to-end ECG automatic diagnosis through deep learning is essential due to the time-consuming and labour-intensive characteristics of manual diagnosis of ECG records. This paper presents the design of a hybrid residual recurrent convolutional neural network (HRRCNN) for automatically diagnosing single-lead ECG with multiple labels. Firstly, the convolutional layers and residual networks extract the spatial and short-term temporal features. Subsequently, a hybrid recurrent neural network (RNN) comprised of a double-layer bidirectional gated recurrent unit (GRU) and a bidirectional long short-term memory (LSTM) is employed to capture long and short-term temporal features. Finally, an output layer, comprising an attention layer and a fully connected layer, converts the extracted features into classification results. To evaluate the performance of HRRCNN, a dataset from the Shanghai First People’s Hospital Affiliated to Shanghai JiaoTong University(Shanghai General Hospital, SGH) is utilized. The experimental results demonstrate that HRRCNN achieves an accuracy of 95.08 %, F1-macro of 87.22 %, Sensitivity of 88.51 %, and a positive prediction rate of 86.13 %. Comparative analysis with existing methods underscores the superior performance of HRRCNN, thereby establishing its potential as an out-of-hospital heart health monitoring tool and an auxiliary tool in clinical settings.

Identification of Counterfeit Videos using Deep Learning Methodology

The rise of deep learning has ushered in a proliferation of deep fake videos, posing significant challenges to the credibility of visual content. Our research introduces a groundbreaking approach by merging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance the accuracy of deepfake prediction. This unique integration, which has not been previously implemented, significantly boosts the model's capability to discern deepfakes. The synergy of CNNs and RNNs in our methodology represents an advancement, contributing to increased accuracy in detecting synthetic content. We leverage CNNs and RNNs for an efficient solution. First, we employ a Res-Next CNN to extract distinctive features from individual video frames, effectively encoding spatial information. These features are then used in the subsequent phase, where a LSTM-RNN models temporal dynamics within the videodata.The temporal aspect is crucial in differentiating deep fake videos due to subtle inconsistencies over time. The LSTM RNN processes the feature sequence, enabling the model to identify temporal patterns unique to deep fakes. This holistic approach, combining spatial and temporal analysis, enhances the model's ability to detect even highly convincing synthetic content. Our model is trained on a comprehensive dataset with rigorous evaluations, demonstrating competitive performance through standard metrics such as accuracy, precision. Practically, our model offers real- time video analysis, automatically identifying deep fake content and mitigating potential risks. Importantly, our approach is simple and robust, suitable for deployment across diverse scenarios. In summary, our research provides an effective solution to the critical issue of deepfake detection. By synergizing CNNs and LSTM-based RNNs, we offer a practical means to uphold the integrity of visual content in an era where digital information authenticity is paramount. Keywords— Deep Learning, CNN, RNN, Deepfake, LSTM, accuracy, precision, visual content, digital information

Multi-scene application of intelligent inspection robot based on computer vision in power plant

As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

A Deep Learning Model with Axial Attention for Radar Echo Extrapolation

ABSTRACT Radar echo extrapolation is an important approach in precipitation nowcasting which utilizes historical radar echo images to predict future echo images. In this paper, we introduce the self-attention mechanism into Trajectory Gated Recurrent Unit (TrajGRU) model. Under the sequence-to-sequence framework, we have developed a novel convolutional recurrent neural network called Self-attention Trajectory Gated Recurrent Unit (SA-TrajGRU), which incorporates the self-attention mechanism. The SA-TrajGRU model which combines spatiotemporal variant structure in TrajGRU and self-attention module is simple and effective. We evaluate our approach on the Moving MNIST-2 dataset and the CIKM AnalytiCup 2017 radar echo dataset. The experimental results show that the performance of the proposed SA-TrajGRU model is comparable to other convolutional recurrent neural network models. HSS and CSI scores of the SA-TrajGRU model are higher than scores of other models under the radar echo threshold of 25 dBZ, indicating that the SA-TrajGRU model has the most accurate prediction results under this threshold.

Joint Inversion of Surface-Wave Dispersions and Receiver Functions Based on Deep Learning

Abstract We proposed a deep learning (DL) method to derive VS models from joint inversion of Rayleigh-wave dispersions and receiver functions, which is based on multilabel convolutional neural network and recurrent neural network. We used a spline-based approach to generate synthetic models instead of directly using existing models to build the training data set, which improves the generalization of the method. Unlike the traditional methods, which usually set a fixed VP/VS ratio, our method makes full use of the powerful data mining ability of DL to invert the VS models assuming different VP/VS ratios. A loss function is specially designed that focuses on key features of the model space, for example, the shape and depth of Moho. Synthetic tests demonstrate that the proposed method is accurate and fast. Application to the southeast margin of the Tibetan Plateau shows results consistent with the previous joint inversion with P constraints, indicating the proposed method is reliable and robust.

Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks

Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks

Prediction of Parking Space Availability Using Improved MAT-LSTM Network

Prediction of Parking Space Availability Using Improved MAT-LSTM Network

A Novel Probabilistic Approach for Driving the Sustainable Energy Circular Economy: Innovating Efficiency in Renewable Resource Markets

Our research introduces a novel hybrid model aimed at driving the sustainable economy by innovating efficiency in forecasting renewable resource market trends for recovery. The fusion of Hierarchical Recurrent Neural Networks (RNNs) and LeNet-5 Convolutional Neural Networks (CNNs) capitalizes on the distinct advantages of both architectures. RNNs capture temporal dependencies, while CNNs identify spatial patterns, providing a holistic understanding of market dynamics. This symbiosis significantly enhances predictive accuracy, empowering stakeholders with more reliable insights into the trajectory of renewable resource markets. Efficient feature selection is achieved through the incorporation of the Modified Hill Climbing (MHC) algorithm. As an evolutionary feature selection technique, MHC dynamically refines input features during the learning process, adapting to the evolving nature of renewable resource markets. This adaptive feature selection not only improves computational efficiency and interpretability but also ensures a focus on the most relevant information, aligning with the goal of driving sustainable circular economic recovery. The fortified hybrid model, harmonizing RNNs, CNNs, and MHC, showcases heightened robustness and generalization across diverse market conditions. By effectively merging deep learning architectures with an evolutionary feature selection process, the model adeptly captures intricate patterns while mitigating overfitting. This contribution underscores the applicability of the proposed approach to real-world scenarios, positioning it as a catalyst for innovative efficiency in renewable resource markets—a pivotal driver for the recovery of the sustainable economy. Our research marks a crucial advancement in the realm of reliable and adaptable forecasting models, essential for informed decision-making in the dynamic landscape of renewable energy markets.

Discerning Deception: A Face-Centric Deepfake Detection Approach with ResNeXt-50 and LSTMs

Abstract: A.I. has grown to epidemic proportions over the last years as its applied in almost all sectors to allocate workload from humans but end up being done effectively with no human intervention. A branch of A.I. called deep learning, which operates by mimicking human judgment and action through neural network systems. Nonetheless, with the increase height of the two platforms have been experienced sufficient cases of misguided individuals using tools to recycle videos, audios, and texts to achieve their agendas. This insinuates a due assumption that Generative Adversarial Networks, GANs, are central to the development of believable deepfakes. GANs have developed a crucial ability to generate videos that replace frames with material from another video source to create deepfakes videos. While GANs serves various purposes such as entertainment, teaching, and experimentation, malicious actors can misuse these deep learning techniques to manipulate videos, impacting the privacy of individuals in society. This paper conducts an analysis of different deepfake detection models, comparing their efficacy and discussing potential future extensions of deepfake technology. The study presents a novel deepfake detection approach utilizing a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This method utilizes ResNext50 for extracting features at the frame level, while employing LSTM (Long Short-Term Memory) for video classification based on these extracted features. Various datasets are incorporated, including the deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++), combining them to achieve a high-accuracy model capable of accurately discerning between real and deepfake videos. The results of this study make a valuable contribution to the continuous endeavors aimed at improving deepfake detection abilities and ensuring privacy protection in a time heavily influenced by artificial intelligence

A Deep Approach to Deep Fake Detection

In recent months, the proliferation of free deep learning-based software tools has facilitated the creation of credible face exchanges in videos, resulting in what are known as "DeepFake" (DF) videos. While manipulations of digital videos have been demonstrated for several decades through the use of visual effects, recent advances in deep learning have significantly increased the realism of fake content and the accessibility with which it can be created. These AI-synthesized media, popularly referred to as DF, pose a significant challenge for detection. Detecting DF is a major challenge due to the complexity of training algorithms to spot them. In this work, we propose a detection approach using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Our system leverages a CNN to extract features at the frame level, which are then used to train an RNN. The RNN learns to classify whether a video has been manipulated, detecting temporal inconsistencies between frames introduced by DF creation tools. We evaluate our approach against a large set of fake videos collected from standard datasets and demonstrate competitive results using a simple architecture.

Emotion Symphony: Deep Learning Journey Through Human Facial Emotions

Abstract: In today's technologically advanced world, understanding human emotions holds paramount importance in various domains ranging from human-computer interaction to mental health diagnosis. Emotions are primarily conveyed through facial expressions, making facial emotion recognition a crucial area of research. This project, "Emotion Symphony," embarks on a deep learning journey aimed at developing an efficient system for recognizing and interpreting human facial emotions. The project employs state-of-the-art deep learning techniques to extract meaningful features from facial images and map them to corresponding emotional states. Leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the system is designed to analyse subtle nuances in facial expressions, capturing both spatial and temporal dependencies for accurate emotion recognition. To train the model, extensive datasets comprising diverse facial expressions are utilized, ensuring robustness and generalization of the proposed system. Additionally, data augmentation techniques are employed to enhance the model's ability to handle variations in lighting conditions, poses, and facial expressions.

Interpreting Doctor Notes using Handwriting Recognition

Abstract: Handwriting recognition of medical prescriptions has been a challenging problem over the recent years with constant research in providing possible accurate solutions. Indecipherable handwritten prescription and inefficiency of Pharmacist to understand the medical prescription can lead to serious and harmful effect to the patients. Even in the recognition of handwriting, mainly doctors notes, they are very difficult for everyone to understand and it takes time for a person to analyse it. So, this idea mainly focused on interpreting doctor’s notes using handwritten recognition and deep learning techniques. The handwritten or printed document pictures are transformed into their electronic counterparts using an optical character recognition (OCR) system. Due to individuals' inconsistent writing styles, dealing with handwritten texts is significantly more difficult than dealing with printed ones. Handwritten text recognition could be done by Image processing, Machine Learning or Deep Learning Techniques. Out of these Deep Learning remains to be the most popular and prominent. Some of the Deep Learning techniques includes Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This gives a review of the various recognition methodologies used for interpreting handwritten texts. It includes the most important algorithms that could be used for detecting the handwritten word/text/character by using various approaches for the recognition process. In the end we are thus comparing the accuracies provided by these systems.

SST-CRAM: spatial-spectral-temporal based convolutional recurrent neural network with lightweight attention mechanism for EEG emotion recognition

SST-CRAM: spatial-spectral-temporal based convolutional recurrent neural network with lightweight attention mechanism for EEG emotion recognition

Transforming oral cancer care: The promise of deep learning in diagnosis

The diagnosis and treatment of oral cancer present significant challenges, including delayed diagnosis at advanced stages and limited access to healthcare. Deep learning (DL), a subset of artificial intelligence, holds promise for transforming medical image analysis and predictive analytics. In this perspective, we examine the applications of DL in oral cancer. Specifically, we explore the efficacy of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in diagnosing and predicting the prognosis of oral cancer in the last five years. Additionally, we underscore the importance of oral cancer databases in advancing research and clinical practice. However, DL methods face constraints related to input variability and model interpretability. Addressing these issues is crucial to harnessing the full potential of DL in oral cancer treatment. In summary, this article underscores the innovative contributions of DL in revolutionizing oral cancer management and advocating for precision medicine in oncology.

Text Classification in Natural Language Processing

This paper presents an overview of text classification techniques, focusing on the pre-processing steps, feature extraction methods, and model selection strategies employed in the process. Algorithms such as Naive Bayes, Support Vector Machines (SVM), logistic regression, and neural networks are used. Furthermore, recent advancements in deep learning models for text classification, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used. Comprehensive understanding of text classification methodologies in NLP and insights into current trends and challenges in the field are mentioned

Vision Saver Deep Learning Approaches for Diabetic Retinopathy Detection

One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy. In order to preserve eyesight, diabetic retinopathy must be identified and treated early. In this study, "Vision Saver," a deep learning-based method for the automated detection and treatment of diabetic retinopathy, is introduced. Vision Saver provides a comprehensive solution for the detection of retinal problems by utilizing deep neural networks, enabling prompt patient care and intervention. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two cutting-edge deep learning approaches, are used by the proposed system to evaluate retinal images and provide precise diagnoses. Additionally, we investigate how generative adversarial networks (GANs) might improve image quality and produce artificial data for training. The architecture of Vision Saver is built to handle both fundus photos and optical coherence tomography (OCT) images, enabling a flexible method of evaluating diabetic retinopathy. Vision Saver's management component includes not only detection but also monitoring of therapy progression. The system aids medical personnel in making educated judgments about interventions like laser therapy, injections, or surgical operations through ongoing monitoring and follow-up. The development and assessment of Vision Saver are covered in this essay, with a focus on how well it performs in terms of sensitivity, specificity, and accuracy. We also discuss concerns with model interpretability, data protection, and integration into clinical workflows. Ultimately, the objective is to improve patient outcomes and lessen the burden of blindness brought on by diabetic retinopathy by offering a comprehensive solution that improves screening and management of the illness. Deep learning in healthcare has a lot of potential, and Vision Saver is an example of how it can significantly improve the lives of people with diabetic retinopathy.

Memorability shapes perceived time (and vice versa).

Visual stimuli are known to vary in their perceived duration. Some visual stimuli are also known to linger for longer in memory. Yet, whether these two features of visual processing are linked is unknown. Despite early assumptions that time is an extracted or higher-order feature of perception, more recent work over the past two decades has demonstrated that timing may be instantiated within sensory modality circuits. A primary location for many of these studies is the visual system, where duration-sensitive responses have been demonstrated. Furthermore, visual stimulus features have been observed to shift perceived duration. These findings suggest that visual circuits mediate or construct perceived time. Here we present evidence across a series of experiments that perceived time is affected by the image properties of scene size, clutter and memorability. More specifically, we observe that scene size and memorability dilate time, whereas clutter contracts it. Furthermore, the durations of more memorable images are also perceived more precisely. Conversely, the longer the perceived duration of an image, the more memorable it is. To explain these findings, we applied a recurrent convolutional neural network model of the ventral visual system, in which images are progressively processed over time. We find that more memorable images are processed faster, and that this increase in processing speed predicts both the lengthening and the increased precision of perceived durations. These findings provide evidence for a link between image features, time perception and memory that can be further explored with models of visual processing.

Multi-resolution sequence and structure feature extraction for binding site prediction

Circular ribonucleic acids (circRNAs) are single-stranded RNA molecules that form loops and are widely expressed in various cells and tissues. They interact with RNA-binding proteins (RBPs) and play a vital regulatory role in the onset and development of several diseases. Researchers have proposed various hybrid architecture prediction methods based on convolutional neural networks and recurrent neural networks to recognize the interactions and sites between circRNAs and RBPs and thus reveal the biological functions of circRNAs. However, existing methods usually ignore the structural information of circRNA, which may affect the modeling of circRNA and RBP binding modes. To address these problems, we propose a prediction model based on multi-resolution feature extraction. First, it generates sequence features using unsupervised word embedding and nucleotide density. Then, it uses implicit and explicit pseudo-secondary structure hybrid encoding to fuse sequence and structural information and better simulate circRNA-RBP binding patterns. Second, it uses an enhanced bidirectional sample convolution and interaction network encoder to capture and integrate high-order features of distinct resolutions from the multi-scale convolution module. This provides rich semantic input to the downstream bidirectional long short-term memory network to improve prediction accuracy. Experimental results on 37 circRNA and 31 linear RNA datasets show that our method has significant advantages in identifying RNA-RBP interactions. Furthermore, the four motifs learned by our method are verified against existing motif databases, indicating that it can discover biologically meaningful circRNA-RBP binding patterns.

A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

Enhances the recognition accuracy of the complex text for online production labels by improving convolutional recurrent neural network

Enhances the recognition accuracy of the complex text for online production labels by improving convolutional recurrent neural network