Convolutional Recurrent Neural Network Research Articles

Humanactivity Recognition using Deep Learning

Human activity recognition (HAR) plays a crucial role in various fields, including healthcare, surveillance, and human-computer interaction. This study explores the application of deep learning techniques for accurate and efficient human activity recognition. Leveraging the capabilities of deep neural networks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the proposed approach aims to capture both spatial and temporal features from sensor data. The dataset utilized in this research comprises multi-model sensor inputs, such as accelerometer and gyroscope readings, collected from wearable devices. The deep learning model is designed to automatically learn hierarchical representations of the raw sensor data, enabling robust feature extraction and discrimination between different human activities. Transfer learning is employed to enhance model generalization across diverse activity categories and varying sensor setups. Experimental evaluations are conducted on real-world datasets, demonstrating the effectiveness of the proposed deep learning framework in accurately classifying activities, including walking, running, sitting, and standing. Comparative analyses against traditional machine learning methods underscore the superior performance and adaptability of deep learning in handling complex and dynamic activity patterns. The results showcase the potential of deploying deep learning models in real-time human activity recognition systems, highlighting their scalability and efficiency. The study contributes to the advancement of HAR methodologies, paving the way for the development of more reliable and robust systems in applications such as healthcare monitoring, assisted living, and smart environments.

Machine Learning Approaches for Fault Detection and Diagnosis in Electrical Machines: A Comparative Study of Deep Learning and Classical Methods

Fault detection and diagnosis in electrical machines are crucial for ensuring their safe and reliable operation. In recent years, machine learning techniques have emerged as powerful tools for addressing this challenge, offering the potential for more accurate and efficient fault detection and diagnosis compared to traditional methods. Among these techniques, deep learning has gained significant attention due to its ability to automatically learn relevant features from raw data. However, the performance of deep learning models in this domain has not been extensively compared to classical methods. This paper presents a comparative study of deep learning and classical methods for fault detection and diagnosis in electrical machines. The study evaluates the performance of various machine learning algorithms, including deep neural networks, support vector machines, decision trees, and ensemble methods, in detecting and diagnosing faults such as stator winding faults, rotor faults, and bearing faults. The experimental evaluation is conducted using real-world datasets obtained from electrical machines in industrial settings. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each approach in detecting and diagnosing faults accurately and efficiently. The results of the study indicate that deep learning approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), outperform classical methods in terms of fault detection and diagnosis accuracy. These deep learning models demonstrate the ability to automatically extract informative features from raw sensor data, enabling them to effectively identify subtle patterns indicative of faults. The study investigates the interpretability of deep learning models compared to classical methods, examining the extent to which the models can provide insights into the underlying causes of faults. While deep learning models typically operate as black boxes, techniques such as layer-wise relevance propagation (LRP) are employed to enhance their interpretability and facilitate the identification of relevant features contributing to fault detection and diagnosis. This comparative study provides valuable insights into the strengths and limitations of deep learning and classical methods for fault detection and diagnosis in electrical machines, offering guidance for practitioners and researchers in selecting appropriate approaches for their specific applications.

Convolutional Recurrent Neural Network espoused Image Encryption System for Secured Image Retrieval

Convolutional Recurrent Neural Network espoused Image Encryption System for Secured Image Retrieval

Machine Transliteration of Handwritten MODI Script to Devanagari using Deep Neural Networks

The transliteration process involves transcribing words from the source language into the target language that uses a different script. Language and scriptural hurdles can be overcome via transliteration systems. There is a demand for automated transliteration systems due to the existence of several languages and the growing number of multilingual speakers. This study focuses on the Machine Transliteration of handwritten MODI script to Devanagari. MODI script was the official script for Marathi till 1950. Although Devanagari has, since then, taken over as the Marathi language's official script, the MODI script has historical significance as large volumes of its manuscripts are preserved in libraries across different parts of India. However, MODI into Devanagari transliteration is a difficult task because MODI script documents are complex in nature and there is no standard dataset available for the experiment. Machine Transliteration can be approached either as a Natural Language Processing task or as a pattern recognition task. In this research work, the transliteration task is carried out using the pattern recognition technique. The transliteration of MODI script to Devanagari is implemented using Convolutional Recurrent Neural Network (CRNN) based Calamari OCR, which is open-source software. An accuracy of 88.14% is achieved in character level matching of each word in the MODI to Devanagari transliteration process. When considering the entire word matching, the accuracy achieved is 61%. Machine Transliteration of

Direct observation of atmospheric turbulence with a video-rate wide-field wavefront sensor

Turbulence is a complex and chaotic state of fluid motion. Atmospheric turbulence within the Earth’s atmosphere poses fundamental challenges for applications such as remote sensing, free-space optical communications and astronomical observation due to its rapid evolution across temporal and spatial scales. Conventional methods for studying atmospheric turbulence face hurdles in capturing the wide-field distribution of turbulence due to its transparency and anisoplanatism. Here we develop a light-field-based plug-and-play wide-field wavefront sensor (WWS), facilitating the direct observation of atmospheric turbulence over 1,100 arcsec at 30 Hz. The experimental measurements agreed with the von Kármán turbulence model, further verified using a differential image motion monitor. Attached to an 80 cm telescope, our WWS enables clear turbulence profiling of three layers below an altitude of 750 m and high-resolution aberration-corrected imaging without additional deformable mirrors. The WWS also enables prediction of the evolution of turbulence dynamics within 33 ms using a convolutional recurrent neural network with wide-field measurements, leading to more accurate pre-compensation of turbulence-induced errors during free-space optical communication. Wide-field sensing of dynamic turbulence wavefronts provides new opportunities for studying the evolution of turbulence in the broad field of atmospheric optics.

Financial Fraud Detection Based on Machine and Deep Learning: A Review

Financial fraud detection is crucial for protecting the integrity of financial markets and institutions globally. Recent advancements in machine learning (ML) and deep learning (DL) have dramatically enhanced the ability to detect and prevent fraudulent activities across various sectors. This review paper examines the implementation of ML and DL in fraud detection, highlighting the evolution from traditional methods to sophisticated models like neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We explore different ML techniques such as supervised, unsupervised, and hybrid approaches, their effectiveness in handling large, imbalanced datasets, and their application in real-world scenarios. Special attention is given to the integration of technologies like blockchain and IoT with AI to innovate fraud detection frameworks. Despite the promising advancements, challenges remain, such as the need for large volumes of labeled data, potential model bias, and the black-box nature of many deep learning models. Future directions focus on enhancing model transparency, addressing privacy concerns, and expanding the use of federated learning. This review aims to demonstrate the effectiveness of current technologies and encourage their adoption in enhancing global financial security

Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP

Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics—Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R2)—to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature.

EEG Emotion Recognition Employing RGPCN-BiGRUAM: ReliefF-Based Graph Pooling Convolutional Network and BiGRU Attention Mechanism

Emotion recognition plays a crucial role in affective computing, and electroencephalography (EEG) signals are increasingly applied in this field due to their effectiveness in reflecting brain activity. In this paper, we propose a novel EEG emotion recognition model that combines the ReliefF-based Graph Pooling Convolutional Network and BiGRU Attention Mechanisms (RGPCN-BiGRUAM). RGPCN-BiGRUAM effectively integrates the advantages of graph convolutional networks and recurrent neural networks. By incorporating ReliefF weights and an attention mechanism into graph pooling, our model enhances the aggregation of high-quality features while discarding irrelevant ones, thereby improving the efficiency of information transmission. The implementation of a multi-head attention mechanism fusion in BiGRU addresses the limitations of single-output features, achieving optimal selection of global features. Comparative experiments on public datasets SEED and DEAP demonstrate that our proposed RGPCN-BiGRUAM significantly improves classification performance compared to classic algorithms, achieving state-of-the-art results. Ablation studies further validate the design principles of our model. The results of this study indicate that RGPCN-BiGRUAM has strong potential for EEG emotion recognition, offering substantial possibilities for future applications.

Uncertainty graph convolution recurrent neural network for air quality forecasting

Accurate and reliable air quality forecasts are essential for informed decision-making in urban planning and environmental engineering. However, predicting air quality faces challenges due to significant fluctuations in data, including the impact of extreme weather. To improve the precision of air quality predictions, it is important to account for the long- and short-term temporal dependencies within historical data from stations, as well as the local and global spatial structures among different stations in a specific region. In this paper, we propose a novel uncertainty graph convolution recurrent neural network for air quality forecasting (AirNN). Concretely, we design an uncertainty-guided graph convolution module to enhance the robustness of air quality forecasting. It treats the parameters on each graph convolution layer as probability distributions and employs variational inference to derive optimal estimates for these distributions. In addition, to capture the global correlation of stations in the whole region, we develop a spatial information modulator. It captures the spatial correlation between long-range stations in graph data. Extensive experiments on different real-world datasets demonstrate the proposed AirNN performs favorably against state-of-the-art methods. Especially in the 24-hour prediction, AirNN reduced the root mean square error (RMSE) by 0.5 on two real datasets, indicating that our proposed AirNN exhibits good performance in long-term forecasting.

GLFER-Net: a polyphonic sound source localization and detection network based on global-local feature extraction and recalibration

Polyphonic sound source localization and detection (SSLD) task aims to recognize the categories of sound events, identify their onset and offset times, and detect their corresponding direction-of-arrival (DOA), where polyphonic refers to the occurrence of multiple overlapping sound sources in a segment. However, vanilla SSLD methods based on convolutional recurrent neural network (CRNN) suffer from insufficient feature extraction. The convolutions with kernel of single scale in CRNN fail to adequately extract multi-scale features of sound events, which have diverse time-frequency characteristics. It results in that the extracted features lack fine-grained information helpful for the localization of sound sources. In response to these challenges, we propose a polyphonic SSLD network based on global-local feature extraction and recalibration (GLFER-Net), where the global-local feature (GLF) extractor is designed to extract the multi-scale global features through an omni-directional dynamic convolution (ODConv) layer and multi-scale feature extraction (MSFE) module. The local feature extraction (LFE) unit is designed for capturing detailed information. Besides, we design a feature recalibration (FR) module to emphasize the crucial features along multiple dimensions. On the open datasets of Task3 in DCASE 2021 and 2022 Challenges, we compared our proposed GLFER-Net with six and four SSLD methods, respectively. The results show that the GLFER-Net achieves competitive performance. The modules we designed are verified to be effective through a series of ablation experiments and visualization analyses.

A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery

PurposeThis study aimed to propose a novel deep learning-based approach to assess the extent of abduction in patients with abducens nerve palsy before and after strabismus surgery. MethodsThis study included 13 patients who were diagnosed with abducens nerve palsy and underwent strabismus surgery in a tertiary hospital. Photographs of primary, dextroversion and levoversion position were collected before and after strabismus surgery. The eye location and eye segmentation network were trained via recurrent residual convolutional neural networks with attention gate connection based on U-Net (R2AU-Net). Facial images of abducens nerve palsy patients were used as the test set and parameters were measured automatically based on the masked images. Absolute abduction also was measured manually, and relative abduction was calculated. Agreements between manual and automatic measurements, as well as repeated automatic measurements were analyzed. Preoperative and postoperative results were compared. ResultsThe intraclass correlation coefficients (ICCs) between manual and automatic measurements of absolute abduction ranged from 0.985 to 0.992 (P＜0.001), and the bias ranged from −0.25 ​mm to −0.05 ​mm. The ICCs between two repeated automatic measurements ranged from 0.994 to 0.997 (P＜0.001), and the bias ranged from −0.11 ​mm to 0.05 ​mm. After strabismus surgery, absolute abduction of affected eye increased from 2.18 ​± ​1.40 ​mm to 3.36 ​± ​1.93 ​mm (P＜0.05). The relative abduction was improved in 76.9% patients (10/13) after surgery (P＜0.01). ConclusionsThis image analysis technique demonstrated excellent accuracy and repeatability for automatic measurements of ocular abduction, which has promising application prospects in objectively assessing surgical outcomes in patients with abducens nerve palsy.

Advancements in Anomaly Detection Techniques in Network Traffic: The Role of Artificial Intelligence and Machine Learning

Purpose: This paper examines the most recent techniques for identifying irregularities in network data, with an emphasis upon machine learning (ML) and artificial intelligence (AI). Understanding how these technologies improve anomaly detection and overall network security is the goal of the study.Design/Methodology/Approach: A thorough examination of scholarly works, business analyses, and conference proceedings from the previous ten years was carried out. The study looks into supervised learning, unsupervised learning, and deep learning, among other AI and ML approaches. In order to evaluate these techniques' efficacy, advantages, and disadvantages in network anomaly detection, a comparative analysis was carried out.Findings/Results: The analysis shows that the identification of anomalies in network traffic is greatly enhanced by the use of AI and ML approaches. Methods such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have shown to be very successful in recognizing intricate patterns. Nonetheless, issues with data quality, computational complexity, and interpretability of models continue to exist.Originality/Value: This paper offers a current assessment of machine learning and artificial intelligence applications in network anomaly detection, emphasizing emerging trends and areas for further study. For academics and practitioners looking to improve network security using sophisticated detection methods, it provides insightful information.

Transcribing Handwritten Medical Prescription using Convolutional Neural Network AlexNet Architecture and Canny Edge Detection

Misinterpreted medical prescriptions had led to casualties due to the illegible cursive handwriting of medical practitioners. Many studies focused on this problem. However, the accuracy was unsatisfactory and needed improvement. The study evaluated the performance of the Canny edge detection with other preprocessing methods, including RGB to Grayscale Conversion, Binarization, and Inversion, which was used to process the images of cursive handwritten medical prescriptions using Alexnet Convolutional Recurrent Neural Network (ACoRNN). The CRNN model developed by previous researchers was used as the basis for comparison, and the researchers created a faster and more accurate model. The best combination of preprocessing methods for ACoRNN was with RGB to Grayscale Conversion, Binarization, Canny edge detection, and Inversion. The researchers’ model had faster preprocessing and testing time and achieved 90.76% average accuracy through five trials.

An improved dung beetle optimization with recurrent convolutional neural networks for efficient detection and classification of undersea water object images

An improved dung beetle optimization with recurrent convolutional neural networks for efficient detection and classification of undersea water object images

A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India

AbstractAccurate rainfall‐runoff analysis is essential for water resource management, with artificial intelligence (AI) increasingly used in this and other hydrological areas. The need for precise modelling has driven substantial advancements in recent decades. This study employed six AI models. These were the support vector regression model (SVR), the multilinear regression model (MLR), the extreme gradient boosting model (XGBoost), the long‐short‐term memory (LSTM) model, the convolutional neural network (CNN) model, and the convolutional recurrent neural network (CNN‐RNN) hybrid model. It covered 1998–2006, with 1998–2004 for calibration/training and 2005–2006 for validation/testing. Five metrics were used to measure model performance: coefficient of determination (R2), Nash‐Sutcliffe efficiency (NSE), mean absolute error (MAE), root‐mean square error (RMSE), and RMSE‐observations standard deviation ratio (RSR). The hybrid CNN‐RNN model performed best in both training and testing periods (training: R2 is 0.92, NSE is 0.91, MAE is 10.37 m3s−1, RMSE is 13.13 m3s−1, and RSR is 0.30; testing: R2 is 0.95, NSE is 0.94, MAE is 12.18 m3s−1, RMSE is 15.86 m3s−1, and RSR is 0.25). These results suggest the hybrid CNN‐RNN model is highly effective for rainfall‐runoff analysis in the Potteruvagu watershed.

Sequential node search for faster neural architecture search

Neural Architecture Search (NAS) has progressed significantly by reducing search costs since it was first introduced. The faster NAS algorithms use cell-type architectures. The cells are made up of nodes, and typically, all the node operations are searched together. This paper proposes a novel algorithm that searches the nodes of the cell type NAS independently of one another. This is done in a sequential manner, hence named SeQuential Neural Architecture Search (SQNAS). It is hypothesized that the hierarchical nature of nodes’ feature tensors is the proper guide to organize the sequence of the nodes. SQNAS reduces the size of the super graph search space at any instant of search, thus reducing the search cost considerably. The independence assumption is justified considering the competitive performance obtained. SQNAS search is also done in cycles of several restarts for better regularization. With a single NVIDIA GTX 1080Ti GPU, the search is completed in 6 h for the image classification task, obtaining a 2.55% test error on CIFAR10. For the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) cells searched on smaller datasets, the transferability to deeper models with larger datasets is tested with ImageNet and WikiText-2. Partial channel SQNAS was also implemented, which completed the search in just 1.5 h on the CIFAR10 dataset with competitive test results. This is one of the fastest searches achieved on CIFAR10.

Intelligent Advanced Attack Detection Technology based on Multi-modal Data Fusion

This paper proposes an adaptive Wedman intrusion detection algorithm (AID-DFS) for data fusion. Firstly, feature extraction of abnormal text detection is carried out using a BI-gated loop (Bi-GRU). Multi-branch convolutional recurrent neural network (CNN-RNN) extracts hierarchical features from abnormal images. The multi-mode dynamic fusion uses the intermodal and intramodal attention mechanisms. In this way, a joint representation of multiple modes is obtained. The visual perception mechanism is used to realize multichannel integration and strengthen the function of original information in multichannel. The experimental results show that the proposed method has 99.6% accuracy and 94.9% accuracy. Compared with other algorithms, the proposed method can improve the performance of the intrusion system by about 10.2%.

Utilizing a CNN-RNN machine learning approach for forecasting time-series outlet fluid temperature monitoring by long-term operation of BHEs system

The Borehole Heat Exchanger (BHE) plays a pivotal role in enhancing heat exchange efficiency within Ground Source Heat Pump (GSHP) systems. The accurate prediction of the BHE's outlet fluid temperature is crucial for optimizing GSHP performance, energy storage, and resource conservation. However, conventional machine learning methods encounter challenges in manual feature extraction, learning complex nonlinear relationships, and adapting to real-world scenarios. To address these limitations, this research proposes a crossbreed model integrating Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures to forecast long-term outlet fluid temperature in BHE systems. The model framework encompasses data preprocessing, utilizing refined data in the CNN module for temporal feature extraction, subsequently passed to the RNN module to capture sequential and temporal patterns from each dataset. Specifically, the advanced CNN-RNN architecture is designed to establish a comprehensive input-output mapping, leveraging essential input features such as inlet fluid, ambient air, and subsurface temperatures at varying depths (0, 10, and 20 m). Performance evaluation metrics, including R2, RMSE, MAE, and AARE, are employed to compare and assess prediction accuracy across various models, including LSTM, CNN, and SimpleRNN. The obtained results demonstrate the superior performance of the proposed model, achieving an RSME of 0.818, MAE of 0.642, AARE of 0.0305, and an R2 value of 98.75 %. This surpasses the performance of traditional prediction models (LSTM, CNN, and SimpleRNN) by 3.01 %, 5.80 %, and 19.52 %, respectively. Notably, the remarkably low MAE of 0.642 exhibited by a CNN-RNN model underscores its capability to outperform traditional approaches, especially when handling large datasets. These findings emphasize the significance of the developed model in facilitating efficient operation, positioning it as a valuable tool for advancing the long-term sustainability of BHE systems.

Enhanced Intrusion Detection System Using Deep Learning Algorithms : A Review

Intrusion Detection Systems (IDS) are crucial for protecting network infrastructures from advanced cyber threats. Traditional IDS, largely reliant on static signature detection, fail to effectively counter novel cyber attacks, leading to high false positive rates and missed zero-day exploits. This study investigates the integration of deep learning technologies into IDS to enhance their detection capabilities. By employing advanced deep learning frameworks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and other algorithms , the research explores their efficacy in identifying complex data patterns and anomalies. Furthermore, the use of big data analytics is assessed for its potential to significantly augment the predictive power of these systems, aiming to set new benchmarks in cybersecurity defenses tailored for contemporary threats.

A Review of Text Classification Based on ML & Data Mining Algorithms

In the digital era, the field of text classification has experienced transformative growth through the application of Machine Learning (ML) and Data Mining (DM) algorithms. This review traces the evolution from traditional data mining methods to sophisticated ML strategies that significantly enhance the analysis and categorization of textual data. We discuss pivotal technologies including Bayesian classifiers, Support Vector Machines (SVM), and contemporary advances such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The integration of Natural Language Processing (NLP) techniques is highlighted for their critical role in enriching semantic analysis capabilities, a necessity for effective text classification. Additionally, the paper addresses challenges like handling high-dimensional data, dealing with imbalanced datasets, and confronting ethical issues such as bias and privacy in automated systems. By synthesizing the latest research, this review identifies current gaps, proposes practical solutions, and forecasts future trends in text classification to support ongoing research and application across various sectors.