Recurrent Neural Network Technique Research Articles

GRU Based MCS Selection in Tactical Vehicle Communication

In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.

GRU Based MCS Selection in Tactical Vehicle Communication

In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.

Electricity price forecast on day-ahead market for mid- and short terms: capturing spikes in data sequences using recurrent neural network techniques

Electricity price forecast on day-ahead market for mid- and short terms: capturing spikes in data sequences using recurrent neural network techniques

Prediction of multiple transverse cracks in a composite beam using hybrid RNN-mPSO technique

The presence of faults is common in engineering composite structures. Precise detection of faults prior to failure has been the focal point for researchers working in this domain. Detection of damage from the modal characteristics is an age-old promising technique besides the artificial intelligence techniques such as neural network (NN), Artificial neural network (ANN), recurrent neural network (RNN), and particle swarm optimization (PSO) methods. However, multiple detection techniques combined can give better results. In this regard, recurrent neural networks (RNN) and modified particle swarm optimization (mPSO) are combined to detect damages in a glass fiber reinforced polymer (GFRP) composite cantilever beam. The proposed hybrid model consists of two stages; indices of relative natural frequencies applied to locate crack parameters (position and severity) by recurrent neural network technique and optimization of the results (position and severity) by mPSO. The results illustrate this method can be implemented to predict multiple transverse cracks and their parameters in a composite beam, manifested in the change of natural frequencies where the error is limited to 0.97% only.

Fault Diagnosis of Three-Phase Induction Motor (IM) Using a Hybrid ELSE-RNN Technique

This manuscript proposes a hybrid technique for fault detection and classification in a three-phase induction motor (IM). The proposed hybrid technique combines enhanced ladder spherical evaluation (ELSE) and recurrent neural networks (RNN); hence, it is called the ELSE–RNN method. The main goal of the manuscript is to detect and categorize the faults that occur in the IM. The key objective of the proposed method is to enhance the performance of supercapacitor (SC) storage technology and fuzzy-tuned proportional–integral (PI) supervision over conventional control. The main contribution of this paper is developing an effective fault diagnosis method for three-phase IMs. The presence of stator, rotor, winding, and bearing faults is employed using signal processing techniques, such as the Hilbert transform and SIFT. The proposed ELSE–RNN technique is utilized with the end goal of detecting and classifying faults. Here, the proposed ELSE–RNN technique recognizes motors’ healthy or unhealthy conditions in many situations to distinguish faults for protection. The proposed ELSE–RNN technique reduces the complexity of detecting and classifying faults with a validated system and increases the system's accuracy. The ELSE–RNN technique is implemented in MATLAB, and its performance is compared to existing techniques.

RNN-Based Approach for Broccoli Harvest Time Forecast

This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study’s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli growing cycle observations made over seven years. This research incorporates models such as the persistence, thermal, and calendar models, demonstrating their strengths and limitations in calculating the optimal broccoli harvest day. Additionally, Recurrent Neural Network (RNN) models with Long Short-term Memory (LSTM) layers were developed, showcasing enhanced accuracy with an error of less than 2.5 days on average when combined with outputs from the calendar model. In the final comparison, the RNN models outperformed both the thermal and calendar models, with an error of 3.14 and 2.5 days, respectively. Furthermore, this article explores the impact of utilizing Global Ensemble Forecast System forecast weather data as a supplementary source to the in situ observations on model accuracy. The analysis revealed the limited effect of extension with a 9-day forecast on the experimental field, reaching an error reduction of up to 0.04 days. The findings provide insights into the effectiveness of different modeling approaches for optimizing broccoli harvest times, emphasizing the potential of RNN techniques in agricultural decision making.

Exploration of movie evaluation analysis and data preprocessing impact based on RNN technology

In order to provide valuable models for the film and television industry, this study aims to introduce recurrent neural network (RNN) techniques for effective movie evaluation analysis. Semantic analysis using machine learning is a very important means of extracting and understanding the meaning behind the text. The study evaluates different RNN techniques to identify the optimal neural network model. Data preprocessing includes tokenization and embedding, including dataset partitioning, tokenization process and word embedding techniques. The comparative analysis involves the predictive performance of simple RNN, Long Short-Term Memory Network (LSTM) and LSTM with attention. This study also explores the impact of including emoji and punctuation analysis in the data preprocessing process on semantic analysis. The results of the study show that preprocessing emoticons and punctuation improves accuracy, and LSTM with attention shows excellent performance. Notably, the study concludes that LSTM with attention performs well in terms of runtime efficiency, convergence speed, and accuracy compared to other models. The effect of punctuation and emoticons is that it will improve the accuracy. This study helps to improve the quality of the movie by constructing an effective analytical model thus.

A systematic literature review on the applications of recurrent neural networks in code clone research.

Code clones, referring to code fragments that are either similar or identical and are copied and pasted within software systems, have negative effects on both software quality and maintenance. The objective of this work is to systematically review and analyze recurrent neural network techniques used to detect code clones to shed light on the current techniques and offer valuable knowledge to the research community. Upon applying the review protocol, we have successfully identified 20 primary studies within this field from a total of 2099 studies. A deep investigation of these studies reveals that nine recurrent neural network techniques have been utilized for code clone detection, with a notable preference for LSTM techniques. These techniques have demonstrated their efficacy in detecting both syntactic and semantic clones, often utilizing abstract syntax trees for source code representation. Moreover, we observed that most studies applied evaluation metrics like F-score, precision, and recall. Additionally, these studies frequently utilized datasets extracted from open-source systems coded in Java and C programming languages. Notably, the Graph-LSTM technique exhibited superior performance. PyTorch and TensorFlow emerged as popular tools for implementing RNN models. To advance code clone detection research, further exploration of techniques like parallel LSTM, sentence-level LSTM, and Tree-Structured GRU is imperative. In addition, more research is needed to investigate the capabilities of the recurrent neural network techniques for identifying semantic clones across different programming languages and binary codes. The development of standardized benchmarks for languages like Python, Scratch, and C#, along with cross-language comparisons, is essential. Therefore, the utilization of recurrent neural network techniques for clone identification is a promising area that demands further research.

Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations

We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques.

Detection of Myocardial Infarction Using Hybrid Models of Convolutional Neural Network and Recurrent Neural Network

Myocardial Infarction (MI) is the death of the heart muscle caused by lack of oxygenated blood flow to the heart muscle. It has been the main cause of death worldwide. The fastest way to detect MI is by using an electrocardiogram (ECG) device, which generates graphs of heartbeats morphology over a certain period of time. Patients with MI need fast intervention as delay will lead to worsening heart conditions or failure. To improve MI diagnosis, much research has been carried out to come up with a fast and reliable system to aid automatic MI detection and prediction from ECG readings. Recurrent Neural Network (RNN) with memory has produced more accurate results in predicting time series problems. Convolutional neural networks have also shown good results in terms of solving prediction problems. However, CNN models do not have the capability of remembering temporal information. This research proposes hybrid models of CNN and RNN techniques to predict MI. Specifically, CNN-LSTM and CNN-BILSTM models have been developed. The PTB XL dataset is used to train the models. The models predict ECG input as representing MI symptoms, healthy heart conditions or other cardiovascular diseases. Deep learning models offer automatic feature extraction, and our models take advantage of automatic feature extraction. The other superior models used their own feature extraction algorithm. This research proposed a straightforward architecture that depends mostly on the capability of the deep learning model to learn the data. Performance evaluation of the models shows overall accuracy of 89% for CNN LSTM and 91% for the CNN BILSTM model.

Utilizing Recurrent Neural Network Technique for Predicting Strand Settlement on Brittle Sand and Geocell

Utilizing Recurrent Neural Network Technique for Predicting Strand Settlement on Brittle Sand and Geocell

Utilising recurrent neural network technique for predicting strand settlement on brittle sand and geocell

Utilising recurrent neural network technique for predicting strand settlement on brittle sand and geocell

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images.

COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and -ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

Comparative Performance Analysis of Vibration Prediction Using RNN Techniques

Drones are increasingly used in several industries, including rescue, firefighting, and agriculture. If the motor connected to a drone’s propeller is damaged, there is a risk of a drone crash. Therefore, to prevent such incidents, an accurate and quick prediction tool of the motor vibrations in drones is required. In this study, normal and abnormal vibration data were collected from the motor connected to the propeller of a drone. The period and amplitude of the vibrations are consistent in normal vibrations, whereas they are irregular in abnormal vibrations. The collected vibration data were used to train six recurrent neural network (RNN) techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gated recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional GRU (Bi-GRU). Then, the simulation runtime it took for each RNN technique to predict the vibrations and the accuracy of the predicted vibrations were analyzed to compare the performances of the RNN model. Based on the simulation results, the Attn.-LSTM and Attn.-GRU techniques, incorporating the attention mechanism, had the best efficiency compared to the conventional LSTM and GRU techniques, respectively. The attention mechanism calculates the similarity between the input value and the to-be-predicted value in advance and reflects the similarity in the prediction.

Performance evaluation of CNN and R-CNN based line by line analysis algorithms for fibre placement defect classification

The Automated Fibre Placement process is commonly used in aerospace for the manufacturing of structural components, but requires a subsequent inspection to meet the corresponding safety requirements. In order to improve this mostly manual inspection step, machine learning methods for the interpretation of 2D surface images are being increasingly utilised in research. Depending on the manufacturing process, a very long time can elapse between the appearance of a manufacturing defect and its recognition. Hence, in this paper Convolutional and Recurrent Neural Network techniques are presented that allow a line-by-line analysis of the incoming height profile scans of a Laser Line Scan Sensor as a 1D signal, which enables a direct reaction to a defect, even if only one or a few individual height profiles of the defect have been recorded. The combination of Convolutional and Recurrent Neural Network structures is particularly beneficial for this application. The investigations in this paper are especially interesting for developers of automated inspection systems in composite engineering.

A Gaussian High-Dimensional Random Matrix-Based Method for Detecting Abnormal Student Behaviour in Chinese Language Classrooms

In this paper, a Gaussian high-dimensional random matrix approach is used to conduct an in-depth study and analysis of the detection of abnormal student behaviour in Chinese language classrooms, and a corresponding model is designed for practical application. The Gaussian high-dimensional random matrix technique and recurrent neural network technique are applied to build a basic technical framework for intelligent proctoring. An innovative method of human posture estimation is used to complete the proctoring task, and the structure of the Gaussian high-dimensional random matrix method is simplified and the model is compressed to achieve real-time and parallelism of the method. A PolSAR sparse representation classification (OGRM_SRC) algorithm based on orthogonal Gaussian random matrices (OGRM) is proposed to construct an observation dictionary based on polarisation features and OGRM to select typical ground target samples in polarized feature images. The system not only functions as a psychometric assessment of students but also is capable of detecting and analysing abnormal student behaviour based on the improved forest algorithm. The residuals of the pixel to be classified are calculated relative to each atom in the observation dictionary, and the minimum reconstruction residual is used as the classification criterion. The OGRM_SRC algorithm is applied to classify the pixel, and the final classification results are obtained and evaluated for accuracy. We propose a fine-grained action recognition optimisation method for recurrent neural networks, fusing temporal attention information and spatial attention information to improve the action recognition method and improve the model’s ability to recognise fine-grained abnormal behaviours such as looking left, looking right, and copying with head down. Based on this, we conducted some comparative experiments to validate the effectiveness of our work and verified that the system achieved a recognition efficiency of 76.8 FPS and a recognition accuracy of 98.5% in a graphical computing processor.

Omicron Virus Data Analytics Using Extended RNN Technique

The OMICRON case that tainted human beings become first observed in China towards the end of 2021. From that point, OMICRON has spread practically all nations on the planet. To conquer this issue, it requires a fast work to recognize people tainted with OMICRON all the more rapidly. This research article proposes that RNN techniques to be utilized for rapid detection and predicting of OMICRON infections. RNN is finished utilizing the Elman agency and implemented to the OMICRON dataset gathered from Kaggle. The dataset accommodates of 75% preparing information and 25% analyzing information. The learning boundaries utilized were the most extreme age, secret hubs, and late learning. Results are for this exploration results show the level of precision is 88.28. Oddity is one of the elective conclusions for potential OMICRON illness is Recurrent Neural Network (RNN).

Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems

Recurrent neural network (RNN) is one of the most used deep learning techniques in fault detection and diagnosis (FDD) of industrial systems. However, its implementation suffers from some limitations presented in the hard training step and the high time complexity. Besides, most used RNN-based FDD techniques do not deal with system uncertainties. Therefore, this paper proposes enhanced RNN techniques that detect and classify faults in wind energy conversion (WEC) systems. First, we develop a reduced RNN in order to simplify the model in terms of training and complexity time as well. Reduced RNN is based on Hierarchical K-means clustering to treat the correlations between samples and extract a reduced number of observations from the training data matrix. Second, two reduced RNN-based interval-valued-data techniques are proposed to distinguish between the different WEC system operating modes. The proposed techniques for interval-valued data are able to improve both fault diagnosis robustness and susceptibility while maintaining a satisfactory and stable performance over long periods of process operation. The presented results confirm the high feasibility and effectiveness of the proposed FDD techniques (an accuracy greater than 98% for all the proposed methods).

Cash stock strategies during regular and COVID-19 periods for bank branches by deep learning

Determining the optimal amount of cash stock reserved in each bank branch is a strategic decision. A certain level of cash stock must be kept and ready for cash withdrawal needs at a branch. However, holding too much cash not only forfeits opportunities to make profit from the exceeding amount of cash in the stock but also increases insurance cost. This paper presents cash stock strategies for bank branches by using deep learning. Deep learning models were applied to historical data collected by a retail bank to predict the cash withdrawals and deposits. Data preparation and feature selection to identify important attributes from the bank branch data were performed. In the prediction process, two Recurrent Neural Network techniques—Long Short-Term Memory and Gated Recurrent Units methods—were compared. Then prediction errors were measured and statistically tested for their probability distributions. These distributions together with the predicted values were used in determining the lower and upper bounds for holding the cash stock. These bounds were employed to recommend the cash stock level strategies by having two options for different situations. The impacts of COVID-19 were also tested and discussed. According to the bank under this study, the proposed strategies can reduce the amount of cash stock by more than 10% for which was their initial target. Hence, the costs of cash management such as insurance cost and cash transportation cost were reduced. Moreover, the excess cash could be used for other purposes of the bank.

A Novel Approach of Text to Audio Conversion for Blindly Disable Using Raspberry Pi

One of the most difficult tasks for visually impaired people is reading. Blind persons may now recognise boundaries, read labels or certain currencies, and even read written, typed, or printed information, thanks to a technology developed by a group of academics. In this work, an assistance system is designed for blind persons who are visually challenged by using Raspberry Pi as a 32-bit micro controller that utilizes the character recognition technology for image to audio conversion; this work proposes a more effective solution. A camera is the heart of the system, serving as the primary vision for identifying product reports, bills, bank account information, menu boards, school handouts, product packaging, and medication bottle directions. The proposed technique consists of four major steps: i) object identification, ii) text localization, iii) text extraction, iv) text to speech conversion. An optical character recognition (OCR) after recognizing printed text with a technique, the image is digitally processed to extract the label with the use of an open CV library. After the Region of Interest is extracted from the congested backdrop, the text localization technique is utilized to find and extract the text. The ROI's text is retrieved and converted into speech. For training the words independently, a convolutional recurrent neural network technique is proposed.