Bidirectional Gated Recurrent Unit Research Articles

Diverse ensemble classifier driven Email spam classification using multiple word embedding’s with COCOB optimizer

Spam Email is a serious concern which can steal user’s personal information and cause huge financial loss due to the increasing rate of internet users. Therefore, the demand for accurate spam filtering has become more sophisticated for the Email spam detection. In the existing techniques, it is difficult to intricate the relationship between words in the Email using certain word embedding techniques and learning rate tuning is one of the greatest challenges of stochastic optimization. To overcome this difficulty, the proposed framework uses diverse ensemble based Email spam classification by incorporating multiple word embedding’s with Continuous Coin Betting optimizer. Word2Vec is used to produce the first set of 200D, next set of 200D word embedding is produced by Glove and 768D is produced by using Bidirectional Encoder Representations from Transformers (BERT) respectively. After generating word embedding, then it is classified through diverse ensemble based classifier with base level classifier consists of Long Short Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) and Bi-directional Gated Recurrent Unit (Bi-GRU) and LSTM as Meta-classifier using COCOB optimizer. Experiments were conducted on 3 benchmark Email dataset and result shows that the proposed system outperforms well with a low false positive rate.

Sentiment Analysis Using Deep Learning Approaches on Multi-Domain Dataset in Telugu Language

Recent advancements in Natural Language Processing (NLP) have made sentiment analysis an essential component of a variety of NLP jobs, including recommendation systems, question answering, and business intelligence products. While sentiment analysis research has, to put it mildly, been widely pursued in English, Telugu has barely ever attempted the task. The majority of research works concentrate on analysing the sentiments of Tweets, news, or reviews containing Hindi and English words. There is a growing interest among academics in studying how people express their thoughts and views in Indian languages like Bengali, Telugu, Malayalam, Tamil and so on. Due to a paucity of labelled datasets, microscopic investigation on Indian languages has been published to our knowledge. This work suggested a sentence-level sentiment analysis on multi-domain datasets that has been collected in Telugu. Deep learning models have been used in this work because it demonstrates the significant expertise in sentiment analysis and is widely regarded as the cutting-edge model in Telugu Sentiment Analysis. Our proposed work investigates a productive Bidirectional Long Short-Term Memory (BiLSTM) Network and Bidirectional GRU Network (BiGRU) for improving Telugu Sentiment Analysis by encapsulating contextual information from Telugu feature sequences using Forward-Backward encapsulation. Further, the model has been deployed by merging the domains so as to predict the accuracy and other performance metrics. The experimental test findings show that the deep learning models outperform when compared with the baseline traditional ML methods in four benchmark sentiment analysis datasets. There is evidence that the proposed sentiment analysis method has improved precision, recall, F1-score and accuracy in certain cases. The proposed model has achieved the F1-score of 86% for song datasets when compared with the other existing models.

Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model

Edge computing has emerged as an attractive alternative to traditional cloud computing by utilizing processing, network, and storage resources close to end devices, such as Internet of Things (IoT) sensors. Edge computing is still in its infancy, and resource provisioning and service scheduling remain research concerns. Kubernetes is a container orchestration tool in distributed environments. Proactive auto-scaling techniques in Kubernetes improve utilization by allocating resources based on future workload prediction. However, prediction models run on central cloud nodes, necessitating data transfer between edge and cloud nodes, which increases latency and response time. We present FedAvg-BiGRU, a proactive auto-scaling method in edge computing based on FedAvg and multi-step prediction by a Bidirectional Gated Recurrent Unit (BiGRU). FedAvg is a technique for training machine learning models in a Federated Learning (FL) model. FL reduces network traffic by exchanging only model updates rather than raw data, relieving learning models of the need to store data on a centralized cloud server. In addition, a technique for determining the number of Kubernetes pods based on the Cool Down Time (CDT) concept has been developed, preventing contradictory scaling actions. To our knowledge, our work is the first to employ FL for proactive auto-scaling in cloud and edge computing. The results demonstrate that the FedAvg-BiGRU method has a slightly higher prediction error than the load centralized processing mode, although this difference is not statistically significant. At the same time, it reduces the amount of data transmission between the edge nodes and the cloud server.

Evaluating and forecasting methods for assessing the health status of cables under the load of large-scale electric vehicle charging

The assessment of the health status and prediction of the lifespan of cable equipment are critical for ensuring the stability and efficiency of the power grid. This paper develops a temperature-current-capacity-life calculation model for cables, considering the fast and slow charging demands of electric vehicles (EVs). Analyses under scenarios of rapid and slow charging demands are conducted, introducing a cable health index and establishing a health status assessment framework based on this index. The framework accounts for various factors leading to cable faults, offering a comprehensive evaluation of the health status of cables with different fault rates. Building upon this, a prediction method using the Fire Hawk Optimization (FHO) Algorithm and Convolutional Neural Network (CNN) is proposed. This method enhances performance by optimizing the hyperparameters of Bidirectional Gated Recurrent Unit (BiGRU) through FHO, effectively searching and determining the optimal hyperparameter configuration. The impact of different scenarios and varying EV penetration rates on cable temperature is analyzed through case studies, facilitating the assessment and prediction of health status.

Stratification of Depressed and Non-Depressed Texts from Social Media using LSTM and its Variants

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models’ precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis.

Data tweet clustering using bidirectional gated recurrent unit and k-prototype for the Indonesian political year

As time passes, social media, which was formerly used as a means of communication between users, is experiencing a transition as a means for broadcasting information, conducting business, advertising, and even political campaigning. In elections, social media is also used to discredit political opponents to reduce the electability of opposing candidate. Spreading hate speech and fake news to undermine the electability of opposing candidate is a common violation of the law committed by supporters of one candidate over another. Considering that the number of social media users increases annually at a very rapid rate, the hazard of social media abuse has the potential to grow. In 2022, Indonesia had 191 million social media users in January 2022. Obviously, this will make the election situation more tumultuous and has the potential to cause societal divisions. The government must have a control system in place to screen social media content that can be considered illegal. In this study, fake news and hate speech are classified using the Bidirectional Gated Recurrent Unit (BiGRU). Lastly, K-Prototype was used to do clustering based on categorization dimensions and probable distribution to identify which clusters had the greatest risk of breaking the law, creating confusion, and dispersing broadly throughout society. It is hoped that the clusters that are created will represent the levels of priority of tweet data that requires prompt attention from the government to prevent it from spreading and inciting social unrest. Based on the results of the analysis, the BiGRU fake news model yields a F1-score of 95%, while the BiGRU hate speech model yields a F1-score of 90%. Clustering data using K-Prototype in this research can reduce the number of tweet data from 13,183 to 1,791 data. These new data are considered as a priority that must be pursued in preventing social media disputes.

A multi-indicator prediction method for NOx emission concentration and ammonia escape value for cement calciner system

It is particularly important to accurately predict NOx emission concentration in cement production. As the cement industry is a process industry, there are many chemical reactions, coupling of front and back processes, and large fluctuations in production, resulting in time-delayed denitration data, strong coupling, and uncertainty, which makes it difficult to adequately extract the characteristics of the denitration data to achieve accurate prediction of NOx concentration. At the same time, excessive ammonia is used in production to reduce NOx concentration, resulting in increased ammonia escape and secondary hazards to the environment, therefore it is necessary to perform accurate multi-step prediction of NOx concentration and ammonia escape. To address these issues, this paper presented a new multicriteria deep learning hybrid model MI-ITBA. The model is constructed by connecting an improved causal dilated temporal convolutional network (ITCN) with bidirectional gated recurrent units (BiGRU) and a special attention mechanism, which enhances the ability to extract features from denitration sequence, using the attention mechanism to weigh the importance of different time steps and highlight the influence of key features on the prediction results. In addition, the time series (TS) of denitration containing time-delay information is designed in the input of the model to minimize the influence of time delay. Dynamic information support for denitration control was provided by multi-step prediction of NOx concentration at seven moments and ammonia escape value at three moments to retain the dynamics of the data. Compared with TCN, BiGRU, and TCN-BiGRU, MI-ITBA has a high prediction accuracy, which can provide an effective guide to realizing NOx emission reduction and accurate ammonia injection.

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

Sentiment Analysis (SA) is a crucial task in understanding public opinions and perceptions towards emerging technologies. In this study, we focus on SA for a self-driving car dataset as it provides valuable insights into public perceptions and opinions towards a transformative technology. The dataset consists of textual reviews associated with sentiment labels, providing insights into how people perceive self-driving car technology. Our objective is to analyze the sentiments expressed in these reviews using Deep Learning (DL) models, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). We compared our results with an existing technique in the field of self-driving car sentiment classification, that implemented various Machine Learning (ML) and DL models, including Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), CNN, and LSTM. In our study, we expanded upon this research by evaluating the performance of ANN, BiLSTM, GRU, and BiGRU. Results reveal that BiLSTM, GRU, and BiGRU exhibit superior performance in sentiment classification within the self-driving car dataset. These findings offer valuable insights into public sentiment towards self-driving cars, contributing significantly to the advancement of SA techniques in the domain of autonomous vehicles. Additionally, the results are statistically tested and are statistically significant.

Integrating Ebola optimization search algorithm for enhanced deep learning-based ransomware detection in Internet of Things security

<abstract> <p>With the widespread use of Internet, Internet of Things (IoT) devices have exponentially increased. These devices become vulnerable to malware attacks with the enormous amount of data on IoT devices; as a result, malware detection becomes a major problem in IoT devices. A reliable and effective mechanism is essential for malware detection. In recent years, research workers have developed various techniques for the complex detection of malware, but accurate detection continues to be a problem. Ransomware attacks pose major security risks to corporate and personal information and data. The owners of computer-based resources can be influenced by monetary losses, reputational damage, and privacy and verification violations due to successful assaults of ransomware. Therefore, there is a need to swiftly and accurately detect the ransomware. With this motivation, the study designs an Ebola optimization search algorithm for enhanced deep learning-based ransomware detection (EBSAEDL-RD) technique in IoT security. The purpose of the EBSAEDL-RD method is to recognize and classify the ransomware to achieve security in the IoT platform. To accomplish this, the EBSAEDL-RD technique employs min-max normalization to scale the input data into a useful format. Also, the EBSAEDL-RD technique makes use of the EBSA technique to select an optimum set of features. Meanwhile, the classification of ransomware takes place using the bidirectional gated recurrent unit (BiGRU) model. Then, the sparrow search algorithm (SSA) can be applied for optimum hyperparameter selection of the BiGRU model. The wide-ranging experiments of the EBSAEDL-RD approach are performed on benchmark data. The obtained results highlighted that the EBSAEDL-RD algorithm reaches better performance over other models on IoT security.</p> </abstract>

Scraper factors investigation on Al2O3 paste flow based on marine predators algorithm-bidirectional gated recurrent unit pseudo-lattice Boltzmann method for stereolithography molding

The coating process is an essential part of stereolithography technology to mold alumina paste, achieved using a scraper. The characteristics of the scraper (geometry and roughness) are investigated to discover the potential influence mechanism on the coating process. To complete the analysis, the bidirectional gated recurrent unit (BiGRU) optimized by the marine predator algorithm (MPA) is proposed to provide a predicted model for the rheological data instead of the traditional fitting by using the known rheological equations. Then, a pseudo-lattice Boltzmann (pseudo-LB) method is proposed to conduct the coating flow simulation without a specific rheological equation, where the predicted rheological model is applied to obtain the relaxation time for each iteration. Based on the above MPA-BiGRU pseudo-LB method, three geometries and different roughnesses are explored for the coating process. The results indicate that the rectangle geometry suits the large coating speed, and the trapezium geometry is recommended for conditions with the small coating speed. The roughness significantly affects the coating process with the rectangle geometry, and the smoother scraper generates a minor disturbance. However, it is almost ineffective in the cases with trapezium geometry.

PV power prediction based on feature selection and GMM clustering

The increase in the installed capacity of photovoltaics (PV) brings great challenges to the stable operation of the power system. To improve the accuracy of PV power prediction, Spearman’s rank correlation coefficient and Grey Relation analysis (GRA) are combined to analyze and screen each meteorological feature to ensure the effectiveness and reasonableness of the input features. Based on the analysis of meteorological features, the Gaussian Mixture Models (GMM) clustering method is proposed to cluster the historical data sets. The historical data are clustered into two categories: simple PV power change and complex PV power change, which facilitates the model to learn the power change characteristics under different weather conditions. A Bidirectional Gated Recurrent Unit (BiGRU) prediction model is built to mine the timing properties of the two types of data and trained and tested separately. By setting several error evaluation indexes for comprehensive judgment, the methods in this paper are better than several other classical prediction methods and have a strong adaptability to different weather conditions.

MIDGNWD : Design of an efficient multimodal iterative deep graph network wearable interface for detecting drowsy drivers

Drowsy driving is a significant contributor to road traffic accidents, necessitating the development of robust and efficient detection systems. Existing approaches for detecting drowsy drivers often rely on single modalities such as image-based facial analysis or vehiclebased metrics, which may lack precision and be susceptible to environmental variations. Furthermore, the existing models might be computationally expensive and result in delayed responses. To address these limitations, this paper proposes a novel Multimodal Iterative Deep Graph Network Wearable Interface that leverages a rich set of data including heart rate, respiration, accelerometer movement, location (speed), traffic, and weather data. These data samples are processed through a fusion of different Recurrent Neural Network processes. The Bidirectional Long Short-Term Memory (BiLSTM) which is used to generate augmented features, is fused with Bidirectional Gated Recurrent Unit (BiGRU) which assists in estimating multidomain feature sets. These features are then used to train an efficient Deep Graph Recurrent Network (DGRN), which facilitates the real-time identification of drowsy drivers. The proposed model significantly enhances the precision of drowsy driver detection by 8.5%, accuracy by 9.5%, recall by 8.3%, and Area Under the Curve (AUC) by 4.9%, while also improving specificity by 5.5% and reducing delay by 10.4%. These improvements collectively contribute to a substantial enhancement in road safety, potentially saving lives by preventing accidents due to drowsy driving. The proposed system, with its real-time processing capability and high accuracy, stands as a significant improvement for identifying drowsy drivers, paving the way for safer and smarter transportation systems.

Sarcasm Detection in Indonesian-English Code-Mixed Text Using Multihead Attention-Based Convolutional and Bi-Directional GRU

Sarcasm Detection in Indonesian-English Code-Mixed Text Using Multihead Attention-Based Convolutional and Bi-Directional GRU

An intelligent chatbot using deep learning with bidirectional GRU and CNN model

An intelligent chatbot using deep learning with bidirectional GRU and CNN model

Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU

Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU

Bidirectional Gated Recurrent Unit with Glove Embedding and Attention Mechanism for Movie Review Classification

During the current Internet era, enormous volume of structured and unstructured textual data is generated and exchanged online that made the text classification more crucial. With the massive amount of user-generated content on social media platforms, businesses and organizations can leverage sentiment analysis to understand customer opinions and attitudes towards the products, services, or brand. Text classification is a natural language processing task in which a machine learning model is trained to categorize the text into predefined classes or categories. Due to the advent of promising Deep Learning techniques, intelligent and accurate text classification system can be developed. In movie review classification, algorithms are used to automatically categorize movie reviews into positive, negative, or sometimes neutral sentiments based on the opinions expressed in the text. The goal is to automate the process of determining whether a review reflects a positive or negative sentiment, helping users quickly understand the overall reception of a movie. This work proposes an automated movie review classification system based on Bidirectional Gated Recurrent Unit encoder with attention module. This method encodes the words using GloVe word embedding technique. Context information is captured in an efficient manner and the encoding is more reliable. Bidirectional GRU layer captures long-term dependencies in sentences, followed by a custom Attention layer that assigns higher weights to key components. The attention mechanism allows the model to selectively focus on important parts of the input sequence, by dynamically determining attention weights based on task relevance. The model was individually trained and evaluated using popular benchmark IMDb dataset. Experimental studies have proved the better performance of proposed movie review classification system that obtained accuracy of 98%.

DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

DeepBiG: A Hybrid Supervised CNN and Bidirectional GRU Model for Predicting the DNA Sequence

Research on Automatic Annotation Method of Korean Language under Data Driving and Fusion

Abstract In the quest to streamline Korean text and speech annotation, this research introduces innovative automatic annotation methods that promise to revolutionize efficiency and technical prowess in constructing Korean annotation datasets. By leveraging the sophisticated Seq2Seq architecture with BERT and bidirectional GRU models, we significantly enhance the model’s ability to grasp contextual nuances, ensuring precise text annotations. The speech annotation frontier benefits from a novel amalgamation of the Hidden Markov Model’s forced alignment and semi-supervised learning, perfected with Seneff auditory features for meticulous phonological consonant boundary detection. Empirical validation across diverse datasets showcases our methodology’s superiority, achieving a remarkable 96.01% accuracy in text annotation and setting a new benchmark for phonological boundary detection at a 14.5ms minimum distance threshold. Our approach outperforms traditional algorithms, marking a pivotal step forward in Korean automatic annotation.

Enhancing User-Item Interaction Through Counterfactual Classifier For Sequential Recommendation

Abstract Sequential recommendation systems are vital for enhancing user experience and efficiency in e-commerce and online platforms by providing personalized suggestions. However, these systems often encounter challenges such as data sparsity and inaccuracy in identifying user preferences, leading to suboptimal recommendation quality. Existing solutions, like data augmentation and causal inference, have attempted to tackle these issues but have frequently neglected the crucial aspect of user feedback, which could potentially misrepresent user interests. In this paper, a new approach is introduced that incorporates a counterfactual classifier, textual information, and user feedback to enhance user-item interactions and the effectiveness of sequential recommendation systems. Our proposal is EUICC-SRec, a new methodology that incorporates a counterfactual classifier, a text module with bidirectional GRU channels, and a cross-attention mechanism. This method is designed to address data sparsity and enhance the precision of sequential recommendations by improving accuracy and relevance. The proposed approach has been evaluated through extensive experiments, demonstrating its superiority in mitigating data sparsity issues and accurately capturing user preferences compared to existing methods.

TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs

With the continuous growth in global population and productivity, the demand for natural gas, the cleanest fossil fuel, is expected to increase significantly. Accurate daily gas production forecasting of shale and sandstone reservoirs ensures a reliable gas supply. However, the complex and non-linear gas data (reservoir and production data) makes this difficult. To address these challenges, we propose a novel model named TimeNet, which utilizes a mix of convolutional neural networks (CNN), bidirectional gated recurrent units (BiGRU), attention mechanisms (AM), and Time2Vec. Time2Vec is integrated to automatically capture important complex and non-linear temporal information and mitigate burdensome time series pre-processing. The CNN layer extracts spatial features influencing gas production, while the BiGRU captures high-level temporal features and irregular trends in the time series data. The AM helps in understanding embedded information for accurate learning. Each component of the TimeNet model serves a distinct function in the prediction task, optimizing its strengths. Testing on two real-world datasets from the Fenchuganj conventional sandstone gas field and the Marcellus shale gas field confirms the proposed model's effectiveness. Comparative analysis demonstrates the superior performance of the proposed model on the two datasets, exhibiting an R2 of 97.25 % and 97.57 % in shale and sandstone gas, respectively.