Attention-based Bidirectional Long Short-term Memory Research Articles

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Deep Neural Networks for Location Reference Identification From Bilingual Disaster-Related Tweets

Twitter is increasingly being used during disasters to communicate with authorities, ascertain the ground reality, and coordinate real-time rescue and recovery activities. Geographical location information about users and events is critical in these scenarios. Geotagged tweets are extremely infrequent, and other location fields, such as user location and place name, are unreliable. The extraction of geographical information from tweet text is limited by the fact that individuals frequently publish multilingual tweets that contain numerous grammatical and spelling errors, as well as nonstandard acronyms. As a result, determining the geographical location of the tweet is a challenging problem. This article presents a technique based on deep neural networks for extracting geographical references mentioned in bilingual tweets. Several deep learning-based models, including convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and attention-based Bi-LSTM, are implemented on real-world English and Hindi language tweets to determine their suitability for extracting location references. The proposed CNN, along with a conditional random field at the last layer, is found to perform better than other models, with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_1$</tex-math> </inline-formula> -score of 0.858. The findings of this study can aid in early event detection, pinpointing the area of devastation and victims, real-time traffic management, and a number of other location-based applications. The suggested system’s code and trained model may be obtained at https://github.com/Abhinavkmr/Bi-lingual-location-reference-identification.git.

Privacy-Preserving Federated Learning for Value-Added Service Model in Advanced Metering Infrastructure

Advanced metering infrastructure (AMI) is the backbone of the next generation smart city and smart grid; it not only provides near real-time two-way communication between the consumers and the energy systems, but also enables the third parties (TPs) to provide relevant value-added services to the consumers to improve user satisfaction. However, the existing services are implemented in a centralized manner, which has potential and associated security and privacy risks also increased with Internet-of-Things (IoT) devices. To better balance the quality of the services and ensure users’ privacy, a TP AMI service model based on differentially private federated learning (FL) is proposed in this article. Instead of sending the private energy data to the cloud server, the proposed service model trains the neural network models locally, and only model parameters are shared with the central server. Moreover, the identity of individuals is eliminated by adding random Gaussian noise during the secure aggregation. Furthermore, an attention-based bidirectional long short-term memory (ATT-BLSTM) neural network model is adopted to solve the long-range dependency problem of conventional neural networks. In the case study, a residential short-term load forecasting (STLF) task is implemented to evaluate the performance of the proposed model. Compared with other state-of-the-art energy service models, the proposed one can achieve similar accuracy as the typical centralized model and balances the trade-off between privacy loss and prediction accuracy flexibly.

Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning

Wireless Body Sensor Network (BSN) comprises wearables with different sensing, processing, storing, and broadcast abilities. Once several devices acquire the data, multi-sensor fusion was needed for transforming erroneous sensor information into maximum quality fused data. Deep learning (DL) approaches are utilized in different application domains comprising e-health for applications like activity detection, and disease forecast. In recent times, it can be demonstrated that the accuracy of classification techniques is enhanced by the combination of feature selection (FS) approaches. This article develops a Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning (MDFMDC-GTODL) algorithm. The proposed MDFMDC-GTODL method enables collection of various daily activity data using different sensors, which are then fused to produce high-quality activity data. In addition, the MDFMDC-GTODL technique applies optimal attention based bidirectional long short term memory (ABLSTM) for heart disease prediction. In this study, Gorilla Troops Optimization Algorithm based FS (GTOA-FS) technique is involved to improve the classification performance. The simulation outcome of the MDFMDC-GTODL technique are validated and the results are investigated in different prospects. A wide-ranging simulation analysis stated the better performance of the MDFMDC-GTODL method over other compared approaches.

Golden jackal optimization algorithm with deep learning assisted intrusion detection system for network security

Network security is essential to our daily communications and networks. Cybersecurity researchers initiate the significance of emerging proficient network intrusion detection systems (IDS) for providing secure networks. While attackers endure to progress novel kinds of attacks and the network sizes endure to develop, the necessity for effectual IDS becomes significant. Additionally, IDS aims to offer confidentiality, integrity, and availability for the data communicated in networked computers by avoiding illegal access to the network. Several studies executed machine learning (ML) systems for emerging effectual IDS; then, with the advent of deep learning (DL) systems and artificial neural networks (ANNs) that create features automatically without human interference, researchers started to depend on DL approaches. This study introduces a new Golden Jackal Optimization Algorithm with Deep Learning Assisted Intrusion Detection System for Network Security (GJOADL-IDSNS) technique. The major intention of the GJOADL-IDSNS system lies in the effectual recognition and classification of the intrusions, to achieve network security. Primarily, data normalization is performed to scale the input data into a useful format. In the presented GJOADL-IDSNS technique, the GJOA-based feature selection (GJOA-FS) approach can be employed to elect an optimum subset of features. Next, the GJOADL-IDSNS methodology applies the attention-based bi-directional long short-term memory (A-BiLSTM) model. For hyperparameter tuning of the A-BiLSTM model, the GJOADL-IDSNS technique uses the salp swarm algorithm (SSA). The simulation value of the GJOADL-IDSNS technique has been tested utilizing benchmark datasets. The comparative results stated that the GJOADL-IDSNS technique achieves better performance than other models.

Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model

In the domain of network security, intrusion detection systems (IDSs) play a vital role in data security. While the utilization of the internet amongst consumers is increasing on a daily basis, the significance of security and privacy preservation of system alerts, due to malicious actions, is also increasing. IDS is a widely executed system that protects computer networks from attacks. For the identification of unknown attacks and anomalies, several Machine Learning (ML) approaches such as Neural Networks (NNs) are explored. However, in real-world applications, the classification performances of these approaches are fluctuant with distinct databases. The major reason for this drawback is the presence of some ineffective or redundant features. So, the current study proposes the Network Intrusion Detection System using a Lion Optimization Feature Selection with a Deep Learning (NIDS-LOFSDL) approach to remedy the aforementioned issue. The NIDS-LOFSDL technique follows the concept of FS with a hyperparameter-tuned DL model for the recognition of intrusions. For the purpose of FS, the NIDS-LOFSDL method uses the LOFS technique, which helps in improving the classification results. Furthermore, the attention-based bi-directional long short-term memory (ABiLSTM) system is applied for intrusion detection. In order to enhance the intrusion detection performance of the ABiLSTM algorithm, the gorilla troops optimizer (GTO) is deployed so as to perform hyperparameter tuning. Since trial-and-error manual hyperparameter tuning is a tedious process, the GTO-based hyperparameter tuning process is performed, which demonstrates the novelty of the work. In order to validate the enhanced solution of the NIDS-LOFSDL system in terms of intrusion detection, a comprehensive range of experiments was performed. The simulation values confirm the promising results of the NIDS-LOFSDL system compared to existing DL methodologies, with a maximum accuracy of 96.88% and 96.92% on UNSW-NB15 and AWID datasets, respectively.

Attention-based Spatialized Word Embedding Bi-LSTM Model for Sentiment Analysis

Movie reviews provide a medium of communication for the movie fans community. Movie reviews not only help viewers and potential viewers to obtain a general opinion about a movie but also allow the fans to construct an opinion of the movie. In this work, an analysis of over 60,000 movie reviews has been implemented to find meaningful text representation via text embedding. We improved the text embedding by proposing an attention-based Bidirectional Long-Short Term Memory (Bi-LSTM) network by using over 60,000 movie review text data as the training set and over 20,000 movie review text data as the testing set. Based on the data features, we performed a probabilistic analysis of the information related to words and phrases, combined the analysis results with text embedding, spatialized the text embedding, and compared the performance of the proposed attention-based spatialized word embedding Bi-LSTM model with several traditional machine learning models. The attention-based spatialized word embedding Bi-LSTM model proposed in this paper achieves an F1 score of 0.91 on the movie review sentiment classification dataset, with a prediction accuracy of 91%, outperforming the results of the current state-of-the-art research. The model can effectively identify the sentimental tendencies of movie reviews and use the analyzed sentimental tendencies to guide consumers in their consumption and obtain feedback on movie content.

TB-MFCC multifuse feature for emergency vehicle sound classification using multistacked CNN – Attention BiLSTM

Vehicles equipped for emergencies like ambulances, fire engines, and police cruisers play a vital role in society by responding quickly to emergencies and helping to prevent loss of life and maintain order. Vehicle sound identification and classification are very important in the cities to identify emergency vehicles easily and to clear the traffic effectively. Convolutional Neural Network plays an important role in the accurate prediction of vehicles during an emergency. The main motive of this paper is to develop a suitable model and algorithms for data augmentation, feature extraction, and classification. The proposed TB-MFCC multifuse feature is comprised of data augmentation and feature extraction. First, in the proposed signal augmentation, each audio signal uses noise injection, stretching, shifting, and pitching separately and this process increases the number of instances in the dataset. The proposed augmentation reduces the overfitting problem in the network. Second, Triangular Bluestein Mel Frequency Cepstral Coefficients (TB-MFCC) are proposed and fused with Zero Crossing Rate (ZCR), Mel-frequency cepstral coefficients (MFCC), Root Mean Square (RMS), Chroma, and Tempogram to extract the exact feature which increases the accuracy and reduces the Mean Squared Error (MSE) of the model during classification. Finally, the proposed Multi-stacked Convolutional Neural Network (MCNN) with Attention-based Bidirectional Long Short Term Memory (A-BiLSTM) improves the nonlinear relationship among the features. The proposed Pooled Multifuse Feature Augmentation (PMFA) with MCNN & A-BiLSTM increases the accuracy (98.66 %), reduces the False Positive Rate (FPR) by 1.01 %, and loss (0 %). Thus the model predicts the sound without overfitting, underfitting, and vanishing gradient problems.

Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment

The Internet of Things (IoT) refers to the network of interconnected physical devices that are embedded with software, sensors, etc., allowing them to exchange and collect information. Although IoT devices have several advantages and can improve people’s efficacy, they also pose a security risk. The malicious actor frequently attempts to find a new way to utilize and exploit specific resources, and an IoT device is an ideal candidate for such exploitation owing to the massive number of active devices. Especially, Distributed Denial of Service (DDoS) attacks include the exploitation of a considerable number of devices like IoT devices, which act as bots and transfer fraudulent requests to the services, thereby obstructing them. There needs to be a robust system of detection based on satisfactory methods for detecting and identifying whether these attacks have occurred or not in a network. The most widely used technique for these purposes is artificial intelligence (AI), which includes the usage of Deep Learning (DL) and Machine Learning (ML) to find cyberattacks. The study presents a Piecewise Harris Hawks Optimizer with an Optimal Deep Learning Classifier (PHHO-ODLC) for a secure IoT environment. The fundamental goal of the PHHO-ODLC algorithm is to detect the existence of DDoS attacks in the IoT platform. The PHHO-ODLC method follows a three-stage process. At the initial stage, the PHHO algorithm can be employed to choose relevant features and thereby enhance the classification performance. Next, an attention-based bidirectional long short-term memory (ABiLSTM) network can be applied to the DDoS attack classification process. Finally, the hyperparameter selection of the ABiLSTM network is carried out by the use of a grey wolf optimizer (GWO). A widespread simulation analysis was performed to exhibit the improved detection accuracy of the PHHO-ODLC technique. The extensive outcomes demonstrated the significance of the PHHO-ODLC technique regarding the DDoS attack detection technique in the IoT platform.

Accurate water quality prediction with attention-based bidirectional LSTM and encoder–decoder

Accurate prediction of water quality indicators can effectively predict sudden water pollution events and reveal them to water users for reducing the impact of water quality pollution. Neural networks, e.g., Long Short-Term Memory (LSTM) and encoder–decoder, have been widely used to predict time series data. However, as the water quality data increases, it becomes unstable and highly nonlinear, and therefore, its accurate prediction becomes a big challenge. To solve it, this work proposes a hybrid prediction method called VBAED to predict the water quality time series. VBAED combines Variational mode decomposition (VMD), a Bidirectional input Attention mechanism, an Encoder with bidirectional LSTM (BiLSTM), and a Decoder with a bidirectional temporal attention mechanism and BiLSTM. The definition of VBAED is an Encoder–Decoder model that uses VMD as mode decomposition, combining BiLSTM with a bidirectional attention mechanism. Specifically, VBAED first adopts VMD to decompose historical data of a predicted factor, and its decomposed results are adopted as the input along with other features. Then, a bidirectional input attention mechanism is adopted to add weights to input features from both directions. VBAED adopts BiLSTM as an encoder to extract hidden features from input features. Finally, the predicted result is obtained by a BiLSTM decoder with a bidirectional temporal attention mechanism. Real-life data-based experiments demonstrate that VBAED obtains the best prediction results compared with other widely used methods.

Assessment of bidirectional transformer encoder model and attention based bidirectional LSTM language models for fake news detection

Fake news arouses to be untrue with the point of deceiving it openly which is now viewed as the greatest threat to society by cultivating the political division and doubts in government. Since this kind of news is disseminated in sheer volume through social media, driving the improvement of strategies for the recognizable proof of false news is necessary. Therefore, this study focuses on text analytics to derive the hidden properties of stylistic content to detect fake and real news. An erudite literature study of fake news detection diverted towards issues such as attention, context, and parallelization. In this same direction, the assessment evaluates the sequential memory-based deep learning model in comparison to the parallel memory-based deep learning model. For sequential, Long Short-Term Memory (LSTM), Bi-Directional LSTM, and Attention-based Bi-directional LSTM are taken into consideration. Besides, for parallel, the transformer-based BERT model is examined. To identify the efficacy of applied approaches, four datasets are taken from diverse domains such as political news, entertainment news, satire news, conspiracy news, and global pandemic news. The experimental analysis of real-world information demonstrates that the pre-trained transformer encoder-based BERT model outperforms with a quite significant margin of improvement. Also, as inspected Attention-based Bi-directional approach provides state-of-the-art results with good training accuracy.

Computer-aided diagnosis using white shark optimizer with attention-based deep learning for breast cancer classification

Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models.

An Attention-based Bidirectional LSTM Model for Continuous Cross-Subject Estimation of Knee Joint Angle during Running from sEMG Signals.

Accurate and robust estimation of joint kinematics via surface electromyogram (sEMG) signals provides a human-machine interaction (HMI)-based method that can be used to adequately control rehabilitation robots while performing complex movements, such as running, for motor function restoration in affected individuals. To this end, this paper proposes a deep learning-based model (AM-BiLSTM) that integrates a bidirectional long short-term memory (BiLSTM) network and an attention mechanism (AM) for robust estimation of joint kinematics. The proposed model was appraised using knee joint kinematic and sEMG signals collected from fourteen subjects who performed running at the speed of 2 m/s. The proposed model's generalizability was tested for both within- and cross-subject scenarios and compared with long short-term memory (LSTM) and multi-layer perceptron (MLP) networks in terms of normalized root-mean-square error and correlation coefficient metrics. Based on the statistical tests, the proposed AM-BiLSTM model significantly outperformed the LSTM and MLP methods in both within- and cross-subject scenarios (p<0.05) and achieved state-of-the-art performance.Clinical Relevance- The promising results of this study suggest that the AM-BiLSTM model has the potential for continuous cross-subject estimation of lower limb kinematics during running, which can be used to control sEMG-driven exoskeleton robots oriented towards rehabilitation training.

Pelican Optimization Algorithm with Deep Learning for Aspect based Sentiment Analysis on Asian Low Resource Languages

Recently, sentiment analysis (SA) has become more popular as it is crucial to moderate and examine the data from the internet. It contains several applications, such as social media monitoring, market research, and opinion mining. Aspect Based Sentiment Analysis (ABSA) is a domain of SA that manages sentiment at a better level. ABSA classifies sentiment in terms of all the aspects for obtaining superior insights as sentiment expressed. A major contribution has been developed in ABSA, and then this progress can be restricted only to some languages with suitable resources. One common method is to utilize machine learning (ML) approaches, namely Neural Networks (NN), Support Vector Machines (SVM), and Naive Bayes (NB), together with Asian and low language-specific resources. These resources offer data on the sentiment polarity (neutral, positive, or negative) of phrases and words that are generally utilized in low-resource languages. In this aspect, this study develops a new Pelican Optimization Algorithm with Deep Learning for ABSA (POADL-ABSA) on Asian and Low Resource Languages. The proposed POADL-ABSA technique focuses on the detection and classification of sentiments. To accomplish this, the POADL-ABSA technique encompasses various levels of operations such as pre-processed, feature vector conversion, and classification. In addition, the POADL-ABSA technique employs the BERT model for feature vector extraction. Besides, attention-based bi-directional long short-term memory (ABiLSTM) system was used for the recognition and classification of sentiments. Finally, the POA was utilized for optimum hyperparameter selection of the ABiLSTM model, and it helps in attaining enhanced sentiment classification results. To ensure the improvised performance of the IAOADL-ABSA technique, an extensive experimental outcome the IAOADL-ABSA technique surpassed other models with acc{u}_y , pre{c}_n , rec{a}_l , and {F}_{score} of 98.72%, 98.71%, 98.72%, and 98.71%, respectively. Therefore, the IAOADL-ABSA technique can be employed for accurate classification results.

Track Signal Intrusion Detection Method Based on Deep Learning in Cloud-Edge Collaborative Computing Environment

Aiming at the low accuracy of the track signal intrusion detection (IDe) algorithm in the traditional cloud-side collaborative computing environment, this paper proposes a deep learning (D-L)-based track signal IDe method in the cloud edge collaborative computing environment. First, the main framework of the IDe method is constructed by comprehensively considering the backbone network, network transmission and ground equipment, and edge computing (EC) is introduced to cloud services. Then, the The CNN (Convolutional Neural Networks)-attention-based BiLSTM (Bi-directional Long Short-Term Memory) neural network is used in the cloud center layer of the system to train the historical data, a D-L method is proposed. Finally, a pooling layer and a dropout layer are introduced into the model to effectively prevent the overfitting of the model and achieve accurate detection of track signal intrusion. The purpose of introducing the pooling layer is to accelerate the model convergence, remove the redundancy and reduce the feature dimension, and the purpose of introducing the dropout layer is to prevent the overfitting of the model. Through simulation experiments, the proposed IDe method and the other three methods are compared and analyzed under the same conditions. The results show that the F1 value of the method proposed in this paper is optimal under four different types of sample data. The F1 value is the lowest of 0.948 and the highest of 0.963. The performance of the algorithm is better than the other three comparison algorithms. The method proposed in this paper is important for solving the IDe signal in the cloud-edge cooperative environment, and also provides a theoretical basis for tracking the signal IDe direction.

Phonocardiogram signal classification for the detection of heart valve diseases using robust conglomerated models

The diagnosis of cardiovascular diseases is quite important in the field of medical community. An important physiological signal of human body is heart sound and it arises due to the blood turbulence and pulsing of cardiac structures. For the early diagnosis of heart diseases, the analysis of heart sounds play an important role as they contain a huge quantity of pathological information associated with heart. To detect heart sounds, Phonocardiogram (PCG) is used as it is a highly useful and non-invasive technique and can be easily analyzed well. In this paper, some efficient models are proposed for the classification of PCG signals. Two important and robust conglomerated models are proposed initially, wherein the first strategy utilizes the concept of semi-supervised Non-negative Matrix Factorization (NMF) along with Brain Storming (BS) optimization algorithm and an advanced version of BS termed as Advanced BS (ABS) is proposed and then it is merged with Genetic Programming (GP) so that new algorithms such as BS-GP and ABS-GP are formed and finally the features selected through it are fed to classification through machine learning. The second strategy utilizes the concept of using three dimensionality reduction techniques along with Fuzzy C-means (FCM) clustering and then an Advanced Sine-Cosine (ASC) optimization algorithm with three different modifications is proposed for the purpose of feature selection and finally it is classified. Deep learning techniques were also employed in the study such as the usage of an Attention based Bidirectional Long Short-Term Memory (A-BLSTM), Ordinal Variational Autoencoder (O-VAE), Conditional Variational Autoencoders (CVAE), Hyperspherical CVAE (H-CVAE) and the Restricted Boltzmann Machine based Deep Belief Network (RBM-DBN) for the classification of PCG signals. The experiment is conducted on a publicly available dataset and results show that a high classification accuracy of 95.39% is obtained for the semi-supervised NMF concept with ABS-GP technique and Support Vector Machine (SVM) classifier.

Enhancing the Performance of Pathological Voice Quality Assessment System Through the Attention-Mechanism Based Neural Network

Doctors, nowadays, primarily use auditory-perceptual evaluation, such as the grade, roughness, breathiness, asthenia, and strain scale, to evaluate voice quality and determine the treatment. However, the results predicted by individual physicians often differ, because of subjective perceptions, and diagnosis time interval, if the patient's symptoms are hard to judge. Therefore, an accurate computerized pathological voice quality assessment system will improve the quality of assessment. This study proposes a self_attention-based system, with a deep learning technology, named self_attention-based bidirectional long-short term memory (SA BiLSTM). Different pitches [low, normal, high], and vowels [/a/, /i/, /u/], were added into the proposed model, to make it learn how professional doctors evaluate the grade, roughness, breathiness, asthenia, and strain scale, in a high dimension view. The experimental results showed that the proposed system provided higher performance than the baseline system. More specifically, the macro average of the F1 score, presented as decimal, was used to compare the accuracy of classification. The (G, R, and B) of the proposed system were (0.768±0.011, 0.820±0.009, and 0.815±0.009), which is higher than the baseline systems: deep neural network (0.395±0.010, 0.312±0.019, 0.321±0.014) and convolution neural network (0.421±0.052, 0.306±0.043, 0.3250±0.032) respectively. The proposed system, with SA BiLSTM, pitches, and vowels, provides a more accurate way to evaluate the voice. This will be helpful for clinical voice evaluations and will improve patients' benefits from voice therapy.

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic.

Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the 'bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text. Neural word embedding has been employed in literature, since it is less labor-intensive than automatic feature engineering. By ignoring the context of sentiment, most of the word-embedding models follow syntactic data of words. The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets (DFODL-SAAT) model. The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language. At first, data cleaning and pre-processing steps are performed to convert the input tweets into a useful format. In addition, TF-IDF model is exploited as a feature extractor to generate the feature vectors. Besides, Attention-based Bidirectional Long Short Term Memory (ABLSTM) technique is applied for identification and classification of sentiments. At last, the hyperparameters of ABLSTM model are optimized using DFO algorithm. The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects. The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.

Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series

Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.