Deep Learning-Based Language Identification in English-Hindi-Bengali Code-Mixed Social Media Corpora

Sequence labeling to detect stuttering events in read speech

Navigating Social Media in #Ophthalmology

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.

In the process of air combat intention identification, expert experience and traditional algorithm are relied on to analyze enemy aircraft combat intention in a single moment, but the identification time and accuracy are not excellent. In this paper, from the dynamic attributes of an airspace fighter air combat target and the dynamic and time series changing characteristics of the battlefield environment, we introduce the bidirectional long short-term memory neural network (BiLSTM + Attention) intention identification method based on the attention mechanism for air combat intention identification. In this method, five kinds of state parameters, including target maneuver type, distance, flight velocity, altitude and heading angle, were taken as datasets. The BiLSTM + Attention was used to extract enemy aircraft intention features. By introducing attention mechanism, the weight coefficients of characteristic states corresponding to air combat victories were corrected. Finally, it was input into the SoftMax function to obtain the category of the enemy’s intention. Experimental results showed that the proposed method can effectively identify enemy aircraft in the case of high complexity, multidimensional and large amount of data. Compared with bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), long short-term memory based on attention mechanisms (LSTM + Attention) and support vector machine (SVM) classification, the proposed method had higher accuracy and lower loss value.

Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in challenges in generating appropriate price predictions. Numerous studies have been conducted on cryptocurrency price prediction using different Deep Learning (DL) based algorithms. This study proposes three types of Recurrent Neural Networks (RNNs): namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-Directional LSTM (Bi-LSTM) for exchange rate predictions of three major cryptocurrencies in the world, as measured by their market capitalization—Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The experimental results on the three major cryptocurrencies using both Root Mean Squared Error (RMSE) and the Mean Absolute Percentage Error (MAPE) show that the Bi-LSTM performed better in prediction than LSTM and GRU. Therefore, it can be considered the best algorithm. Bi-LSTM presented the most accurate prediction compared to GRU and LSTM, with MAPE values of 0.036, 0.041, and 0.124 for BTC, LTC, and ETH, respectively. The paper suggests that the prediction models presented in it are accurate in predicting cryptocurrency prices and can be beneficial for investors and traders. Additionally, future research should focus on exploring other factors that may influence cryptocurrency prices, such as social media and trading volumes.

The developed research includes an Long Short-Term Memory (LSTM) network with IoT sensor data for the management of rice crop irrigation. Optimizing the amount of water required for rice crops is very critical, based on real-time predictions of the actual environmental and soil parameters including moisture, temperature, humidity, and rainfall as well as all other possible related factors. IoT sensors installed in agricultural fields gathered data, which was transmitted to a controller and then uploaded to the cloud for further processing. The training dataset contained 5,600 records. In this research, 70% was used for training and the rest 30% for testing. Three LSTM models were used for analyzing sensor data and predicting the need for irrigation and controlling the irrigation pump accordingly. These were Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM. It was evaluated the performance of the models developed using precision, recall, F1 score, accuracy, and other performance measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score. Bidirectional LSTM was determined to have the best performance, with MAE at 0.15, RMSE at 0.24, and an R2 score of 0.93, exhibiting high accuracy and predictive power. The Stacked LSTM and Vanilla LSTM also gave fair results, in terms of having R2 values of 0.89 and 0.84, respectively. The number of training and memory used for the computation were also given, with Vanilla LSTM taking least, followed by Stacked LSTM and Bidirectional LSTM. These results suggest that the LSTM networks can potentially be capable of improving the irrigation management such that more water is conserved and, subsequently, more suitable for agricultural productivity.

Small and Medium-sized Enterprises (SMEs) in Sri Lanka are increasingly turning to social media to gain insights into consumer preferences; however, they frequently lack sophisticated analytical tools to derive actionable insights. This research introduces a hybrid deep learning framework aimed at sentiment and emotion analysis specifically for the Sri Lankan cake sector. Customer reviews and social media comments were gathered and processed through various methods including text cleaning, emoji translation, stopword elimination, tokenization, and sequence padding. Several deep learning models— Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU) were assessed, along with hybrid models that combined CNN and BiLSTM with GPT-2 embeddings. The CNN+GPT-2 model achieved the highest accuracy in sentiment classification at 96.14%, while BiLSTM performed best in emotion classification with an accuracy of 91.57%. In terms of trend forecasting, Long Short-Term Memory (LSTM) outshone both SARIMA and Prophet, yielding the lowest error metrics and the most reliable sentiment predictions. These results underscore the potential of hybrid deep learning and transformer-based models to effectively capture complex consumer sentiments and provide SMEs with practical, AI-driven insights. This study is among the first to implement such hybrid NLP models within a localized Sri Lankan context, presenting a scalable approach for business intelligence and informed decision-making.

Tuberculosis is an infectious disease and requires serious treatment. Extrapulmonary Tuberculosis is detected using a microscope. Currently it will take a long time because the fluid preparations are viewed in a microscope one by one carefully and in the fluid preparations there are 150 fields of vision. Examination for Extra Pulmonary Tuberculosis by culture takes between 1-2 weeks or even more. Examination by biopsy will take a long time because the fluid preparations are looked at carefully under the microscope one by one. The image of Tuberculosis is expressed if in the image there is a bacillus object in red, and it turns out that apart from the bacillus object there are other objects also in red. So that examinations for tuberculosis can be more efficient, examinations using computer technology are needed. This research aims to compare the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) classification methods in the detection of extra-pulmonary tuberculosis disease to obtain better accuracy results. This research carried out HSI color space transformation, segmentation using global thresholding, feature extraction using 13 features based on shape and texture using the Correlation Based Feature Selection (CFS) feature selection method. The results show that BiLSTM has the best accuracy with a value of 88.40% at the number of features = 3, namely Short Run High Gray-Level Emphasis, Run Length Nonuniformity, Minor axis length, while LSTM produces an accuracy of 63.19% at the number of features = 5. BiLSTM is capable of detecting opposite features, meaning that BiLSTM can detect opposite features in data sequences and BiLSTM's ability to understand multiple contexts, so it tends to provide more accurate results in some data classification tasks.

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification.

This study compares the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models in predicting earthquake occurrences in the Tokai region, using data from the United States Geological Survey (USGS) dataset. Given the importance of accurate earthquake prediction, particularly in high-risk regions, this research focuses on assessing the effectiveness of each model in identifying occurrence and non-occurrence events. Both models were tuned to optimize sensitivity and specificity through adjustments in sequence length, learning rate, and additional hyperparameters, with results evaluated using metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Findings reveal that while both models achieved high sensitivity, the LSTM model demonstrated superior specificity and AUC, indicating a more balanced performance in distinguishing between earthquake occurrences and non-occurrences. The results show that LSTM outperforms Bi-LSTM in terms its classification metrics. LSTM achieved an accuracy of 76%, compared to 55% for Bi-LSTM. For the AUC metric, LSTM scored 66%, while Bi-LSTM scored 67%.

Using online information resources to build knowledge bases to provide knowledge answering services would help auto companies or third-party platforms to gain competitive advantages. Therefore, a construction plan of automobile maintenance expert system based on knowledge graph was proposed by integrated modelling. In terms of the entity recognition algorithm, the BM LSTM (Boyer-Moore Long Short-Term Memory) algorithm was proposed by integrating hidden Markov model, Conditional Random Field (CRF), Bi-directional Long Short-Term Memory (BiLSTM), BiLSTM-CRF and Lattice LSTM, which improved the accuracy index F1-score. In terms of the text quality evaluation algorithm, a secondary text quality evaluation system was designed. It evaluated the matching quality of the problem based on the word toolkit Synonyms and Levenshtein Distance algorithm. And it evaluated the quality of the answer text based on the TF-IDF (Term Frequency-Inverse Document Frequency) similarity algorithm and centered on completeness, accuracy, reliability, and argument strength. Finally, experiments are carried out on the proposed model and algorithm to prove its effectiveness.

The analysis and prediction of industrial production plants are of great significance for reducing energy consumption, improving economic efficiency. Therefore, a production prediction method based on bidirectional long short-term memory (Bi-LSTM) is proposed to accurately analyze and evaluate the energy efficiency status of ethylene production plants in industrial processes. Bi-LSTM is a Indirection ally connected network with two layers of long short-term memory (LSTM), it gives full consideration to the relationship between the current data and the data before and after it. Bi-LSTM solves the gradient disappearance or gradient explosion problem in recurrent neural network (RNN), and overcomes the drawback that LSTM only consider the relationship between the current data and its previous data. The comparison results show that the prediction effect of the Bi-LSTM model is superior to that of the back propagation (BP) neural network model, and the average relative error is reduced by 70%, which proves that the Bi-LSTM can effectively raise the accuracy and stability of the ethylene production prediction.

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

Online leakage current classification using convolutional neural network long short-term memory for high voltage insulators on web-based service

Tanzania heavily depends on United States Dollar (USD) foreign currency to import various goods and services into the country. Failure to correctly forecast exchange rates between USD and Tanzanian Shillings (TZS) may pose risks such as inability to import intended goods and services, possibility of losing money in stock exchange markets and other investment businesses in case of unexpected currency appreciation or depreciation as well as poor investment decisions in foreign exchange markets. To address this, this study has developed and comparatively evaluated performances of LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional LSTM) deep learning models for forecasting daily USD to TZS exchange rates. The findings reveal that, BiLSTM model outperforms LSTM in forecasting daily USD to TZS exchange rates, achieving a MAPE (Mean Absolute Percentage Error) score of 0.363 on test set (unseen data) compared to a MAPE score of 1.471 achieved by LSTM model. This study recommends to the prospective Artificial Intelligence (AI) researchers and software developers to use BiLSTM instead of LSTM model to forecast (predict) USD to TZS exchange rates. Also, this study has developed USD to TZS exchange rates dataset which can be used by AI researchers, saving them time and costs involved with creating datasets from scratch. This study has also developed ready to use BiLSTM and LSTM models which can be used by Tanzanian business men and women involved in stock exchange markets, foreign exchange markets and other businesses, to predict daily USD to TZS exchange rates and make appropriate business and investment decisions.