Deep learning approaches for force feedback based void defect detection in friction stir welding

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.

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.

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%.

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

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.

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

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.

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.

The objective of this research is to develop a Deep Learning model to forecast the stock price, by using the variant of Long Short-Term Memory. This model predicts the close price of the stock for the future selected date, choosing as inputs the following data: open, high, low, adj close and close prices. This model shows a comparative analysis between three different LSTM networks: Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (Stacked LSTM), and Stacked Bi-directional Long Short-Term Memory (Stacked Bidirectional LSTM) concluding which one is the best and implementing the model using that variant. We have used the historical stock prices data from yahoo’s financial website over 5 years, by choosing multiple datasets: Apple, Amazon, Google, Meta, Microsoft and Tesla (daily values). In order to get effective in the forecasting model, we have tested the network with different iterations and epochs. The model represents Multiple Graphs for data visualization in different comparisons. We have estimated the effectiveness of our proposed model by using the following performance indicators: the Mean Square Error (MSE), the Root Mean Square Error (RMSE), and the R-Squared of the model. The experimental results clearly show that our Stacked Bi-LSTM model has the highest accuracy values when comparing with the LSTM and Stacked LSTM Models. Hence, we can conclude that our Stacked Bi-LSTM Model is suitable for accurate prediction of the stock market time series. Key Words: stock price prediction, Machine Learning, stacked LSTM, Bi-directional LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Data Visualization, Training and Testing Set, Financial Time Series, Future prediction

Data available on the internet is growing constantly. This huge amount of data makes it harder for the users to get useful information. Comments and reviews of movies are given by many users, and recommendation systems makes it easier to find useful content, which is fast and relevant for users. Movies with more positive reviews and comments are usually chosen by everyone. The sentiment behind user reviews is useful to know whether a movie is worth watching. This paper outlines an approach for a movie recommendation system that uses cosine similarity technique to recommend similar movies to users based on the movie title , genre, director, actor they choose or search . Deep learning algorithms, GRU (Gated Recurrent unit), LSTM (long short-term memory), RNN (Recurrent neural network) and BI-LSTM (bi-directional long short-term memory), are trained and tested to classify user comments taken from YouTube into positive, negative, and neutral sentiments. Accuracy, F1-score, precision, recall measures are utilized to assess the model from every perspective. After comparing four algorithms, as BI-LSTM outperformed other algorithms with an accuracy, recall, F1 score and precision, of 98.58%, 97.99% 98.26% and 98.54% respectively, Bi-LSTM (bi-directional long short-term memory) is used for performing sentiment analysis on the movie reviews dataset to understand users sentiment.

The radiological features of computed tomography (CT) images and the sequence of radiomics signatures in continuous slices of lung CT lesions are helpful in identifying subtypes of lung adenocarcinoma. A model based on bidirectional long short-term memory (Bi-LSTM) and multihead attention can learn the rules of this sequence well. In this study, 421 patients with 427 lesions confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) were recruited from three hospitals. The radiomics signatures of the identified lesion regions in each CT image were extracted using 'PyRadiomics' software, and the corresponding radiological features were subsequently documented and collected. Then, the top 100 features were extracted by the minimum redundancy maximum relevance (mRMR) feature ranking method. A model based on the radiological and imaging features was established to classify the lesions using Bi-LSTM and multihead attention. The diagnostic performance of the model was measured by the area under the curve (AUC) of the receiver operating characteristic (ROC). The model combined radiological features and radiomics signatures. The AUCs of the model in the training, testing, and validation groups were 0.985, 0.94 and 0.981, respectively, and the accuracy was 0.92, 0.976 and 0.91, respectively. In addition, we trained two other models [convolutional neural network (CNN) + multihead attention, long short-term memory (LSTM) + multihead attention] and compared them using the testing dataset. The precision of the two models was 0.89 and 0.88, respectively, and the accuracy was 0.88 and 0.87, respectively. Bi-LSTM and multihead attention based on radiomics signatures and radiological features provide a way to distinguish AIS, MIA, and IAC.

Parts of Speech (pos) tagging corresponds to techniques of tagging accurate pos tags of each word of a sentence. It is a pivotal and indispensable part of sentiment analysis, synthesizing text to speech, analyzing textual data and many more Natural Language Processing (NLP) tasks. Though there are many researches done in the field of languages like English, Chinese etc., it is still comparatively unexplored for Bangla. An excellent pos tagger in Bangla language can accelerate NLP of Bangla textual data. In this study, we have developed a novel hybrid Deep Learning (DL) model with two parallel input networks with Convolutional Neural Network (CNN), and Bidirectional Long Short Term Memory (BiLSTM), and an output network of CNN. CNN works well regarding the local dependencies of words in sentences, and BiLSTM works well with the global dependencies of words regarding the context in a sentence. No other work has been done with local features of textual data along with long term word dependencies in Bangla pos tagging studies. The model can work with any external Word Embeddings. In this study, it is implemented with both Embedding Layer and Word2Vec layer. In the evaluation process, Mathews Correlation Coefficient (MCC), Precision-Recall curve are used along with accuracy and F1-score due to the dataset's imbalanced characteristic. Our proposed model accompanied by Word2Vec layer has outperformed Vanilla Recurrent Neural Network (RNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), BiLSTM networks, and also one recent study using the same dataset with the highest accuracy of 0.974, F1-score of 0.883, and MCC value of 0.936.

Short Term Stock Price Prediction in Indian Market: A Neural Network Perspective