DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

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Automatic Labeled Dialogue Generation for Nursing Record Systems

Journal of Personalized Medicine ◽

10.3390/jpm10030062 ◽

2020 ◽

Vol 10 (3) ◽

pp. 62

Author(s):

Tittaya Mairittha ◽

Nattaya Mairittha ◽

Sozo Inoue

Keyword(s):

Data Augmentation ◽

Short Term Memory ◽

Generative Models ◽

Abstract Knowledge ◽

Augmentation Techniques ◽

Nursing Record ◽

Long Short Term Memory ◽

The Individual ◽

High Level ◽

Embedding Methods

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

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Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Sensors ◽

10.3390/s21020411 ◽

2021 ◽

Vol 21 (2) ◽

pp. 411

Author(s):

Yunkai Zhang ◽

Yinghong Tian ◽

Pingyi Wu ◽

Dongfan Chen

Keyword(s):

Autism Spectrum Disorder ◽

Action Recognition ◽

Short Term Memory ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Short Term ◽

Term Memory ◽

Clinical Observations ◽

The Neural Network ◽

Long Short Term Memory

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

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Air pollution forecasting application based on deep learning model and optimization algorithm

Clean Technologies and Environmental Policy ◽

10.1007/s10098-021-02080-5 ◽

2021 ◽

Author(s):

Azim Heydari ◽

Meysam Majidi Nezhad ◽

Davide Astiaso Garcia ◽

Farshid Keynia ◽

Livio De Santoli

Keyword(s):

Air Pollution ◽

Wind Speed ◽

Power Plant ◽

Air Temperature ◽

Short Term Memory ◽

Combined Cycle ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

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Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189593 ◽

2021 ◽

pp. 1-10

Author(s):

Hye-Jeong Song ◽

Tak-Sung Heo ◽

Jong-Dae Kim ◽

Chan-Young Park ◽

Yu-Seop Kim

Keyword(s):

Neural Network ◽

Language Processing ◽

Short Term Memory ◽

Parallel Structure ◽

Short Term ◽

Similarity Estimation ◽

Accurate Judgment ◽

Proposed Model ◽

Sentence Similarity ◽

Long Short Term Memory

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.

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An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

International Journal of Distributed Artificial Intelligence ◽

10.4018/ijdai.2021070101 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1-12

Author(s):

Sumit Das ◽

Manas Kumar Sanyal ◽

Sarbajyoti Mallik

Keyword(s):

Artificial Intelligence ◽

Web Application ◽

Short Term Memory ◽

Real Data ◽

Fake News ◽

Artificial Intelligence Technique ◽

News Reports ◽

Proposed Model ◽

Long Short Term Memory ◽

Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

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Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

SPE Reservoir Evaluation & Engineering ◽

10.2118/208596-pa ◽

2021 ◽

pp. 1-17

Author(s):

Enda Du ◽

Yuetian Liu ◽

Ziyan Cheng ◽

Liang Xue ◽

Jing Ma ◽

...

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Production Forecasting ◽

Temporal Correlations ◽

Proposed Model ◽

The Mean ◽

Long Short Term Memory ◽

The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

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Ensemble-Based Feature Selection With Long Short-Term Memory for Classification of Network Intrusion

Advances in Social Networking and Online Communities - E-Collaboration Technologies and Strategies for Competitive Advantage Amid Challenging Times ◽

10.4018/978-1-7998-7764-6.ch008 ◽

2021 ◽

pp. 228-245

Author(s):

Preethi D. ◽

Neelu Khare

Keyword(s):

Feature Selection ◽

Performance Metrics ◽

Short Term Memory ◽

Short Term ◽

Chi Square ◽

Term Memory ◽

Network Intrusion ◽

Proposed Model ◽

Long Short Term Memory

This chapter presents an ensemble-based feature selection with long short-term memory (LSTM) model. A deep recurrent learning model is proposed for classifying network intrusion. This model uses ensemble-based feature selection (EFS) for selecting the appropriate features from the dataset and long short-term memory for the classification of network intrusions. The EFS combines five feature selection techniques, namely information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the standard benchmark NSL-KDD dataset and implemented using tensor flow and python. The proposed model is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection technique and classified using LSTM. The performance study showed that the proposed model performs better, with 99.8% accuracy, with a higher detection and lower false alarm rates.

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A novel fuzzy rough set based long short-term memory integration model for energy consumption prediction of public buildings

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201857 ◽

2020 ◽

pp. 1-15

Author(s):

Hongchang Sun ◽

Yadong wang ◽

Lanqiang Niu ◽

Fengyu Zhou ◽

Heng Li

Keyword(s):

Energy Consumption ◽

Rough Set ◽

Prediction Accuracy ◽

Short Term Memory ◽

Short Term ◽

Fuzzy Rough Set ◽

Proposed Model ◽

Long Short Term Memory ◽

Energy Consumption Prediction ◽

Consumption Prediction

Building energy consumption (BEC) prediction is very important for energy management and conservation. This paper presents a short-term energy consumption prediction method that integrates the Fuzzy Rough Set (FRS) theory and the Long Short-Term Memory (LSTM) model, and is thus named FRS-LSTM. This method can find the most directly related factors from the complex and diverse factors influencing the energy consumption, which improves the prediction accuracy and efficiency. First, the FRS is used to reduce the redundancy of the input features by the attribute reduction of the factors affecting the energy consumption forecasting, and solves the data loss problem caused by the data discretization of a classical rough set. Then, the final attribute set after reduction is taken as the input of the LSTM networks to obtain the final prediction results. To validate the effectiveness of the proposed model, this study used the actual data of a public building to predict the building’s energy consumption, and compared the proposed model with the LSTM, Levenberg-Marquardt Back Propagation (LM-BP), and Support Vector Regression (SVR) models. The experimental results reveal that the presented FRS-LSTM model achieves higher prediction accuracy compared with other comparative models.

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Bimodal Emotion Recognition Model for Minnan Songs

Information ◽

10.3390/info11030145 ◽

2020 ◽

Vol 11 (3) ◽

pp. 145 ◽

Cited By ~ 1

Author(s):

Zhenglong Xiang ◽

Xialei Dong ◽

Yuanxiang Li ◽

Fei Yu ◽

Xing Xu ◽

...

Keyword(s):

Neural Network ◽

Emotion Recognition ◽

Short Term Memory ◽

Music Appreciation ◽

Research Papers ◽

Audio Features ◽

Analysis Theory ◽

Proposed Model ◽

Song Lyrics ◽

Long Short Term Memory

Most of the existing research papers study the emotion recognition of Minnan songs from the perspectives of music analysis theory and music appreciation. However, these investigations do not explore any possibility of carrying out an automatic emotion recognition of Minnan songs. In this paper, we propose a model that consists of four main modules to classify the emotion of Minnan songs by using the bimodal data—song lyrics and audio. In the proposed model, an attention-based Long Short-Term Memory (LSTM) neural network is applied to extract lyrical features, and a Convolutional Neural Network (CNN) is used to extract the audio features from the spectrum. Then, two kinds of extracted features are concatenated by multimodal compact bilinear pooling, and finally, the concatenated features are input to the classifying module to determine the song emotion. We designed three experiment groups to investigate the classifying performance of combinations of the four main parts, the comparisons of proposed model with the current approaches and the influence of a few key parameters on the performance of emotion recognition. The results show that the proposed model exhibits better performance over all other experimental groups. The accuracy, precision and recall of the proposed model exceed 0.80 in a combination of appropriate parameters.

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iRNAD: a computational tool for identifying D modification sites in RNA sequence

Bioinformatics ◽

10.1093/bioinformatics/btz358 ◽

2019 ◽

Vol 35 (23) ◽

pp. 4922-4929 ◽

Cited By ~ 31

Author(s):

Zhao-Chun Xu ◽

Peng-Mian Feng ◽

Hui Yang ◽

Wang-Ren Qiu ◽

Wei Chen ◽

...

Keyword(s):

Computational Models ◽

Operating Characteristic ◽

Cross Validation ◽

Characteristic Curve ◽

Support Vector ◽

Final Model ◽

Rna Sequence ◽

Functional Roles ◽

Proposed Model ◽

User Friendly

Abstract Motivation Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA. Results We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model. Availability and implementation A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

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