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Journal articles on the topic 'Recurrent neural networks BLSTM'

Author: Grafiati

Published: 25 May 2022

Last updated: 30 July 2025

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1

Guo, Yanbu, Bingyi Wang, Weihua Li, and Bei Yang. "Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks." Journal of Bioinformatics and Computational Biology 16, no. 05 (2018): 1850021. http://dx.doi.org/10.1142/s021972001850021x.

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Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectiona

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Zhong, Cheng, Zhonglian Jiang, Xiumin Chu, and Lei Liu. "Inland Ship Trajectory Restoration by Recurrent Neural Network." Journal of Navigation 72, no. 06 (2019): 1359–77. http://dx.doi.org/10.1017/s0373463319000316.

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The quality of Automatic Identification System (AIS) data is of fundamental importance for maritime situational awareness and navigation risk assessment. To improve operational efficiency, a deep learning method based on Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) is proposed and applied in AIS trajectory data restoration. Case studies have been conducted in two distinct reaches of the Yangtze River and the capability of the proposed method has been evaluated. Comparisons have been made between the BLSTM-RNNs-based method and the linear method and classic Artif

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Reddy, K. Jeevan. "Text To Speech Synthesis with Bidirectional LSTM based Recurrent Neural Networks." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 270–76. https://doi.org/10.22214/ijraset.2025.72981.

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According to recent studies, feed-forward Deep neural networks (DNNs) perform better than text-to-speech (TTS) systems that use decision-tree clustered context-dependent hidden Markov models (HMMs). The feed-forward aspect of DNNbased models makes it difficult to incorporate the long-span contextual influence into spoken utterances. Another typical strategy in HMM-based TTS for establishing a continuous speech trajectory is using the dynamic characteristics to constrain the production of speech parameters. Parametric time-to-speech synthesis is used in this study by capturing the co-occurrence

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R.Ankush, Banger1 Mansi Singh1 Kirnesh Sharma1 Satvik Singla1 Mrs.Shikha Rastogi2. "HINDI LANGUAGE RECOGNITION SYSTEM USING NEURAL NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 98–103. https://doi.org/10.5281/zenodo.1241407.

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In this paper, we propose a recognition scheme for the Indian script of Hindi. Recognition accuracy of Hindi script is not yet comparable to its Roman counterparts. This is mainly due to the complexity of the script, writing style etc. Our solution uses a Recurrent Neural Network known as Bidirectional Long Short Term Memory (BLSTM). Our approach does not require word to character segmentation, which is one of the most common reason for high word error rate. We report a reduction of more than 20% in word error rate and over 9% reduction in character error rate while comparing with the best ava

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KADARI, REKIA, YU ZHANG, WEINAN ZHANG, and TING LIU. "CCG supertagging with bidirectional long short-term memory networks." Natural Language Engineering 24, no. 1 (2017): 77–90. http://dx.doi.org/10.1017/s1351324917000250.

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AbstractNeural Network-based approaches have recently produced good performances in Natural language tasks, such as Supertagging. In the supertagging task, a Supertag (Lexical category) is assigned to each word in an input sequence. Combinatory Categorial Grammar Supertagging is a more challenging problem than various sequence-tagging problems, such as part-of-speech (POS) tagging and named entity recognition due to the large number of the lexical categories. Specifically, simple Recurrent Neural Network (RNN) has shown to significantly outperform the previous state-of-the-art feed-forward neu

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Shchetinin, E. Yu. "EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 199 (January 2021): 44–51. http://dx.doi.org/10.14489/vkit.2021.01.pp.044-051.

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The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotion

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Dutta, Aparajita, Kusum Kumari Singh, and Ashish Anand. "SpliceViNCI: Visualizing the splicing of non-canonical introns through recurrent neural networks." Journal of Bioinformatics and Computational Biology 19, no. 04 (2021): 2150014. http://dx.doi.org/10.1142/s0219720021500141.

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Most of the current computational models for splice junction prediction are based on the identification of canonical splice junctions. However, it is observed that the junctions lacking the consensus dimers GT and AG also undergo splicing. Identification of such splice junctions, called the non-canonical splice junctions, is also essential for a comprehensive understanding of the splicing phenomenon. This work focuses on the identification of non-canonical splice junctions through the application of a bidirectional long short-term memory (BLSTM) network. Furthermore, we apply a back-propagatio

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Zhang, Ansi, Honglei Wang, Shaobo Li, et al. "Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation." Applied Sciences 8, no. 12 (2018): 2416. http://dx.doi.org/10.3390/app8122416.

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Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different

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Janardhanan, Jitha, and S. Umamaheswari. "Exploration of Deep Learning Models for Video Based Multiple Human Activity Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 422–28. http://dx.doi.org/10.17762/ijritcc.v11i8s.7222.

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Human Activity Recognition (HAR) with Deep Learning is a challenging and a highly demanding classification task. Complexity of the activity detection and the number of subjects are the main issues. Data mining approaches improved decision-making performance. This work presents one such model for Human activity recognition for multiple subjects carrying out multiple activities. Involving real time datasets, the work developed a rapid algorithm for minimizing the problems of neural networks classifier. An optimal feature extraction happens and develops a multi-modal classification technique and

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Rathika, M., P. Sivakumar, K. Ramash Kumar, and Ilhan Garip. "Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks." Mathematical Problems in Engineering 2022 (December 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/1864290.

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In recent years, cooperative communication (CC) technology has emerged as a hotspot for testing wireless communication networks (WCNs), and it will play an important role in the spectrum utilization of future wireless communication systems. Instead of running node transmissions at full capacity, this design will distribute available paths across multiple relay nodes to increase the overall throughput. The modeling WCNs coordination processes, as a recurrent mechanism and recommending a deep learning-based transfer choice, propose a recurrent neural network (RNN) process-based relay selection i

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Xuan, Wenjing, Ning Liu, Neng Huang, Yaohang Li, and Jianxin Wang. "CLPred: a sequence-based protein crystallization predictor using BLSTM neural network." Bioinformatics 36, Supplement_2 (2020): i709—i717. http://dx.doi.org/10.1093/bioinformatics/btaa791.

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Abstract Motivation Determining the structures of proteins is a critical step to understand their biological functions. Crystallography-based X-ray diffraction technique is the main method for experimental protein structure determination. However, the underlying crystallization process, which needs multiple time-consuming and costly experimental steps, has a high attrition rate. To overcome this issue, a series of in silico methods have been developed with the primary aim of selecting the protein sequences that are promising to be crystallized. However, the predictive performance of the curren

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Varshney, Abhishek, Samit Kumar Ghosh, Sibasankar Padhy, Rajesh Kumar Tripathy, and U. Rajendra Acharya. "Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals." Electronics 10, no. 9 (2021): 1079. http://dx.doi.org/10.3390/electronics10091079.

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The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN)

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Vendrame, Alessandra, Cristina Cappelletto, Paola Chiovati, et al. "Artificial Intelligence-Based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy from Respiratory Tracks." Applied Sciences 13, no. 8 (2023): 4962. http://dx.doi.org/10.3390/app13084962.

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Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT) treatment of patients with left breast cancer from analysis of respiratory signal, using Deep Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks. Methods: The respiratory traces from 36 patients who underwent DIBH RT were collected. The patients’ RT treatment plans were generated for both DIBH and free-breathing (FB) modalities. The patients were divided into two classes (patient eligible or not), based on the decrease of maximum dose to the left anterior descending (LAD) artery achiev

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Esmaeili, Fatemeh, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth, and Alan Wang. "Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest." Bioengineering 10, no. 12 (2023): 1348. http://dx.doi.org/10.3390/bioengineering10121348.

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Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring unif

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Zulqarnain, Muhammad, Rozaida Ghazali, Yana Mazwin Mohmad Hassim, and Muhammad Rehan. "Text classification based on gated recurrent unit combines with support vector machine." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3734. http://dx.doi.org/10.11591/ijece.v10i4.pp3734-3742.

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As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term depe

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Muhammad, Zulqarnain, Ghazali Rozaida, Mazwin Mohmad Hassim Yana, and Rehan Muhammad. "Text classification based on gated recurrent unit combines with support vector machine." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3734–42. https://doi.org/10.11591/ijece.v10i4.pp3734-3742.

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As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-ter

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Mahmoud, Adnen, and Mounir Zrigui. "BLSTM-API: Bi-LSTM Recurrent Neural Network-Based Approach for Arabic Paraphrase Identification." Arabian Journal for Science and Engineering 46, no. 4 (2021): 4163–74. http://dx.doi.org/10.1007/s13369-020-05320-w.

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Chingamtotattil, Rahul, and Rajamma Gopikakumar. "Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1709. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1709-1719.

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Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN)

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Chingamtotattil, Rahul, and Rajamma Gopikakumari. "Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1709–19. https://doi.org/10.11591/ijeecs.v28.i3.pp1709-1719.

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Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN)

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Li, Shuyu, and Yunsick Sung. "Transformer-Based Seq2Seq Model for Chord Progression Generation." Mathematics 11, no. 5 (2023): 1111. http://dx.doi.org/10.3390/math11051111.

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Machine learning is widely used in various practical applications with deep learning models demonstrating advantages in handling huge data. Treating music as a special language and using deep learning models to accomplish melody recognition, music generation, and music analysis has proven feasible. In certain music-related deep learning research, recurrent neural networks have been replaced with transformers. This has achieved significant results. In traditional approaches with recurrent neural networks, input sequences are limited in length. This paper proposes a method to generate chord prog

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Brocki, Łukasz, and Krzysztof Marasek. "Deep Belief Neural Networks and Bidirectional Long-Short Term Memory Hybrid for Speech Recognition." Archives of Acoustics 40, no. 2 (2015): 191–95. http://dx.doi.org/10.1515/aoa-2015-0021.

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Abstract This paper describes a Deep Belief Neural Network (DBNN) and Bidirectional Long-Short Term Memory (LSTM) hybrid used as an acoustic model for Speech Recognition. It was demonstrated by many independent researchers that DBNNs exhibit superior performance to other known machine learning frameworks in terms of speech recognition accuracy. Their superiority comes from the fact that these are deep learning networks. However, a trained DBNN is simply a feed-forward network with no internal memory, unlike Recurrent Neural Networks (RNNs) which are Turing complete and do posses internal memor

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Gao, Shenghan, Changyan Zheng, Yicong Zhao, Ziyue Wu, Jiao Li, and Xian Huang. "Comparison of enhancement techniques based on neural networks for attenuated voice signal captured by flexible vibration sensors on throats." Nanotechnology and Precision Engineering 5, no. 1 (2022): 013001. http://dx.doi.org/10.1063/10.0009187.

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Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech. However, high-frequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction. In this paper, speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared. Four kinds of speech enhancement algorithms based on a fully connected neural network (FCNN), a long shor

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Alsubari, Saleh Nagi, Theyazn H. H. Aldhyani, Sachin N. Deshmukh, Mashael Maashi, Sadeen Alharbi, and Heyam H. Al-Baity. "Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain." Computational Intelligence and Neuroscience 2022 (November 18, 2022): 1–14. http://dx.doi.org/10.1155/2022/4656846.

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Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. T

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Yurianta, Abdufattah, Anaqi Syaddad Ihsan, Arijal Ibnu Jati, Osmalina Nur Rahma, and Aji Sapta Pramulen. "Speech Synthesis Based on EEG Signal for Speech Impaired Patients by Using bLSTM Recurrent Neural Network." Indonesian Applied Physics Letters 3, no. 1 (2022): 11–15. http://dx.doi.org/10.20473/iapl.v3i1.40257.

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The disability rate in Indonesia is still relatively high and is one of the main health problems which reaches 30.38 million people or 14.2% of the Indonesian population. One of these types of disabilities is speech impairment. There are several possible causes for speech impairment, including the focal disturbance. This situation occurs because of disturbances in the vocal cords caused by injuries due to accidents and other conditions, such as throat cancer, which of course will reduce the productivity of the sufferer. Sign language can be used to communicate, but it still has limitations for

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Terra Vieira, Samuel, Renata Lopes Rosa, Demóstenes Zegarra Rodríguez, Miguel Arjona Ramírez, Muhammad Saadi, and Lunchakorn Wuttisittikulkij. "Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning." Sensors 21, no. 5 (2021): 1880. http://dx.doi.org/10.3390/s21051880.

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A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complain

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Liu, Yunjie, Mu Shengdong, Gu Jijian, and Nadia Nedjah. "Intelligent Prediction of Customer Churn with a Fused Attentional Deep Learning Model." Mathematics 10, no. 24 (2022): 4733. http://dx.doi.org/10.3390/math10244733.

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In recent years, churn rates in industries such as finance have increased, and the cost of acquiring new users is more than five times the cost of retaining existing users. To improve the intelligent prediction accuracy of customer churn rate, artificial intelligence is gradually used. In this paper, the bidirectional long short-term memory convolutional neural network (BiLSTM-CNN) model is integrated with recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in parallel, which well solves the defective problem that RNNs and CNNs run separately, and it also solves the probl

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Aldhahri, Eman. "The Use of Recurrent Nets for the Prediction of e-Commerce Sales." Engineering, Technology & Applied Science Research 13, no. 3 (2023): 10931–35. http://dx.doi.org/10.48084/etasr.5964.

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The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite

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Otte, S., L. Wittig, G. Hüttmann, et al. "Investigating Recurrent Neural Networks for OCT A-scan Based Tissue Analysis." Methods of Information in Medicine 53, no. 04 (2014): 245–49. http://dx.doi.org/10.3414/me13-01-0135.

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Summary Objectives: Optical Coherence Tomography (OCT) has been proposed as a high resolution image modality to guide transbronchial biopsies. In this study we address the question, whether individual A-scans obtained in needle direction can contribute to the identification of pulmonary nodules. Methods: OCT A-scans from freshly resected human lung tissue specimen were recorded through a customized needle with an embedded optical fiber. Bidirectional Long Short Term Memory networks (BLSTMs) were trained on randomly distributed training and test sets of the acquired A-scans. Patient specific tr

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Lynn, Htet Myet, Pankoo Kim, and Sung Bum Pan. "Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication." Applied Sciences 11, no. 3 (2021): 1125. http://dx.doi.org/10.3390/app11031125.

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In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can b

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Maiora, Josu, Chloe Rezola-Pardo, Guillermo García, Begoña Sanz, and Manuel Graña. "Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data." Bioengineering 11, no. 10 (2024): 1000. http://dx.doi.org/10.3390/bioengineering11101000.

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Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the dat

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Coman, Daniela Andreea, Silviu Ionita, and Ioan Lita. "Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks." Sensors 24, no. 11 (2024): 3316. http://dx.doi.org/10.3390/s24113316.

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Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classification methods, using diverse characteristic parameters of EEG signals in the time, frequency, and statistical domains. This paper explores the results of an experiment that aimed to highlight arithmetic mental tasks contained in the PhysioNet database, performed on a group of 36 subjects. The majority of publications on this topic deal with machi

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Li, Yue, Xutao Wang, and Pengjian Xu. "Chinese Text Classification Model Based on Deep Learning." Future Internet 10, no. 11 (2018): 113. http://dx.doi.org/10.3390/fi10110113.

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Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neur

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Esmaeili, Fatemeh, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth, and Alan Wang. "Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks." Bioengineering 9, no. 10 (2022): 529. http://dx.doi.org/10.3390/bioengineering9100529.

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Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor’s performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, an

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Jaiswal, Sushma, Harikumar Pallthadka, and Rajesh P.Chinhewadi. "Enhanced Image Captioning using Bidirectional Long Short-Term Memory and Convolutional Neural Networks." International Journal of Scientific Methods in Engineering and Management 02, no. 03 (2024): 18–32. http://dx.doi.org/10.58599/ijsmem.2024.2303.

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The utilization of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BLSTM) networks in image captioning has significantly enhanced the quality and relevance of generated captions. In this approach, a CNN serves as the encoder to extract meaningful features from the input image, capturing its visual information. These features are then fed into a BLSTM network, acting as the decoder, which processes the features in a bidirectional manner to generate descriptive and coherent captions by considering both past and future context. The model is trained on a dataset of i

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Grossberg, Stephen. "Recurrent neural networks." Scholarpedia 8, no. 2 (2013): 1888. http://dx.doi.org/10.4249/scholarpedia.1888.

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Nanyonga, Aziida, Keith Joiner, Ugur Turhan, and Graham Wild. "Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia." Modelling 6, no. 2 (2025): 40. https://doi.org/10.3390/modelling6020040.

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This study investigates the application of advanced deep learning models for the classification of aviation safety incidents, focusing on four models: Simple Recurrent Neural Network (sRNN), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BLSTM), and DistilBERT. The models were evaluated based on key performance metrics, including accuracy, precision, recall, and F1-score. DistilBERT achieved perfect performance with an accuracy of 1.00 across all metrics, while BLSTM demonstrated the highest performance among the deep learning models, with an accuracy of 0.9896, followed by

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Long, Haixia, Zhao Sun, Manzhi Li, Hai Yan Fu, and Ming Cai Lin. "Predicting Protein Phosphorylation Sites Based on Deep Learning." Current Bioinformatics 15, no. 4 (2020): 300–308. http://dx.doi.org/10.2174/1574893614666190902154332.

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Background: Protein phosphorylation is one of the most important Post-translational Modifications (PTMs) occurring at amino acid residues serine (S), threonine (T), and tyrosine (Y). It plays critical roles in protein structure and function predicting. With the development of novel high-throughput sequencing technologies, there are a huge amount of protein sequences being generated and stored in databases. Objective: It is of great importance in both basic research and drug development to quickly and accurately predict which residues of S, T, or Y can be phosphorylated. Methods: In order to so

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Passricha, Vishal, and Rajesh Kumar Aggarwal. "A Hybrid of Deep CNN and Bidirectional LSTM for Automatic Speech Recognition." Journal of Intelligent Systems 29, no. 1 (2019): 1261–74. http://dx.doi.org/10.1515/jisys-2018-0372.

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Abstract Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional neural networks (CNNs) are the advanced version of DNNs that achieve 4–12% relative gain in the word error rate (WER) over DNNs. Existence of spectral variations and local correlations in speech signal makes CNNs more capable of speech recognition. Recently, it has been demonstrated that bidirectional long short-term memory (BLSTM) produces higher recognition rate in acoustic modeling because they are adequate to reinforce higher-level representations of acoustic data. Spatial and temp

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Bitzer, Sebastian, and Stefan J. Kiebel. "Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks." Biological Cybernetics 106, no. 4-5 (2012): 201–17. http://dx.doi.org/10.1007/s00422-012-0490-x.

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Erbay, Hasan, Rezan Bakır, and Halit Bakır. "ALBERT4Spam: A Novel Approach for Spam Detection on Social Networks." Bilişim Teknolojileri Dergisi 17, no. 2 (2024): 81–94. http://dx.doi.org/10.17671/gazibtd.1426230.

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Engaging in social media browsing stands out as one of the most prevalent online activities. As social media increasingly integrates into our daily routines, it opens up numerous opportunities for spammers seeking to target individuals through these platforms. Given the concise and sporadic nature of messages exchanged on social networks, they fall within the realm of short text classification challenges. Effectively addressing such issues requires appropriately representing the text to enhance classifier efficiency.Accordingly, this study utilizes robust representations derived from contextua

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Rotman, Michael, and Lior Wolf. "Shuffling Recurrent Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9428–35. http://dx.doi.org/10.1609/aaai.v35i11.17136.

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We propose a novel recurrent neural network model, where the hidden state hₜ is obtained by permuting the vector elements of the previous hidden state hₜ₋₁ and adding the output of a learned function β(xₜ) of the input xₜ at time t. In our model, the prediction is given by a second learned function, which is applied to the hidden state s(hₜ). The method is easy to implement, extremely efficient, and does not suffer from vanishing nor exploding gradients. In an extensive set of experiments, the method shows competitive results, in comparison to the leading literature baselines. We share our imp

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Schuster, M., and K. K. Paliwal. "Bidirectional recurrent neural networks." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2673–81. http://dx.doi.org/10.1109/78.650093.

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Ziafat, Nishmia, Hafiz Farooq Ahmad, Iram Fatima, Muhammad Zia, Abdulaziz Alhumam, and Kashif Rajpoot. "Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning." Applied Sciences 11, no. 6 (2021): 2508. http://dx.doi.org/10.3390/app11062508.

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Automatic speech recognition for Arabic has its unique challenges and there has been relatively slow progress in this domain. Specifically, Classic Arabic has received even less research attention. The correct pronunciation of the Arabic alphabet has significant implications on the meaning of words. In this work, we have designed learning models for the Arabic alphabet classification based on the correct pronunciation of an alphabet. The correct pronunciation classification of the Arabic alphabet is a challenging task for the research community. We divide the problem into two steps, firstly we

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Chang, Xunyun, and Liangqing Peng. "Evaluation Strategy of the Piano Performance by the Deep Learning Long Short-Term Memory Network." Wireless Communications and Mobile Computing 2022 (June 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/6727429.

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With the development of society and the progress of technology, the piano education industry has a large market. In view of the problem of high payment fees in the piano education industry, the scientific and automatic nature of piano performance evaluation has attracted people’s attention. However, since most of the piano performance evaluation schemes are based on rules, the continuity of the piano music and the accuracy of playing are ignored. Therefore, the purpose is to design a scientific piano performance evaluation scheme that can play a certain role in the sustainable development of t

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Nazmin, Begum, and Syed Mustafa A. "CNN-BLSTM Joint Technique on Dynamic Shape and Appearance of FACS." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1754–57. https://doi.org/10.35940/ijeat.D7308.049420.

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Facial recognition is a process where we can identify or verify a person from digital image or videos and is used in ID verification services , protecting law enforcement ,preventing retail crime etc. Past work on automatic analysis of facial expression focuses on detecting the facial expression and exploiting the dependencies among AU’s. But, spontaneous detection of facial expression depending on various factors such as shape, appearance and dynamics is very difficult. Joint learning of shape , appearance and dynamics is done by a deep learning technique .This includes a convolutional

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Chhetri, Manoj, Sudhanshu Kumar, Partha Pratim Roy, and Byung-Gyu Kim. "Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan." Remote Sensing 12, no. 19 (2020): 3174. http://dx.doi.org/10.3390/rs12193174.

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Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated

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Shchetinin, Eugene Yu, and Leonid Sevastianov. "Improving the Learning Power of Artificial Intelligence Using Multimodal Deep Learning." EPJ Web of Conferences 248 (2021): 01017. http://dx.doi.org/10.1051/epjconf/202124801017.

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Computer paralinguistic analysis is widely used in security systems, biometric research, call centers and banks. Paralinguistic models estimate different physical properties of voice, such as pitch, intensity, formants and harmonics to classify emotions. The main goal is to find such features that would be robust to outliers and will retain variety of human voice properties at the same time. Moreover, the model used must be able to estimate features on a time scale for an effective analysis of voice variability. In this paper a paralinguistic model based on Bidirectional Long Short-Term Memory

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As'ari, Muhammad Amir, Nur Anis Jasmin Sufri, and Guat Si Qi. "Emergency sign language recognition from variant of convolutional neural network (CNN) and long short term memory (LSTM) models." International Journal of Advances in Intelligent Informatics 10, no. 1 (2024): 64. http://dx.doi.org/10.26555/ijain.v10i1.1170.

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Sign language is the primary communication tool used by the deaf community and people with speaking difficulties, especially during emergencies. Numerous deep learning models have been proposed to solve the sign language recognition problem. Recently. Bidirectional LSTM (BLSTM) has been proposed and used in replacement of Long Short-Term Memory (LSTM) as it may improve learning long-team dependencies as well as increase the accuracy of the model. However, there needs to be more comparison for the performance of LSTM and BLSTM in LRCN model architecture in sign language interpretation applicati

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KAWAMURA, Yoshiaki. "Learning for Recurrent Neural Networks." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 1 (1995): 52–56. http://dx.doi.org/10.3156/jfuzzy.7.1_52.

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Ma, Xiao, Peter Karkus, David Hsu, and Wee Sun Lee. "Particle Filter Recurrent Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5101–8. http://dx.doi.org/10.1609/aaai.v34i04.5952.

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Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and multi-modal real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that explicitly models uncertainty in its internal structure: while an RNN relies on a long, deterministic latent state vector, a PF-RNN maintains a latent state distribution, approximated as a set of particles. For effective learning, we provide a fully differentiable particle filter algorithm that updates the PF-RNN latent state distribution accor

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