Short-term Memory Recurrent Neural Network Research Articles

ScDLC: a deep learning framework to classify large sample single-cell RNA-seq data

BackgroundUsing single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.ResultsWe propose a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Our new scDLC does not require a prior knowledge on the data distribution, but instead, it takes into account the dependency of the most outstanding feature genes in the LSTMs model. LSTMs is a special recurrent neural network, which can learn long-term dependencies of a sequence.ConclusionsSimulation studies show that our new scDLC performs consistently better than the existing methods in a wide range of settings with large sample sizes. Four real scRNA-seq datasets are also analyzed, and they coincide with the simulation results that our new scDLC always performs the best. The code named “scDLC” is publicly available at https://github.com/scDLC-code/code.

An Improved Music Composing Technique Based on Neural Network Model

Traditionally, music was considered an analog signal that had to be made by hand. In recent decades, music has been highlighted by technology that can autonomously compose a suite of music without any human interaction. To achieve this goal, this article suggests an autonomous music composition technique based on long short-term memory recurrent neural networks. Firstly, the music collection is split into music sequences based on unit time, and the Meier cepstrum coefficients of music audio are retrieved as features during music preprocessing. Secondly, the training samples composed of feature vectors processed by data were trained and predicted by short- and long-term memory models. Finally, the generated music sequence is spliced and fused to get new music. This article designs and performs experiments to demonstrate that our results are promising. From experimental results, this work gained that our model has the maximum accuracy of 99% and the lowest loss rate of 0.03.

Modeling of Intelligent Sensor Duty Cycling for Smart Home Automation

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users’ living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors’ energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users’ future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users’ unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor’s role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme’s work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors’ energy consumption and prolongs the battery lifetime to approximately 137 days. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article presented an energy-efficient duty-cycling scheme for automating sensors’ operations in a smart home scenario. The traditional duty-cycling schemes mainly provide solutions based on appointing sentries or predicting smart home users’ next activity using models, such as Bayesian networks. We design a system that integrates the advantages of both sentry and prediction-based schemes to reduce the amount of energy required by sensors to detect and automate smart home users’ activities with high accuracy and precision. The active sensors are appointed using a bidirectional long short-term memory recurrent neural network. Similarly, the guard sensors are assigned to detect unusual activities using the similarities among idle sensors. This study could be used to automate the smart home sensors for detecting home user’s activities with less energy, which ultimately prolongs the battery lifetime of the sensors.

Latent Representation of the Human Pan-Celltype Epigenome Through a Deep Recurrent Neural Network.

The availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were cell type-specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-cell type representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions, and evolutionary conservation. These representations outperform existing methods in a majority of cell types while yielding smoother representations along the genomic axis due to their sequential nature.

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Autonomous credit card fraud detection using machine learning approach☆

Credit card fraud has risen in vulnerable effects in recent years as more people use credit cards to pay for products. This is owing to advancements in technology and growths in internet transactions, both of which have resulted in massive financial losses due to fraud. To reduce such losses, an effective fraud detection system must be designed and implemented. Machine learning approaches used to detect credit card fraud automatically and do not take into account deception process or behavioral problem, which might lead to alerts. The goal of this study is to figure out how to spot credit card fraud. To detect the occurrence of fraud, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) is proposed. In addition, an attention mechanism has been included to increase performance even more. In instances like fraud detection, where the information sequence is made up of vectors with complicated interrelated properties, models with this structure have proven to be particularly efficient. LSTM-RNN is compared to other classifiers such as Naive Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN). Experiments reveal that our proposed model produces powerful results and has a high level of accuracy.

Theoretical and Experimental Analysis of a New Intelligent&#x0D; Charging Controller for Off-Board Electric Vehicles Using&#x0D; PV Standalone System Represented by a Small-Scale&#x0D; Lithium-Ion Battery

Electric vehicles are rapidly infiltrating the power grid worldwide, initiating an immediate need for a smart charging technique to maintain the stability and robustness of the charging process despite the generation type. Renewable energy sources (RESs), especially photovoltaic (PV), are becoming the essential source for electric vehicle charging points. The stochastic behavior of the PV output power affects the power conversion for regulating the battery charger voltage levels, which influences the battery to overheat and degrade. This study presents a PV standalone smart charging process for off-board plug-in electric vehicles, represented by a small-scale lithium-ion battery based on the multistage charging currents (MSCC) protocol. The charger comprises a DC–DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory recurrent neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. Additionally, it was used as an alarm flag for any possible PV output shortage during the charging process in the long- and short-term prediction to be supported by any other electricity source. The NNPC–LSTM controller was compared with the fuzzy logic and the conventional PID controllers while varying the input voltage and implementing the MSCC protocol. The proposed charging controller perfectly ensured that the minimum battery terminal voltage ripple and charging current ripple reached 1 mV and 1 mA, respectively, with a very high-speed response of 1 ms in reaching the predetermined charging current stages. The present simulated and experimental results are in good agreement with the previous related work in the literature survey.

Deep Learning Approaches for Air-Writing Using Single UWB Radar

Air-writing is an emerging and promising method for contactless human-machine interaction. This paper proposes a novel air-writing framework based on a single ultra wide-band radar (UWB). The framework employs a simple data capture and a processing pipeline facilitated by deep learning approaches, where a number of data representations and models are explored. Two different data representations are proposed, two-dimensional and three-dimensional range-Doppler spectrogram. The deep learning approaches include, fully connected neural networks, convolutional neural networks, three-dimensional convolutional neural networks, and hybrid two-dimensional and three-dimensional convolutional neural networks long short-term memory recurrent neural networks. A dataset of 1,800 samples containing 10 air-written numbers is collected to train, validate, and test the performance of the proposed methods. It is shown that hybrid convolutional neural networks long short-term memory recurrent neural networks architectures can effectively predict air-written numbers with an accuracy of 98.5%. The experimental results suggest the efficacy of the proposed approaches for practical and convenient air-writing applications.

Prediction of growth in grower-finisher pigs using recurrent neural networks

Accurate prediction of pig growth is an important tool for supporting sustainable global pork production, due to the potential benefits to farm management in monitoring growth according to target, promoting efficient use of resources and planning sale of finished animals. In this paper, a long short-term memory (LSTM) recurrent neural network architecture was designed to predict how long it would take for grower-finisher pigs housed in a climate-controlled building reflecting commercial practice to reach a target liveweight of 90 kg. To make these predictions, data on pig liveweight and food consumption were integrated, along with various combinations of environmental temperature recorded by the building control system and, to represent seasonality, encodings of day of the year. Also, ensembles were created and evaluated by averaging the predictions of multiple models, each trained with different combinations of input features, and comparisons were made between the performance of the LSTM models and other machine learning algorithms. Overall, the LSTM model achieved the best prediction performance, with a RMSE of 2.470 days and a Pearson's correlation coefficient of 0.962. Integrating both environmental temperature and seasonality data into the models generally resulted in an improvement in predictive performance. The results demonstrate the potential of LSTM models as a precision livestock farming tool for the early prediction of growth in commercial finisher pigs. • A machine learning pipeline for the forecasting of pig growth was designed. • Predicting the time to reach a target weight can improve data availability. • Specialised neural network architectures are very effective at predicting growth. • Utilising environmental temperatures can improve growth prediction accuracy. • Specialised algorithms can be used to remove outliers from non-cyclical time series.

882-P: Deep Learning Model for Predicting Hospital Readmission among People with Diabetes

Current models for predicting 30-day readmission risk among people with diabetes vary in performance. We previously published the Diabetes Early Readmission Risk Indicator (DERRITM) , a logistic regression (LR) model with modest predictive performance (C-statistic 0.69) . The current study aims to develop a more accurate model using deep learning (DL) on electronic health record (EHR) data. We analyzed electronically abstracted data from 36,563 patients with diabetes and at least 1 hospitalization at an urban, academic medical center between 7/1/2010-12/31/2020. One hospitalization per patient was randomly selected for analysis. A DL, long short-term memory Recurrent Neural Network (RNN) was developed and compared to traditional models: LR, and Multi-layer Perceptron (MLP) . Models were developed using demographics, vitals, diagnostic and procedure codes, medications, laboratory tests, and administrative data as defined by the PCORnet Common Data Model. A look-back time of 1 year before the index hospitalization was used. Data dimensionality was reduced to 2,500 features by Singular Value Decomposition. The RNN C-statistic is significantly greater than that of the other models (Figure) . The RNN model outperforms the DERRI and is based on more generalizable EHR data. Varying the compression and time window might improve performance. RNN models may outperform traditional models at predicting readmission risk. Disclosure A.Abdel hai: None. M.Weiner: None. A.Paranjape: None. A.Livshits: None. J.Brown: None. Z.Obradovic: None. D.J.Rubin: Research Support; AstraZeneca. Funding National Institutes of Health (R01DK122073)

Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System

A reliable power supply has long been identified as an important economic growth parameter. Electricity load forecasts predict the future behavior of the electricity load. Carrying out a forecast is important for real-time dispatching of power, grid maintenance scheduling, grid expansion planning, and generation planning depending on the forecasting horizon. Most of the methods used in long-term load forecasting are regressions and are limited to predicting peak loads of a yearly or monthly resolution with low accuracy. In this paper, we propose a method based on long short-term memory-recurrent neural networks (LSTM-RNN) cells with relations between identified influential econometric load-driving parameters which includes: the Gross Domestic Product (GDP), Population (H), and past Electric Load Data. To the best of our knowledge, the use of the GDP and H as two additional independent variables in load forecast modelling using machine learning techniques is a novelty in Cameroon. A comparison was performed between a linear regression (LR)-based long-term load forecast model (a model currently used by the Transmission System Operator of Cameroon) and LSTM-RNNs model constructed. The results generated were evaluated using a Mean Absolute Percentage Error (MAPE) within the same period of evaluation, and the overall value of the MAPE obtained for LSTM-RNNs model was 5.4962 whereas that for the LR model was 7.5422. Based on these results, the LSTM-RNN model is considered highly accurate and competent. The model was used to generate a forecast for the period of 2022&amp;ndash;2026 with an hourly resolution. A MAPE of 5.4962 was obtained with a computational time of approximately ten minutes, making the model vital for offline use by utilities due to its capacity to quantitatively and accurately predict long-term load with an hourly resolution.

Evaluation of clinical named entity recognition methods for Serbian electronic health records

Background and objectivesThe importance of clinical natural language processing (NLP) has increased with the adoption of electronic health records (EHRs). One of the critical tasks in clinical NLP is named entity recognition (NER). Clinical NER in the Serbian language is a severely under-researched area. The few approaches that have been proposed so far are based on rules or machine-learning models with hand-crafted features, while current state-of-the-art models have not been explored. The objective of this paper is to assess the performance of state-of-the-art NER methods on clinical narratives in the Serbian language. Materials and methodsWe designed an experimental setup for a comprehensive evaluation of state-of-the-art NER models. The gold standard corpus we used for the evaluation is comprised of discharge summaries from the Clinic for Nephrology at the University Clinical Center of Serbia. The following models were evaluated: conditional random fields (CRF), multilingual transformers (BERT Multilingual and XLM RoBERTa), and long short-term memory (LSTM) recurrent neural networks, and their ensembles. In addition, we investigated the necessity of the pretraining task of transformer based models and the use of pretrained word embeddings with LSTM model. ResultsOur results show that individually CRF had the best precision, the pretrained BERT Multilingual model had the best recall values, and the LSTM model had the best F1 score. The best performance was achieved by combining the existing models in a majority voting ensemble with an F1 score of 0.892. The presented results are similar to the inter annotator agreement on our gold standard corpus and are comparable to existing state-of-the-art results for clinical NER reported in literature. ConclusionExisting state-of-the-art models can provide viable results for clinical named entity recognition when applied to languages with the complexity of the Serbian language without major modifications.

Food Nitrogen Footprint of the Indian Subcontinent Toward 2050.

Substantial loss of nitrogen (N) in reactive forms (nitrogen species except for N2) induced by agro–food system is a cause of the environmental degradation and harms human health. The main factors influencing the food N footprint of the Indian Subcontinent (ISC) are the nitrogen use efficiency (NUE) of crop cultivation and religious dietary cultures. In this study, we assess the food N footprint of the ISC and establish reduction scenarios toward 2050. We used a religion-sensitive N-Calculator method and food consumption data from the Food and Agriculture Organization of the United Nations to estimate the food N footprint of the ISC of different religious communities. We developed four reduction scenarios as follows: The business-as-usual scenario; a 30% increase in the crop cultivation NUE; altered protein supplies to the healthy EAT–Lancet reference diet considering religious food taboos; and an integrated approach with a 30% increase in the NUE increase and the altered diet. We used the long short-term memory recurrent neural network approach to predict the future. The study revealed that the average food N footprint per-capita per-year increased from 7.94 kg-N in the 1960s to 8.43 kg-N in the early 2010s, and the crop cultivation NUE was reduced to less than 40%. Buddhists had the lowest footprint over the period. An increase in the NUE of the crop cultivation and an altered diet results in a 13% reduction in the N footprint compared to the business-as-usual scenario. We conclude that improved crop cultivation NUEs and an altered religion-specific healthy diet would reduce the N footprint.

Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulnerabilities of current authentication methods such as physiological biometrics or passwords. Instead of these methods, behavioral biometrics has recently been researched as a solution to these vulnerable authentication methods. In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement, and we evaluate the performance of different single-modal and multi-modal combinations of the two biometrics. Using two publicly available datasets - BioIdent and Hand Movement Orientation and Grasp (H-MOG), this study uses seven common machine learning algorithms to evaluate performance. The algorithms used in the evaluation include Random Forest, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Multilayer Perceptron, and Long Short-Term Memory Recurrent Neural Networks, with accuracy rates reaching as high as 86%.

Fault Prediction of Rolling Element Bearings Using the Optimized MCKD–LSTM Model

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out reliability maintenance activities. Many methods have been used to solve the problem of reliability prediction. Due to its convenience and efficiency, the data-driven method is increasingly widely used in practical reliability prediction. In order to ensure the reliability of bearing operation, the main objective of the present study is to establish a novel model based on the optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to realize early bearing fault warnings by predicting bearing fault time series. The proposed model is based on the lifecycle vibration signal of the bearing. In the first step, the cuckoo search (CS) is utilized to optimize the parameter filter length and deconvolution period of MCKD, considering the influence of periodic bearing time series, and to improve the fault impact component of the optimized MCKD deconvolution time series. Then the LSTM learning rate is selected according to the deconvolution time series. Finally, the dataset obtained through various preprocessing approaches is used to train and predict the LSTM model. The analyses performed using the XJTU-SY bearing dataset demonstrate that the prediction results are in good consistency with real fault data, and the average prediction accuracy of the optimized MCKD–LSTM model is 26% higher than that of the original time series.

Improving Jakarta’s Katulampa Barrage Extreme Water Level Prediction Using Satellite-Based Long Short-Term Memory (LSTM) Neural Networks

Jakarta, the capital region of Indonesia, is experiencing recurring floods, with the most extensive recording loss as high as 350 million dollars. Katulampa Barrage’s observation of the Upper Ciliwung River plays a central role in reducing the risk of flooding in Jakarta, especially flowing through the Ciliwung River. The peak flow measured in the barrage would travel 13–14 h to the heart of the city, providing adequate time for the government officials and the residents to prepare for the flood risk. However, Jakarta is continually pressed by the population growth, averaging 1.27% in the past 20 years. The constant growth of Jakarta’s population continually develops slums in increasingly inconvenient locations, including the riverbanks, increasing vulnerability to floods. This situation necessitates a more advanced early warning system that could provide a longer forecasting lead time. Satellite remote sensing data propose a promising utility to extend the prediction lead time of extreme events. In the case of this study, Sadewa data is used to predict the water level of Katulampa Barrage using long short-term memory (LSTM) recurrent neural networks (RNN). The results show that the model could predict Katulampa Water Level accurately. The model presents a potential for implementation and additional lead time to increase flood mitigation preparedness.

Hybrid deep recurrent neural networks for COVID-19 detection and diagnosis

Corona virus Disease (COVID-19) is an acute pandemic which has put the lives of millions of people worldwide at risk during recent times. There is a high demand to develop effective tools and methods to diagnose the COVID infection in people at an early stage to prevent the spread of the disease to a larger community. This paper aims to provide a systematic method for COVID diagnosis using machine learning and deep learning algorithms. The proposed method Hybrid Deep Recurrent Neural Network (HDRNN) is a fusion of Convolution Neural Networks (CNN) and Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN) to detect COVID infection efficiently from X-ray samples. CNN is employed in the proposed method primarily to extract the essential features from the X-ray images and LSTM is suitable to classify the COVID affected patients with more fidelity. The dataset used in this work consists of an aggregate of 3470 images including COVID affected and Pneumonia affected samples. The experimental results carried out on the collected dataset with the proposed HDRNN method demonstrated an accuracy of 99.4%, F1 Score 98.7%, Sensitivity of 99.3% and Specificity of 99.2 %.

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models.

We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the norm and less than 6.3% in the norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.

MP41-09 COMBINING VIDEO AND FORCE SENSOR DATA WITHIN A MULTILABEL MULTIMODAL DEEP LEARNING ALGORITHM TO PROVIDE AUTOMATED FEEDBACK FOR ROBOT ASSISTED RADICAL PROSTATECTOMY SIMULATIONS

You have accessJournal of UrologyCME1 May 2022MP41-09 COMBINING VIDEO AND FORCE SENSOR DATA WITHIN A MULTILABEL MULTIMODAL DEEP LEARNING ALGORITHM TO PROVIDE AUTOMATED FEEDBACK FOR ROBOT ASSISTED RADICAL PROSTATECTOMY SIMULATIONS Patrick Saba, Beilei Xu, Rachel Melnyk, Christopher Wanderling, and Ahmed Ghazi Patrick SabaPatrick Saba More articles by this author , Beilei XuBeilei Xu More articles by this author , Rachel MelnykRachel Melnyk More articles by this author , Christopher WanderlingChristopher Wanderling More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002607.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Our approach in combining 3D printing and hydrogel casting allows us to create realistic robot assisted radical prostatectomy (RARP) simulations with force sensors embedded into the neurovascular bundles (NVB). We sought to develop an algorithm that combines video and sensor data to provide quantitative and objective feedback by automatically identifying and grading actions performed on a validated RARP simulation. METHODS: 531 data points corresponding to instrument interactions with the NVB (scissor cut, sharp dissection, blunt dissection, & traction) were segmented from expert simulations and synced endoscopic video and sensor readings were collected (Figure 1A). A severity grade, ranging from 1 (minimal) to 3 (severe injury), was assigned to each interaction. 80% of segments were chosen to train four Long Short-Term Memory (LSTM) recurrent neural networks. One LSTM was analyzed for each modality (video or sensor) and each task (identification or grading of actions) before a Multilabel Multimodal Deep Learning (MMDL) technique was used to fuse the individual LSTMs into a single algorithm. The remaining 20% were used to evaluate the MMDL combinations. RESULTS: Sensor and video data analysis alone were limited in their ability to accurately identify surgical actions while also quantifying their respective forces. The MMDLs that combined both data sets into one analyzation system were evaluated for accuracy, precision, and recall, to quantify fully correct, incorrect, or partially correct answers (actions could be correctly identified but simultaneously misrepresent the severity grade) (Figure 1B). Overall, the MMDL combination of all four LSTMs offered the best accuracy, precision, and recall (76.5%, 81.6%, 83.8%, respectively) for determining the action and their severity grade. CONCLUSIONS: MMDLs provide the basis for an automated and accurate method to identify and quantify instrument actions during RARP NVB dissection which can be used to develop procedure specific assessments and feedback for trainees (ex: quantifying positive/negative action occurrences and/or significant forces on the NVB leading to neuropraxia). This approach can ultimately lead to independent training during realistic RARP simulations. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e722 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Patrick Saba More articles by this author Beilei Xu More articles by this author Rachel Melnyk More articles by this author Christopher Wanderling More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF DownloadLoading ...

A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.