scholarly journals Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Md Fazle Rabby ◽  
Yazhou Tu ◽  
Md Imran Hossen ◽  
Insup Lee ◽  
Anthony S. Maida ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Recurrent Neural Network ◽  
Insulin Infusion ◽  
Diabetes Management ◽  
Short Term Memory ◽  
Artificial Pancreas ◽  
Time Interval ◽  
Kalman Smoothing ◽  
Deep Recurrent Neural Network

Abstract Background Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. Methods In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. Results For the OhioT1DM (2018) dataset, containing eight weeks’ data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively. Conclusions To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings—the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.

scholarly journals A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2195
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh Kumar Ochani
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Estimation Accuracy ◽  
Post Processing ◽  
Input Space ◽  
Deep Recurrent Neural Network ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

2020 ◽  
Vol 17 (8) ◽  
pp. 3421-3426
Author(s):  
D. Deva Hema ◽  
J. Tharun ◽  
G. Arun Dev ◽  
N. Sateesh
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Online Reviews ◽  
Physical Contact ◽  
Proposed Model ◽  
Deep Recurrent Neural Network ◽  
Bayes Algorithm ◽  
Fake Reviews

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

scholarly journals Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

2021 ◽  
Author(s):  
Kevin B. Dsouza ◽  
Adam Y. Li ◽  
Vijay K. Bhargava ◽  
Maxwell W. Libbrecht
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Replication Timing ◽  
Cell Types ◽  
Data Sets ◽  
Deep Recurrent Neural Network ◽  
Long Short Term Memory ◽  
Latent Representations

AbstractThe 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 celltype 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-celltype 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.

scholarly journals Power System Load Forecasting Method Based on Recurrent Neural Network

2020 ◽  
Vol 182 ◽  
pp. 02007
Author(s):  
Chuanjun Pang ◽  
Tie Bao ◽  
Lei He
Keyword(s):  
Neural Network ◽  
Power System ◽  
Recurrent Neural Network ◽  
Electricity Market ◽  
Short Term Memory ◽  
Sequence Data ◽  
Load Forecasting ◽  
System Load ◽  
Forecasting Method ◽  
Deep Recurrent Neural Network

Power system load forecasting plays an important role in the power dispatching operation. The development of the electricity market and the increasing integration of distributed generators have increased the complexity of power consumption model and put forward higher requirements for the accuracy and stability of load forecasting. A load forecasting method based on long-short term memory (LSTM) is proposed. This method uses deep recurrent neural network from the artificial intelligence field to establish a load forecasting model. Using the LSTM network to memorize the long-term dependence of the sequence data, the intrinsic variation of the load itself is identified from both the horizontal and vertical dimensions within a longer historical time period, while considering various influencing factors. Actual load data is used to verify the forecasting performance of different historical date windows and different network architectures.

scholarly journals Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Erik Marchi ◽  
Fabio Vesperini ◽  
Stefano Squartini ◽  
Björn Schuller
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Novelty Detection ◽  
Reconstruction Error ◽  
Generative Models ◽  
Audio Signals ◽  
Short Term ◽  
Present Contribution ◽  
Deep Recurrent Neural Network

In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% averageF-measure over the three databases.

Formal Verification of a Multi-Basal Insulin Infusion Control Model

10.29007/kcrp ◽  
2018 ◽  
Author(s):  
Xin Chen ◽  
Souradeep Dutta ◽  
Sriram Sankaranarayanan
Keyword(s):  
Blood Glucose ◽  
Insulin Infusion ◽  
Infusion Pump ◽  
Artificial Pancreas ◽  
Small Time ◽  
Time Interval ◽  
State Variables ◽  
Continuous Glucose Monitor ◽  
Glucose Levels ◽  
Blood Glucose Levels

The artificial pancreas concept automates the delivery of insulin to patients with type-1 diabetes, sensing the blood glucose levels through a continuous glucose monitor (CGM) and using an insulin infusion pump to deliver insulin. Formally verifying control algorithms against physiological models of the patient is an important challenge. In this paper, we present a case study of a simple hybrid multi-basal control system that switches to different preset insulin delivery rates over various ranges of blood glucose levels. We use the Dalla- Man model for modeling the physiology of the patient and a hybrid automaton model of the controller. First, we reduce the problem state space and replace nonpolynomial terms by approximations with very small errors in order to simplify the model. Nevertheless, the model still remains nonlinear with up to 9 state variables.Reachability analysis on this hybrid model is used to verify that the blood glucose levels remain within a safe range overnight. This poses challenges, including (a) the model exhibits many discrete jumps in a relatively small time interval, and (b) the entire time horizon corresponding to a full night is 720 minutes, wherein the controller time period is 5 minutes. To overcome these difficulties, we propose methods to effectively handle time- triggered jumps and merge flowpipes over the same time interval. The evaluation shows that the performance can be improved with the new techniques.

Jaya Ant Lion Optimization-Driven Deep Recurrent Neural Network for Cancer Classification Using Gene Expression Data (Preprint)

2020 ◽  
Author(s):  
Ramachandro Majji
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Early Stage ◽  
Data Transformation ◽  
Cancer Classification ◽  
Data Normalization ◽  
Ant Lion Optimization ◽  
Deep Recurrent Neural Network ◽  
Novel Strategy ◽  
Feature Dimension

BACKGROUND Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. OBJECTIVE Propose an automatic prediction system for classifying cancer to malignant or benign. METHODS This paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is the data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer-based on the reduced dimension features to produce a satisfactory result. RESULTS The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, the maximal sensitivity of 95.95%, and the maximal specificity of 96.96%. CONCLUSIONS The resulted output of the proposed JayaALO-based DeepRNN is used for cancer classification.

scholarly journals Recurrent neural network-based acute concussion classifier using raw resting state EEG data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karun Thanjavur ◽  
Arif Babul ◽  
Brandon Foran ◽  
Maya Bielecki ◽  
Adam Gilchrist ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Resting State ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Health Concern ◽  
Adolescent Athletes ◽  
Individual Level ◽  
Diffuse Brain Injury ◽  
Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

scholarly journals Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rakesh David ◽  
Rhys-Joshua D. Menezes ◽  
Jan De Klerk ◽  
Ian R. Castleden ◽  
Cornelia M. Hooper ◽  
...  
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Data Dissemination ◽  
Short Term Memory ◽  
Biological Data ◽  
Experimental Methodology ◽  
Subcellular Localisation ◽  
Crop Species ◽  
Deep Recurrent Neural Network ◽  
Functional Features

AbstractThe increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.

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