scholarly journals DEVELOPMENT OF A DEEP-LEARNING BASED WAVE FORECASTING MODEL USING LSTM NETWORK

2020 ◽  
pp. 31
Author(s):  
Thit Oo Kyaw ◽  
Tomoya Shibayama ◽  
Yoko Shibutani ◽  
Yasuo Kotake
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Numerical Models ◽  
Storm Surges ◽  
Forecasting Model ◽  
Feed Forward Neural Networks ◽  
Wave Conditions ◽  
Lstm Network ◽  
Wave Forecasting

Forecasting of wave conditions plays an essential role for offshore construction and maintenance. Recently, machine learning-based wave forecasting models have been developed and their integrated usage with physics-based numerical models has become popular. These studies mostly apply Feed Forward Neural Networks (FFNNs) with an emphasis on prediction of time-series of waves, tides and storm surges. As a particularly different approach, we develop a deep learning-based wave forecasting model using Long Short-Term Memory (LSTM) network under Recurrent Neural Networks. As a case study, the model will be utilized to predict the wave conditions (low or high) near the Tottori Port, Japan.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/oMvIS9zkIOs

scholarly journals Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 149 ◽  
Author(s):  
Salah Bouktif ◽  
Ali Fiaz ◽  
Ali Ouni ◽  
Mohamed Adel Serhani
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Time Lags ◽  
Electric Load ◽  
Learning Approaches ◽  
Electric Load Forecasting

Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a lack of validity because it neglects several characteristics of the electric load exhibited by time series. In this work, we propose a load-forecasting model based on enhanced-LSTM that explicitly considers the periodicity characteristic of the electric load by using multiple sequences of inputs time lags. An autoregressive model is developed together with an autocorrelation function (ACF) to regress consumption and identify the most relevant time lags to feed the multi-sequence LSTM. Two variations of deep neural networks, LSTM and gated recurrent unit (GRU) are developed for both single and multi-sequence time-lagged features. These models are compared to each other and to a spectrum of data mining benchmark techniques including artificial neural networks (ANN), boosting, and bagging ensemble trees. France Metropolitan’s electricity consumption data is used to train and validate our models. The obtained results show that GRU- and LSTM-based deep learning model with multi-sequence time lags achieve higher performance than other alternatives including the single-sequence LSTM. It is demonstrated that the new models can capture critical characteristics of complex time series (i.e., periodicity) by encompassing past information from multiple timescale sequences. These models subsequently achieve predictions that are more accurate.

scholarly journals Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

2021 ◽  
Vol 15 ◽  
Author(s):  
Karun Thanjavur ◽  
Dionissios T. Hristopulos ◽  
Arif Babul ◽  
Kwang Moo Yi ◽  
Naznin Virji-Babul
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Resting State ◽  
Short Term Memory ◽  
State Data ◽  
Eeg Data ◽  
Clinical Environments ◽  
Lstm Network

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals Detection of dementia on raw voice recordings using deep learning: A Framingham Heart Study

2021 ◽  
Author(s):  
Chonghua Xue ◽  
Cody Karjadi ◽  
Ioannis Ch. Paschalidis ◽  
Rhoda Au ◽  
Vijaya B. Kolachalama
Keyword(s):  
Deep Learning ◽  
Framingham Heart Study ◽  
Short Term Memory ◽  
Neuropsychological Testing ◽  
Heart Study ◽  
Audio Recordings ◽  
Lstm Network ◽  
Sensitivity Specificity ◽  
Normal Cognition ◽  
Mean Sensitivity

AbstractBackgroundIdentification of reliable, affordable and easy-to-use strategies for detection of dementia are sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data without any pre-processing are not readily available.MethodsWe used a subset of 1264 digital voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 minutes in duration, on average, and contained at least two speakers (participant and clinician). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia. We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the raw audio recordings to classify if the recording included a participant with only NC or only dementia, and also to differentiate between recordings corresponding to non-demented (NC+MCI) and demented participants.FindingsBased on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the sensitivity-specificity curve (AUC) of 0.744±0.038, mean accuracy of 0.680±0.032, mean sensitivity of 0.719±0.112, and mean specificity of 0.652±0.089 in predicting cases with dementia from those with normal cognition. The CNN model achieved a mean AUC of 0.805±0.027, mean accuracy of 0.740±0.033, mean sensitivity of 0.735±0.094, and mean specificity of 0.750±0.083 in predicting cases with only dementia from those with only NC. For the task related to classification of demented participants from non-demented ones, the LSTM model achieved a mean AUC of 0.659±0.043, mean accuracy of 0.701±0.057, mean sensitivity of 0.245±0.161 and mean specificity of 0.856±0.105. The CNN model achieved a mean AUC of 0.730±0.039, mean accuracy of 0.735±0.046, mean sensitivity of 0.443±0.113, and mean specificity of 0.840±0.076 in predicting cases with dementia from those who were not demented.InterpretationThis proof-of-concept study demonstrates the potential that raw audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can provide a level of screening for dementia.

scholarly journals Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

2021 ◽  
Vol 6 ◽  
Author(s):  
Tahani Aljohani ◽  
Alexandra I. Cristea
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Methodological Approach ◽  
High Accuracy ◽  
Directional Model ◽  
Textual Representations ◽  
The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

scholarly journals Ensemble Malware Classification System Using Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 721 ◽  
Author(s):  
Barath Narayanan Narayanan ◽  
Venkata Salini Priyamvada Davuluru
Keyword(s):  
Neural Networks ◽  
Classification System ◽  
Short Term Memory ◽  
Ensemble Classification ◽  
Machine Language ◽  
Support Vector ◽  
Automated Classification ◽  
Malware Classification ◽  
Lstm Network ◽  
Computational Resources

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft’s Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using Long Short-Term Memory (LSTM) networks after converting them into sequences. In addition, features are extracted from these architectures (CNNs and LSTM) and are classified using a support vector machine or logistic regression. An accuracy of 97.2% is achieved using LSTM network for distinguishing assembly files, 99.4% using CNN architecture for classifying compiled files and an overall accuracy of 99.8% using the proposed ensemble approach thereby setting a new benchmark. An independent and automated classification system for assembly and/or compiled files provides the luxury to anti-malware industry experts to choose the type of system depending on their available computational resources.

Définition des conditions de vagues pour la conception d'un havre de pêche à Sept-îles

1991 ◽  
Vol 18 (5) ◽  
pp. 851-863 ◽  
Author(s):  
Y. Ouellet ◽  
A. Drouin
Keyword(s):  
Key Words ◽  
Numerical Models ◽  
Wave Climate ◽  
Wave Transformation ◽  
Forecasting Model ◽  
Wave Modeling ◽  
Long Period ◽  
Numerical Studies ◽  
Small Craft ◽  
Wave Forecasting

This paper presents the results of numerical studies to define the wave climate inside the Bay of Sept-îles, where it is proposed to build a small craft harbour. This wave climate is relatively complex as it results from waves coming from outside the Bay, that is from the estuary of the St. Lawrence, or generated inside the Bay itself. Such information is required to select the configuration of the harbour and the best location among the various sites proposed. Waves have been recorded at a station outside the Bay in 1966 – 1967 and inside the Bay in 1983. These records were used to calibrate the wave forecasting model based on wind data recorded at Sept-îles airport. They were also used to validate results obtained from a refraction model used to determine wave transformation from outside to inside the Bay. Then waves were forecasted over a long period (1953 – 1984), for the ice-free season, and used to study wave agitation inside different schemes proposed for harbour configuration and site. The study shows that there is a need to obtain better wave information and to improve the numerical models. Key words: wave forecasting, wave transformation, wave records, wave modeling, harbor, Sept-îles.

scholarly journals Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models

2021 ◽  
Author(s):  
Qihang Wang ◽  
Feng Liu ◽  
Guihong Wan ◽  
Ying Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
General Anesthetics ◽  
Learning Models ◽  
Multi Stage ◽  
Meta Learning ◽  
Brain States ◽  
High Level

AbstractMonitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.

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