A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and Grasshopper Optimization Algorithm

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a “time-gate” is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians’ decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models.

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Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Sensors ◽

10.3390/s20195611 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5611 ◽

Cited By ~ 1

Author(s):

Mihail Burduja ◽

Radu Tudor Ionescu ◽

Nicolae Verga

Keyword(s):

Neural Network ◽

Intracranial Hemorrhage ◽

Network Architecture ◽

Short Term Memory ◽

Ct Scans ◽

Short Term ◽

Term Memory ◽

Deep Model ◽

Long Short Term Memory ◽

Ct Slices

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

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Application of deep learning methods to predict ionosphere parameters in real time

E3S Web of Conferences ◽

10.1051/e3sconf/202019602007 ◽

2020 ◽

Vol 196 ◽

pp. 02007

Author(s):

Vladimir Mochalov ◽

Anastasia Mochalova

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Architecture ◽

Short Term Memory ◽

Neural Network Architecture ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

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Semantic Sequence Labeling Model of Power Dispatching Based on Deep Long Short Term Memory Network

Journal of Interconnection Networks ◽

10.1142/s0219265921430180 ◽

2021 ◽

Author(s):

Hu Feifei ◽

Zeng Shibo ◽

Hong Danke ◽

Zhang Situo ◽

Song yongwei ◽

...

Keyword(s):

Decision Making ◽

Short Term Memory ◽

Mechanism Analysis ◽

Short Term ◽

Term Memory ◽

Control Center ◽

Fine Grained ◽

Sequence Labeling ◽

Long Short Term Memory ◽

Power Dispatching

As the decision-making brain for power system operation, grid regulation and operation is a comprehensive decision-making control that combines a large amount of data, mechanism analysis, operating procedures and professional experience, and a new generation of artificial intelligence development ideas and evolution characterized by data-driven and knowledge-guided. The directions are very close. However, the current scheduling control is still based on experience and manual analysis. The massive and diverse data of the control center and the lack of logical models between the plans require a large amount of experience and knowledge associations by the control personnel. There are more repetitive human brain labor and relatively low intelligence. Therefore, deep learning is applied to the learning of power control knowledge, and a semantic understanding network based on deep Long Short Term Memory is proposed. It uses sequence labeling to extract in-depth semantic related information of different keywords and query questions, and finds key information about language problems in order to achieve fine-grained and precise query. Experiments show that the proposed network model is superior to the previous methods, and it achieves better performance in the joint extraction of fine-grained evaluation words and evaluation objects, extracts the key information and deep semantic information of query problems and corresponding cases, and realizes power scheduling based on voice interaction The model can be effectively applied in the field of power dispatching and solve a large number of problems in power dispatching and control.

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Improving the Learning Power of Artificial Intelligence Using Multimodal Deep Learning

EPJ Web of Conferences ◽

10.1051/epjconf/202124801017 ◽

2021 ◽

Vol 248 ◽

pp. 01017

Author(s):

Eugene Yu. Shchetinin ◽

Leonid Sevastianov

Keyword(s):

Neural Network ◽

Network Architecture ◽

Short Term Memory ◽

Short Term ◽

Security Systems ◽

Term Memory ◽

Human Voice ◽

Long Short Term Memory ◽

Effective Analysis

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 (BLSTM) neural network is described, which was trained for vocal-based emotion recognition. The main advantage of this network architecture is that each module of the network consists of several interconnected layers, providing the ability to recognize flexible long-term dependencies in data, which is important in context of vocal analysis. We explain the architecture of a bidirectional neural network model, its main advantages over regular neural networks and compare experimental results of BLSTM network with other models.

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A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

10.3233/faia210458 ◽

2021 ◽

Author(s):

Yan Yan ◽

Hongzhong Ma

Keyword(s):

Fault Diagnosis ◽

Power Systems ◽

Short Term Memory ◽

Permutation Entropy ◽

Short Term ◽

Term Memory ◽

Mode Decomposition ◽

Diagnosis Method ◽

Long Short Term Memory ◽

Grasshopper Optimization

Recently, long short-term memory (LSTM) networks have been widely adopted to help with fault diagnosis for power systems. However, the parameters of LSTM networks are determined by prior knowledge and experience and thereby not capable of dealing with unexpected faults in volatile environments. In this paper, we propose and apply an improved grey wolf optimization (IGWO) algorithm to optimize the parameters of LSTM networks, aiming to circumvent the drawback of empirical LSTM parameters and enhance the fault diagnosis accuracy for on-load tap changers (OLTCs). The composite multiscale weighted permutation entropy and energy entropy yielded by the grasshopper optimization algorithm and variational mode decomposition (GOA-VMD) method are used as the inputs of LSTM networks. The IGWO algorithm is applied in an iterative manner to optimize the relevant super arithmetic of the LSTM. In this way, an IGWO-LSTM combination model is constructed to classify different faults diagnosed in OLTCs. Experimental results verify the diagnosis performance superiority of the proposed method over several widely used comparison benchmarks

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Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v3i3.1086 ◽

2019 ◽

Vol 3 (3) ◽

pp. 357-363

Author(s):

Soffa Zahara ◽

Sugianto ◽

M. Bahril Ilmiddafiq

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Optimization Algorithm ◽

Price Index ◽

Short Term Memory ◽

Wholesale Price ◽

Stochastic Gradient Descent ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

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