scholarly journals An Automatic Assessment System for Alzheimer’s Disease Based on Speech Using Feature Sequence Generator and Recurrent Neural Network

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yi-Wei Chien ◽  
Sheng-Yi Hong ◽  
Wen-Ting Cheah ◽  
Li-Hung Yao ◽  
Yu-Ling Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Cognitive Skills ◽  
Selection Process ◽  
Characteristic Curve ◽  
Optimal Solution ◽  
Assessment System ◽  
Automatic Assessment ◽  
Data Driven Approach ◽  
Feature Sequence

AbstractAlzheimer disease and other dementias have become the 7th cause of death worldwide. Still lacking a cure, an early detection of the disease in order to provide the best intervention is crucial. To develop an assessment system for the general public, speech analysis is the optimal solution since it reflects the speaker’s cognitive skills abundantly and data collection is relatively inexpensive compared with brain imaging, blood testing, etc. While most of the existing literature extracted statistics-based features and relied on a feature selection process, we have proposed a novel Feature Sequence representation and utilized a data-driven approach, namely, the recurrent neural network to perform classification in this study. The system is also shown to be fully-automated, which implies the system can be deployed widely to all places easily. To validate our study, a series of experiments have been conducted with 120 speech samples, and the score in terms of the area under the receiver operating characteristic curve is as high as 0.838.

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 Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function

VLSI Design ◽  
1995 ◽  
Vol 3 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Pong P. Chu
Keyword(s):  
Neural Network ◽  
Boolean Function ◽  
Input Data ◽  
Selection Process ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Hopfield Network ◽  
Neural Network Approach ◽  
Complete Problem ◽  
Np Complete

To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an “energy function” and map it to a modified Hopfield network, which will automatically search for the minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time.

scholarly journals Recurrent Neural Network for Inertial Gait User Recognition in Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4054 ◽  
Author(s):  
Fernandez-Lopez ◽  
Liu-Jimenez ◽  
Kiyokawa ◽  
Wu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Sensors ◽  
Selection Process ◽  
Gait Recognition ◽  
Recognition Algorithm ◽  
Tuning Method ◽  
Random Grid ◽  
Hyperparameter Selection ◽  
User Recognition

In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) for hyperspectral image based face recognition in real time door locking applications

2021 ◽  
pp. 1-11
Author(s):  
Ashok Kumar Rai ◽  
Radha Senthilkumar ◽  
A. Kanan
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recurrent Neural Network ◽  
Hyperspectral Image ◽  
Selection Process ◽  
Facial Feature ◽  
Recognition System ◽  
Training Data ◽  
Search Optimization ◽  
Feature Extraction And Selection

Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods.

A One-Layer Recurrent Neural Network with a Discontinuous Activation Function for Linear Programming

2008 ◽  
Vol 20 (5) ◽  
pp. 1366-1383 ◽  
Author(s):  
Qingshan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Linear Programming ◽  
Recurrent Neural Network ◽  
Optimal Solution ◽  
Activation Function ◽  
High Gain ◽  
Discontinuous Activation ◽  
The Neural Network ◽  
Decision Variables ◽  
Discontinuous Activation Function

A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.

scholarly journals Online Life Forecasting Method and State Assessment Architecture of Power Cable Based on Recurrent Neural Network and Internet of Things

2021 ◽  
Author(s):  
Changcheng Song ◽  
Binglei Xue ◽  
Fumu Lu ◽  
Feng Lan ◽  
Zheng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Life Cycle ◽  
Internet Of Things ◽  
Recurrent Neural Network ◽  
Life Cycle Management ◽  
Assessment System ◽  
Assessment Process ◽  
Power Cable ◽  
Forecasting Method ◽  
Status Assessment

For the purpose of increasing the accuracy of power cable life forecasting and status assessment, improving its life cycle management process, this paper proposes a power cable online life forecasting method and status assessment system based on recurrent neural network and Internet of Things (IoTs). Power cable electrical insulation online monitoring system is established on the first place. Then, recurrent neural network and fuzzy analytical hierarchy process are used in the IoTs based power cable online status assessment architecture to proceed life forecasting and status assessment process. Lastly, example analysis is presented to verify the effectiveness and superiority of the methodology introduced in this paper. It is shown that artificial intelligence and IoTs will also have broad development and application prospect when combined with power cable life cycle management.

scholarly journals Automated Adenoid Hypertrophy Assessment with Lateral Cephalometry in Children Based on Artificial Intelligence

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1386
Author(s):  
Tingting Zhao ◽  
Jiawei Zhou ◽  
Jiarong Yan ◽  
Lingyun Cao ◽  
Yi Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Assessment Tool ◽  
Characteristic Curve ◽  
High Sensitivity ◽  
Assessment System ◽  
Adenoid Hypertrophy ◽  
Obstructive Sleep ◽  
Lateral Cephalograms ◽  
Lateral Cephalometry

Adenoid hypertrophy may lead to pediatric obstructive sleep apnea and mouth breathing. The routine screening of adenoid hypertrophy in dental practice is helpful for preventing relevant craniofacial and systemic consequences. The purpose of this study was to develop an automated assessment tool for adenoid hypertrophy based on artificial intelligence. A clinical dataset containing 581 lateral cephalograms was used to train the convolutional neural network (CNN). According to Fujioka’s method for adenoid hypertrophy assessment, the regions of interest were defined with four keypoint landmarks. The adenoid ratio based on the four landmarks was used for adenoid hypertrophy assessment. Another dataset consisting of 160 patients’ lateral cephalograms were used for evaluating the performance of the network. Diagnostic performance was evaluated with statistical analysis. The developed system exhibited high sensitivity (0.906, 95% confidence interval [CI]: 0.750–0.980), specificity (0.938, 95% CI: 0.881–0.973) and accuracy (0.919, 95% CI: 0.877–0.961) for adenoid hypertrophy assessment. The area under the receiver operating characteristic curve was 0.987 (95% CI: 0.974–1.000). These results indicated the proposed assessment system is able to assess AH accurately. The CNN-incorporated system showed high accuracy and stability in the detection of adenoid hypertrophy from children’ lateral cephalograms, implying the feasibility of automated adenoid hypertrophy screening utilizing a deep neural network model.

Letter to the Editor: A DISCRETE-TIME LAGRANGIAN NETWORK FOR SOLVING CONSTRAINED QUADRATIC PROGRAMS

2000 ◽  
Vol 10 (04) ◽  
pp. 261-265 ◽  
Author(s):  
WAI SUM TANG ◽  
JUN WANG
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Optimal Solution ◽  
Optimization Method ◽  
Quadratic Program ◽  
Penalty Parameter ◽  
Quadratic Programs ◽  
The Neural Network ◽  
Simulation Results

A discrete-time recurrent neural network which is called the discrete-time Lagrangian network is proposed in this letter for solving convex quadratic programs. It is developed based on the classical Lagrange optimization method and solves quadratic programs without using any penalty parameter. The condition for the neural network to globally converge to the optimal solution of the quadratic program is given. Simulation results are presented to illustrate its performance.

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