scholarly journals Activity Classification Using Backpropagation Neural Networks for the Daily Lives of the Elderly

2021 ◽  
Vol 11 (3) ◽  
pp. 188-193
Author(s):  
Porawat Visutsak ◽  
Keyword(s):  
Neural Networks ◽  
Systems Design ◽  
The Elderly ◽  
Daily Activities ◽  
The Novel ◽  
Daily Lives ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Future Extension ◽  
Novel Method

Activity Analysis Systems or Activity Recognition Systems for the elderly is recently a part of the smart home systems design. This assisted system normally helps the senior people to live alone in a house, safely and improve a quality of life. Therefore, learning to recognize which activities are safe is necessary for classifying the activities of the elderly. This information will give the researchers in the assistive technology some insights to understand the basic daily lives of the elderly. Moreover, it is also help the caregivers to monitor activities of the senior people while they live alone in the house. In this paper, the novel method for detecting and recognizing the activities using Backpropagation Neural Networks has been proposed. The proposed model was tested on a set of basic daily activities (lie, stand, sit, walk and dine). The proposed model was trained to construct the Backpropagation Neural Networks model and used the trained model to classify basic daily activities of the elderly. The proposed model gives the results of 0.78, 0.72 and 0.74 of precision, recall and F1 score, respectively. The discussion and future extension are also given in this paper.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

The Novel Method of Structural Health Monitoring Using FEM and Neural Networks

2005 ◽  
Vol 475-479 ◽  
pp. 2099-2102 ◽  
Author(s):  
Shijie Zheng ◽  
Hong Tao Wang ◽  
Lifeng Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Beam ◽  
Epoxy Composite ◽  
The Novel ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis ◽  
Novel Method ◽  
Modal Values

In this paper, a new method of combining computational mechanics and neural networks for prediction of composite beam delamination is proposed. One beam with delamination, as well as a ‘healthy’ beam with no delamination, had a four-ply symmetric carbon/epoxy composite design, were fabricated simultaneously. The delamination was assumed at different location of the beam, and then the finite element analysis was performed and the modal frequencies of the composite beam were obtained, which were used to train the neural network. The piezoelectric patch was attached to the top of the composite beam to measure its modal frequencies. A feedforward backpropagation neural network was designed, trained, and used to predict the delamination location using the experimental modal values as inputs. The experimental results demonstrate that the predicted delamination location and size error is small.

Structured Pyramidal Neural Networks

2018 ◽  
Vol 28 (05) ◽  
pp. 1750021 ◽  
Author(s):  
Alessandra M. Soares ◽  
Bruno J. T. Fernandes ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Processing Time ◽  
Receptive Fields ◽  
Memory Capacity ◽  
The Other ◽  
Support Vector ◽  
Proposed Model ◽  
Novel Method ◽  
Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

Towards the Importance of the Type of Deep Neural Network and Employment of Pre-trained Word Vectors for Toxicity Detection: An Experimental Study

2021 ◽  
Author(s):  
Abdullah Talha Kabakus
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Deep Neural Networks ◽  
Word Embedding ◽  
Experimental Result ◽  
Daily Lives ◽  
Gold Standard Dataset ◽  
Detection Approach ◽  
Proposed Model ◽  
Social Media Platforms

As a natural consequence of offering many advantages to their users, social media platforms have become a part of daily lives. Recent studies emphasize the necessity of an automated way of detecting the offensive posts in social media since these ‘toxic’ posts have become pervasive. To this end, a novel toxic post detection approach based on Deep Neural Networks was proposed within this study. Given that several word embedding methods exist, we shed light on which word embedding method produces better results when employed with the five most common types of deep neural networks, namely,  , , , , and a combination of  and . To this end, the word vectors for the given comments were obtained through four different methods, namely, () , () , () , and () the  layer of deep neural networks. Eventually, a total of twenty benchmark models were proposed and both trained and evaluated on a gold standard dataset which consists of  tweets. According to the experimental result, the best , , was obtained on the proposed  model without employing pre-trained word vectors which outperformed the state-of-the-art works and implies the effective embedding ability of s. Other key findings obtained through the conducted experiments are that the models, that constructed word embeddings through the  layers, obtained higher s and converged much faster than the models that utilized pre-trained word vectors.

scholarly journals Faster Convergent Artificial Neural Networks

2018 ◽  
Vol 17 (1) ◽  
pp. 7126-7132
Author(s):  
Dolores De Groff ◽  
Perambur Neelakanta
Keyword(s):  
Neural Networks ◽  
Input Data ◽  
Hessian Matrix ◽  
Research Work ◽  
Learning Curves ◽  
Fast Convergence ◽  
Learning Rate ◽  
The Novel ◽  
Learning Rates ◽  
Novel Method

Proposed in this paper is a novel fast-convergence algorithm applied  to neural networks (ANNs) with a learning rate based on the eigenvalues of the associated Hessian matrix of the input data.   That is, the learning rate applied to the backpropagation algorithm changes dynamically with the input data used for training.  The best choice of learning rate to converge to an accurate value quickly is derived. This newly proposed fast-convergence algorithm is applied to a traditional multilayer ANN architecture with feed-forward and backpropagation techniques.  The proposed strategy is applied to various functions learned by the ANN through training.  Learning curves obtained using calculated learning rates according to the novel method proposed are compared to learning curves utilizing an arbitrary learning rate to demonstrate the usefulness of this novel technique.  This study shows that convergence to accurate values can be achieved much more quickly (a reduction in iterations by a factor of  hundred) using the techniques proposed here.  This approach is illustrated in this research work with derivations and pertinent examples to illustrate the method and learning curves obtained. 

scholarly journals Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

2020 ◽  
Author(s):  
Junghwan Lee ◽  
Casey Ta ◽  
Jae Hyun Kim ◽  
Cong Liu ◽  
Chunhua Weng
Keyword(s):  
Neural Networks ◽  
Mechanical Ventilation ◽  
Risk Management ◽  
Recurrent Neural Networks ◽  
Healthcare Systems ◽  
The Novel ◽  
Health Records ◽  
Proposed Model ◽  
Severity Prediction ◽  
Novel Coronavirus

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) using recurrent neural networks (RNN). The predicted severity risk score represents the probability for a person to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission

The Novel Method of Adaptive Multiplayer Games for Mobile Application using Neural Networks

2013 ◽  
Vol 5 (1) ◽  
pp. 10-24 ◽  
Author(s):  
Widodo Budiharto ◽  
Michael Yoseph Ricky ◽  
Ro’fah Nur Rachmawati
Keyword(s):  
Neural Networks ◽  
Mobile Application ◽  
Educational Games ◽  
Rapid Development ◽  
Difficulty Level ◽  
The Novel ◽  
Multiplayer Games ◽  
Mobile Platforms ◽  
Development Goals ◽  
Novel Method

The rapid development of the games industry and its development goals were not just for entertainment, but also used for educational of students interactively. Unfortunately the development of adaptive educational games on mobile platforms in Indonesian language that interesting and entertaining for learning process is very limited. This paper shows the research of development of novel adaptive multiplayer games for students who can adjust the difficulty level of games based on the ability of the user, so that it can motivate students to continue to play these games. The authors propose a method where these games can adjust the level of difficulty, based on the assessment of the results of previous problems using neural networks with three inputs in the form of percentage correct, the speed of answer and interest mode of games (animation / lessons) to produce 1 output. The experimental results are presented and show the adaptive multiplayer games are running well on mobile devices based on BlackBerry platform.

A novel method for the determination of linting propensity of paper

TAPPI Journal ◽  
2012 ◽  
Vol 11 (10) ◽  
pp. 9-17
Author(s):  
ALESSANDRA GERLI ◽  
LEENDERT C. EIGENBROOD
Keyword(s):  
Ash Content ◽  
The Novel ◽  
Offset Printing ◽  
Excellent Correlation ◽  
End Conditions ◽  
Bottom Side ◽  
Novel Method ◽  
Total Fraction

A novel method was developed for the determination of linting propensity of paper based on printing with an IGT printability tester and image analysis of the printed strips. On average, the total fraction of the surface removed as lint during printing is 0.01%-0.1%. This value is lower than those reported in most laboratory printing tests, and more representative of commercial offset printing applications. Newsprint paper produced on a roll/blade former machine was evaluated for linting propensity using the novel method and also printed on a commercial coldset offset press. Laboratory and commercial printing results matched well, showing that linting was higher for the bottom side of paper than for the top side, and that linting could be reduced on both sides by application of a dry-strength additive. In a second case study, varying wet-end conditions were used on a hybrid former machine to produce four paper reels, with the goal of matching the low linting propensity of the paper produced on a machine with gap former configuration. We found that the retention program, by improving fiber fines retention, substantially reduced the linting propensity of the paper produced on the hybrid former machine. The papers were also printed on a commercial coldset offset press. An excellent correlation was found between the total lint area removed from the bottom side of the paper samples during laboratory printing and lint collected on halftone areas of the first upper printing unit after 45000 copies. Finally, the method was applied to determine the linting propensity of highly filled supercalendered paper produced on a hybrid former machine. In this case, the linting propensity of the bottom side of paper correlated with its ash content.

Highlighting the Correlation Between Telomere Shortening and the Susceptibility to the Novel SARS-CoV-2 Virus (COVID-19)

2020 ◽  
Author(s):  
Aml Ghanem
Keyword(s):  
Telomere Length ◽  
Stress Responses ◽  
Viral Infections ◽  
Deep Understanding ◽  
The Elderly ◽  
The Novel ◽  
Telomere Shortening ◽  
Infection Pathways ◽  
And Gender ◽  
Demographic Distribution

COVID-19 is a global crisis that requires a deep understanding of infection pathways to facilitate the development of effective treatments and vaccines. Telomere, which is regarded as a biomarker for other respiratory viral infections, might influence the demographic distribution of COVID-19 infection and fatality rates. Viral infection can induce many cellular remodeling events and stress responses, including telomere specific alterations, just as telomere shortening. In brief, this letter aims to highlight the connection between telomere shortening and susceptibility to COVID-19 infection, in addition to changes in telomeric length according to the variation of age and gender of confirmed cases with COVID-19 infection. To sum up, the correlation is revealed from the available data that connect telomere length and COVID-19 infection, demonstrated in the fact that the elderly patients and males are more susceptible to COVID-19 due to shortening in their telomere length.

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