Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model

Movassagh, Ali Akbar; Alzubi, Jafar A.; Gheisari, Mehdi; Rahimi, Mohamadtaghi; Mohan, Senthilkumar; Abbasi, Aaqif Afzaal; Nabipour, Narjes

doi:10.1007/s12652-020-02623-6

Cited by 124 publications

(29 citation statements)

References 21 publications

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“…It was verified that feature-level fusion outperforms all the single-modality classification algorithms in cognitive workload measurement, as indicated by its best and average accuracy. The best accuracy (93.21%) was obtained using ANN, which agrees with the research conducted by Movassagh et al [ 54 ]. In their research, they revealed that ANN is more convergent with the neural network coefficient than the existing algorithms.…”

Section: Discussionsupporting

confidence: 90%

Cognitive workload evaluation of landmarks and routes using virtual reality

2022

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Investigating whether landmarks and routes affect navigational efficiency and learning transfer in traffic is essential. In this study, a virtual reality-based driving system was employed to determine the effects of landmarks and routes on human neurocognitive behavior. The participants made four (4) journeys to predetermined destinations. They were provided with different landmarks and routes to aid in reaching their respective destinations. We considered two (2) groups and conducted two (2) sessions per group in this study. Each group had sufficient and insufficient landmarks. We hypothesized that using insufficient landmarks would elicit an increase in psychophysiological activation, such as increased heart rate, eye gaze, and pupil size, which would cause participants to make more errors. Moreover, easy and difficult routes elicited different cognitive workloads. Thus, a high cognitive load would negatively affect the participants when trying to apply the knowledge acquired at the beginning of the exercise. In addition, the navigational efficiency of routes with sufficient landmarks was remarkably higher than that of routes with insufficient landmarks. We evaluated the effects of landmarks and routes by assessing the recorded information of the drivers’ pupil size, heart rate, and driving performance data. An analytical strategy, several machine learning algorithms, and data fusion methods have been employed to measure the neurocognitive load of each participant for user classification. The results showed that insufficient landmarks and difficult routes increased pupil size and heart rate, which caused the participants to make more errors. The results also indicated that easy routes with sufficient landmarks were deemed more efficient for navigation, where users’ cognitive loads were much lower than those with insufficient landmarks and difficult routes. The high cognitive workload hindered the participants when trying to apply the knowledge acquired at the beginning of the exercise. Meanwhile, the data fusion method achieved higher accuracy than the other classification methods. The results of this study will help improve the use of landmarks and design of driving routes, as well as paving the way to analyze traffic safety using the drivers’ cognition and performance data.

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Section: Discussionsupporting

confidence: 90%

Cognitive workload evaluation of landmarks and routes using virtual reality

2022

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“…The footages of networked surveillance systems are used as input. The main goal in the paper published by Movassagh et al [35] is to train the neural network by using metaheuristic approaches and to enhance the perceptron neural network precision. Ant colony and invasive weed optimization algorithms are used for performance evaluation.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Patil¹,

Rekha²,

Solanki³

et al. 2022

Computers, Materials &Amp; Continua

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In the past few decades, climatic changes led by environmental pollution, the emittance of greenhouse gases, and the emergence of brown energy utilization have led to global warming. Global warming increases the Earth's temperature, thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people. Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer, causing the untimely death of thousands of people. To forecast weather conditions, researchers have utilized machine learning algorithms, such as autoregressive integrated moving average, ensemble learning, and long short-term memory network. These techniques have been widely used for the prediction of temperature. In this paper, we present a swarm-based approach called Cauchy particle swarm optimization (CPSO) to find the hyperparameters of the long shortterm memory (LSTM) network. The hyperparameters were determined by minimizing the LSTM validation mean square error rate. The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City. The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset. The experimental evaluation on MAT-LABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output. The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training. The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error (RMSE) value of 0.250 compared with the traditional LSTM of 0.35 RMSE.

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“…Gu et al [18] analyzed the text sentiment of commodity evaluation, combining the neural network model with the semantic rules, context, and other factors. e improved artificial neural network (ANN) also has great advantages in research such as prediction [19].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Blog Sentiment Classification Based on MD-HFCE

Yang

Tian

2021

Security and Communication Networks

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In recent years, the rapid growth of multimodal information has become an important factor affecting the results of sentiment analysis. However, a few state-of-the-art works take into account the multimodal features and sentiment fuzziness. To this end, a fuzzy method is proposed for assessing sentiment intensity in this paper. Firstly, based on the visual-text conversion network (CNN-LSTM), as well as sentiment optimization through SentiBank and SentiBridge, the visual features are normalized to the text features. At the same time, the emotional features of the extracted audio will be predicted by the random forest algorithm. Subsequently, the sentiment characteristics are processed by dual hesitant fuzzification to form positive and negative sentiment intensity factors. Finally, a classification method, that is, MD-HFCE (multilayer dual hesitant fuzzy comprehensive evaluation), fuzzy comprehensive evaluation method improved by Mamdani fuzzy reasoning, is proposed to realize the multifeature fuzzy sentiment classification based on the comprehensive sentiment dictionary. The classification results are applicable to the topics of sentiment monitoring. The experimental results show that the proposed algorithm can effectively realize feature integration and improve the average sentiment classification accuracy of multimodal blogs to 82.2%.

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Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model

Cited by 124 publications

References 21 publications

Cognitive workload evaluation of landmarks and routes using virtual reality

Cognitive workload evaluation of landmarks and routes using virtual reality

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

Multimodal Blog Sentiment Classification Based on MD-HFCE

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