Tuning Multi-Layer Perceptron by Hybridized Arithmetic Optimization Algorithm for Healthcare 4.0

Stankovic, Marko; Gavrilović, Jelena; Jovanovic, Dijana; Živković, Miodrag; Antonijevic, Milos; Bačanin, Nebojša; Stanković, Miloš S.

doi:10.1016/j.procs.2022.12.006

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…These algorithms, and algorithms derived from their base have been applied in several fields with promising outcomes. Some noteworthy examples of metaheuristics applied to optimization problems include examples for crude oil price forecasting ( Jovanovic et al, 2022 ; Al-Qaness et al, 2022 ), Ethereum and Bitcoin prices predictions ( Stankovic et al, 2022b ; Milicevic et al, 2023 ; Petrovic et al, 2023 ; Gupta & Nalavade, 2022 ), industry 4.0 ( Jovanovic et al, 2023b ; Dobrojevic et al, 2023 ; Para, Del Ser & Nebro, 2022 ), medicine ( Zivkovic et al, 2022a ; Bezdan et al, 2022 ; Budimirovic et al, 2022 ; Stankovic et al, 2022a ), security ( Zivkovic et al, 2022b ; Savanović et al, 2023 ; Jovanovic et al, 2023c ; Zivkovic et al, 2022c ), cloud computing ( Thakur & Goraya, 2022 ; Mirmohseni, Tang & Javadpour, 2022 ; Bacanin et al, 2022d ; Zivkovic et al, 2021 ), and environmental sciences ( Jovanovic et al, 2023d ; Bacanin et al, 2022b ; Kiani et al, 2022 ).…”

Section: Background and Preliminariesmentioning

confidence: 99%

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

Damaševičius,

Jovanovic,

Petrovic

et al. 2024

PeerJ Computer Science

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Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations.

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Section: Background and Preliminariesmentioning

confidence: 99%

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

Damaševičius,

Jovanovic,

Petrovic

et al. 2024

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

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“…Deepika and Balaji [ 182 ] used a multi-layer perceptron to obtain good performance in terms of accuracy, sensitivity, F1-score, specificity, and Kappa (all of them above 96.40%). The two proposals which reported lower performance were those of Theerthagiri [ 181 ] and Stankovic et al [ 185 ], achieving a performance above 83% but lower than 89% in accuracy, precision, F1-score, and recall. Theerthagiri [ 181 ] also obtained lower performance in AUC, MSE, and RMSE, compared to other proposals.…”

Section: Examples Of Cvd Detection Utilizing Machine Learningmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

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“…Besides the number of hidden layers and the number of neurons in the hidden layer, there are other hyperparameters that influence the performance of an MLP. These hyperparameters are the activation function, optimizer, algorithm, and learning rate value [16]. The range of hyperparameter values can be seen in table 1.…”

Section: Multi Layer Perceptron (Mlp)mentioning

confidence: 99%

Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm

Jondri,

Indwiarti,

Puspandari

2023

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Retweets are a way to spread information on Twitter. A tweet is affected by several features which determine whether a tweet will be retweeted or not. In this research, we discuss the features that influence the spread of a tweet. These features are user-based, time-based and content-based. User-based features are related to the user who tweeted, time-based features are related to when the tweet was uploaded, while content-based features are features related to the content of the tweet. The classifier used to predict whether a tweet will be retweeted is Multi Layer Perceptron (MLP) and MLP which is optimized by the swarm intelligence algorithm. In this research, data from Indonesian Twitter users with the hashtag FIFA U-20 was used. The results of this research show that the most influential feature in determining whether a tweet will be retweeted or not is the content-based feature. Furthermore, it was found that the MLP optimized with the swarm intelligence algorithm had better performance compared to the MLP.

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Tuning Multi-Layer Perceptron by Hybridized Arithmetic Optimization Algorithm for Healthcare 4.0

Cited by 10 publications

References 18 publications

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm

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