Short-Term Traffic Forecasting in Optical Network using Linear Discriminant Analysis Machine Learning Classifier

Szostak, Daniel; Walkowiak, Krzysztof; Włodarczyk, Adam

doi:10.1109/icton51198.2020.9203040

Cited by 23 publications

(10 citation statements)

References 7 publications

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“…All experiments were conducted using datasets, which were generated based on real traffic characteristics. The results reported in the next sections prove that the proposed approaches outperform the methods described in [16].…”

Section: Related Worksupporting

confidence: 58%

“…This work is a continuation and extension of our recent paper [16]. The contribution of this paper is threefold.…”

Section: Introductionmentioning

confidence: 65%

“…Such a concept follows from characteristics of optical networks and other transport network technologies. According to the best of our knowledge, the only paper that focuses on the prediction of traffic levels in optical networks is our previous paper [16], where we presented some preliminary results on this problem. To fill the research gap, in this paper we introduce and examine two supervised ML approaches: classification and regression.…”

Section: Related Workmentioning

confidence: 99%

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Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

2021

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Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

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Section: Related Worksupporting

confidence: 58%

“…This work is a continuation and extension of our recent paper [16]. The contribution of this paper is threefold.…”

Section: Introductionmentioning

confidence: 65%

Section: Related Workmentioning

confidence: 99%

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Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

2021

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“…We used a 10-fold cross-validation training strategy, and each model was trained one hundred times, separately. First, we train ten classical machine learning models using LightGBM Classifier (LGBM) [ 50 ], Gradient Boosting Classifier (GBC) [ 51 ], XGBoost Classifier (XGB) [ 52 ], Extra Tree Classifier (ETC) [ 53 ], k Neighbors Classifier (KNN) [ 54 ], Decision Tree Classifier (DT) [ 55 ], Random Forest Classifier (RF) [ 56 ], Linear Discriminant Analysis (LDA) [ 57 ], Support Vector Classifier (SVC) [ 58 ], and Logistic Regression (LR) [ 59 ]. Then, we used the three models with the highest average accuracy for stacking.…”

Section: Materials and Methodsmentioning

confidence: 99%

Hybrid Exercise Program for Sarcopenia in Older Adults: The Effectiveness of Explainable Artificial Intelligence-Based Clinical Assistance in Assessing Skeletal Muscle Area

Wei

Meng

Guo

et al. 2022

IJERPH

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Background: Sarcopenia is a geriatric syndrome characterized by decreased skeletal muscle mass and function with age. It is well-established that resistance exercise and Yi Jin Jing improve the skeletal muscle mass of older adults with sarcopenia. Accordingly, we designed an exercise program incorporating resistance exercise and Yi Jin Jing to increase skeletal muscle mass and reverse sarcopenia in older adults. Additionally, machine learning simulations were used to predict the sarcopenia status after the intervention. Method: This randomized controlled trial assessed the effects of sarcopenia in older adults. For 24 weeks, 90 older adults with sarcopenia were divided into intervention groups, including the Yi Jin Jing and resistance training group (YR, n = 30), the resistance training group (RT, n = 30), and the control group (CG, n = 30). Computed tomography (CT) scans of the abdomen were used to quantify the skeletal muscle cross-sectional area at the third lumbar vertebra (L3 SMA). Participants’ age, body mass, stature, and BMI characteristics were analyzed by one-way ANOVA and the chi-squared test for categorical data. This study explored the improvement effect of three interventions on participants’ L3 SMA, skeletal muscle density at the third lumbar vertebra (L3 SMD), skeletal muscle interstitial fat area at the third lumbar vertebra region of interest (L3 SMFA), skeletal muscle interstitial fat density at the third lumbar vertebra (L3 SMFD), relative skeletal muscle mass index (RSMI), muscle fat infiltration (MFI), and handgrip strength. Experimental data were analyzed using two-way repeated-measures ANOVA. Eleven machine learning models were trained and tested 100 times to assess the model’s performance in predicting whether sarcopenia could be reversed following the intervention. Results: There was a significant interaction in L3 SMA (p < 0.05), RSMI (p < 0.05), MFI (p < 0.05), and handgrip strength (p < 0.05). After the intervention, participants in the YR and RT groups showed significant improvements in L3 SMA, RSMI, and handgrip strength. Post hoc tests showed that the YR group (p < 0.05) yielded significantly better L3 SMA and RSMI than the RT group (p < 0.05) and CG group (p < 0.05) after the intervention. Compared with other models, the stacking model exhibits the best performance in terms of accuracy (85.7%) and F1 (75.3%). Conclusion: One hybrid exercise program with Yi Jin Jing and resistance exercise training can improve skeletal muscle area among older adults with sarcopenia. Accordingly, it is possible to predict whether sarcopenia can be reversed in older adults based on our stacking model.

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“…In order to obtain an accurate assessment of the performance of the model, we carried out the stratified 10-fold cross-validation 100 times. In the beginning, traditional machine learning modeling was carried out with the assistance of the LightGBM Classifier (LGBM) [ 57 ], Gradient Boosting Classifier (GBC) [ 58 ], XGBoost Classifier (XGB) [ 59 ], Extra Tree Classifier (ETC) [ 60 ], Decision Tree Classifier (DT) [ 61 ], Random Forest Classifier (RF) [ 62 ], Linear Discriminant Analysis (LDA) [ 63 ], and Logistic Regression (LR) [ 64 ]. Following this, the three models that had the best overall effectiveness within these records were chosen for stacking modeling.…”

Section: Materials and Methodsmentioning

confidence: 99%

The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly

Wang

Meng

et al. 2022

IJERPH

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Background: Frailty is a serious physical disorder affecting the elderly all over the world. However, the frail elderly have low physical fitness, which limits the effectiveness of current exercise programs. Inspired by this, we attempted to integrate Baduanjin and strength and endurance exercises into an exercise program to improve the physical fitness and alleviate frailty among the elderly. Additionally, to achieve the goals of personalized medicine, machine learning simulations were performed to predict post-intervention frailty. Methods: A total of 171 frail elderly individuals completed the experiment, including a Baduanjin group (BDJ), a strength and endurance training group (SE), and a combination of Baduanjin and strength and endurance training group (BDJSE), which lasted for 24 weeks. Physical fitness was evaluated by 10-meter maximum walk speed (10 m MWS), grip strength, the timed up-and-go test (TUGT), and the 6 min walk test (6 min WT). A one-way analysis of variance (ANOVA), chi-square test, and two-way repeated-measures ANOVA were carried out to analyze the experimental data. In addition, nine machine learning models were utilized to predict the frailty status after the intervention. Results: In 10 m MWS and TUGT, there was a significant interactive influence between group and time. When comparing the BDJ group and the SE group, participants in the BDJSE group demonstrated the maximum gains in 10 m MWS and TUGT after 24 weeks of intervention. The stacking model surpassed other algorithms in performance. The accuracy and precision rates were 75.5% and 77.1%, respectively. Conclusion: The hybrid exercise program that combined Baduanjin with strength and endurance training proved more effective at improving fitness and reversing frailty in elderly individuals. Based on the stacking model, it is possible to predict whether an elderly person will exhibit reversed frailty following an exercise program.

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Short-Term Traffic Forecasting in Optical Network using Linear Discriminant Analysis Machine Learning Classifier

Cited by 23 publications

References 7 publications

Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Hybrid Exercise Program for Sarcopenia in Older Adults: The Effectiveness of Explainable Artificial Intelligence-Based Clinical Assistance in Assessing Skeletal Muscle Area

The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly

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