Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Akçay, Mehmet Taciddin

doi:10.35377/saucis.03.03.805598

Cited by 3 publications

(2 citation statements)

References 16 publications

(26 reference statements)

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“…The conventional methods utilized for the comparative assessment of the intelligent lead-based BiLSTM are KNN (Shamrat et al 2021 ), stochastic gradient descent-based neural network (SGD-NN) (Akçay 2020 ), Random Forest (Neogi et al 2021 ), TD-LSTM (Wang et al 2016 ), ATAE-LSTM (Tang et al 2015 ), BiLSTM (Li et al 2021 ), Spy based BiLSTM (Pambudi and Kawamura 2022 ), and King based BiLSTM (Soradi-Zeid et al 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

Kumari

Pushphavathi²

2022

Soc. Netw. Anal. Min.

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Social media is an online platform with millions of users and is utilized to spread news, information, world events, discuss ideas, etc. During the COVID-19 pandemic, information and ideas are shared by users both officially and by citizens. Here, the detection of useful content from social media is a challenging task. Hence, natural language processing (NLP) and deep learning are widely utilized for the analysis of the emotions of people during the COVID-19 pandemic. Hence, this research introduces a deep learning mechanism for identifying the sentiment of the people by considering the online Twitter data regarding COVID-19. The intelligent lead-based BiLSTM is utilized to analyze people's sentiments. Here, the loss of the classifier while learning the data is eliminated through the incorporation of the intelligent lead optimization. Hence, the loss is reduced, and a more accurate analysis is obtained. The intelligent lead optimization is devised by considering the role of the informer in identifying the enemy base to safeguard the territory from attack along with the Monarch's knowledge. The performance of the intelligent lead-based BiLSTM for the sentiment analysis is assessed using the metrics like accuracy, sensitivity, and specificity and obtained the values of 96.11, 99.22, and 95.35%, respectively, which are 14.24, 10.45, and 26.57% enhanced performance compared to the baseline KNN technique.

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Section: Resultsmentioning

confidence: 99%

Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

Kumari

Pushphavathi²

2022

Soc. Netw. Anal. Min.

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“…where Ti is the targets, 𝑇 ̅ is the mean of all target values, Yi is the neural network outputs and N is the number of samples. These performance results were used as a reference for the comparisons about the all methods [25].…”

Section: Statistical Performance Validationmentioning

confidence: 99%

Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Akçay

Akgündoğdu

2022

Sakarya University Journal of Computer and Information Sciences

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Among the electromechanical components of the rail system, the rail system vehicle is one of the most important units that carrying the passenger load. In terms of the efficiency of the signalization system, it is very critical to create the optimum vehicle driving profile. While many parameters of the vehicle are important during the design of the driving profile, determining the acceleration and braking accelerations directly affects this characteristic. The use of programmable devices and software instead of human factors is becoming more and more widespread day by day with the developed technology in rail transportation systems. Among the software used, artificial intelligence and machine learning applications constitute a large share in the general distribution. Especially, in the driverless (GOA4) signaling systems, these softwares become more important. In this study, the estimation of Vehicle Acceleration and Braking Acceleration with travel time has been done by using Machine Learning Methods. The ideal results obtained were given comparatively and interpreted on the graphics.

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Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

Akçay

Akgündoğdu

Tiryaki

2021

Demiryolu Mühendisliği

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Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Cited by 3 publications

References 16 publications

Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Estimation of the Average Speed for a Railway Signaling System by Using Gaussian Process Regression Methods with Bayesian Optimization

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