A Performance Comparison of Optimization Algorithms on a Generated Dataset

Gaddam, Dharma Karan Reddy; Ansari, Mohd Dilshad; Vuppala, Sandeep; Gunjan, Vinit Kumar

doi:10.1007/978-981-16-3690-5_135

Cited by 42 publications

(5 citation statements)

References 10 publications

(10 reference statements)

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“…We have taught the model in the rst 100 rounds of training. Our model's performance improved with every 20th epoch, resulting in higher accuracies according to our optimized methods[13,15].…”

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confidence: 84%

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New developed Methods used for Data Science Optimization

Mahboub,

krishna

2023

Preprint

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Nowadays, there is a growing need to introduce new methods and algorithms in the field of data science. Optimization solutions can be utilized to construct data science models. Our primary emphasis lies in the examination and enhancement of cutting-edge techniques and methods utilized in the field of data science, with the aim of effectively tackling problems. These alternatives consist of algorithms that are based on rule based and the different methods of preprocessing for data science that do not rely on derivatives. We think that the best way to reach our goals for making things better was by using machine learning. By utilizing optimization methods and algorithms, it becomes feasible to locate enhanced solutions to problems in machine learning optimization. These methods can lead to increased improvements in how machines learn and apply knowledge.

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confidence: 84%

“…Dharma. et al [13] have introduced various optimization algorithms like gradient descent, mini-batch gradient descent, momentum, NAG, RMS prop, Adagrad, and Adam. Abdu-rakhmon Sadiev et al [14] introduced federated learning (FL) as a framework for distributed learning and optimization.…”

Section: Objectivesmentioning

confidence: 99%

New developed Methods used for Data Science Optimization

Mahboub,

krishna

2023

Preprint

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“…After calculating the gradient, the squared gradient is accumulated to apply RMSprop: [30] r ← ρr + (1 − ρ)g Ⓢ g (6) where the rate of deterioration is. The following is how the parameter update is calculated and used: (7) θ ← θ + ∆θ (8)…”

Section: E Rmsprop Optimizationmentioning

confidence: 99%

“…Therefore, this study aims to compare the performance of RMSProp and Adam optimization in neural machine translation from Minangkabau to Indonesian. In this study, we will use existing translation data to train a neural machine translation model and then compare the model's performance using RMSProp and Adam optimization [7]. We will analyze the results of both optimization techniques and evaluate translation performance based on standard evaluation metrics [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Ahda,

Wibawa,

Dwi Prasetya

et al. 2024

JOIV : Int. J. Inform. Visualization

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Language is a tool humans use to establish communication. Still, the language used is one language and between regions or nations with their languages. Indonesia is a country that has a diversity of second languages and is the fourth most populous country in the world. It is recorded that Indonesia has nearly 800 regional languages, but research activities in natural language processing are still lacking. Minangkabau is an endangered language spoken by the Minangkabau people in Indonesia's West Sumatra province. According to UNESCO, the Minangkabau language is listed as a language that is "definitely endangered," with only around 5 million speakers worldwide. This study uses neural machine translation (NMT) to create a formula based on this information. Neural machine translation, in contrast to conventional statistical machine translation, intends to build a single neural network that can be built up to achieve the best performance. Because it can simultaneously hold memory for a long time, comprehend complicated relationships in data, and provide information that is very important in determining the outcome of translation, LSTM is one of the most powerful machine-learning techniques for translating languages. The BLUE score is utilized in the NMT evaluation. The test results use 520 Minangkabau sentences, conducting tests based on the number of epochs ranging from 100-1000, resulting in optimization using Adam being better than optimization RMSprop. This is evidenced by the results of the best BLUE-1 score of 0.997816 using 1000 epochs.

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“…Other related works include Ansari et al (2021), Ting et al (2022), Dabbu et al (2022), Gaddam et al (2021a), Gaddam et al (2021b), Zhu et al (2021), Sethi et al (2020), Kapil et al (2016), Karuppusamy et al (2021), Gautam et al (2019).…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of a cloud-based network analytics system with multiple-source data aggregation

Fowdur

Babooram

2022

IJPCC

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Purpose The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters. Design/methodology/approach The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases. Findings Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic. Originality/value Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.

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A Performance Comparison of Optimization Algorithms on a Generated Dataset

Cited by 42 publications

References 10 publications

New developed Methods used for Data Science Optimization

New developed Methods used for Data Science Optimization

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Performance analysis of a cloud-based network analytics system with multiple-source data aggregation

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