Measure the Usability of Graphical User Interface

Sharipbay, Altynbek; Barlybayev, Alibek; Sabyrov, Talgat

doi:10.1007/978-3-319-31232-3_98

Cited by 15 publications

(4 citation statements)

References 0 publications

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“…The following formula is used to calculate RSI values: RSI = 100-100/(1+RS). RS is the exponential average of the price when the asset rose over the considered time period divided by the exponential average of the price when the asset fell over the considered time period [41,42].…”

Section: Methods For Calculating Technical Indicatorsmentioning

confidence: 99%

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Barlybayev,

Zhetkenbay,

Karimov

et al. 2023

EEJET

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Adaptive neuro-fuzzy inference system (ANFIS) it is a type of neural network that combines the strengths of both fuzzy logic and artificial neural networks. ANFIS is particularly useful in stock trading because it can handle uncertainty and imprecision in the data, which is common in stock market data. In stock trading, ANFIS can be used for a variety of purposes, such as predicting stock prices, identifying profitable trades, and detecting stock market trends. One of the key advantages of using ANFIS for stock trading is that it can handle both linear and non-linear relationships in the data. This is particularly useful in the stock market, where the relationships between different variables are often complex and non-linear. ANFIS can also be updated and retrained as new data becomes available, which allows it to adapt to changing market conditions. Therefore, the main hypothesis of this work is to understand whether it is possible to predict the dynamics of prices for stocks of companies in the electric vehicle (EV) sector using technical analysis indicators. The purpose of this work is to create a model for predicting the prices of companies in the EV sector. The technical analysis indicators were processed by several machine learning models. Linear models generally perform worse than more advanced techniques. Decision trees, whether fine or coarse, tend to yield poorer performance results in terms of RMSE, MSE and MAE. After conducting a data analysis, the ANFIS and Bayesian regularization back propagation Neural Network (BR-BPNN) models were seen to be the most effective. The ANFIS was trained for 2000 epochs which yielded a minimum RMSE of 5.90926

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Section: Methods For Calculating Technical Indicatorsmentioning

confidence: 99%

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Barlybayev,

Zhetkenbay,

Karimov

et al. 2023

EEJET

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“…ANFIS and discrete particle swarm optimization were applied to forecasting the closing prices of stock indices in India [34]. When developing such systems, it is important to use highperformance programming languages, for example, Python [35], as well as pay attention to the convenience of the user interface [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

Matkarimov,

Barlybayev,

Karimov

2024

Journal of Electrical and Computer Engineering

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The object of research is the fundamental and technical indicators of companies after the release of the earnings report. This study attempts to address the issue of understanding the impact of fundamental and technical analysis indicator dynamics on profits and loss news releases. This research provides an in-depth analysis of stock price forecasting models, focusing on the influence of earning report seasons as catalysts for stock price growth. The study explores the relationship between key financial indicators, including earnings per share (EPS), revenue, and the maximum price observed in the 52-week period of the previous year (MaxW52). A trading algorithm is developed based on the adaptive neurofuzzy inference system (ANFIS). Through a comprehensive analysis of the neural network’s training sample, it is concluded that abnormally large negative indicators have a profound impact on traders’ emotional reactions. This results leads to a hypothesis for further research, suggesting that report indicators may be processed by computational algorithms, potentially including artificial intelligence (AI). Consequently, the emergence of emotional trading robots managed by investment funds becomes a crucial area for investigation. Understanding the behavior of these algorithms enables proactive decision-making, allowing traders to leverage their knowledge and sell-purchased securities to these algorithms before their transactions occur. The implications of this research shed light on the evolving landscape of trading strategies and the role of emotionality in financial markets.

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“…As we initially assumed, the results showed that the price is very strongly correlated with the brand name of the auto manufacturer, with the mileage, and the year of production. To build pair plots, we will use Matlab, which has a very user-friendly interface [24].…”

Section: Data Preprocessingmentioning

confidence: 99%

Predicting Used-Vehicle Resale Value in Developing Markets: Application of Machine Learning Models to the Kazakhstan Car Market

Barlybayev,

Sankibayev,

Kadyr

et al. 2023

ISI

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The burgeoning trade in used vehicles has necessitated further research into price prediction. In developing nations, the abundance of second-hand cars and limited supply of new ones has led to a preference for used vehicles. Consequently, the analysis of vendor data becomes imperative for gaining valuable insights. Sellers are increasingly seeking accurate price predictions to maximize their profits. The assessment of used car prices necessitates a thorough understanding of the features that influence value. Although the inclusion of multiple features can enhance prediction accuracy, the list of these features is non-exhaustive. This study seeks to examine the effectiveness of various regression techniques such as Linear, Decision Tree, SVM machines, Neural Network, and Bagged Trees, alongside machine learning algorithms, in predicting the selling price of used cars based on the associated features. Evaluation metrics will be utilized to identify the most proficient model by examining the performance and error rate of each model. The deep neural network model demonstrates exceptional performance, as indicated by its low RMSE and MSE values, suggesting high efficiency. Some models, including cubic SVM, fine Gaussian SVM, and wide neural network, exhibit a robust correlation (R) in accurately connecting input and output variables. Furthermore, narrow, medium, bilayered, and trilayered neural networks display commendable performance in recording variable correlations. After comparing various models, Bagged Trees were identified as the most cost-effective option per square meter, due to their advantageous pricing and performance.

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Measure the Usability of Graphical User Interface

Cited by 15 publications

References 0 publications

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation

Predicting Used-Vehicle Resale Value in Developing Markets: Application of Machine Learning Models to the Kazakhstan Car Market

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