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Research Article
Bidirectional LSTM-based Sentiment Analysis for Assamese Text
Manashi Talukdar*
,
Shikhar Kumar Sarma
Issue:
Volume 7, Issue 2, June 2024
Pages:
29-37
Received:
20 March 2024
Accepted:
8 April 2024
Published:
28 April 2024
DOI:
10.11648/j.ajcst.20240702.11
Downloads:
Views:
Abstract: With the enhanced exploration of the new generation of the web, people are free to state their opinion on any particular topic like product, services, organization and even on other people online in different social media platform and thus an innumerous amount of user generated contents are being created each moment. Hence, the need for mining this information has become the priority of the researcher so that they can identify the user’s sentiments and guide other people in various fields. Sentiment analysis deals with analyzing the review, opinion, attitude and emotions of a person from a given set of text by categorizing those on the basis of polarity as positive, negative and neutral. In this paper, sentiment of the social media text in Assamese Language is being analyzed because most of the communication is done through regional language and as a researcher from this region it is utmost concern to mine this information. To analyze the sentiments from the manually prepared datasets, LSTM- deep learning algorithm is used and implemented it in Python environment and also overall performance is measured in terms of accuracy, precision, recall and f1-score.
Abstract: With the enhanced exploration of the new generation of the web, people are free to state their opinion on any particular topic like product, services, organization and even on other people online in different social media platform and thus an innumerous amount of user generated contents are being created each moment. Hence, the need for mining this...
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Research Article
A Method for Solving the Generalized Weng Model
Zeng Hongwu,
Xu Qiuyu*
Issue:
Volume 7, Issue 2, June 2024
Pages:
38-42
Received:
2 April 2024
Accepted:
22 April 2024
Published:
17 May 2024
Abstract: The Generalized Weng Model is one of the basic models for oil production forecasting. Professor Chen Yuanqian first proposed the linear iterative trial-and-error method to solve the generalized Weng Model, and scholar Zhao Lin proposed the method to solve the Weng model based on binary regression. In this paper, a new method for solving Weng Model is put forward. Taking Liaohe Oilfield in China as an example, the process and results of the three methods are compared, and the advantages and disadvantages of the three methods are analyzed. The results show that when the original linear iterative trial and error method solves the model, it needs to simulate the value of parameter b with computer software, and then select a judgment criterion to find the optimal b value. In this paper, a method based on binary regression is proposed which can directly calculate parameter b. The new method can directly calculate the parameter b better than the method based on binary regression. The method in this paper is to fit all the data at one time, avoiding the above two kinds of uncertainties, and the calculation workload is small and can be realized by EXCEL, which is convenient for technical personnel.
Abstract: The Generalized Weng Model is one of the basic models for oil production forecasting. Professor Chen Yuanqian first proposed the linear iterative trial-and-error method to solve the generalized Weng Model, and scholar Zhao Lin proposed the method to solve the Weng model based on binary regression. In this paper, a new method for solving Weng Model ...
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Research Article
Test Maturity Model integration (TMMi): Test Maturity in the Financial Domain
Erik Van Veenendaal*
Issue:
Volume 7, Issue 2, June 2024
Pages:
43-50
Received:
3 May 2024
Accepted:
17 May 2024
Published:
30 May 2024
Abstract: Software quality is of utmost importance to the financial sector. Software testing plays a critical role in achieving software product quality. Financial institutions benefit from rigorous testing by having confidence in the reliability and performance of the software. This can lead to improved customer experience, increased operational efficiency, and reduced risks of system failures or security breaches. A questionnaire-based survey was designed and subsequently an international survey was conducted involving sixty financial institutions, e.g., banking, insurance companies and pension funds, from across the globe to understand their level of test maturity. As a reference framework against which to measure their test maturity, the Test Maturity Model integration (TMMi) was used. In this paper their motivations for doing test process improvement and the benefits they achieved are discussed. Concrete examples of the benefits achieved are provided. The role of test automation with test process improvement at the financial institutions is also reported upon in this paper. The most common level of test maturity achieved, measured against the TMMi, is TMMi level 3 “Defined” which represents a more than average level of test maturity. Benefits are reported by the financial institutions, especially in the areas of software quality and testing productivity. The benefits achieved show a high level of correlation with the motivations for investing in test process improvement. Almost all of financial institutions also use test automation to improve their testing in parallel with process improvement, with test automation at system level being by far the most popular.
Abstract: Software quality is of utmost importance to the financial sector. Software testing plays a critical role in achieving software product quality. Financial institutions benefit from rigorous testing by having confidence in the reliability and performance of the software. This can lead to improved customer experience, increased operational efficiency,...
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Research Article
An Investigation of Predictability of Traders' Profitability Using Deep Learning
David Ademola Oyemade*,
Eseoghene Ben-Iwhiwhu
Issue:
Volume 7, Issue 2, June 2024
Pages:
51-61
Received:
4 June 2024
Accepted:
24 June 2024
Published:
8 July 2024
Abstract: Trading in the financial market is a daunting task in spite of the attracting increase of the daily turnover of the Forex financial market from 6.5 trillion USD in 2022 to approximately 7.5 trillion USD in 2024. About 80% of retail investors lose money. However, to minimize the risk of losses, investors explore the possibility of profitable trading by resorting to social trading. In social trading of the financial market, the performance statistics and performance charts of traders with diverse trading strategies, methods and characteristics are showcased by the financial market brokers to enable investors decide on which trader’s signal to adopt or copy for profitable investment. However, investors are often faced with the problem of choosing a set of profitable traders among thousands with different past hypothetical results, in spite of the provision of traders’ performance ranking, made available by the brokers. The investors have serious concern on the stability, sustainability and predictability of a trader’s future performance which will eventually determine the investors profit or loss if the trader’s signals are copied or followed. This paper applies three deep learning models: the multilayer perceptron, recurrent neural network and long short term memory for the prediction of traders’ profitability to provide the best model for investment in the financial market, and reports the experience. The results of the study show that recurrent neural network performs best, followed by long short term memory while multilayer perceptron yields the least results for the prediction. These three models yield a mean squared error of 0.5836, 0.7075 and 0.9285 respectively in a test scenario for a trader.
Abstract: Trading in the financial market is a daunting task in spite of the attracting increase of the daily turnover of the Forex financial market from 6.5 trillion USD in 2022 to approximately 7.5 trillion USD in 2024. About 80% of retail investors lose money. However, to minimize the risk of losses, investors explore the possibility of profitable trading...
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