The Impacts of Machine Learning in Financial Crisis Prediction

Vadlamudi, Siddhartha; Xandr,

doi:10.18034/abr.v10i3.528

Cited by 16 publications

(9 citation statements)

References 50 publications

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“…Financial crisis prediction is the most complex and promising problem for companies and small-scale enterprises. Vadlamudi [28] investigated how the newly adopted machine learning technology addresses this issue in all areas of private and public business. e author uses systematic literature evaluation to study the impact of machine learning on financial crisis prediction.…”

Section: Machine-learning-based Economic Situation Predictionmentioning

confidence: 99%

Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics

Wang

Zhao

2022

Mobile Information Systems

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Big data economy meets artificial intelligence, making the traditional statistical economy gradually evolve into an intelligent economy. Limited by human consciousness, traditional economic models have low prediction accuracy. In traditional statistical methods, the limited sample data also makes it impossible to effectively control and comprehensively forecast macroeconomic and development trends. Data economy has fundamentally transformed the traditional means of economic analysis. This is because the digital economy enables economic connectivity and precise data sharing, which can be used for precise economic statistics and mathematical analysis. Meanwhile, in terms of statistical methods, artificial intelligence methods no longer rely on human consciousness, but more objectively pay attention to economic cause and effect and are more accurate and comprehensive. This paper proposes an economic forecasting method based on artificial intelligence methods combined with big data analytics. In our model, we consider the economic statistics, equilibrium, and future prediction with the big data. Through the artificial intelligence method based on deep learning, the possible political factors, human activity factors, and social environmental factors in actual economic activities are effectively combined to form the main analysis subject affecting the economy. The results show that our model can be used as a basic model for economic statistics, economic analysis, economic decision-making, economic self-regulation, and other functions under the current development trend of the data economy.

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Section: Machine-learning-based Economic Situation Predictionmentioning

confidence: 99%

Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics

Wang

Zhao

2022

Mobile Information Systems

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“…The indexing process generally has a consolidation mechanism that allows using ideas from different domains. It's been defined that there are numerous indexing techniques used in the research and academic disciplines (Vadlamudi, 2020). In this section, we will talk about every strategy in detail and also describe the way they work.…”

Section: Different Techniques Of Indexingmentioning

confidence: 99%

Intelligent Indexing and Sorting Management System – Automated Search Indexing and Sorting of Various Topics

Ganapathy

Fadziso

2020

Eng. int. (Dhaka)

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An issue that the majority of the databases face is the static and manual character of indexing activities. This traditional method of indexing and sorting different topics is confirmed to shake the dataset performance somewhat, making downtime and a potential effect in the presentation that is normally addressed by manually indexing operations. Numerous data mining methods can accelerate this process by using proper indexing structures. Choosing the appropriate index generally relies upon the kind of operation that the algorithm performs against the dataset. Topic indexing is the operation of recognizing the principal topics covered by a document. These are helpful for some reasons: as subject headings in libraries, as keywords in scholarly articles, and as hashtags on social media platforms. Knowing a document’s topic assists individuals with deciding its importance quickly. In any case, assigning topics manually is a tedious and redundant task. This paper shows the best way to create them automatically in a way that contends with manual indexing done by humans. This paper also talks about the issues and the techniques for identifying applicable data in a huge variety of documents. The contribution of this thesis to this issue is to foster better content analysis techniques that can be utilized to describe document content with automated index terms. Index terms can be used as meta-data that defines documents and is utilized for seeking various topics. The main point of this paper is to show the way toward creating an automatic indexer which analyzes the topic of documents by integrating proof from word frequencies and proof from the linguistic analysis given by a syntactic parser. The indexer weighs the expressions of a document as per their assessed significance for depicting the topic of a given document based on the content analysis.

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“…Over the last few years, the most important constraint for positioning resourceful devices is extremely pertinent to small and medium-sized enterprises (SMEs) for predicting economical faults and business loss. SMEs need business management for observing the modus operandi and inspect whether it is relevant to attain the determined objectives [ 3 ]. This model is portrayed through a series of firm rules, and some approaches are called “controls,” which guarantee the structure of the enterprise organization.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment

Venkateswarlu¹,

Baskar²,

Anupong

et al. 2022

Computational Intelligence and Neuroscience

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As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country’s economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.

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The Impacts of Machine Learning in Financial Crisis Prediction

Cited by 16 publications

References 50 publications

Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics

Digital Economy Meets Artificial Intelligence: Forecasting Economic Conditions Based on Big Data Analytics

Intelligent Indexing and Sorting Management System – Automated Search Indexing and Sorting of Various Topics

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment

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