Applications of artificial intelligence in business management, e-commerce and finance

Pallathadka, Harikumar; Ramírez-Asís, Edwin; Loli-Poma, Telmo Pablo; Kaliyaperumal, Karthikeyan; Ventayen, Randy Joy Magno; Naved, Mohd

doi:10.1016/j.matpr.2021.06.419

Cited by 114 publications

(57 citation statements)

References 12 publications

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“…The essential non‐linear system in online applications is stock marketing. Predicting the stock of each product is very important to improve the business 30 . Moreover, several investors have been made to predict the stocks, but those models have taken more time to execute.…”

Section: System Model and Problem Statementmentioning

confidence: 99%

A Buffalo‐based bi‐directional recurrent paradigm for Indian stock market prediction

Pattewar

Jain

Kiranmayee³

2023

Concurrency and Computation

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Earlier prediction of stock market details is an important factor in finance intersection.Moreover, predicting stock details is difficult as the stock datasets are unstructured and the entities differ based on each application. Hence, the conventional prediction model requires additional features for predicting stock prices. To end these issues, the presented article has focused on developing the novel African Buffalo-based Bi-Directional Recurrent Model (ABBRM) to predict stock details efficiently. Once the dataset is collected, preprocessing is performed in the hidden layer of the novel ABBRM. Moreover, the refined data is imported to the classification layer for feature selection and prediction processes. The incorporation of Buffalo fitness has afforded the most satisfactory stock price prediction outcome. Furthermore, the designed procedure is executed in the python environment with appropriate packages. Significant parameters like prediction accuracy, recall, precision, execution time, and error rate verified the projected model's robustness. Moreover, the presented model has attained an average accuracy of 93.5% for the short term and 99.91% for the long term. ABBRM was compared with existing techniques to identify the presented framework's effectiveness and has attained better results than other models. Here, the significance of the novel ABBRM is better prediction, which means the stock features were stored in the Buffalo memory fitness phase; during the execution phases, it helps to attain the exact prediction. Also, the Buffalo algorithm iterations were repeated continuously till a suitable prediction outcome was gained, and better training and testing outcomes were gained by the deep recurrent features in the designed model. Hence, this model is highly suitable for all prediction applications.

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Section: System Model and Problem Statementmentioning

confidence: 99%

A Buffalo‐based bi‐directional recurrent paradigm for Indian stock market prediction

Pattewar

Jain

Kiranmayee³

2023

Concurrency and Computation

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“…Potential applications of machine learning in the e-commerce sector have been researched extensively from different perspectives (e.g., chatbots [11], recommendation engines [12][13][14], applications for intelligent logistics [15,16] and pricing [17][18][19][20][21]). The application of machine learning, as in other sales business models, extends to almost every area of e-commerce (e.g., security [22], fraud detection [23,24], profit maximisation [25], sales prediction [26,27], inventory management [28,29], product categorisation [30], and portfolio management [31]). Literature reviews exploring machine learning applications in different e-commerce scenarios can mainly be found in [32][33][34][35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

2022

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Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.

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“…Hari Kumar Pallathadka et al (10) an application of Machine Learning and deep Learning tool of AI for the advancement of E-Commerce Management and Finance sector. Both government and industry are following this tool for their goods and supply chain mechanism for effective performances.…”

Section: E-commerce and Financial Industriesmentioning

confidence: 99%

Application of Artificial Neural Network with Various Algorithms Tools in Numerous Sectors – A Review

2022

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This study reports collective information of the Artificial Neural Network-(ANN) model implemented toimprove the management and provide suggestions to the government and corporates. The ANN tool is an awesome architect model that helps collect a high volume of information from independent such as civilians, input parameters in manufacturing, etc to dependent such as government, corporates, machinery, etc. The ANN model has various architect tools that help to regulate and monitor the demand for government and the public through the weighted link, like brain neurons. It's an optimized computation study to predict less uncertainty than experimental values. This literature study reports diverse methods of algorithms available in ANN models and briefly disseminated the procedure specifically to collect a high volume of inputs transferred to hidden neurons and passed to output terminal neurons with accurate solutions to the enhancement of government decision making.Besides, the ANN is a perfect substitute tool for experimental and numerical analysis procedures to reduce high expenses for collecting data in high volume. This literature report briefly disseminates different methods of ANN tools implemented in various applications with significant conclusions, considering many techniques for collecting data and suggestions provided for future research work.

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Applications of artificial intelligence in business management, e-commerce and finance

Cited by 114 publications

References 12 publications

A Buffalo‐based bi‐directional recurrent paradigm for Indian stock market prediction

A Buffalo‐based bi‐directional recurrent paradigm for Indian stock market prediction

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

Application of Artificial Neural Network with Various Algorithms Tools in Numerous Sectors – A Review

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