Improving Business Intelligence through Machine Learning Algorithms

Tamang, Minsang D.; Shukla, Vinod Kumar; Anwar, Shaista; Punhani, Ritu

doi:10.1109/iciem51511.2021.9445344

Cited by 20 publications

(11 citation statements)

References 7 publications

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“…In the organization's performance sector, these papers [1], [24], [25], [29], [32] focus on various aspects of business intelligence (BI) and how it can be improved and automated using machine learning algorithms (ML), data mining techniques, and knowledge management practices [36], [37]. They identify problems such as integrating ML with BI, automating manual processes, extracting hidden information from business data, ensuring data security and privacy, and leveraging critical knowledge for organizational performance.…”

Section: B Discussionmentioning

confidence: 99%

“…Business Intelligence (BI) and Deep Learning (DL) are two concepts that have gained recent interest in the field of information technology. BI is a data-driven approach that leverages tools to aid in decision-making, while DL is a subfield of machine learning that uses neural networks to solve complex problems [1]. DL leverages NN models to automatically extract features from input data, obviating the need for feature extraction stages.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector. Index Terms— Business Intelligence Tools; decision making; deep learning algorithms.

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Section: B Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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“…Other than that, we did an extensive review of existing literature on AI in retail as well, including [6,9], revealed a persistent need for improvements in business using more advanced AI algorithms, and cost-effectiveness across retails. This paper addresses this need by highlighting the potential of generative AI in retail sector, highlighting continue improvement required on AI and machine learning algorithms.…”

Section: From Data To Decisions: Enhancing Retail With Ai and Machine...mentioning

confidence: 99%

From Data to Decisions: Enhancing Retail with AI and Machine Learning

Dhoni

2024

IJCE

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Purpose: The recent advancements in computational power have presented unprecedented opportunities for businesses to harness data. A noteworthy development in December 2022 was the introduction of OpenAI's [1] ChatGPT, signifying the rise of generative AI tools including, but not limited to, Bard [2], Midjourney [3], GitHub Copilot [4], Amazon Bedrock, and Google's Gemini [5]. This research paper aims to harness AI capabilities within retail organizations, using data (customer) to expand business reach and enhance customer satisfaction. Data and AI form the core of this research. Methodology: In this research, we have trained a Large Language Model (LLM) by providing it with database schemas, including tables, to interact with centralized data and gain insights through simple prompts. We can leverage data for data Analysis and create reports, dashboards, understand customer behavior. Findings: Our research findings that AI serves as a pivotal force in amplifying the retail industry's potential. AI's applications span from improving customer experience by enabling voice orders, emotional insight, exclusive Deals just for you, product design, email campaign, optimizing inventory to facilitating targeted marketing strategies, list goes on. Yet, as we navigate this AI-augmented retail landscape, it is imperative to address challenges related to data privacy, algorithmic bias, implementation costs, and the need for expertise. Unique contributor to theory, policy and practice: In essence, generative AI is more than a fleeting trend; it epitomizes the future of retail, demanding both adoption and circumspection. Our recommendation to use AI for enhancing retail business and use it ethically.

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“…After companies are transparent about their question, they can evaluate different aspect models, such as how big the training data is, how long it would take to train a model, and so on. [13] Thus, when companies want to make improvements like the examples above, they need first to identify the purpose of what they wish their machine learning models to do for them. For the movie recommendation system example, three authors want their algorithm to help optimize their working process.…”

Section: Applicationmentioning

confidence: 99%

Overview of potential customer prediction for products based on machine learning

Wang

2024

ACE

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With the development of the Internet, e-commerce, as a new way of online shopping, has provided great convenience to people's lives. Due to the complex potential relationship between consumers and products, it is difficult to recommend products according to consumers' needs, which increases the difficulty of online shopping. Therefore, how to predict the potential consumers of commodities has attracted the wide attention of researchers. Fortunately, machine learning-based approaches have made text and images that can model complex underlying relationships between data. Therefore, researchers have introduced machine learning into the product potential consumer prediction field and achieved good results. This paper first introduces the relevant data set and the product potential users' forecast evaluation index. Then, it summarizes the relevant product potential consumer prediction methods based on machine learning. Finally, the paper summarizes the whole article and looks forward to future research methods.

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Improving Business Intelligence through Machine Learning Algorithms

Cited by 20 publications

References 7 publications

A Survey on Business Intelligence Approach Based on Deep Learning

A Survey on Business Intelligence Approach Based on Deep Learning

From Data to Decisions: Enhancing Retail with AI and Machine Learning

Overview of potential customer prediction for products based on machine learning

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