An analysis on business intelligence predicting business profitability model using Naive Bayes neural network algorithm

Mishan, Mohd Taufik; Kushan, Albin Lemuel; Fadzil, Ahmad Firdaus Ahmad; Amir, Aimi Liyana Binti; Anuar, Nurhilyana

doi:10.1109/icsengt.2017.8123421

Cited by 8 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It analyzes the similarities and patterns in user-item interactions to identify items that are likely to be of interest to a particular user [23,28,29]. Content-based filtering, on the other hand, focuses on the characteristics of the items themselves [30,31]. It recommends items to users based on the similarity between the content attributes of items and the users' preferences [25,32,33].…”

Section: Traditional Recommender Systemsmentioning

confidence: 99%

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Al-Hasan,

Sayed,

Bensaali

et al. 2024

BDCC

View full text Add to dashboard Cite

Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems.

show abstract

Section: Traditional Recommender Systemsmentioning

confidence: 99%

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Al-Hasan,

Sayed,

Bensaali

et al. 2024

BDCC

View full text Add to dashboard Cite

show abstract

“…Machine learning methods are an essential tool in emerging disciplines such as data science [12] and business intelligence [13] since they provide efficient predictive models constructed from the data previously collected. DTs, ANN, and SVMs, as well as clustering methods, have been widely used to build these models.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution Algorithm in the Construction of Interpretable Classification Models

Rivera-López¹,

Canul-Reich²

2018

Artificial Intelligence - Emerging Trends and Applications

View full text Add to dashboard Cite

In this chapter, the application of a differential evolution-based approach to induce oblique decision trees (DTs) is described. This type of decision trees uses a linear combination of attributes to build oblique hyperplanes dividing the instance space. Oblique decision trees are more compact and accurate than the traditional univariate decision trees. On the other hand, as differential evolution (DE) is an efficient evolutionary algorithm (EA) designed to solve optimization problems with real-valued parameters, and since finding an optimal hyperplane is a hard computing task, this metaheuristic (MH) is chosen to conduct an intelligent search of a near-optimal solution. Two methods are described in this chapter: one implementing a recursive partitioning strategy to find the most suitable oblique hyperplane of each internal node of a decision tree, and the other conducting a global search of a near-optimal oblique decision tree. A statistical analysis of the experimental results suggests that these methods show better performance as decision tree induction procedures in comparison with other supervised learning approaches.

show abstract

Multi-domain Business Intelligence Model for Educational Enterprise Resource Planning Systems

Abdullah

Ta’a

Mohammed

2021

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

An analysis on business intelligence predicting business profitability model using Naive Bayes neural network algorithm

Cited by 8 publications

References 5 publications

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Differential Evolution Algorithm in the Construction of Interpretable Classification Models

Multi-domain Business Intelligence Model for Educational Enterprise Resource Planning Systems

Contact Info

Product

Resources

About