Customer Segmentation Using K Means Algorithm and RFM Model

Chalechema, Sasidhar; Saini, Madan Lal; Perla, Indukumar; Shivanand, Avula Venkata

doi:10.1109/icccis60361.2023.10425556

2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) 2023

DOI: 10.1109/icccis60361.2023.10425556

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Customer Segmentation Using K Means Algorithm and RFM Model

Sasidhar Chalechema,

Madan Lal Saini,

Indukumar Perla

et al.

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2024

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Cited by 4 publications

References 11 publications

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A Comparative Study of Various Text Summarization Methods

Mohith,

Chinni,

Sai Sambha Shiva Redddy

2024

SSRN Journal

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A Comparative Study of Various Text Summarization Methods

Mohith,

Chinni,

Sai Sambha Shiva Redddy

2024

SSRN Journal

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Building Face Ageing Model Using Face Synthesis

Mishra,

Chahar,

Jaswal

2024

International Journal of Innovative Science and Research Techno

710

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Advancements in face synthesis technology have enabled innovative methods for modeling facial aging. This research paper focuses primarily on creating a robust face aging model using deep learning and Generative Adversarial Networks (GANs), trained on a diverse dataset of facial images. The proposed approach captures both global features and local textures to produce realistic age-progressed images while preserving the subject's identity. This paper also examines face synthesis techniques, with specific emphasis for the various practical usage of GANs. The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age- progressed face images. We evaluated the model using quantitative metrics and qualitative assessments, demonstrating its effectiveness. Additionally, we address ethical considerations, proposing guidelines for responsible use. Our study offers a novel framework with significant applications in security, forensics, and entertainment, and suggests future research directions to improve accuracy and ethical standards.

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Abstractive Text Summarization Using GAN

Bharti,

Kumar Sinha,

Singhal

et al. 2024

International Journal of Innovative Science and Research Techno

710

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In the field of natural language processing, the task of writing long concepts into short expressions has attracted attention due to its ability to simplify the processing and understanding of information. While traditional transcription techniques are effective to some extent, they often fail to capture the essence and nuances of the original texts. This article explores a new approach to collecting abstract data using artificial neural networks (GANs), a class of deep learning models known for their ability to create patterns of real information. We describe the fundamentals of text collection through a comprehensive review of existing literature and methods and highlight the complexity of GAN-based text. Our goal is to transform complex text into context and meaning by combining the power of GANs with natural language understanding. We detail the design and training of an adaptive GAN model for the text recognition task. We also conduct various experiments and evaluations using established metrics such as ROUGE and BLEU scores to evaluate the effectiveness and efficiency of our approach. The results show that GANs can be used to improve the quality and consistency of generated content, data storage, data analysis paper, etc. It shows its promise in paving the way for advanced applications in fields. Through this research, we aim to contribute to the continued evolution of writing technology, providing insights and innovations that support the field to a new level of well-done.

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Customer Segmentation Using K Means Algorithm and RFM Model

Cited by 4 publications

References 11 publications

A Comparative Study of Various Text Summarization Methods

A Comparative Study of Various Text Summarization Methods

Building Face Ageing Model Using Face Synthesis

Abstractive Text Summarization Using GAN

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