Examining Machine Learning Techniques for Body Odor Detection

EasyChair Preprint 13151

6 pages•Date: May 1, 2024

Abstract

Body odor detection plays a crucial role in various fields, including healthcare, personal hygiene, and forensic science. Traditional methods for detecting body odor are often subjective and rely heavily on human judgment. With the advancements in machine learning (ML) techniques, there is a growing interest in exploring their potential for more objective and efficient body odor detection. This paper presents a comprehensive review of recent studies that have applied ML techniques for body odor detection. The review covers a range of ML algorithms, including deep learning, support vector machines, and decision trees, and discusses their performance in terms of accuracy, sensitivity, and specificity. Additionally, the paper highlights the challenges and future directions in the field, such as dataset limitations, cross-cultural variations in body odor perception, and the integration of sensor technologies. Overall, this review provides valuable insights into the current state of ML-based body odor detection and offers recommendations for future research in this area.

Keyphrases: Technology, body, machine learning

Links:

https://easychair.org/publications/preprint/zZc9

BibTeX entry

BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:

@booklet{EasyChair:13151,
  author    = {Favour Olaoye},
  title     = {Examining Machine Learning Techniques for Body Odor Detection},
  howpublished = {EasyChair Preprint 13151},
  year      = {EasyChair, 2024}}

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