Sejal Gilda1,
Meghali Waghmode,
Arpita Sable,
- Student Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Pune Maharashtra India
- Assistant Professor Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Pune India
- Student Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Pune Maharashtra India
Abstract
In the creation of new products, yogurt makers need to prioritize consumer preferences to enhance their market share. An advanced prediction method will help in grasping the fundamental connection between consumer preferences and sensory attributes. This study introduces a new deep learning approach that employs an autoencoder to derive product features from expert-scored sensory attributes. These sensory features are then analyzed through support vector machine regression to align them with consumer preferences. Manual distance measurement is often prone to human error. This project aims to achieve precise and consistent measurements for short distances. The device can measure distances between 0.5 meters and 4 meters with an accuracy of 1 centimeter. This project employs ultrasonic sensors to determine distance by emitting ultrasonic waves at a frequency of 40 kHz. The circuit, which is controlled by an ATmega microcontroller, calculates the distance using the speed of sound at 25°C, factoring in the ambient temperature and the time taken for the waves to return. The measured distance is then displayed on an LCD module. The importance of this project lies in its capability to accurately measure distances to different obstacles. This device is applicable in numerous fields, including construction for distance measurement, robotics, car sensors for obstacle avoidance, and many other uses
Keywords: Yogurt; Model-Train; Consumer Preference; Autoencoder; Support Vector Machine.
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Research & Reviews : Journal of Dairy Science & Technology
| Volume | |
| Received | July 22, 2024 |
| Accepted | August 1, 2024 |
| Published | August 20, 2024 |