Information Package / Course Catalogue
| Course Code | : ZST527 |
| Course Type | : Area Elective |
| Couse Group | : Second Cycle (Master's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 6 |
It is aimed for students taking this course to understand artificial intelligence techniques and to learn how to use these techniques in solving various problems in milk and dairy products technology. Especially artificial neural networks, fuzzy logic and genetic algorithm are used in the Dairy Industry. Modeling applications such as product grading, classification, process modeling and optimization, quality control monitoring, conversion of images to numerical data, product design, control of storage systems, mastitis prediction, effectiveness of heat treatment are carried out in dairy products. These topics are explained in line with the purpose of the course.
In this course, the use of artificial neural networks, fuzzy logic and genetic algorithm in dairy technology is examined. Applications of artificial intelligence in milk and dairy products technology include product grading, classification, process modeling and optimization, monitoring quality control, converting images into digital data, product design, temperature and humidity control of storage systems, control of the effectiveness of heat treatment, raw milk quality and mastitis control. topics are examined.
| 1. | Understands artificial intelligence techniques and can use these techniques to solve various problems in milk and dairy products technology. |
| 2. | To provide students with the necessary creativity to model the problems they will encounter in the future and produce solutions. |
| 3. | Ability to analyze developing technology and use it in one's own field |
| 4. | Ability to contribute to new product development and create ideas to help product decisions |
| 5. | Ability to manage projects and develop projects with the right moves |
| 1. | Daniel Hefft, ?Charles Oluwaseun Adetunji ·2023. Sensing and Artificial Intelligence Solutions for Food Manufacturing. CRC Press, |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %70 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 3 | 3 | 84 |
| Assignment | 1 | 10 | 3 | 13 |
| Individual Work | 4 | 6 | 2 | 32 |
| Midterm Examination | 1 | 5 | 3 | 8 |
| Final Examination | 1 | 5 | 3 | 8 |
| TOTAL WORKLOAD (hours) | 145 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | |
OÇ-1 | 5 | 2 | 4 | 4 | 5 |
OÇ-2 | 5 | 4 | 4 | ||
OÇ-3 | 5 | 4 | 4 | 5 | |
OÇ-4 | 4 | 4 | 4 | 5 | |
OÇ-5 | 5 | 4 | 4 | 5 | |