ARCHIVES

1. Zhang, L., & Li, H. (2019). Design of Food Safety
2. Detection Systems Based on IoT. SP, 15, 89. Focuses on the design and implementation of IoT-based food safety detection systems,
relevant for understanding how IoT can be applied to food poisoning detection.
3. Wang, Y. (2018). Study on Sensor Technologies in Food Safety. JUSHE, 37, 44+50. Explores the role of sensor technologies in food
safety, providing context for integrating sensors with IoT devices for food poisoning detection.
4. Li, M., & Chen, J. (2020). Research on Real-Time Monitoring Systems for Food Safety. JUSHE, 09, 23. Investigates real-time monitoring
systems for food safety, which can inform the design of IoT-based detection devices.
5. Liu, Q., & Zhao, W. (2019). Smart Food Storage Systems Using IoT. Home Drama, 22, 215-216. Discusses smart food storage systems
with a focus on using IoT for monitoring and maintaining food safety.
6. Yang, X., Zhou, F., & Ai, L. (2017). User Experience Design for Food Safety Detection Devices. CNKI. Examines user experience in
designing food safety detection devices, useful for enhancing the user interface and experience of the IoT device.
7. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. A foundational text on deep learning techniques, including
those used in anomaly detection and sensor data analysis for food safety.
8. Ian, J., & Siddiqui, H. (2018). Machine Learning Yearning. Self-published. Provides insights into machine learning strategies and best
practices, useful for developing and refining AI models for food safety detection.
9. TensorFlow Documentation. (n.d.). Retrieved from https://www.tensorflow.org/ Official documentation for TensorFlow, a key library
used for building and training machine learning models.
10. PyTorch Documentation. (n.d.). Retrieved from https://pytorch.org/ Provides resources for PyTorch, another popular library for
machine learning and neural network development.
11. OpenCV Documentation. (n.d.). Retrieved from https://opencv.org/ Documentation for OpenCV, a library used for computer vision tasks
such as image segmentation and feature extraction.
12. Gartner. (2023). Magic Quadrant for IoT in Food Safety. Gartner Research. Offers insights into the latest trends and technologies in
IoT, including applications relevant to food safety and monitoring.
13. Forrester. (2023). The Future of AI-Driven Food Safety. Forrester Research. Discusses emerging trends and future directions for AI in
food safety, providing context for advancements in IoT-based detection devices.
14. Hershey, J., & Tannenbaum, M. (2023). How IoT is Transforming Food Safety. Food Tech Weekly. An article exploring current
applications of IoT in food safety, including case studies and real-world.