## 1 Introduction  
In this age of technical innovation from a fitness tracker to the international space station, we are enjoying the wave of change in every aspect of our lives. Electromagnetic radiation in the microwave frequency range is used in a microwave oven (known as a microwave) to heats food. Since 1967, it has been used in houses and also for commercial purposes [1]. To control how long the microwave heats the food, some sort of timing mechanism using dials or push-button programming is used. To do this, the user needs to guess the required time mentally by considering several factors such as the present temperature of the food (for instance, the food could be taken out from refrigerator, freezer, or could be at room temperature), amount and thermal properties of the food, power level of the microwave oven, and finally the desired target temperature of the food. This thinking process of calculating the exact required time is complex and may be guessed inaccurately. If a longer time is guessed than the required time, then the food becomes too hot to eat. If the food is eaten with a spoon without checking the temperature by touching it first, it may burn the mouth. The user needs to wait until the food cools down when the food is too hot. If a shorter time is guessed than the required time, the user needs to repeat the process of checking the present temperature of the food by touch, guessing time, and then heating again until the desired target temperature is reached. This process is tedious and tiresome.  
This paper proposes a solution to the above problems. The proposed system's overall operation in Figure 1. The key contributions of this research are stated below.  
* A closed-loop microwave oven is designed which continuously measures the food temperature using a thermal camera while the food is being heated, and stops the heating automatically when the food temperature reaches the target temperature. Thus, the user does not need to calculate the exact needed time in the head. This is easier for the user and ensures the precise target temperature of the food. On a graphical liquid crystal display (LCD), the thermal image of the food is shown in real-time while the food is being heated.
* The paper also proposes an automatic target temperature recommendation method. After food is put in and the door is shut, it captures an image of the food using a camera and trains a deep learning-based image classifier. When a target temperature is set for that food by the user for the first time, that temperature is assigned to that image class. Later, when the user heats the same class of food again (even if the food is on a different stir or transformation condition), the proposed method automatically classifies the food and recommends the target temperature which was assigned to that food class previously. Thus, the user does not need to recall and re-enter the target temperature. The method uses a Convolutional Neural Network (CNN) to classify the images. Whenever a new food item is inserted or an image is misclassified, the method retrains the deep learning model in real-time. In this way, the proposed microwave oven progressively learns the food items that are consumed in that family and becomes smarter in recommending the target temperature.
* The expected shipment of microwaves is 13.5 million units in 2019 [2] and 96% of Americans use this product [3]. According to the survey in [4], microwaves are used by the Americans to warm and heat more, rather than cook dishes. The microwave oven is behind compared with other major appliances in terms of smart and Artificial Intelligence (AI) features [5]. The proposed machine learning-based intelligent microwave can fill-up this gap in research.