A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is then passed through a set of sensors that measure 3 properties of the fruit: shape, texture, and weight. The sensor system is somehow rather primitive: Shape sensor: -1 if the fruit is round and 1 if it is more elliptical Texture sensor: -1 if the surface is smooth, 1 if it is rough Weight sensor: -1 if the fruit is > 500g, 1 if is < 500g The sensor output will then be input to a Neural Networks based classifying system. As an AI Engineer you are supposed to design (draw the architecture and determine the optimal weight W and bias b) a simple neural network (could be a single perceptron) that can be used to recognize the fruit so that it can be directed to the correct storage bin. As a startup case, the simple network will only be used for two types of fruit i.e. banana and apple. Employ initial weight W = (0.5 -1.0 -0.5) and b = 0.5. Datasets from the sensor are as follows: banana = (-1, 1, -1); apple = (1, 1, -1).

*Please do it correctly, answer in a proper format, and answer in a typed or softcopy format