Solve the following projects: 1- WEKA Find the dataset glass.arff and load it into the Explorer interface. For your own information, answer the following exercises. 1. How many attributes are there in the dataset? 2. What are their names? 3. What is the class attribute? 4. Run the classification algorithm IBk (weka.classifiers.lazy.IBk). Use cross-validation to test its performance, leaving the number of folds at the default value of 10. Hint: you can examine the classifier options in the Generic Object Editor window that pops up when you click the text beside the Choose button. The default value of the KNN field is 1: This sets the number of neighboring instances to use when classifying. 5. What is the accuracy of IBk (given in the Classifier Output box)? 6. Visualize the classifier errors and show how many errors in tableware? 7. Run IBk again, but increase the number of neighboring instances to k = 5 by entering this value in the KNN field. Here and throughout this section, continue to use cross-validation as the evaluation method. 8. What is the accuracy of IBk with five neighboring instances (k = 5)? 9. Build a J48 decision tree for this data. 2- Cooja Simulation - Simulate a Wireless Sensor Network WSN with random topology. - Simulate at least 10 mots and 1 base station. - Simulated sensors should send sensing data (e.g. Temperature) to the base station every 20 seconds. - Play around motes codes (Contiki OS) as much as you can to improve your skills.