## Develop a context diagram and level-0 Data flow diagram for following case study

Develop a context diagram and level-0 Data flow diagram for following case study:
“Drugstore at PIMS Hospital fills medical prescriptions for all hospital patients and distributes these medications to the nurses’ sections responsible for the patients’ care. Doctors write prescriptions and send them to the drugstore. A drugstore technician reviews each prescription and sends it to the appropriate drugstore section. Prescriptions for drugs that must be formulated within the hospital are sent to the lab section, prescriptions for off-the-shelf drugs are sent to the LP (local purchase) section, and prescriptions for narcotics are sent to the LP (local purchase) section. At each section, a pharmacist reviews the order, checks the patient’s file to determine the appropriateness of the prescription, and fills the order if the dosage is at a safe level and it will not negatively interact with the other medications or allergies indicated in the patient’s file. If the pharmacist does not fill the order, the prescribing doctor is contacted to discuss the situation. In this case, the order may ultimately be filled, or the doctor may write another prescription depending on the outcome of the discussion. Once filled, a prescription label is generated listing the patient’s name, the drug type and dosage, an expiration date, and any special instructions. The label is placed on the drug container, and the order is sent to the appropriate nurse section. The patient’s admission number, the drug type and amount dispensed, and the cost of the prescription are then sent to the Billing department”

### Compute the information gain of the term “elections” according to Eq. 5.7.

Consider the term “elections” which is present in only 50 documents in a corpus of 1000 documents. Furthermore, assume that the corpus contains 100 documents belonging to the Politics category,….

### Predict the probabilities of categories Cat and Car of Test2 on the toy corpus example in Sect. 5.3.5.2. You can use the multinomial na¨ıve Bayes model with the same level of smoothing as used in the example in the book. Return normalized probabilities that sum to 1 over the two categories. 2.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a fully supervised generalization of the nai¨ıve Bayes model in which each of the k classes contains exactly b > 1 mixture components for a total of b · k mixture components. How would you perform parameter estimation in this model?

1.       Predict the probabilities of categories Cat and Car of Test2 on the toy corpus example in Sect. 5.3.5.2. You can use the multinomial na¨ıve Bayes model with the same….

### What are the possible advantages and disadvantages of using such an approach?

1.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a semi-supervised generalization of the nai¨ıve Bayes model in which each of the….