In metallurgy, materials are made with mixtures of various metals and other elements to achieve certain desirable properties. In a particular preparation of steel, three elements, namely, iron, manganese, and carbon, are mixed in two different proportions. The samples obtained from these two different proportions are placed on a normalized scale, as shown in Figure P4.9 and are represented as fuzzy sets Aj and A2. You are interested in finding some sort of "average" steel proportion. For the logical union of the membership functions shown, we want to find the defuzzified quantity. For each of the seven methods presented in this chapter, assess (a) whether each is applicable and, if so, (b) calculate the defuzzified value, z".

The code given below represents a saveTransaction() method which is used to save data to a database from the Java program. Given the classes in the image as well as an image of the screen which will call the function, modify the given code so that it loops through the items again, this time as it loops through you are to insert the data into the salesdetails table, note that the SalesNumber from the AUTO-INCREMENT field from above is to be inserted here with each record being placed into the salesdetails table. Finally, as you loop through the items the product table must be update because as products are sold the onhand field in the products table must be updated. When multiple tables are to be updated with related data, you should wrap it into a DMBS transaction.  The schema for the database is also depicted.  public class PosDAO {    private Connection connection;        public PosDAO(Connection connection) {        this.connection = connection;    }        public void…

List all the ways to select two different members from S without repetition. The order in which members are selected is important. For​ example, AB is not the same selection as BA.   S={Q,R,S,T,U}

KNN  is a technique used to estimate new values based on the similarity of known ones. In this assignment, your company wants you to estimate the selling price of a customer's building   The price you calculate will be given to the customer as the company selling price recommendation.  You decide to use Data Science techniques such as the K-Nearest Neighbor.(KNN)  You will need to: Import the necessary libraries from your program. (You can use the model class sklearn.neighbors.KNeighborsClassifier,   part of the package  sci-kit-learn 1.1.1 (Links to an external site) or any other. Train/test the model with the data included in the module (cal_housing.tgz).   The house you need to estimate the value for has the following properties: longitude: 120.75latitude: 39.34housingMedianAge: 35.5total rooms: 260totalBedrooms:120  population:540households: 12medianIncome:1.8 K BuildingValue:  ?   What is the recommended price? You need to provide the code, properly commented.   You could use…

A typical roulette wheel used in a casino has 38 slots that are numbered 1,2,3,....,36,0,00, respectively.  Half of the remaining slots are red and half are black.  Also, half of the integers between 1 and 36 inclusive are odd, half are even, and 0 and 00 are defined to be neither odd nor even.  A ball is rolled around the wheel and ends up in one of the slots; we assume each slot has equal probability of 1/38, and we are interested in the number of the slot into which the ball falls. (a)Define the Sample space S. (b)Let A = {0,00}. Give the value of P(A). (c) Let B = {14,15,17 18}. Give the value of P(B). (d) Let D = {x:x is odd}. Give the value of P(D).

A basic wooden railway set contains the pieces shown in figure below. It identifies the quantityof each type of piece denoted by ‘x’. Each piece is not uniquely identified by a primary keyvalue only labeled by type. i.e., Input information only states quantity of types such as twelvestraight pieces, two branch pieces, two merge pieces, and sixteen curve pieces. Pieces connect the knob to opening, with subtend curves extending the arc from start to end by45 degrees, and the pieces can be flipped over for track direction. The task is to connect these pieces into a railway that has no overlapping tracks and no looseends where a train could run off onto the floor. Question: Suppose that the pieces fit together exactly with no slack. Give a precise formulation of thetask as a search problem. Write a Java algorithm to assemble optimally solving the problem.

Let's revisit our first problem, where we want to set up a series of chess matches so we can rank six players in our class. As we did before, we will assume that everyone keeps their chess rating a private secret; however, when two players have a chess match, the person with the higher rating wins 100% of the time. But this time, we are only interested in identifying the BEST of these six players and the WORST of these six players. (We don't care about the relative ordering or ranking of the middle four players.) Your goal is to devise a comparison-based algorithm that is guaranteed to identify the player with the highest rating and the player with the lowest rating. Because you are very strong at Algorithm Design, you know how to do this in the most efficient way. Here are five statements. A. There exists an algorithm to solve this problem using 6 matches, but there does not exist an algorithm using only 5 matches. B. There exists an algorithm to solve this problem using 7 matches,…

The for construct is a kind of loop that iteratively processes a sequence of items. So long as there are things to process, it will continue to operate. In your opinion, how true is this statement?

Write a Jupyter code for the following.

Step 1. Intersection over Union   # def intersection_over_union(dt_bbox, gt_bbox):  ---> return iou Step 2. Evaluate Sample    We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy()   # predictions dt_bboxes =…