Publications by Fares A
605_week7_discussion
Chapter 5, Problem 14: On the average, only 1 person in 1000 has a particular rare blood type. Find the probability that, in a city of 10,000 people, no one has this blood type. How many people would have to be tested to give a probability greater than 1/2 of finding at least one person with this blood type? Solution: Part a Using the Poisson Dis...
408 sym
607_project2_FDA_AI(resubmit)
dataset#1 FDA Approval of AI Algorithms in Medicine data set. This is the wide table of the data set I have chosen: Here it is in table format form the loaded data frame from the CSV file imported into R: library(kableExtra) df <- read.csv("https://raw.githubusercontent.com/unsecuredAMRAP/607/main/AI-FDA_wide-table.csv") df %>% kable("html"...
8038 sym R (68504 sym/62 pcs) 14 img 1 tbl
607_project1_resubmission
I used the same chess text data file suggested in the project 1 page on blackboard (so that the data is available to whoever going to run the code again). I’m first going to try to clean the data and turn into into a structured form. # structuring the data library(stringr) # indicating the separator lines separator_indices <- grep("^-+$",...
1529 sym R (9974 sym/18 pcs) 1 img
607_project2_FDA_AI
dataset#1 FDA Approval of AI Algorithms in Medicine data set. This is the wide table of the data set I have chosen: Here it is in table format form the loaded data frame from the CSV file imported into R: library(kableExtra) df <- read.csv("https://raw.githubusercontent.com/unsecuredAMRAP/607/main/AI-FDA_wide-table.csv") df %>% kable("html"...
6570 sym R (21270 sym/54 pcs) 14 img 1 tbl
605_week6_hm
Problem 1 So it can be RRRRR or GRRRR (no order). I used paper and by hand, using binomial distribution: ways of getting 4 red = 7!(4!*(7-4)!) = 35 ways of getting 1 green = 5 ways of getting 5 red = 1 total ways = 35*5 + 1 = 176 Using R: green_jellybeans <- 5 red_jellybeans <- 7 # no green jellybeans are withdrawn (5 reds) ways_1 <- 1 # 1 gree...
2559 sym
607_wek5hm
Building the table and creating a CSV file from it, then saving it.The wide structure of the table is preserved while creating the table and df. library(tibble) library(readr) library(tidyr) library(dplyr) library(kableExtra) data <- list( Airline = c("ALASKA", "ALASKA", "AM WEST", "AM WEST"), Status = c("on time", "delayed", "on time...
1419 sym R (4309 sym/11 pcs) 3 tbl
607_week5Discussion
This is the wide table of the data set I have chosen: Here it is in table format form the loaded data frame from the CSV file imported into R: Type.of.FDA.approval Mention.of.AI.in.announcement X X.1 X.2 X.3 Modality X.4 X.5 X.6 X.7 X.8 Clinical.Discipline X.9 X.10 X.11 artificial intelligence machine learning ...
149 sym 1 img 1 tbl
605_week5hm
Problem 1: 1- (Bayesian). A new test for multinucleoside-resistant (MNR) human immunodeficiency virus type 1 (HIV-1) variants was recently developed. The test maintains 96% sensitivity, meaning that, for those with the disease, it will correctly report “positive” for 96% of them. The test is also 98% specific, meaning that, for those withou...
12274 sym 1 img
605_M5_post
Exercise 3 chapter 1: In the early 1600s, Galileo was asked to explain the fact that, although the number of triples of integers from 1 to 6 with sum 9 is the same as the number of such triples with sum 10, when three dice are rolled, a 9 seemed to come up less often than a 10|supposedly in the experience of gamblers. Write a program to simula...
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605_week4hm
The exercise was to “With the attached data file, build and visualize eigenimagery that accounts for 80% of the variability. Provide full R code and discussion.” To start with, I will begin by loading the images, converting them into a suitable numerical format for analysis, performing Principal Component Analysis (PCA) to identify the eige...
1415 sym R (3497 sym/8 pcs) 1 img