Introduction:
The paper entails a detailed discussion and analysis of “Natural Language Processing” within Big Data analysis. Big Data Analysis is basically referred to as the use of various advanced “analytical techniques” against very large and diverse sets of data and which tends to include “semi-structured”, ‘structured”, and “unstructured data” (Ghavami, 2019). These data have a wide range of sources that vary in size tremendously starting from terabytes to zettabytes. Continue reading
;linear search
.model small;set model small
.stack 110h;set or leave 100 bytes for stack Continue reading
class ListNode:
def __init__(self, data, priority):
self.data = data
self.priority = priority
self.next = None Continue reading
#include <stdio.h>
#include <string.h> //for string manipulation
#include <unistd.h>
#include <stdlib.h> Continue reading
/* Contains basic functions to read/write integer, floating-point and
* string data types from/to the UART. Continue reading
A=imread(‘one.png’)
signImage=rgb2gray(A);
figure(1)
imshow(signImage)
%Detect features of first image that is read above%
signPoints=detectSURFFeatures(signImage)
figure(3)
imshow(signImage)
title(‘100 strongest features from sign image’)
hold on
%Top 100 strongest features
plot(selectStrongest(signPoints,100))
[signFeatures2, signPoints2] = extractFeatures(signImage, signPoints)
B=imread(‘two.png’)
signImage2=rgb2gray(B);
figure(4)
imshow(signImage2)
%Detect features of first image that is read above%
signPoints2=detectSURFFeatures(signImage2)
figure(5)
imshow(signImage2)
title(‘100 strongest features from sign image’)
hold on
%Top 100 strongest features
plot(selectStrongest(signPoints2,300))
[signFeatures2, signPoints2] = extractFeatures(signImage2, signPoints2)
%Punitive point matches in both the images%
picPairs=matchFeatures(signFeatures, signFeatures2);
matchedSignPoints = signPoints(picPairs(:,1),:);
matchedFindPoints = signPoints2(picPairs(:,2),:);
figure(6)
showMatchedFeatures(signImage,signImage2,matchedSignPoints, matchedFindPoints, ‘montage’)
title(‘Matched points both images’)
%Locate objects using Punitive matches%
%[tform, inlierBoxPoints, inlierScenePoints] =estimateGeometricTransform(matchedSignPoints, matchedFindPoints);
%figure;
%showMatchedFeatures(signImage, signImage2, inlierBoxPoints,inlierScenePoints, ‘montage’);
%title(‘Matched Points (Inliers Only)’);
files=["in_abc10.txt","in_abc100.txt"] outFiles=["out_abc10a.txt","out_abc100a.txt"] finalOutputFile=["out10b.txt","out100b.txt"] Continue reading
import java.util.ArrayList;
import java.util.Scanner;
public class BubbleSotr
{
public static void main(String[] args) Continue reading
library(readxl)
CSDATA <- read_excel(“CSDATA.xlsx”)
dim(CSDATA)
#Fitting the linear regression on all the independent variables Continue reading
#include <iostream>
#include<fstream>
#include<vector>
#include<string>
#include<stdio.h>
#include <string>
using namespace std; Continue reading
import java.io.*;
import java.util.Scanner;
// Class for Doubly Linked List
public class DoublyLinkedList {
// class for Node
class Node {
int data;
Node prev;
Node next; Continue reading
Worksheet 3A
Who is asking evaluation questions of the program?
Engage stakeholder
Who will use the evaluation results and for what purpose?
The purpose is that evolution results in tobacco and cigarettes Continue reading
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(cvTools)
#library(glmnet)
library(sandwich) Continue reading
/*Assignm
* run many code samples & provide output
*
* L: the list
* n: number of items stored in the list, not the size of the array Continue reading
#include <stdio.h>
#include <string.h>
// To return value for a character. 10 is returned for 'A'
int val(char c)
{ Continue reading