Knowledge Graph is a knowledge base of entities and the relationships between them. It is a graph formed by representing entities (like people, places, objects) as nodes, and relationships between entities (like is located in, is a, etc) as edges. It contains Facts where Facts are typically represented as “SPO” triples: (Subject, Predicate, Object).
It actually contains a head entity, relation and a tail entity, or in simpler terms: subject, relation and object.

It acquires and integrates information into an Ontology and applies a reasoner to derive new knowledge.(Lisa Ehrlinger and Wolfram Wöß – University of Linz in Austria)


We will be using React Native CLI for generating an apk.

First, we need to make sure that the project is running successfully without any errors. Then, generate a private signing key to generate an apk for an React Native app.


We can generate a private signing key using keytool on our project directory as :

sudo keytool -genkey -v -keystore my-upload-key.keystore -alias my-key-alias -keyalg RSA -keysize 2048 -validity 10000

We can change your_key_name with any name we want, as well as your_key_alias. This command prompts us through the following :

Enter your keystore password: 12345Re-enter new password…


Let’s get started with some basic topics that we should know before diving into the coding and deploying section.

What is Flask?

Flask is a simple and powerful micro web framework. It is mostly used for creating APIs in Python. Flask depends on the Jinja template engine and the Werkzeug WSGI toolkit. The “micro” in micro web framework means Flask aims to keep the core simple but extensible.

Flask does not include a database abstraction layer, form validation or anything else where different libraries already exist that can handle that. Instead, Flask supports extensions to add such functionality to your…

Structuring element

It is a small binary image or a small matrix of pixels, each with value of zero or one.

It is positioned at all possible locations in the image and it is compared with the corresponding neighborhood of pixels.

Some operations test whether the element ‘fits’ within the neighborhood, while others test whether it “hits” or intersects the neighborhood.

When a structuring element is placed in a binary image, it is said to ‘fit’ the image if, for each of its pixels set to 1 , the corresponding image pixel is same. …

Contrast stretching is an Image Enhancement method which attempts to improve an image by stretching the range of intensity values.

Here, we stretch the minimum and maximum intensity values present to the possible minimum and maximum intensity values.

Example: If the minimum intensity value(r min ) present in the image is 100 then it is stretched to the possible minimum intensity value 0. Likewise, if the maximum intensity value(r max) is less than the possible maximum intensity value 255 then it is stretched out to 255.(0–255 is taken as standard minimum and maximum intensity values for 8-bit images)

Note: Contrast…

Image Thresholding simply means setting an intensity value for intensity value lower than a certain value(Threshold value) and another intensity value for intensity value higher than the threshold value.

Here, Threshold value acts as a point of intensity value which separates the intensity values of pixels into two parts.

Threshold value can be selected by different methods and Image Thresholding can also be done by different ways. In this context, we are only taking a grayscale image(or monochrome image).

Some basic Image Thresholding techniques are:

a) Global Thresholding:

We select the Threshold value manually which seems suitable for the given…

A negative of an image is an image where its lightest areas appear as darkest and the darkest areas appear as lightest.

The appearance change from lightest to darkest and darkest to lightest is basically done in gray scale image and refers to the change of pixel intensity values from highest to lowest and lowest to highest.

In case of colour image, different colour is represented as negative of different colours according to their intensity values.

Let us take an 8-bit image with intensity range of 0–255.

For gray image(or monochrome image), we can convert it into its negative form…

An image is denoted by f(x,y) and p,q are used to represent individual pixels of the image.

Neighbours of a pixel

A pixel p at (x,y) has 4-horizontal/vertical neighbours at (x+1,y), (x-1,y), (x,y+1) and (x,y-1). These are called the 4-neighbours of p : N4(p).

A pixel p at (x,y) has 4 diagonal neighbours at (x+1,y+1), (x+1,y-1), (x-1,y+1) and (x-1,y-1). These are called the diagonal-neighbours of p : ND(p).

The 4-neighbours and the diagonal neighbours of p are called 8-neighbours of p : N8(p).

Adjacency between pixels

Let V be the set of intensity values used to define adjacency.

In a binary image, V ={1} if we are…

Digital image is an 2-dimensional matrix of sampled intensity(gray/colour) values.

The process of improvement of pictorial information using different algorithms and methods for different purposes is Digital Image Processing.

Digital image processing is subdivided into different steps as shown in the diagram below.

Fundamental steps in digital image processing[1]

a) Image acquisition:

Here, the acquired image is already in digital form. This step also involves preprocessing, such as scaling.

b) Image enhancement:

It is the process of manipulating of an image for specific purposes(results). There are variety of image enhancement methods. We only use specific(required) methods suitable for required good result(output). It is based on human subjective preferences regarding what constitutes a good result.

c) Image…

samir khanal

AI Enthusiasts, Software Developer

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