=Paper=
{{Paper
|id=Vol-3126/paper39
|storemode=property
|title=Machine learning concepts and applications
|pdfUrl=https://ceur-ws.org/Vol-3126/paper39.pdf
|volume=Vol-3126
|authors=Yusifov Mahammad,Lala Bakirova
}}
==Machine learning concepts and applications==
https://ceur-ws.org/Vol-3126/paper39.pdf
Machine Learning Concepts and Applications
Yusifov Mahammad1, Lala Bakirova 2
1,2
Azerbaijan State Oil and Industry University 1, 34 Azadliq Avenue, Baku, AZ1000, Azerbaijan
Abstract
The thesis includes the main basic concepts of machine learning and its applications. For
simplest possible explanation of machine learning concepts, handwritten number recognition
software has been represented and on this application, concepts like artificial neurons, weights,
edges, connections and others has been explained. Layered structure of artificial network,
artificial neuron activation value. What is edges shapes? How the recognition process
happening, what is neural network and neurons? What is weight and activation value, Sigmoid
and ReLU function? What is the parameter of connections between neurons? These questions
and others has been covered in this article.
Keywords 1
Neural network, artificial intelligence, neurons, auto recognition, activation, weight
1. Introduction considered as a function that takes input as a
number and passes as output to the next so-called
artificial neuron [1]. Figure 1 is an example of
Machine learning is a field of science that
artificial neural network that consists of input,
covers mathematical complex calculations and
output and some additional hidden layers that can
equations in order to make computers imitate the
be considered as black box for now.
“learning” process of humans. This process
involves a computer algorithm that builds models
based on given training data to make proper
decisions without being manually programmed by
a developer [2]. Machine learning is considered as
an important functionality and branch of Artificial
Intelligence and the applications of this
interesting technology are very wide: Speech
recognition and other image recognition
techniques, computer vision, email spam, and
malware filtering, virtual personal assistant,
online fraud detection, self-driving cars, stock
market trading, and medicine. And this list can be Figure 1: Artificial neural network
expandable. In this thesis, main concepts and
applications of machine learning will be covered. In every layer, neurons are connected to each
other via weights that can be modified with result
2. Neural network of learning or training process. Next, we can take
simple example application handwritten number
recognition and go through these concepts of
The artificial neural network is a set of
neurons.
connected units that each represents a neuron, like
in the human brain. Every neuron can be
ISIT 2021: II International Scientific and Practical Conference
«Intellectual Systems and Information Technologies», September
13–19, 2021, Odesa, Ukraine EMAIL: yusifov.19@mail.ru (A. 1);
lala_bekirova@mail.ru (A. 2)
ORCID: 0000-0002-4295-8642 (A. 1); 0000-0003-0584-7916 (A.
2)
©️ 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
2.1. Handwritten number consists of 10 neurons that each represents a
number as a result. The activation of neurons in
recognition the output layer also represents grayscale value,
the neuron with the highest activation number is
Although it is not so challenging to humans to the “choice” of the machine. For example in our
understand the number on the left which is sloppy case, the number is 9 and the neural network of
written by me, for computers it is not an easy task. the decision process has been shown in Figure 4.
Figure 2 shows an example for number
recognition which has been taken from this site:
https://www.i-am.ai/neural-numbers.html.
Figure 2: Screenshot of number recognition AI
How this software converts or translates the Figure 4: Artificial network with result “9”
analog number given on the left to the digital
mathematical number that has been given on the When we try to recognize a number, our brain
right by the machine? Let’s start with looks for certain patterns like loops or shapes. For
conceptualizing the term “neuron”. example, in our example, the number 9 has circle
up top and a line on the right. The number 8 has 2
circles or loops one on top and the other at the
bottom. So our next inner layer can be responsible
for holding each one of these patterns shapes in a
neuron. If the activation of the specific neuron is
close to 1, the possibility of that corresponding
pattern is high. For example, suppose there is one
certain neuron that represents a circle pattern, and
the activation is 0.9. That means the resulting
number can be: 0, 6, 8, 9.
Figure 3: Number on pixels or on “neurons”
We can consider a neuron as a unit that holds
a number between 0 and 1. In figure 3 we have 28
x 28 = 784 neurons and each one of these neurons
holds a value that represents the greyscale value
of the corresponding pixel. For example, the
neuron that value is 0 means it is black and the one
with value 1 means it is white. This number is
called “activation” [3]. Figure 5: Second last layer holding patterns
All of these 784 neurons represent the input
layer of our artificial network and the output layer
So we can suppose, our second last layer is neurons: weight which is also number. And from
responsible for recognizing patterns as shown in mathematical approach:
Figure 5. 𝑎1𝑤1 + 𝑎2𝑤2 + … + 𝑎𝑛𝑤𝑛 , (1)
But, these patterns themselves also consist of This equation is weighted sum [4].
little shapes, which we can call edges. For
example circle can consists of a bunch of edges.
Figure 6: Little edges of patterns
So we also need to have a layer that is
responsible for recognizing these little edges. The
layer that comes after the input layer can be
responsible for that operation. So with 2 layered
16 neurons structure, we can suppose the working
principle of our little AI is in this way: In the first Figure 8: Edge recognition with weights
layer we get input as a handwritten number with
784 neurons, and then it sends a signal to the next Weight with the color green means positive
layer in order to detect edges and then comes third vale and with the red color means negative so
layer to detect bigger shapes as patterns and at the weights with the pixel color red means darker are
output we have our result as a number. and it distinguishes darker and lighter areas on
pixels so that it can detect edges.
When we compute weighted sum in equation
1, we can come up with any number, but for our
network, we need activation values to be in the
range of 0 and 1 and here we need to use the
specific function that minimizes real number to
the range between 0 and 1. A common function is
the sigmoid function.
Figure 7: Overall of 2 layered neural network
structure.
This structure can be also applicable for other
recognition tasks like image and voice Figure 9: Sigmoid function
recognition. For example, detecting shapes in the
pictures, or identifying syllables in human voice Negative inputs end up close to 0 and positive
can be our “patterns” or “edges” in this structure. inputs end up close to 1 [5]. So the activation of a
To be able to detect that edges, we need to neuron in our network is a measure of how
assign new parameter to the connections between positive the corresponding weighted sum is. But
also we need bias for inactivity. We just need to
extract that value from the weighted sum before
processing the sigmoid function [1].
𝜎(𝑎1𝑤1 + 𝑎2𝑤2 + … + 𝑎𝑛𝑤𝑛 − (2)
𝑏𝑖𝑎𝑠) ,
So, the weights indicate what pattern, edges this
specific neuron in the second layer is picking up
on and the bias indicates how high the weighted
sum needs to be before the neuron starts getting
active.
But there is another better function called Figure 11: Some numbers from MNIST database.
ReLU – REctified Linear Unit.
The more training data we give to the machine,
the more it evolves itself by doing feedback:
taking input from the picture does its operation
and match the result with the real idealistic value
behind the number and correct itself [6].
3. Applications
Very popular example of how powerful
artificial intelligence can be is GPT-3 (Generative
Pre-trained Transformer 3) launched by OpenAI
company. This model uses deep learning to
produce texts that are very similar to human words
[9]. GPT-3 can even code by its own, you just
Figure 10: ReLU function have to give input commands like create a button
that looks like rectangular and when it is pressed
Using sigmoid function, was very difficult to increase certain value or do some other
train at some point, but ReLU solved this problem operations. For example debuild.co is interesting
[10]. website that uses GPT-3 to write code. User gives
Every neuron in the first layer is connected to orders as input and it generates JavaScript code
the 784 neurons. And each one of these 784 according to orders and visually shows the result
connections has weight and bias. So we have 784 at a time [10].
x 16 weights with 16 biases and the connection
between other layers also has weights and biases
according to them. Our network has 13002
weights and biases in total.
Learning in here is, getting the machine to find
a true setting for all of these many numbers so that
it can solve the problem at a time [7].
The training process of the network can be
done with the help of certain special databases that
store collections of images of numbers
handwritten. So the machine can train itself to
become a better “predictor” with both having
input and output from the database [8].
MNIST database is well-known example of Figure 12: Describing google UI to GPT-3
these databases which stores 70000 images of
digits handwritten by people like high school GPT-3 functionalities are very wide and that
students and employees of the American Census shows how amazing works can be done using
Bureau that are published to help AI researchers. Machine Learning and Artificial Intelligence.
In medical field, machine learning has so many
applications and they are currently being
developed. One of the proper examples is
Microsoft's InnerEye Project that identifies
differences between healthy cells and tumor cells analyses of human and better treatment for each
by using 3D radiological images as shown in specific patient. There are 3 tables in this simple
Figure 13. represented database:
1. patients: stores basic information for
other relations
2. health_parameters: stores vital
information about a human this data is
essential for identifying potential diseases
3. other_parameters: stores information
about daily activites, and possible decisions
that the human can make.
The second and third tables are connected to
Figure 13: Screenshot from Inner Eye first table via foreign key on id of patient. With
only this small data a well-structured AI can
Pfizer uses machine learning to research about predict the cause of psychological disease and
how the immune system of human body can fight develop more relevant therapy for patient. For
cancer. Insitro is a startup uses data science and example: a married man with weight of 120kg
machine learning to develop drugs that cures who is accountant may have heart disease and
people much faster and with high level of success with having no hobbies is additional risk
[11]. parameter that causes psychological disease on
Besides medical field, there are many him. That one example can be extended widely
interesting fields that use machine learning. with the growth of the database. More data
Navigation programs like google maps analyzes provided to model, more it will be decisive and
all roads and transportation ways and brings most correct. If there are 2 persons that have relation
relevant choice of travel from location to another. they can also have same problem. For instance, a
Social media programs has many features that use partner of current patient had this certain disease
machine learning to process its functionality like 4 months ago as she registered in this database. So
bringing “people you may know”, face detection, this AI can read all relations of humans even if
image recognition and others. Self-driving cars they are not bound together but have same
like Tesla works on deep learning and also workplace that other patient had, this can be
includes IoT because of intelligent sensors. important information for model to decide. This
Language translation apps, online video complex relation may be difficult for us to see and
streaming apps and many others have a touch on decide but it is not the same for a machine. It can
machine learning and expands quickly. perform big analysis and diagnostic on a human
Another additional machine learning and identify multiple treatment or therapy
application can be in psychology field. Model can methods. And a doctor who is psychologist can
be established in order to read and store different choose one or more of those method even modify
parameters of humans in a database. Database them. This kind of machine learning application is
structure is shown in a following figure. not only about psychology field, it also can be
about other specific fields of medical applications.
4. Conclusions
In the new era of technology, Artificial
Intelligence will help humans to solve so many
problems in a short time without errors. It can
affect the human life as given example about
identifying the disease on human by giving
database as a training data for machine to learn. In
medicine, special surgeries, in oil manufacturing,
Figure 14: Simple database for model to learn or in some dangerous places that humans should
not be operate, machines will replace and make
appropriate decisions like, or better than humans.
This database can be input for model and it can
And that complex operations is starting with
use machine learning to improve more correct
handwritten number recognition task. It is "Envisioning an artificial intelligence
considered as “Hello-World” of Machine documentation assistant for future primary
Learning. But as we know every big roads starts care consultations: A co-design study with
with first little steps. general practitioners". Journal of the
American Medical Informatics Association.
5. Acknowledgements 2020
[15] Kobie N. "DeepMind's new AI can spot
breast cancer just as well as your doctor".
Thanks to head of cathedra: Doctor of Wired UK. Wired. 2020
technical sciences Mrs. Lala Bakirova.
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