1.1 statistics advanced methods. Figure 1. Artificial

1.1  Introduction:


Artificial intelligence (AI) is the intelligence demonstrated by
machines which is an effective approach to human learning and reasoning 1. In
1950, “The Turing Test” was proposed as an good explanation of how a computer
could perform a human cognitive reasoning 2. AI can be divided into specific
research sub-fields. For example: Natural Language Processing (NLP) 3 can
enhance the writing experience in various applications 4,5. The most important
part of NLP is machine translation, which is the translation between languages.
Machine translation algorithms aides applications which can consider grammar
structure as well as spelling mistakes. Also computer suggests words to writer
or editor to make changes 6. Figure 1 shows how AI covers seven subfields of
computer sciences.

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Recent studies in big data and machine learning analyzes
multiple possibilities of characterization of databases 7. For many the
years, databases are collected for statistical purposes. Statistical curves can
describe past, and present in order to predict future behaviors. However the
techniques developed to process this data by algorithms is very recent, an
optimization of those algorithms could lead on an effective self–learning 8. Decision
making by robots can be implemented based on existing values, multiple criteria
and statistics advanced methods.

























Figure 1.  Artificial intelligence
and its subfields.



3.2 What is deep learning?


Deep learning is an emerging area of machine learning research. It
comprises of multiple hidden layers of artificial neural networks. The deep
learning methodology applies nonlinear transformations and model abstractions
of high level in large databases. The recent development in deep learning
architectures within numerous fields have already provided significant
contributions in artificial intelligence1.


Deep learning method’s ultimate goal is to give rise to artificial
intelligence with prime focus on 
mathematical and computational principles to learn from examples to
acquire knowledge9.


Deep  learning first appeared in the year 2006 and
it was known as hierarchical learning which is usually used in the fields of
pattern recognition 2. Deep learning consists of mainly two aspects 4:


processing in multi-layers.

and Unsupervised learning.


Nonlinear processing in multiple layers means an algorithm where
the current layer takes the output of the previous layer as an input. Hierarchy
is developed among the layers of  data
based on importance criteria. On the other hand, supervised and unsupervised learning is related
with the class target label, its availability means a supervised system,
whereas its absence means an unsupervised system.




Applications of Deep Learning in engineering problems:



Image Processing:


2003, experiments were done by applying particle filtering and Bayesian –
belief propagation.  The main concept of
this experiment was that if human can detect the face of person by watching
only half cropped photo, a computer can also reconstruct the photo from half
cropped photo10.

2006, greed algorithm and hierarchy was developed to process handwritten

Convolutional Neural Networks for iris recognitions increases the accuracy up
to 99.35% 11.

location recognition nowadays allows the user to know a determined address
based on a picture. A Supervised Semantics – Preserving Deep Hashing (SSPDH)
algorithm has proved a considerable improvement in comparison with Visual Hash

 (VHB) and Space – Saliency Fingerprint
Selection (SSFS). The accuracy of SSPDH is even           70% more efficient 12.

Facebook and Microsoft all have developed face detection as security lock
system for the cell phones this digital image processing is done by the use of
deep learning method.





2009, automatic speech recognition application was carried out to reduce the
Phone Error Rate by using two different architectures of deep belief networks

was employed to speed up the developing and optimization of FaceSentinel face
recognition devices. There devices could expand the face recognition process
from one -to-one to one -to- many in nine months 14. Without deep learning it
would have taken 10 years. These devices are used at Heathrow airport in London
and have the potential to be used as time and attendance and in banking sector 3.


Natural Language Processing:


Google translate
uses large end-to- end long short-term memory network. It translates the whole sentences
rather than some part of the sentence. It supports over 100 languages 15.



Several important applications of deep learning in the field of speech
recognition and image processing are summarized in the Table -1 16.







Table 1: Deep learning applications 2003-2017



























Publications in Deep learning


Figure 2 shows database of springer’s publications in deep learning.
We can see that there is a huge increase in publications which shows the keen interests
of researchers 16.



2: Data of number of publications in deep learning from
Springer database :2006 to 2017.




learning is one of the fastest growing applications of machine learning. Large number
of publications prove that a lot of research is going on in this subject.

of layers and supervision in learning are the two key aspects to develop a good
application of deep learning. Hierarchy is essential for data classification whereas
supervision checks for the importance of the data in the process.

learning has given concrete results in the field of image processing and speech

learning is also an important method for designing security tools as it can do facial
recognitions which is already a feature in today’s smartphones.