1. The document discusses various topics related to artificial intelligence including its definition, applications in different fields like agriculture, education, information technology and entertainment. 2. Key concepts discussed include machine learning, deep learning, neural networks, supervised and unsupervised learning, computer vision and natural language processing. 3. Applications of AI mentioned include image and speech recognition, predictive analysis, personalized learning, chatbots, targeted advertising and automated tasks to aid professionals.

1. 5th International
Conference on Industrial
and Mechanical
Engineering and
Operations Management
(IMEOM), 2022
IEOM
Artificial Intelligence: Classification,
Applications, Opportunities, and
Challenges
Presentation Title
Presented By
Abdullah al Mamun
Professional Software Engineer and Researcher
Manager, Robi Axiata Limited
B.Sc. In CSE from RUET

2. Elon Musk
CEO of Tesla & SpaceX
AI is a fundamental risk to the existence of human civilization.

3. I, Robot
2004 ‧ Sci-fi/Action

4. ?
?
?
What is AI?

5. Recognition Letter
A
Procedure to recognition the image is Letter “A”
 Make the logic
 Read the image
pixel
 Return result

6. Take a Deep
Look
Letter “A” make by three lines

7. Different Front
Style Letter A
Baby Learn pattern to detect Object.
 So is your programming login is
capable to detect these type of letter?
 If No, then what is the lacking for
your logic?
 What should be add to detect
object dynamically?

8. Programming
Logic
Dynamically
Recognition able,
Self-learning Model.
Design Dynamic Model

9. What is exactly
Artificial Intelligence?
Artificial Intelligence is a
model/procedure/tool who has
capability for self learning,
dynamically detect the pattern/object
and take decision by own knowledge
just like human brain.
“So according to the definition, is it proved that AI is
really threat for human existence?”

10. Human Intelligence Curve

11. Human Brain Learning Process
Input Image Feature Extraction Learning
Human Brain Neuron
Recognition

12. Artificial Neural Network vs human brain Neural

13. Learning Vs Recognition
Learning
Learning is a search
through the space of
possible hypotheses for
one that will perform
well, even on new
examples beyond the
training set. To
measure the accuracy
of a hypothesis we give
it a test set of examples
that are distinct from
the training set.
Recognition
According to the
training dataset
learning process is
performed and engine
is updated. By pass
through the input
sample over the engine
and it will return an
output according to the
learning accuracy.

14. AI Classification
AI
Symbolic
Learning
Machine
Learning
Computer
Vision
Robotics
Statistical
Learning
Deep
Learning
Speech
Recognition
NLP CNN RNN
Object
Recognition
Humans can
speak & listen to
communicate
through language.
Much of speech
recognition is
statistically based
Human can
write & read text
in a language
Humans can
see with their
eyes & process
what they see.
Computer Vision falls
under the symbolic
way for computers to
process information.
Image
Processin
g
Humans recognize the
scene around them through
their eyes which create
images of that world.
Humans can
understand their
environment and
move around fluidly.
Pattern
Recognition
Humans have the
ability to see patterns
such as grouping of
like objects.
Machines are even
better at pattern
recognition because it
can use more data
and dimensions of
data.
ANN
The human brain is a network of
neurons and we use these to learn
things if we replicate the structure
and function of the human brain
we might be able to get cognitive
capabilities in machines.
ANN’s are more
complex & deeper,
we use those to
learn complex thing
To replicate the human
brain if we get the
network to scan images
from left-right, top-
bottom.
With accomplished
by CNN &
computer vision.
Neural Network to
remember a limited
past

15. Artificial Neural Network
(ANN)
ANN, is a group of multiple perceptrons/ neurons at each
layer. ANN can be used to solve problems related to:
 Tabular data
 Text Data
 Image Data
ANN Application:
 Image Recognition
 Natural Language Processing
 Pattern Recognition
 Text to Speech

16. Recurrent Neural Network (RNN)
RNN is a class of artificial neural networks where
connections between nodes form a directed graph along a
temporal sequence.
 Audio data
 Text Data
 Time Series Data
ANN Application:-
 Speech Recognition
ANN can be used to solve problems related to:-
A looping constraint on the hidden layer of
ANN turns to RNN.
 Text Processing(Chatbot)
 Face detection, OCR Applications as Image Recognition
 Music composition

17. Convolution Neural Network (CNN)
A Convolutional Neural Network (ConvNet/CNN) is a
Deep Learning algorithm which can take in an input
image, assign importance (learnable weights and biases)
to various aspects/objects in the image and be able to
differentiate one from the other.
CNN can be used to solve problems related to:
 Image Data
CNN Application:
 Image Recognition
 Image Classification
 Face Recognition

18. Supervised Learning
Supervised Learning use of labeled datasets to train
algorithms that to classify data or predict outcomes
accurately. As input data is fed into the model, it adjusts
its weights through a reinforcement learning process,
which ensures that the model has been fitted
appropriately.
The model first learns from the given training data. The
training data contains different patterns, which the model
will learn.
Application:
 classifying spam in a separate folder from your inbox
 Image- and object-recognition
 Predictive analytics

19. Unsupervised Learning
Unsupervised learning has no training phase; instead, the
algorithm is simply handed a dataset and uses the
variables within the data to identify and separate out
natural clusters.
Application:
 Finding customer segments
 Feature selection

20. Reinforcement Learning
Reinforcement Learning(RL) is a type of machine learning
technique that enables an agent to learn in an interactive
environment by trial and error using feedback from its
own actions and experiences.
Application:
 Robot deciding its path
 Next move in a chess game

21. Agriculture
Finance
Medical
Education
Entertainment
1
2
4
6
7
Application of AI
3
5
Information Technology
Robotics
Transport
8

22. 01
02
03
04
05
06
Application of AI in Agriculture
AI systems are helping to improve the overall harvest quality and accuracy – known as precision agriculture. AI technology helps in detecting
disease in plants, pests and poor nutrition of farms. AI sensors can detect and target weeds and then decide which herbicide to apply within
the region.
Solid and Crop Monitoring
AI models can inform farmers of
specific problem areas so that they can
take immediate action.
Insect & plant disease detection
AI computer vision can detect and
analyse crop maturity and soil quality
Aerial survey and imaging
AI can analyse imagery from drones
and satellites to help farmers monitor
crops and herds.
Weather Forecast for Cultivation
AI farmers can analyse weather conditions by
using weather forecasting which helps they plan
the type of crop can be grown and when should
seeds be sown.
Livestock health monitoring
AI trained to look at video data and
determine Animal indicative of disease
or behavioural problems
Produce grading and sorting
AI computer vision can continue to help
farmers even once the crops have
been harvested.

23. Application of AI in Education
Personalized Learning
AI in education ensures that the educational
software is personalized for every individual.
05
04
03
02
01
Online career counselling
AI and education focuses on every individual’s
requirements through features like AI-embedded games,
customized programs, and more to learn effectively.
Virtual facilitators
With AI in schools and virtual classrooms, the
technology takes up most of the value-added tasks.
Creating Smart Digitization Content
AI can teachers and research experts create
innovative content for convenient preaching and
learning.
Administrative Tasks Automated to Aid Educators
AI can be used to grade smaller tasks like homework
assignments, quizzes, and some minor class tests.
Benefits of Artificial Intelligence in the Education Industry
AI can automate grading so that the
tutor can have more time to teach.
AI chatbot can communicate with
students as a teaching assistant.
AI in the future can be work as a
personal virtual tutor for students,
which will be accessible easily at any
time and any place.

24. Application of AI in Information Technology
AI for IT operations refers to the use of Artificial Intelligence to manage Information Technology based on a multi-based platform.
The main technologies used in AIOps are Machine Learning and Big Data. These automate data processing and decision making, using
both historical and online data.
01
02
03
04
Biometric Identification (Iris Scan, Facial recognition)
AI can help make facial recognition by computers much easier by analysing facial features and matching them with a
database.
Chatbots - Virtual Assistance
Apple's Siri and Amazon's Alexa are examples of consumer-oriented, data-driven, predictive AI chatbots.
Application in E-Commerce
AI can help today's online retailers deliver an optimized customer experience on and off their ecommerce websites by using
collected business and customer data to make better business decisions and more accurately predict the future.
OCR/Handwriting recognition
AI has an ability of a computer to receive and interpret intelligible handwritten input from sources
such as paper documents, photographs, touch-screens and other devices.

25. Application of AI in Entertainment
We are currently using some AI based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms,
these services show the recommendations for programs or shows.
01
02
03
04
05
06
Application in Social Media
An AI-powered social monitoring tool or social
listening tool can deliver insights from your
brand's social media profiles and audience.
Targeted advertising and increasing engagement
AI to launch smart marketing campaigns and to expose
a brand to a broader audience. They analyse consumer
sentiment to use the information for scaling up and
improving services.
Gaming like Chess, AlphaGo
The AI behind AlphaGo uses machine learning and neural
networks to allow itself to continually improve its skills by
playing against itself. The AI won the Go game, but the
human won the future.
Music Composition
AI applications in music that cover not only
music composition, production, and performance
but also how music is marketed and consumed.
human-like intelligence in video games
In video games, artificial intelligence (AI) is used to generate
responsive, adaptive or intelligent behaviours primarily in non-
player characters (NPCs) similar to human-like intelligence.
Video content analysis, surveillance and
manipulated media detection
Object detection models are based on neural networks
that enable recognition and targeting in real-time, even
in cases of blurred images or image noise

26. Carrying goods in factories
or warehouses
AI generates value in the warehouse through various sub-technologies: machine learning, natural language
processing, robotics, and computer vision.
Cleaning offices and
large equipment
AI devices that can detect areas that need heavier cleaning and other areas that may not need as much work. The
devices can learn from its experience in a room and change its future behaviours without having to detect these
differences every time.
Inventory management AI can improve inventory processes. And they do so through demand forecasting. This feature integrates with AI
inventory management software to find out customer preferences.
Home Automation
Systems
AI in managing the smart home infrastructure helps in gathering data from the home automation devices,
predicting user behaviour, providing maintenance data, help enhance data security and privacy.
Assembly and inspection AI-powered visual inspection uses computer vision AI to analyze machinery, production processes, inventory
levels, and workplaces to ensure safe, efficient, and effective business processes.
01
02
03
04
05
Application of AI in Robotics
Humanoid Robots are best examples for AI in robotics, recently the intelligent Humanoid robot named as Erica and Sophia has been
developed which can talk and behave like humans.

27. Application of AI in Medical
Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are
worsening so that medical help can reach to the patient before hospitalization.
01
02
03 04
05
06
In-Patient Mobility Monitoring
AI-based equipment health monitoring and
prediction systems save time and expense by
eliminating equipment failure and downtime.
01
Clinical Trials for Drug Development
AI today not only does flashy gene-sequencing work, it's
being trained to predict drug efficacy and side effects, and to
manage the vast amounts of documents and data that
support any pharmaceutical product.
02
Quality of Electronic Health Records
AI in EHRs (Electronic Health Records) is primarily applied for
the improvement of data discovery, extraction, and
personalized recommendations for treatments.
03
Medical record analysis
AI evaluates an individual patient's record and
predict a risk for a disease based on their
previous information and family history.
04
Heart sound analysis
AI provide new possibilities for echocardiography to generate accurate,
consistent and automated interpretation of echocardiograms, thus
potentially reducing the risk of human error.
05
Treatment plan design
AI can help identify hidden or complex
patterns in diagnostic data to detect diseases
earlier and improve treatments.
06

28. Application of AI in Finance
AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and
machine learning into financial processes.
01
05
03
07
Trading and investment
AI stock trading uses robo-advisors to analyze millions of
data points and execute trades at the optimal price.
Underwriting
AI-driven underwriting systems assist the underwriters by
accurately quantifying unstructured and qualitative data
points
Audit
AI can help automate specific tasks, such as data entry
and analysis, improving accuracy and speeding up the
auditing process.
Identify abnormalities Transaction
AI, anomalous and fraudulent transactions made with
credit cards or online portal can be identified
Detect anti-money laundering patterns
AI and machine learning tools have the potential to enhance risk-
based AML programs by assigning priority risk categories to
customers during onboarding, and by screening for patterns,
connections, and statistical anomalies in transactional activity
Biometric fraud detection
AI allows the banks to estimate the likelihood of
committing fraud by a particular customer.
Digital payment advisers
AI can be used to improve the speed and efficiency of the
payment process, by reducing the extent to which humans
need to be involved.
Consumer and Personal Finance
AI provides a faster, more accurate assessment of a potential borrower, at
less cost, and accounts for a wider variety of factors, which leads to a
better-informed, data-backed decision

29. Application of AI in Transport
AI is becoming highly demanding for travel industries. AI is capable of doing various travel related works such as from making travel
arrangement to suggesting the hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots
which can make human-like interaction with customers for better and fast response.
Self-driving vehicles
AI software in the car is connected to all the sensors and collects input from simulate real-world
conditions to safety-test autonomous vehicles and video cameras inside the car.
Smart Navigation Assistant
The AI personal assistant works on a simple mechanism of receiving the voice or text inputs
and then responding to it with the respective form.
Virtual Travel booking agent
AI in the travel industry can be found in travel chatbots, voice assistants and robots, facial
recognition software, systems for crafting personalized recommendations, sentiment analysis,
luggage handling, and flights forecasting.
Traffic Control System
AI is used in road traffic management to help analyze real-time data from
various means of transportation, including cars, buses and trains.

30. Opportunities of Artificial intelligence (AI)
Artificial intelligence (AI) has the potential to revolutionize many industries and fields by automating tasks,
analysing large amounts of data, and making decisions with a level of accuracy and speed that is difficult for
humans to match. Some of the key opportunities and benefits of AI include:
05
04
03
02
01
Enhanced to improve security & safety
Enhanced customer experience
Improved accuracy and reliability
Improved decision-making
Increased productivity and efficiency

31. Increased productivity
and efficiency
 AI can automate tasks, allowing humans to
focus on more complex and creative work.
 AI can automate certain tasks and processes,
freeing up time for human workers to focus on
more complex and creative tasks.
 AI can be used to optimize resource usage and
reduce waste, leading to improved
environmental sustainability.

32. Improved
decision-making
 AI can analyze large amounts of data quickly
and accurately, helping humans to make
better and recommendations for decision-
making.
 AI can be used to automate routine tasks,
freeing up time for employees to focus on
more complex and creative tasks.
 AI can help doctors and healthcare
professionals to diagnose diseases more
accurately and provide personalized
treatment plans.

33.  AI algorithms can be trained to recognize
patterns and make predictions with a high
degree of accuracy, reducing the risk of
human error.
 AI can help organizations streamline processes
and workflows, leading to increased efficiency
and productivity.
 AI can be used to optimize transportation
routes and reduce traffic congestion,
improving efficiency and reducing emissions.
Improved accuracy
and reliability

34. Enhanced customer experience
 AI can be used to provide personalized
experiences for customers, such as personalized
product recommendations or personalized
customer service.
 AI-powered chatbots and virtual assistants can
handle routine customer inquiries, freeing up
human customer service representatives to
handle more complex issues.
 AI can be used to deliver personalized
experiences to customers, such as personalized
product recommendations or customized
advertising.

35. Enhanced to improve
security & safety
 AI can be used to identify and prevent cyber-attacks, as
well as to monitor and protect critical infrastructure.
 AI can be used to monitor and control various systems,
such as manufacturing processes or transportation
networks, to improve safety and reduce the risk of
accidents.
 AI can be used to monitor and analyze security footage,
detecting potential threats and alerting authorities.

36. Challenges of Artificial Intelligence (AI)
There are a number of challenges that must be addressed in order to fully realize the potential of artificial intelligence (AI). Overall, the
development and implementation of AI is a complex and challenging field and addressing these challenges will require the collaboration of
researchers, policymakers, and other stakeholders. Some of the major challenges include:
Limited ability to learn
Bias in Data and fairness
Ethical considerations
Job displacement and Economic disruption
Security and Lack of transparency
Challenges
of
AI

37. 01
Limited ability to
learn
 Current AI systems are limited in their ability
to learn from experience, and their
performance can be improved by providing
them with more data.
 The adoption of AI can be disruptive and
may require significant changes to processes
and workflows, which can be difficult for
some organizations and individuals to
accept.
 There is a shortage of professionals with the
necessary skills and expertise to develop
and deploy AI systems, which can make it
difficult for organizations to implement AI
projects.

38. 02
Bias in Data and
fairness
 AI systems can reflect the biases of their
creators, and this can lead to unfair or
discriminatory outcomes.
 AI algorithms are only as good as the data they
are trained on, and if the data is biased, the AI
will be biased as well and this can lead to unfair
and discriminatory outcomes.
 AI systems are only as good as the data they are
trained on, and if the data is biased, the AI
system will also be biased. This can lead to
unfair or biased outcomes, particularly in areas
such as hiring and lending.
 AI systems are only as good as the data they are
trained on, and if the data is biased, the AI
system may also be biased. This can lead to
discriminatory outcomes and perpetuate
existing inequalities.

39. 03
Job displacement and
Economic disruption
 AI systems become more advanced, there
are concerns that they could replace human
workers in a wide range of jobs, leading to
widespread unemployment.
 AI has the potential to disrupt labor markets
and create economic disparities, which may
require policy interventions to address.
 There is a shortage of skilled professionals
with expertise in AI, which can make it
difficult for organizations to implement and
utilize AI effectively.

40. 04
Ethical considerations
 As AI systems become more sophisticated, there are
concerns about their potential impact on society and
the ethical implications of their use.
 AI raises a number of ethical concerns, including
issues related to privacy, autonomy, and
accountability. These concerns need to be carefully
considered and addressed in order to ensure that AI
is used ethically and responsibly.
 AI raises a number of ethical concerns, such as the
potential for AI systems to be used for malicious
purposes, the potential for job displacement, and the
potential for AI to be used to make decisions that
affect people's lives in significant ways.
 AI raises a number of ethical concerns, including
issues related to privacy, accountability, and the
potential for AI to be used for malicious purposes.

41. 05
Security and Lack of
transparency
 AI systems can be vulnerable to hacking and
other forms of cyber-attack, which can pose a
threat to sensitive data and systems.
 Some AI systems, particularly those using deep
learning techniques, can be difficult to
understand and explain, which can make it
difficult to identify and correct biases or errors.
 Many AI systems operate using complex
algorithms that are difficult for humans to
understand, making it difficult to explain how
the system arrived at a particular decision or
prediction.
 AI systems can be vulnerable to hacking and
other cyber threats, which can have serious
consequences.

42. Thank You
Thank you very much for
the opportunity to take
part in the Conference!

43. A
Q & A
“The important thing is
not to stop questioning.”
- Albert Einstein
&
Q