This manuscript addresses the wide application areas of machine learning in an industrial context.

1. Machine Learning : A Pioneer for Modern Industry
Dr. Varun Kumar
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2. Outlines
1 Introduction to Learning
2 Motivation:
3 Scope of Machine Learning:
4 Basics of Industrial Requirement
5 ML as Per Industrial Perspective
6 References
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3. Key Agenda
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4. Introduction to Learning:
Key Features of Learning:
1 Learning is a slow and steady process. → Human
2 Learning is a feedback based approach.
3 It is a process for acquiring more information related to the given
problem.
4 Learning provides a methodology for solving any complex challenges.
5 Any problem can be solved quickly, when the probability of learning is
very high.
6 Fast learning is the current demand in global scenario for solving the
complex problem.
7 Lack of fast learning ability in current scenario provides a new
research direction, ie Machine Learning.
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5. Learning Intuition
Basic approach
1 Conventional Approach:
Requirement of proper algorithm (a set of instruction) for solving a
problem. Ex- Sorting numbers
2 Non-conventional Approach:
All real world problem cannot be solved mathematically or through
algorithmic process. Ex- To check a spam emails from legitimate
emails.
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6. Learning as per machine perspective:
1 There are many applications for which we do not have an algorithm
but do have example data.
2 Machine learning is not just a database problem; it is also a part of
artificial intelligence.
3 If the system can learn and adapt to such changes, the system
designer need not foresee and provide solutions for all possible
situations.
4 Machine learning is programming computers to optimize a
performance criterion using example data or past experience.
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7. Introduction to classical learning methodology :
1. Classical approach is based on the statistical analysis.
2. Characteristic of dependent variable is modeled into a well defined
mathematical form. Ex-
y = x + n ⇒ Received signal for guided media
y = hx + n ⇒ Received signal for unguided media
Note : h = f (v, f , d, g(θ)....)
v → Velocity
f → Operating carrier frequency
d → Physical separation between Tx and Rx .
g(θ) → Nature of the wireless media, where θ → (θ1, θ2, ....)
Many more
3. Dependent parameter is considered as a random variable.
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8. Challenges with classical learning model:
⇒ Real world problems can not follow the well defined mathematical
relation at every instant.
⇒ Real world problems are more fuzzy in nature.
⇒ Due to expansion of industrialization with exponential rate globally.
There are lots of disjoint entities that affects the end product yield.
Modern approach
⇒ Now a days, every event (parametric relation) is stored in the form of
data.
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9. Motivation:
Motivation:
1 Can computer take decisions (even smart smarter ones) just like
humans ?
2 Can computer help human in doing their tasks of daily living ?
3 Can we build a smart eco-system where users get feedback and
systems can update their actions ?
4 Can we develop technology to learn from human behavior ?
5 Repetitive task modeling
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10. Scope of Machine Learning
Scope of Machine Learning
1 5G wireless communication
2 Artificial Intelligence
3 E-commerce
4 Industrial 4.0
5 Internet of Thing
6 Health Care Support System
7 Modern Agriculture
8 Satellite Communication
9 DNA Synthesis and Related Research
Many more
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11. Basics for Industrial expectation
⇒ Key source of employment generator
⇒ Cause of socioeconomic upliftment
Quality of successful industrialization
1 Mass production capability
2 Technology driven network
3 Large scale expansion capability
4 Scientific temperament
5 Better future prediction
.
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12. Example
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13. 1 ML in supermarket chain and e-commerce
Ex- A supermarket chain
⇒ For successful operation and better growth
→ Need a advance learning based modern methodology
Important supermarket chain
Future group (Big-Bazar)
D-Mart
V-Mart
Key feature and components of supermarket chain:
1 Hundreds of stores all over a country
2 Selling thousands of goods
3 Millions of customers
4 Details of each transaction: date, customer identification code, goods
bought and their amount, total money spent
5 Consumption of gigabytes or more data every day
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14. Continued–
Challenges of Supermarket Chain:
1 To increase the selling product
2 To find out the potential customer
3 To follow adaptive pattern of marketing, which changes in time and
by geographic location
Many more
Usage of Machine Learning:
1 Stored data (details of transaction): Need a rigorous analysis and
turned into information.
2 This information helps for short and long term prediction.
Note: Behavioral pattern of selling a product has not a fixed algorithm
and does not follow any mathematical expression.
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15. 2 Tele-communication:
Tele-communication:
Call patterns are analyzed for network optimization and maximizing the
quality of service.
Key Wireless Resources:
1 Power
2 Time
3 Frequency
4 Space (Number of antenna element across Tx /Rx )
5 Code
Challenges of Tele-communication
Reduction of recurring cost (fuel cost + maintenance)
Improvisation of quality of service at low power and less bandwidth.
QoS → high data rate, minimum latency, better link reliability
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16. 3 Finance
Wireless Resources Allocation Using ML: Why ??
Network Densfication
Availability of superior computational resource
Prediction of active users in a geographical bound
Many more
Finance:
Financial institution (Bank) has a strategically importance in any country.
Business Model of Financial Institution:
Give & Take
Output > Input → Profit & Output < Input → Loss
Aim of Financial Institution:
Maximizing the profit: Saving account (4%) & On loan (14-18%)
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17. Continued–
Minimizing the loss:
Technology driven network
Reduction of operational expenditure
Earlier prediction of defaulter customer
Developing a mechanism for preventing non-performing assets (NPA)
Finding a potential robust customer using ML
Source of income.
Current debt on customer.
Banks analyze customer’s past data (repay history of previous loan).
Bank also analyzes the loan amount.
To build models to use in credit applications.
Predict the chance for willful defaulter.
Many more
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18. 4 ML in manufacturing sector
Manufacturing:
Industrial manufacturing plays a vital role in country’s economy.
Aim of industrial manufacturing:
To increase the production yield.
Adopting the technology driven techniques, where manual method
slow down the production speed.
Automatic fault detection and correction mechanism.
Minimize the production loss.
Develop proper supply chain.
ML in industrial manufacturing:
Learning models are used for optimization, control, and troubleshooting.
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19. 5 Empowerment of health infrastructure by ML
Medical
Hospital, doctor and medical equipment collectively empower our health
infrastructure.
ML for medical diagnosis:
A simple disease can have multiple reasons. Ex-
Fever
Sex → male/female
Age
Frequency of occurrence
Intake of food supplement
Past history of some disease related to the respective patient, etc
Based on the above input, learning programs helps in medical diagnosis.
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20. 6 ML:
Science:
Large amounts of data can only be analyzed fast enough by computers in
the field of
Physics
Astronomy
Biology and many more
World wide web :
It is constantly growing, and searching for relevant information cannot be
done manually.
⇒ Extensive usage of AI in search engine, like Google, Bing.
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21. Conclusion
Conclusion
1 ML is in toddler stage, but rigorous research on ML can paradigm
shift of current level of learning ability.
2 ML can uplift the social and economic standard of human being.
3 It can reduce the job opportunity in some sector, but it also produce
the new possibility.
4 Extended form of ML is called as AI.
5 Extensive usage of AI may break the fabric of human machine relation.
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22. References
E. Alpaydin, Introduction to machine learning. MIT press, 2020.
J. Grus, Data science from scratch: first principles with python. O’Reilly Media,
2019.
T. M. Mitchell, The discipline of machine learning. Carnegie Mellon University,
School of Computer Science, Machine Learning , 2006, vol. 9.
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