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Application of machine learning in industrial applications

The group will present an introduction to machine learning, the basics of machine learning, and applications of machine learning in industry such as product categorization, improving the accuracy of inertial measurement units using supervised machine learning, data mining techniques, and machine learning for medical diagnosis. They will also discuss the future scope of machine learning.

Related topics:
Data Mining Insights•
GROUP D13-3 (INSTRUMENTATION) WE WILL BE PRESENTING THE FOLLOWING: • INTRODUCTION TO MACHINE LEARNING • THE BASICS OF MACHINE LEARNING • APPLICATIONS OF MACHINE LEARNING IN INDUSTRY o PRODUCT CATEGORIZATION o IMPROVING ACCURACY OF INERTIAL MEASUREMENT UNIT USING SUPERVISED MACHINE LEARNING o DATA MINING TECHNIQUES o MACHINE LEARNING FOR MEDICAL DIAGNOSIS • FUTURE SCOPE OF MACHINE LEARNING
MACHINE LEARNING
DEFINITION OF MACHINE LEARNING Ability of a machine to improve its own performance through the use of a software that employs artificial intelligence techniques to mimic the ways by which humans seem to learn such as repetition and experience.
 Helps in building machines exhibiting intelligent behavior.  Apart from artificial intelligence it is also used in administration, commerce and industry.  The most widely known demonstration of this migration is ‘DATA MINING’.
 Makes human-computer interaction easier  Relatively simple to integrate  Will distinguish your products from others  Increase customer satisfaction  Will improve simple-intelligent systems
 Medical diagnosis  Data mining  Bioinformatics  Speech and handwriting recognition  Product categorization  Inertial measurement unit (IMU)  Information retrieval
CONCEPT OF MACHINE LEARNING
What exactly is “Machine Learning”?? Input Output Database + Set of Rules (sensors) (Predictions) BLACKBOX
• Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms. • Computer system (expert system) which is imbued with decision making ability like a human expert. • Two parts: a. Knowledge base (database) b. Inference engine (predicted with certain probability) • It is a learner (like a small baby) which looks at the examples (obstacles), analyses it using stored data which also includes previous experiences, finds its algorithm and predicts the possible solution with highest probability. • It keeps updating itself with every obstacle solved, enhancing its performance every time. • Its algorithms includes different combinations of logic.
Necessity of Human Machine Interfacing • • Number of types of obstacles in real world are huge, hence it has to proceed to a generalize solution using certain set of rules. • Only disadvantage being its high error- making. • And thus human interfacing with these systems becomes necessary.
So How Does These Expert Systems Differ from Humans...??
Need for Product Categorization
Difference in the Scale of Companies
So How Does it Work ...???
Features of Product Categorization • Classification • Filtering • Suggestions / history • User personal information
IMU SYSTEM USING SUPERVISED MACHINE LEARNING
ANGLE MEASUREMENT
Motion Capture Technology
Disadvantages of Motion Capture • Specific hardware and special software increase cost • Camera field of view is necessary • More space required • No calibrations or manipulation while recording data • Sometimes we require more than one camera for accuracy • needs proper lightning conditions
So What Should We Do...??
Inertial Measurement Unit (IMU) system with Machine Learning
Inertial Measurement Unit (IMU) with Machine Learning • IMU is sensor which measures acceleration and angular velocity rate. • The inertial measurement unit using support vector regression method has the advantages of having a small size as well as quite low cost. • As compared to the motion capture system, it provides better positional data analysis.
Enhancement in Accuracy of IMU Kernel trick -Inner product space -Linear analysis Support vector machine -Classification -Regression analysis
Data Mining
What is Data Mining? • Intersection of computer science and statistics • Data mining software is analytical tools for analyzing data. • Data mining is process of finding correlations or patterns among large databases.
What is a 'Pattern'? It is the probability of distribution of similar data. Or in other words its just a relation between the variables. Machine learning and Data Mining: 1. The computer sorts the data based on the algorithm. 2. If there is some drastic change in data then, the algorithm tries to find relation between them and adapts accordingly.
Identifying non-trivial, valid and useful patterns in a given database is known as 'Knowledge Discovery in Databases (KDD)'. Steps: • Understand and define problem. • Extract Data: We should extract data what we need from the Database. • Data Engineering: Deal with missing variables, rescale data, Combine similar attributes. • Algorithm Engineering (This is the ML part): Figure out what algorithm to use or write one.
An Overview of the Knowledge Discovery in Database (KDD) Process
Applications : • DM for Artificial Neural Networks : In most cases a neural network is an adaptive system that changes its structure so Data Mining is used to model complex relationships between inputs and outputs or to find patterns in data. • Instance-based Learning Algorithms for DM : Instance-Based Learning (IBL) is defined as the generalizing of a new instance to be classified from the stored training examples, which is widely used for classification tasks.Here actually the machine learn from the experience.
Me Machine learning for Medical Diagnosis Medical Diagnosis by Machine Learning
• Medical diagnosis: It is a procedure to identify disorder in a person. • Machine learning for medical diagnosis: It means that the computer will identify the symptoms and tell what that particular person is diagnosed with.
SURVEY OF DIAGNOSIS BY ALGORITHMS/ PHYSICIANS
SELECTION OF THE APPROPRIATE MACHINE LEARNING SYSTEM  Good performance  The transparency of diagnostic knowledge.  The ability to explain decisions  The ability of the algorithm to reduce the number of tests necessary to obtain reliable diagnosis.  The ability to appropriately deal with missing data.
MACHINE LEARNING ALGORITHMS  1. STATISTICAL OR PATTERN RECOGNITION.  2. INDUCTIVE LEARNING OF SYMBOLIC RULES.  3. ARTIFICIAL NEURAL NETWORKS.
Medical Imaging: •Medical Imaging is taking photos of body parts (both internal and external) and analyzing them for a disorder. •CCD and GDV are types of image devices which have found great applications in Machine Learning Systems.
CONCLUSION  IN FUTURE, THE STUDY OF MACHINE LEARNING HOLDS EXCITING PROSPECTS WITH CONSTANT INNOVATIONS IN DIVERSE FIELDS.  WITH BETTER ALGORITHMS, WE CAN COMPLETELY BRIDGE THE GAP BETWEEN MEN AND MACHINES.  BECAUSE IT IS A TYPE OF ADAPTIVE LEARNING, IT WILL FIND APPLICATIONS IN ALL POSSIBLE FIELDS.
THANK YOU!