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Webinar on AI in IoT applications KCG Connect Alumni Digital Series by Rajkumar

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Webinar on AI in IoT applications KCG Connect Alumni Digital Series by Rajkumar

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The Artificial Intelligence in IoT Applications. Take your first step towards a bright future with our renowned alumnus,
Prof R. Raj Kumar on AI for IoT Applications.
He is an award wining author of the book, ‘India 2030’.
To get access to the webinar kindly contact your respective department heads.
Looking forward to having you on the webinar.
.
.
.
#KCGCollege #KCGStudentlife #KCGConnect #Education #EmergingTechnologies #ArtificialIntelligence #IoT #MachineLearning #BlockChain #ElectricVehicle #QuantumTechnology #CAD

The Artificial Intelligence in IoT Applications. Take your first step towards a bright future with our renowned alumnus,
Prof R. Raj Kumar on AI for IoT Applications.
He is an award wining author of the book, ‘India 2030’.
To get access to the webinar kindly contact your respective department heads.
Looking forward to having you on the webinar.
.
.
.
#KCGCollege #KCGStudentlife #KCGConnect #Education #EmergingTechnologies #ArtificialIntelligence #IoT #MachineLearning #BlockChain #ElectricVehicle #QuantumTechnology #CAD

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Webinar on AI in IoT applications KCG Connect Alumni Digital Series by Rajkumar

  1. 1. Time : 6.30 pm – 8.30 pm AI in IOT Applications KCG Connect Alumni Digital Series Date : 24.04.2020 R. Rajkumar AP | CSE | SRM IST KCG Alumni
  2. 2. R. Rajkumar AP | CSE | SRM KCG Connect Alumni Digital Series AI in IOT Applications
  3. 3. Purpose / Objectives of the Webinar Contents  To get knowledge in AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  4. 4. Imagine a world without Internet.! Without Internet it is really hard to imagine life. We are so much dependent on Internet not only in terms of social media but professionally as well If Internet is shut down for a day then imagine about websites like Google, Zoom, Webex, Amazon, Flipkart and many more.
  5. 5. TURING TEST • Can Machine think ? • A way to test machine intelligence. • Todetermine whether responses come from computer or human
  6. 6. Learning What is Human Intelligence? Understanding of Language FeelingPerceiving Reasoning
  7. 7. 1900 Today
  8. 8. For your thinking
  9. 9. For your thinking Touch Sight Hearing Smell Taste
  10. 10. For your thinking Touch Sight Hearing Smell Taste Pressure, temperature, light touch, vibration, pain and other sensations are all part of the touch sense
  11. 11. For your thinking Touch Sight Hearing Smell Taste A 576-megapixel resolution People without sight may compensate with enhanced hearing, taste, touch and smell
  12. 12. For your thinking Touch Sight Hearing Smell Taste The inner ear is connected to the vestibulocochlear nerve, which carries sound and equilibrium information to the brain.
  13. 13. For your thinking Touch Sight Hearing Smell Taste Humans may be able to smell over 1 trillion scents
  14. 14. For your thinking Touch Sight Hearing Smell Taste Salty, sweet, sour, spicy and bitter. Taste is sensed in the taste buds. Adults have 2,000 to 4,000 taste buds
  15. 15. What is Artificial Intelligence? AI = Machine Intelligence • Artificial Intelligence is the science and engineering of making intelligence machine -John McCarthy • A computer performing tasks that are normally though to require human intelligence • Getting a computer to do in real life what computers do in the movies
  16. 16. KNOWLEDGE IS POWER SIR FRANCIS BACON (1561 - 1626)
  17. 17. KNOWLEDGE in Your HAND
  18. 18. IS KNOWLEDGE STILL POWER? SIR FRANCIS BACON (1561 - 1626)
  19. 19. “Artificial Intelligence would be the ultimate version of Google. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on” - Larry Page CEO, Google, October 2000 “We’ve been building the best AI team and tools for years, and recent breakthroughs will allow us to do even more. We will move from mobile first to an AI first world.” - Larry Page CEO, Alphabet, April 28, 2016
  20. 20. Artificial Intelligence Evolution
  21. 21. “Success in creating AI would be the biggest event in human history. Unfortunately, It might also be the last, unless we learn how to avoid the risks” - Steven Hawking
  22. 22. Thinking Goals ofAI System that think like humans System that think rationally System that act like humans System that act rationally Acting Human This model from Russell and Norving. Rational
  23. 23. Thinking Goals ofAI System that think like humans “Cognitive Science” • Neuron Level • Neuroanatomical Level • Mind Level Acting Human Rational
  24. 24. Thinking Goals ofAI System that think like humans “Cognitive Science” • Neuron Level • Neuroanatomical Level • Mind Level System that act like humans “The Turing Test” • Understand language • Game AI, Control NPCs • Control the body Acting Human Rational
  25. 25. Thinking Goals ofAI System that think like humans “Cognitive Science” • Neuron Level • Neuroanatomical Level • Mind Level System that think rationally “Laws of thought” • Logic • A is X and X are Y then A is Y System that act like humans “The Turing Test” • Understand language • Game AI, Control NPCs • Control the body Acting Human Rational
  26. 26. Thinking Goals ofAI System that think like humans “Cognitive Science” • Neuron Level • Neuroanatomical Level • Mind Level System that think rationally “Laws of thought” • Logic • A is X and X are Y then A is Y System that act like humans “The Turing Test” • Understand language • Game AI, Control NPCs • Control the body System that act rationally “Doing the right thing” • Maximize the goal achievement, given information • Doesn’t necessary involve thinking • It involve solving Acting Human Rational
  27. 27. Thinking Goals ofAI System that think like humans “Cognitive Science” • Neuron Level • Neuroanatomical Level • Mind Level System that think rationally “Laws of thought” • Logic • A is X and X are Y then A is Y System that act like humans “The Turing Test” • Understand language • Game AI, Control NPCs • Control the body System that act rationally “Doing the right thing” • Maximize the goal achievement, given information • Doesn’t necessary involve thinking • It involve solving Acting Human Rational
  28. 28. Understand the data
  29. 29. Mathematical Model / Conditions
  30. 30. Answer is “No”
  31. 31. What’s required for a machine to be Intelligent?
  32. 32. • Artificial Intelligence (AI) : A field of computer science dedicated to the study of computer software making intelligent decisions, reasoning, and problem solving. • Machine Learning (ML) : A field of AI focused on getting machines to act without being programmed to do so. Machines "learn" from patterns they recognize and adjust their behavior accordingly. • Natural Language Processing (NLP) : The ability of computers to understand, or process natural human languages and derive meaning from them. NLP typically involves machine interpretation of text or speech recognition. AI Terms
  33. 33. • Data Mining : The process by which patterns are discovered within large sets of data with the goal of extracting useful information from it. • Deep Learning (DL): A subset of machine learning that uses specialized algorithms to model and understand complex structures and relationships among data and datasets. AI Terms
  34. 34. • Algorithm: Formula that represents a relationship between things. It’s self-contained, step- by-step set of operations that automates a function, like a process, recommendation or analysis. • Neural Network: Computational approach that loosely models how the brain solves problems with layers of inputs and outputs. Rather than being programmed, the networks are trained with several thousand cycles of interaction. AI Terms
  35. 35. AI Terms • Intelligent Agents - Example : Taxi Driver • Performance Measure: Safe, Fast, Legal, Comfortable Trip, Maximize profits • Environment: Roads, other traffic, pedestrians, customers • Actuators: Steering wheel, accelerator, brake, signal, horn • Sensors: Cameras, Sonar, Speedometer, GPS, engine sensor
  36. 36. Uses of AI spreading Rapidly andWidely
  37. 37. AI in Big TechCompanies
  38. 38. Facebook CEO Mark Zuckerberg talks about the company's 10-year road map during @ Facebook’s F8 developers conference in April,2016
  39. 39. • A Sub-Field of AI • Construction and study of systems that can learn from data What is MachineLearning?
  40. 40. • Types of Learning • Supervised Learning: Reliance on algorithm trained by human input, reduce expenditure on manual review for relevance and coding • Unsupervised Learning: High reliance on algorithm for raw data, large expenditure on manual review for review for relevance and coding • Semi-Supervised Learning: Reliance on analytics trained by human input, automated analysis using resulting model • Reinforcement Learning: Algorithm is continually trained by human input, can be automated once maximally accurate What is MachineLearning?
  41. 41. What is MachineLearning? • Evolved from pattern recognition and computational learning theory • Subfield of Artificial Intelligence • Study of Algorithms that iteratively learn form data • Make predictions • Find hidden insights without explicit programming
  42. 42. Steps in MachineLearning
  43. 43. In other words… “Learning is any process by which a system improve performance from experience” “Machine Learning is concerned with computer programs that automatically improve their performance through experience” - Herbert Simon
  44. 44. • Google • TensorFlow • Keras • Facebook • Caffe2 • Amazon • DSSTNE • Microsoft • CNTK Deep Learning Framework by Big TechCompanies Credit - https://twitter.com/fchollet/status/776455778274250752/photo/1
  45. 45. Natural Language Processing(NLP) • What is NLP? • Study of interaction between computer and human languages • A Sub-Field of AI • Aim : Tobuild intelligent computer that can interact with human being like a human being !! NLP = Computer Science + AI + Computational Linguistics Language Language Generation (NLG) Understanding (NLU)
  46. 46. Applications of AI
  47. 47. Top10 Hot AI Technologies The 10 Hottest AI Technologies: 1. Natural Language Generation 2. Speech Recognition 3. Virtual Agents 4. Machine Learning Platforms 5. AI-optimized Hardware 6. Decision Management 7. Deep Learning Platforms 8. Biometrics 9. Robotic Process Automation 10. Text Analytics and NLP 38% of enterprises are already using AI, growing to 62% by 2020
  48. 48. Google Voice Search Google’s Self Driving Car Google Translate Gmail Spam Detection
  49. 49. Facebook – ChatbotArmy
  50. 50. • Machine Learning has a wide spectrum of applications including: • Spam Email Detection • Machine Translation (Language Translation) • Image Search (Similarity) • Clustering (KMeans) : Amazon Recommendations • Classification : Google News • Text Summarization : Google News • Fraud detection : Credit card Providers • Speech Understanding : iPhone with Siri • Face Dectection : Facebook’s Photo tagging ML Applications
  51. 51. Amazon Automated Warehouse WAREHOUSE LOGISTICS Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  52. 52. Amazon Prime Air POSTMAN Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  53. 53. KALMAR AUTOMATED VEHICLES, PORT OF LOS ANGELES, CALIFORNIA SHIPPING LOGISTICS Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  54. 54. BOSTON DYNAMICS WILDCAT POLICEMAN Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  55. 55. KNIGHTSCOPE K5 SECURITY GUARD Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  56. 56. GENERAL ATOMICS MQ-9 REAPER SNIPER Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  57. 57. HADRIAN X BUILDER TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  58. 58. FLIRTEY DRONE PIZZA DELIVERY GUY Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  59. 59. PIZZA DELIVERY GUY Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  60. 60. moley robotic CHIEF Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  61. 61. HADRIAN X DRIVER Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  62. 62. LONG-HAUL TRUCKING Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  63. 63. MUSICIAN Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  64. 64. Magnum Autonomous Tractor FARMER Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  65. 65. ROBEAR Care Taker Original from TEDx Manchester: AI & The Future of Work (2017), Volker Hirsch
  66. 66. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  67. 67. Weka Installation Kindly download the WEKA tool and Install in your laptop/ desktop website : www.cs.waikato.ac.nz › weka
  68. 68. SELFIE TIME..!! GET REFRESH BREAK FOR 2 MINS
  69. 69. Shall we continue our learning?
  70. 70. Fact of Learning
  71. 71. WEKA
  72. 72. Content W h a t is WEKA? D a t a set in WEKA T h e Explorer: Preprocess data Classification Clustering Association Rules Attribute Selection D a t a Visualization 8 2
  73. 73. What is WEKA? Waikato Environment for Knowledge Analysis  I t ’ s a data mining/machine learning tool developed by Department of Computer Science, University of Waikato, New Zealand.  W e k a is a collection of machine learning algorithms for data mining tasks.  W e k a is open source software issued under the GNU 8 3
  74. 74. Download and Install WEKA 8 4
  75. 75. Main Features  4 9 data preprocessing tools  7 6 classification/regression algorithms  8 clustering algorithms  3 algorithms for finding association rules  1 5 attribute/subset evaluators + 10 search algorithms for feature selection 8 5
  76. 76. Main GUI • Three graphical user interfaces • “The Explorer” (exploratory data analysis) • “The Experimenter” (experimental environment) • “The KnowledgeFlow” (new process model inspired interface) • Simple CLI (Command prompt) • Offers some functionality not available via the GUI 8 6 01/07/13
  77. 77. Datasets in Weka Each entry in a dataset is an instance of the java class: weka.core.Instance Each instance consists of a number of attributes Nominal: one of a predefined list of values e.g. red, green, blue Numeric: A real or integer number String: Enclosed in “double quotes” Date Relational
  78. 78. ARFF File (Attribute – Relation File Format) @relation weather @attribute outlook { sunny, overcast, rainy } @attribute temperature numeric @attribute humidity numeric @attribute windy { TRUE, FALSE } @attribute play { yes, no } @data sunny, 85, 85, FALSE, no sunny, 80, 90, TRUE, no overcast, 83, 86, FALSE, yes rainy, 70, 96, FALSE, yes rainy, 68, 80, FALSE, yes rainy, 65, 70, TRUE, no overcast, 64, 65, TRUE, yes sunny, 72, 95, FALSE, no sunny, 69, 70, FALSE, yes rainy, 75, 80, FALSE, yes sunny, 75, 70, TRUE, yes overcast, 72, 90, TRUE, yes overcast, 81, 75, FALSE, yes rainy, 71, 91, TRUE, no
  79. 79. WEKA: Explorer Pre-process: Choose and modify the data being acted on. Classify: Train and test learning schemes that classify or perform regression. Cluster: Learn clusters for the data. Associate: Learn association rules for the data. Select attributes: Select the most relevant attributes in the data. Visualize: View an interactive 2D plot of the data.
  80. 80. Explorer: pre-processing the data 90 Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary Data can also be read from a URL or from an SQL database (using JDBC) Pre-processing tools in WEKA are called “filters” WEKA contains filters for: Discretization, normalization, resampling, attribute selection, transforming and combining attributes, …
  81. 81. @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_p resent 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_ present WEKA only deals with “flat” files
  82. 82. @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_p resent 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_ present WEKA only deals with “flat” files
  83. 83. 15 University of Waikato 01/07/13 load filter analyze
  84. 84. University of Waikato
  85. 85. University of Waikato
  86. 86. University of Waikato
  87. 87. University of Waikato
  88. 88. University of Waikato
  89. 89. University of Waikato
  90. 90. University of Waikato
  91. 91. University of Waikato
  92. 92. University of Waikato
  93. 93. University of Waikato
  94. 94. University of Waikato
  95. 95. University of Waikato
  96. 96. University of Waikato
  97. 97. University of Waikato
  98. 98. University of Waikato
  99. 99. University of Waikato
  100. 100. Explorer: building “classifiers” Classifiers in WEKA are models for predicting nominal or numeric quantities Implemented learning schemes include: Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, …
  101. 101. age income student credit_rating buys_computer <=30 high no fair no <=30 high no excellent no 31…40 high no fair yes >40 medium no fair yes >40 low yes fair yes >40 low yes excellent no 31…40 low yes excellent yes <=30 medium no fair no <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent yes 31…40 medium no excellent yes 31…40 high yes fair yes >40 medium no excellent no 112 Decision Tree Induction: Training Dataset
  102. 102. age? overcast student? credit rating? <=30 >40 no yes yes yes 31..40 113 fairexcellentyesno Output: A Decision Tree for “buys_computer”
  103. 103.  B a s i c algorithm (a greedy algorithm)  T r e e is constructed in a top-down recursive divide- and-conquer manner  A t start, all the training examples are at the root Attributes are categorical (if continuous-valued, they are discretized in advance) Examples are partitioned recursively based on selected attributes  Te s t attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain) 114 Algorithm for Decision Tree Induction
  104. 104. University of Waikato
  105. 105. University of Waikato
  106. 106. University of Waikato
  107. 107. University of Waikato
  108. 108. University of Waikato
  109. 109. University of Waikato
  110. 110. University of Waikato
  111. 111. University of Waikato
  112. 112. University of Waikato123 01/07/13
  113. 113. University of Waikato124 01/07/13
  114. 114. University of Waikato125 01/07/13
  115. 115. University of Waikato126 01/07/13
  116. 116. University of Waikato127 01/07/13
  117. 117. University of Waikato128 01/07/13
  118. 118. University of Waikato129 01/07/13
  119. 119. 01/07/1 3 63 Explorer: clustering data WEKA contains “clusterers” for finding groups of similar instances in a dataset Implemented schemes are: k-Means, EM, Cobweb, X-means, FarthestFirst Clusters can be visualized and compared to “true” clusters (if given) Evaluation based on loglikelihood if clustering scheme produces a probability distribution
  120. 120. 65  G i v e n k, the k-means algorithm is implemented in four steps: Partition objects into k nonempty subsets  Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster)  Assign each object to the cluster with the nearest seed point  G o back to Step 2, stop when no more new assignment The K-Means Clustering Method
  121. 121. 66 Explorer: finding associations  W E K A contains an implementation of the Apriori algorithm for learning association rules  Wo r k s only with discrete data  C a n identify statistical dependencies between groups of attributes:  m i l k , butter  bread, eggs (with confidence 0.9 and support 2000) Apriori can compute all rules that have a given minimum support and exceed a given confidence
  122. 122. University of Waikato133 01/07/13
  123. 123. University of Waikato134 01/07/13
  124. 124. University of Waikato135 01/07/13
  125. 125. University of Waikato136 01/07/13
  126. 126. University of Waikato137 01/07/13
  127. 127. 75 Explorer: attribute selection Panel that can be used to investigate which (subsets of) attributes are the most predictive ones Attribute selection methods contain two parts: A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking An evaluation method: correlation-based, wrapper, information gain, chi-squared, … Very flexible: WEKA allows (almost) arbitrary combinations of these two
  128. 128. University of Waikato
  129. 129. University of Waikato140 01/07/13
  130. 130. University of Waikato141 01/07/13
  131. 131. University of Waikato142 01/07/13
  132. 132. University of Waikato143 01/07/13
  133. 133. University of Waikato144 01/07/13
  134. 134. University of Waikato145 01/07/13
  135. 135. University of Waikato146 01/07/13
  136. 136. 85 Explorer: data visualization Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem  W E K A can visualize single attributes (1-d) and pairs of attributes (2-d)  T o do: rotating 3-d visualizations (Xgobi-style) Color-coded class values “Jitter” option to deal with nominal attributes (and to detect“hidden” data points) “Zoom-in” function
  137. 137. Data visualization
  138. 138. University of Waikato149 01/07/13
  139. 139. University of Waikato150 01/07/13
  140. 140. University of Waikato151 01/07/13
  141. 141. University of Waikato152 01/07/13
  142. 142. University of Waikato153 01/07/13
  143. 143. University of Waikato154 01/07/13
  144. 144. University of Waikato155 01/07/13
  145. 145. University of Waikato156 01/07/13
  146. 146. University of Waikato157 01/07/13
  147. 147. University of Waikato158 01/07/13
  148. 148. References and Resources References:  W E K A website: http://www.cs.waikato.ac.nz/~ml/weka/index.html  W E K A Tutorial:  Machine Learning with WEKA: A presentation demonstrating all graphical user interfaces (GUI) in Weka.  A presentation which explains how to use Weka for exploratory data mining.  W E K A Data Mining Book:  I a n H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)  W E K A Wiki: http://weka.sourceforge.net/wiki/index.php/Main_Page Others:  J ia we i Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd ed.
  149. 149. GET REFRESH BREAK FOR 2 MINS
  150. 150. Shall we continue our learning?
  151. 151. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  152. 152. “anytime, any place, anything in connectivity.” IOT
  153. 153. Introduction to IOT Internet of Things (IoT) is the networking of physical objects that contain electronics embedded within their architecture in order to communicate and sense interactions amongst each other or with respect to the external environment. In the upcoming years, IoT-based technology will offer advanced levels of services and practically change the way people lead their daily lives. Advancements in medicine, power, gene therapies, agriculture, smart cities, and smart homes are just a very few of the categorical examples where IoT is strongly established.
  154. 154. Over 9 billion ‘Things’ (physical objects) are currently connected to the Internet, as of now. In the near future, this number is expected to rise to a whopping 20 billion. According to Business Insider, there will be more than 64 billion IoT devices by 2025
  155. 155. Four main components used in IoT: Low-power embedded systems Cloud computing Availability of big data Networking connection
  156. 156. Low-power embedded systems Less battery consumption, high performance are the inverse factors play a significant role during the design of electronic systems. looking for another alternative naming system to represent each physical object.
  157. 157. Low-power embedded systems
  158. 158. Cloud computing Data collected through IoT devices is massive and this data has to be stored on a reliable storage server. This is where cloud computing comes into play. The data is processed and learned, giving more room for us to discover where things like electrical faults/errors are within the system.
  159. 159. Cloud computing
  160. 160. Availability of big data We know that IoT relies heavily on sensors, especially real-time. As these electronic devices spread throughout every field, their usage is going to trigger a massive flux of big data.
  161. 161. Availability of big data
  162. 162. Networking connection In order to communicate, internet connectivity is a must where each physical object is represented by an IP address. However, there are only a limited number of addresses available according to the IP naming.
  163. 163. Networking connection
  164. 164. How it Works? The entire IOT process starts with the devices themselves like smartphones, smartwatches, electronic appliances like TV, Washing Machine which helps you to communicate with the IOT platform.
  165. 165. Fundamental components of an IoT system 1. Sensors/Devices: 2. Connectivity: 3. Data Processing: 4. User Interface:
  166. 166. 1. Sensors/Devices: Sensors or devices are a key component that helps you to collect live data from the surrounding environment. All this data may have various levels of complexities. It could be a simple temperature monitoring sensor, or it may be in the form of the video feed. A device may have various types of sensors which performs multiple tasks apart from sensing. Sensors can be either standalone devices or are embedded in ordinary objects or machines to make them smart.
  167. 167. Types of Sensors Temperature sensors Moisture IoT sensors Light IoT sensors Acoustic & noise IoT sensors Water level IoT sensors Presence & proximity IoT sensors Motion IoT sensors Gyroscope IoT sensors Chemical IoT sensors Image IoT sensors
  168. 168. Temperature sensors This most basic type of sensor finds its application in every kind of IoT use case where keeping track of thermal conditions of air, work environment, machines or other objects is vital. Temperature sensors are particularly useful in manufacturing plants, warehouses, weather reporting systems and agriculture, where soil temperature is monitored to provide balanced and maximised growth.
  169. 169. Moisture IoT sensors While their most obvious and widespread use is in meteorology stations to report and forecast weather, quite surprisingly, moisture and humidity sensors are also being extensively employed in agriculture, environment monitoring, food supply chain, HVAC and health monitoring.
  170. 170. Light IoT sensors Depending on ambient light intensity, smart TVs, mobile phones or computer screens are able to adjust their brightness thanks to light sensors, yet sensors for detecting ambient light are not only commonplace in consumer electronics, but also smart city applications. They are increasingly used for adapting street lights or urban lighting levels for increased economy.
  171. 171. Acoustic & noise IoT sensors Smart acoustic sensors enable to monitor the level of noise in a given environment. Being able to measure and provide data to help noise pollution prevention, acoustic IoT sensor systems are gaining ground in smart city solutions.
  172. 172. Water level IoT sensors To prevent natural disasters, data gathered by the water level monitoring sensors can be used in flood warning systems for analytics and prediction. Apart from environmental protection, this sensor finds its use in a variety of industrial applications to control and optimise manufacturing processes.
  173. 173. Presence & proximity IoT sensors By emitting an electromagnetic radiation beam, this type of sensor is capable of sensing its target object presence and determining the distance that separates both. With their high reliability and long life, it is no wonder that they have quickly made it into so many IoT sectors, such as smart cars, robotics, manufacturing, machines, aviation, and even smart parking solutions.
  174. 174. Motion IoT sensors A smart building system is probably having IoT application for the motion sensor to imagine. While this obviousness holds largely true, apart from helping to monitor private or public spaces from intrusion and burglary, the use of motion sensors use is extending to energy management solutions, smart cameras, automated devices and many others.
  175. 175. Gyroscope IoT sensors The task of this type of sensor is to detect rotation and measure angular velocity, which makes it perfect for navigation systems, robotics, consumer electronics and manufacturing processes involving rotation. For a more day-to-day IoT application, gyroscope sensors are increasingly installed in IoT devices used by athletes for accurate measurements of body movements to analyse and improve their sports performance.
  176. 176. Chemical IoT sensors Sensors to detect chemical compounds (solids, liquids, and gases) are indispensable elements in industrial security systems, environmental protection solutions, and, quite obviously, scientific research. Moreover, they have already gained a foothold in IoT- supported air quality monitoring which helps cities and states fight harmful impact of air and water pollution.
  177. 177. Image IoT sensors Converting optical data to electrical impulses, an image sensor enables the connected object to view the environment around it and act upon it using the intelligence obtained from the analysis of data provided. Image sensors are used whenever there is a need for the smart device to ‘see’ its immediate surroundings, which includes smart vehicles, security systems, military equipment like radars and sonars, medical imaging devices and, of course ____________.
  178. 178. Actuators Linear actuators—these are used to enable motion of object or element in a straight line. Motors—they enable precise rotational movements of device components or whole objects. Relays—this category includes electromagnet-based actuators to operate power switches in lamps, heaters or even smart vehicles. Solenoids—most widely used in home appliances as part of locking or triggering mechanisms, they also act as controllers in IoT-based gas and water leak monitoring systems.
  179. 179. Sensors:
  180. 180. Satellite Sensors Mechanical structure Propulsion subsystem Thermal control subsystem Power supply subsystem Telemetry, tracking and command (TT&C) subsystem Attitude and orbit control subsystem Payload subsystem Antenna subsystem
  181. 181. IOT Connectivity
  182. 182. Connectivity: All the collected data is sent to a cloud infrastructure. The sensors should be connected to the cloud using various mediums of communications. These communication mediums include mobile or satellite networks, Bluetooth, WI-FI, WAN, etc.
  183. 183. Data Processing: Once that data is collected, and it gets to the cloud, the software performs processing on the gathered data. This process can be just checking the temperature, reading on devices like AC or heaters. However, it can sometimes also be very complex like identifying objects, using computer vision on video.
  184. 184. Input For any processing to occur, input must be available. The data collected may be in the form of images, QR codes, text, numbers, or even videos. All these data must be converted into machine readable form before they can be sent for processing.
  185. 185. Process This is the phase where the actual data processing happens. Different techniques like classification, sorting, calculations, etc. are used to get meaningful information from the data received.
  186. 186. Output Although the information is produced in the processing phase itself, it is rendered into human readable format in the output stage. This output maybe in the form of text, graphs, tables, audio, video, etc. Output may also be stored as data for further processing at a later date. This is essential because comparison of current information with historical data can produce useful insights into the overall functioning of a system. ( This comparison can also be used to predict future behaviour ~ ML )
  187. 187. Best Tools for IoT Data Processing Here are some of the best tools and platforms being used for IoT data processing.  Google cloud  IBM Watson IoT  Amazon AWS IoT Core  Microsoft Azure IoT suite  Oracle IoT  Cisco IoT Cloud Connect
  188. 188. IOT Software’s IoT software encompasses a wide range of software and programming languages from general-purpose languages like C++ and Java to embedded-specific choices like Google’s Go language or Parasail.
  189. 189. C & C++ The C programming language has its roots in embedded systems—it even got its start for programming telephone switches. It can be used almost everywhere and many programmers already know it. C++ is the object-oriented version of C, which is a language popular for both the Linux OS and Arduino embedded IoT software systems. These languages were basically written for the hardware systems which makes them so easy to use.
  190. 190. Java While C and C++ are hardware specific, the code in JAVA is more portable. It is more like a write once and read anywhere language, where we can install libraries, invests time in writing codes.
  191. 191. Python There has been a recent surge in the number of python users and has now become one of the best languages in Web development. Its use is slowly spreading to the embedded control and IoT world—specially the Raspberry Pi processor. Python is an interpreted language, which is, easy to read, quick to learn and quick to write. Also, it’s a powerhouse for serving data-heavy applications.
  192. 192. IOT User Interface
  193. 193. User Interface: The information needs to be available to the end-user in some way which can be achieved by triggering alarms on their phones or sending them notification through email or text message. The user sometimes might need an interface which actively checks their IOT system. For example, the user has a camera installed in his home. He wants to access video recording and all the feeds with the help of a web server.
  194. 194. User Interface:
  195. 195. User Interface:
  196. 196. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  197. 197. Choosing the IOT Platform Choosing an IoT platform is a pre-requisite for beginning the development of an end-to-end IoT solution.
  198. 198. Arduino
  199. 199. Arduino Arduino is a microcontroller board that is used for dedicated applications; for example: Actuating small devices like motors, sensors, and lights. The Arduino UNO runs on an 8-bit ATmega328 chip at only 20 MHz. www.arduino.cc
  200. 200. Raspberry Pi
  201. 201. Raspberry Pi Raspberry Pi has a microcontroller, HDMI ports, and RAM. Which means that; with basic coding knowledge. we can configure an OS on Raspberry Pi and use it as a media streaming device, running a web server, or VPNs. Raspberry Pi runs on an ARM 11 CPU with 700 MHz www.raspberrypi.org
  202. 202. Comparison Pi comes complete with ports, like USB, RJ45, HDMI and an SD card reader. But Arduino depends more on external interfaces to provide the necessary connections. Pi is essentially a mini computer so it is more expensive than Arduino
  203. 203. Arduino Sample project
  204. 204. Raspberry Pi
  205. 205. Raspberry Pi Sample project
  206. 206. Is it possible to work together? For a smart home applications we can choose Raspberry PI to be a central server, in charge of communication, data collection and storage from the Arduino, dealing with massive data workload (such as media processing), and handling data from mobile apps to make it more convenient to control applications. Raspberry Pi can work with Arduino Ethernet and Zigbee on data transmission.
  207. 207. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  208. 208. Endless Possibilities of combining AI with IOT
  209. 209. Dataset http://www.timeseriesclassification.com/dataset.php
  210. 210. Data set
  211. 211. a. Smart Home The estimated amount of funding for Smart Home startups exceeds $2.5bn and is ever growing. The list of startups includes prominent startup company names such as AlertMe or Nest as well as a number of multinational corporations like Philips, Haier, or Belkin etc.
  212. 212. b. Wearable devices It make our life easier and healthier
  213. 213. c. Smart City IoT solutions offered in the Smart City area solve various city-related problems comprising of traffic, reduce air and noise pollution and help make cities safer.
  214. 214. d. Smart Grids Electricity suppliers in an automated fashion in order to improve the efficiency, economics, and reliability of electricity distribution. 41,000 monthly Google searches is a testament to this concept’s popularity.
  215. 215. e. Industrial Internet of Things One way to think of the Industrial Internet is, as connecting machines and devices in industries such as power generation, oil, gas, and healthcare.
  216. 216. f. Connected Car Connected car technology is a vast and an extensive network of multiple sensors, antennas, embedded software, and technologies that assist in communication to navigate in our complex world.
  217. 217. g. Connected Health (Digital Health/Tele- health/Telemedicine) IoT has various applications in healthcare, which are from remote monitoring equipment to advance & smart sensors to equipment integration. It has the potential to improve how physicians deliver care and also keep patients safe and healthy
  218. 218. h. Smart Retail Retailers have started adopting IoT solutions and using IoT embedded systems across a number of applications that improve store operations such as increasing purchases, reducing theft, enabling inventory management, and enhancing the consumer’s shopping experience.
  219. 219. i. Smart Supply Chain With an IoT enabled system, factory equipment that contains embedded sensors communicate data about different parameters such as pressure, temperature, and utilization of the machine.
  220. 220. Outcomes we learned how IoT works and an entire IOT system functions. Also, we discussed some real-life examples where we can use AI for IoT. We will be learning more about IOT and AI in detail by start doing projects.
  221. 221. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  222. 222. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  223. 223. Purpose / Objectives of the Webinar Contents  To get knowledge about AI  To get knowledge in WEKA tool  To get knowledge in IOT  To know about Arduino & Raspberry Pi  To know the possibilities of combining AI with IOT Outcomes  To plan for an IOT project  To plan for an AI project  To plan for an AI + IOT project
  224. 224. My research works on IOT and AI Current Projects: 1. Got fund Rs. 9.98 L for Mixed Reality application in Medical Education at SRM Medical College hospital and Research Centre, Kattankulathur. 2. Got fund Rs. 3.14 L for IOT Game simulator for paralysed patients by Department of Science and Technology through SIIC. 3. Executive Member in SRM IOT Centre of Excellence. 4. Core committee member of Industry Institution Summit, IET India. Completed Projects 1. AR – IOT – Smart Home Automation (Patent Published) 2. IOT Speed Breaker for Indian Roads (Patent Published) 3. IOT Dental Chair with Machine Learning (Patent Applied) 4. Smart Horn System (Patent Applied) 5. Keypod (Patent Granted | KCG )
  225. 225. Publications in AI and IOT International Journal Publications ( Scopus / SCI ) R. Rajkumar, V. Ganapathy, BIO-INSPIRING LEARNING STYLE CHATBOT INVENTORY USING BRAIN COMPUTING INTERFACE TO INCREASE THE EFFICIENCY OF E-LEARNING, IEEE – ACCESS. ACCEPTED AT MARCH 20, 2020, ID ACCESS- 2020-13266. R. Rajkumar, V. Ganapathy, CUSTOMIZED SPORTS E-LEARNING PLATFORM: FOR MAKING TEACHING AND LEARNING MOBILE BASED APPLICATION, International Journal of Pure and Applied Mathematics Volume 119 No. 16 2018, 3635- 3643. R. Rajkumar, V. Ganapathy, BRAIN COMPUTER INTERFACE TO IDENTIFY THE STATE OF MIND OF THE LEARNERS, International Journal of Pure and Applied Mathematics Volume 119 No. 16 2018, 3645-3654. R. Rajkumar, V. Ganapathy, DETECTION OF PANIC AND RECOVERY FROM PANIC USING BRAIN COMPUTER INTERFACE, International Journal of Pure and Applied Mathematics Volume 119 No. 16 2018, 3655-3662. R. Rajkumar, V. Ganapathy, VIRTUAL REALITY MULTIPLE QUESTIONNAIRES EXAMINATION PLATFORM, Dubai International education Conclave, Curtin University, Dubai, 2018. R. Rajkumar, V. Ganapathy, BIO INSPIRED BLOOD GROUP PREDICTION, International Journal of Control Theory and Applications. Vol 2. 13. 2017. R. Rajkumar, N. Vivekananthamoorthy, DETERMINANT FACTORS ON STUDENT EMPOWERMENT AND ROLE OF SOCIAL MEDIA AND EWOM COMMUNICATION: MULTIVARIATE ANALYSIS ON LINKEDIN USAGE. Indian Journal of Science and Technology, Vol 9(25), DOI: 10.17485/ijst/2016/v9i25/95318, July 2016 R. Rajkumar, N. Vivekananthamoorthy, THE ROLE OF SOCIAL NETWORKING SITES AND EWOM COMMUNICATION IN ENHANCING STUDENT ENGAGEMENT IN CURRENT LEARNING SCENARIOS, 2015. R. Rajkumar, M. Karthikeyan, N. Vivekantha Moorthy, COGNITIVE SEARCH E-LEARNING FRAMEWORK: AN EFFECTIVE ASSESSMENT BASED LEARNING SYSTEM. International Journal of Computer Applications (0975 – 8887), 2015.
  226. 226. Thank you..! Feedback & Questions? R. Rajkumar rajkumar03r@gmail.com +91 9894808403. https://www.linkedin.com/in/raj-kumar-1251a553/

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