MachineIntelligence powerpoint presentation
- 1. lakshmana@teloxis.com Use and Implementation of Computational Intelligence S. Lakshmanaraj lakshmana@teloxis.com 9225518035
- 2. lakshmana@teloxis.com Use and Implementation of Computational Intelligence 3/2/2024 S. Lakshmanaraj 2 • Agenda • Components of Computation Intelligence • Computation Intelligence-Data Analytics Maturity Path • Example Applications of Computation Intelligence • IoT • Healthcare • eCommerce • Finance • Cyber Security • Education • …and so on • Architecture to Implement • Business Component Architecture of Computation Intelligence • Connectivity Architecture of Computation Intelligence • Data Horizons • Implementation Approach • 9 Steps Approach
- 3. lakshmana@teloxis.com Computational Intelligence 3/2/2024 S. Lakshmanaraj 3 • Fuzzy Logics • Approximate reasoning and Decision making • Neural Networks • Data analysis, Classification, Associative memory, Clustering generation of patterns and Control of patterns • Evolutionary Computation • Natural evolution to bring up new artificial evolutionary methodologies • Learning Theory • Process of bringing together behaviorism, cognitivism, constructivism along with emotional and environmental effects • Probabilistic Methods • Randomness to predict the problem and prescribe the solution combining mathematical relations and or above methods STEP 1 Data Informatio n Knowledg e Units Collection s Relations Patterns Skill Applications Dimension STEP 2 STEP 3 STEP 4 STEP 5 Connectedness Understanding Wisdom Principles Decisions STEP 6
- 4. lakshmana@teloxis.com High Level Machine Intelligence - Data Analytics Maturity Path 3/2/2024 S. Lakshmanaraj 4 STEP 1 Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Feeling and Thinking What Happened? Why Did It Happen? What Will Happen? How Can We Make It Happen? Association Analytics STEP 2 STEP 3 STEP 4 STEP 5 Value Difficulty
- 5. lakshmana@teloxis.com Where Can be Used for IoT? 3/2/2024 S. Lakshmanaraj 5 • Information – Diagnostic Analytics • Moving Speed Detections as well as oscillation frequencies • Removal of Data noise and Self Correctness • Growth/Decline rate – Support Cases, Manufacturing Defects rate, Devices Wear & Tear Rate, Financial Growth • Knowledge – Activation Functions for AI/ML • Preventative Maintenance Schedule - modelling by sound and temperature in motors of fan, washing machine, fridge etc. • Prescriptive Methods - Auto switch on/off A/C based on temperature, products pair well together and how to price products
- 6. lakshmana@teloxis.com Where Can be Used for Healthcare? 3/2/2024 S. Lakshmanaraj 6 • Information – Diagnostic Analytics • Clinical Document Quality Index • Growth/Decline rate – Support Cases, Recovery rate, Readmission rate, Financial Growth • Knowledge – Activation Functions for AI/ML • Preventative and Corrective actions – Diagnosis data with Patient education materials • Predictive Methods- Number of patients visiting hospitals, Diseases seasonal patterns • Prescribing Methods – Number of resources needed like beds, pills, injections, nurses etc.
- 7. lakshmana@teloxis.com Where Can be Used for e-Commerce? 3/2/2024 S. Lakshmanaraj 7 • Information – Diagnostic Analytics • Optimal Logistics Route planner • Decoration Pattern to connect irregular shapes • Product Grouping to maximize Buyers and to minimize stock • Growth/Decline rate – After sales support cases, Financial Growth • Knowledge – Activation Functions for AI/ML • Predictive Method- Where to invest money, Which products can be retired, Customer segmentations • Prescribing Methods – Price response functions, Supply and Demand generating seasonal patterns
- 8. lakshmana@teloxis.com Where Can be Used for Cyber Security? 3/2/2024 S. Lakshmanaraj 8 • Information – Diagnostic Analytics • Network (network traffic analysis and intrusion detection) • Endpoint (anti-malware) • Application, Users, Process (anti-fraud) • At Rest, At Transit or Historical • Knowledge – Activation Functions for AI/ML • Prediction Methods – Anomalies, Forensic analysis • Prescribing Methods – Encrypted Blockchain
- 9. lakshmana@teloxis.com Where Can be Used for Education? 3/2/2024 S. Lakshmanaraj 9 • Information – Diagnostic Analytics • Digital Library • Questions, Answers • Markings / Categorization as Easy to Difficult from Novice to Expertise • Knowledge – Activation Functions for AI/ML • Prediction Methods – Most wanted materials, Attendance, Productive hours, teaching preferences • Prescribing Methods – Assigning Education Materials to overcome Weak Skills, Auto scheduler
- 10. lakshmana@teloxis.com Where Can be Used …and So on… 3/2/2024 S. Lakshmanaraj 10 Society 1.0 • Hunter-Gatherer Society Society 2.0 • Agrarian Society Society 3.0 • Industrial Society Society 4.0 • Digital Society Society 5.0 •Machine Intelligence to expand human capabilities and address social challenges
- 11. lakshmana@teloxis.com High Level Example of Preventative and Maintenance System 3/2/2024 S. Lakshmanaraj 11 Cloud SQL Server Sensitive Pump Data Predictions/ Other data Trained ML Data Streaming Anomaly Detection Real-time actions Maintenance Shut-off Core Engine AI built-in Machine learning for low-latency analytics Built-In Time-Series Streaming and Analytics Cloud SQL Time- Series Time-series built-in Unparalleled performance and security Most secure with industry leading performance
- 12. lakshmana@teloxis.com High Level Generic Business Components Architecture 3/2/2024 S. Lakshmanaraj 12 DATA SOURCES OBJECTS STORAGE RDBMS DATA WAREHOUSE NoSQL COLLECT (Ingest) COLLECT (Store or Destroy) ORGANIZE (Transform & Manage) FUNCTION AS A SERVICE WORKBENCH DATA PREPERATION BIG DATA AS A SERVICE KNOWLEDGE CATALOG EVENT STREAMING SERVERLESS ETL LOG ANALYSIS IoT DATA EXTERNAL DATA KEY MANAGEMENT AUDITING IDENTITY & ACCESS MANAGEMENT AI RULE ENGINE MACHINE LEARNING ANALYZE & INFUSE (Present & Interact) AUTOMATE PROTECT TRUST MONITORING RULE EDITOR TIME SERIES DATA RELATIONAL DATA UNSTRUCTURED DATA ACTIONABLE DATA MEANINGFUL DATA OBJECTS AT REST X
- 13. lakshmana@teloxis.com High Level Generic Connectivity Architecture 3/2/2024 S. Lakshmanaraj 13
- 14. lakshmana@teloxis.com Data Horizons 3/2/2024 S. Lakshmanaraj 14 Data Availability Data Quality Data Consistency Data Security Data Auditability Reporting, Analytics and Data Science Services Data Lineage Data Standards Data Policies and Procedures Business Metadata Technical Metadata Master Data Management, Metadata Management and Data Governance Reporting Self-service BI Open Data API Data Science Relational Dimensional In-memory Polyglot Data Architecture and Data Technology Structured Data Unstructured Data Semi-structured Data Binary Data Data Integration, Data Services and Data Adapters Internal External Third party Future M&A Data Sources Data Grouping and Indexing Data Sharing Process
- 15. lakshmana@teloxis.com High Level Generic Data Analytics 9 Steps Approach 3/2/2024 S. Lakshmanaraj 15 1. Identify the problem and the stakeholders 2. Identify what data are needed and where those data are located 3. Develop a plan for analysis and a plan for offline or periodic or near-time or real-time data retrievals or access 4. Extract, transform, load the data 5. Check, clean and prepare the data for analysis and automate in minimizing time 6. Analyze and interpret the data 7. Visualize the data 8. Disseminate the new knowledge 9. Implement the knowledge in the organization
- 16. lakshmana@teloxis.com 1st and 2nd Step Data Points for UN SDG Goals 3/2/2024 S. Lakshmanaraj 16
- 17. lakshmana@teloxis.com Data Analytics Initial 1st to 5th Steps - IoT 3/2/2024 S. Lakshmanaraj 17
- 18. lakshmana@teloxis.com Data Analytics Initial 1st to 5th Steps - Healthcare 3/2/2024 S. Lakshmanaraj 18 Clinical Intelligence Data access Data/text search & mining Data/text analysis Access & Collaborative Portal Data Integration Data Access Clinical Data Public data, disease, procedure, provider, treatment plan, lab tests, patient history, etc. Data Acquisition Payer Intelligence & Decision Support Financial Data Code, Operation, Billing, Fee, Coverage, Risk Factor, Competitor, Customer Background, etc. Clinical Decision Support Evidences & Knowledge - Guidelines - Rules Integrated Clinical Data Warehouse (HIW, CG, HCN etc.) Physicians/Admin Patients Analysts and Reviewers Patients Solution Portfolio CLINICAL Informatics RESEARCH Informatics ADMINISTRATIVE Informatics Results, medications Vitals, Allergies DC Summaries Clinical Doc Proteomics, SNPs, Publications Clinical Trials PubMEd ADT, Demographics Provider, Scheduling Diagnosis, CPT, AR, Billing, Claims Health Analytics
- 19. lakshmana@teloxis.com Data Analytics Algorithms Used in Step 6 3/2/2024 S. Lakshmanaraj 19 Supervised Unsupervised Linear Nonlinear Single Combined Easy to Interpret Hard to Interpret Linear Regression Logistic Regression Perceptron Bagging Random Forests Boosting (Bootstrap Aggregating) Decision Trees Rule Learning Naïve K-Nearest Bayes Neighbors Multi-Layer SVM Perceptron Hidden Markov Perceptron K-Means Expectation Self-Organizing Maximization Maps
- 20. lakshmana@teloxis.com Data Analytics Mid 6th and 7th Steps 3/2/2024 S. Lakshmanaraj 20 Marts/Cubes Reports Portal/Kiosks Biz Apps Dashboards EDW Unstructured Big Data Integrated Analytics
- 21. lakshmana@teloxis.com AI/ML Initiatives for Step 7 3/2/2024 S. Lakshmanaraj 21 01 02 03 04 08 09 10 11 05 13 06 14 07 15 12 16 20 17 19 18 Unsupervised learning Reinforcement Learning Supervised learning Dimensionally Reduction Clustering Regression Classification 01. Feature Elicitation 02. Structure Discovery 03. Meaningful compression 04. Big data Visualisation 08. Objects Classification 09. Objectives fulfilment 10. Diagnostics 11. Fraud Detection 19. Skill Acquisition 20. Robot Navigation 05. Recommended Systems 06. Targeted Systems 07. Segmented Systems Psychological models data mining Cognitive science Decision theory Information theory databases Machine Learning neuroscience statistics evolutionary models Control theory 12. Forecasting 13. Predictions 14. Process Optimization 15. New Insights 16. Real-Time Decisions 17. Gamifications 18. Learning Tasks
- 22. lakshmana@teloxis.com Data Analytics Final 8th and 9th Steps 3/2/2024 S. Lakshmanaraj 22 • Disseminating the new knowledge • Write up the findings • Disseminate to the stakeholders • Implementing the new knowledge • Requires participation of stakeholders Targeted Content Compelling Calls to Action Predictive Analytics Precision Marketing Findability Self- Management Tools Collaborative Interactions Behavioral Insights Advanced Personalization
- 23. lakshmana@teloxis.com 3/2/2024 S. Lakshmanaraj 23 Thank you For more information, my Concept AI/ML activation models are published in https://www.ijmttjournal.org/Volume-66/Issue-11/IJMTT-V66I11P502.pdf You can connect me at https://www.linkedin.com/in/lakshmanarajsankaralingam/ Final Thoughts... Any Questions?