Digital Fluency Notes PDF

DIGITAL FLUENCY NOTES DIGITAL FLUENCY Compulsory for All UG Courses (BA/B.Com/B.Sc/BBA/BSW etc.,) Module I : Emerging Technologies : Overview of Emerging Technologies: i. Artificial Intelligence, Machine Learning, Deep Learning...

DIGITAL FLUENCY NOTES DIGITAL FLUENCY Compulsory for All UG Courses (BA/B.Com/B.Sc/BBA/BSW etc.,) Module I : Emerging Technologies : Overview of Emerging Technologies: i. Artificial Intelligence, Machine Learning, Deep Learning ii. Database Management for Data Science, Big Data Analytics iii. Internet of Things (IoT) and Industrial Internet of Things (IIoT) iv. Cloud Computing and its service models. v. Cyber Security and Types of Cyber Attack Module II : Applications of Emerging Technologies i. Artificial Intelligence ii. Big Data Analytics iii. Internet of Things iv. Cloud Computing v. Cyber Security Module III : Building Essential Skills Beyond Technology : Importance of the following : i. Effective Communication Skills. ii. Creative Problem Solving & Critical Thinking iii. Collaboration and Teamwork Skills iv. Innovation & Design Thinking v. Use of tools in enhancing skills. References : 1. Digital Fluency : S.P.Sajjan & S.B Ramoshi : Ekalavya E-educate : 2021 2. Digital Fluency : Suresh Palarimath : Siddalingeshwara Book Publications, Gulbarga 3. https://futureskillsprime.in 2 NEP - 2020 DIGITAL FLUENCY NOTES Module I : Emerging Technologies - Artificial Intelligence Meaning : Artificial Intelligence is techniques that help machines and computer mimic human behaviour – Gary Brotman Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. Artificial Intelligence is an attempt to build machines that act like humans who can think and act, able to learn use knowledge to solve problems. Artificial intelligence (AI), also known as machine intelligence, is a branch of computer science that focuses on building and managing technology that can learn to autonomously make decisions and carry out actions on behalf of a human being. John McCarthy is known as Father of Artificial Intelligence. Examples: Siri, Alex and other smart assistants Self-driving cars Robo advisors Conversational bots Email span filters Netflix recommendations How AI Works? In general, AI systems work by ingesting large amounts of labeled training data, analysing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. AI focuses on three cognitive Skills: 1. Learning Process 2. Reasoning Process 3. Self-correction Process 3 NEP - 2020 DIGITAL FLUENCY NOTES 1. Learning Process: This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task. 2. Reasoning Process: This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome. 3. Self-correction Process: This aspect of AI programming is designed to continually finetune algorithms and ensure they provide the most accurate results possible. History of Artificial Intelligence Important research that laid the ground work for AI: ▪ In 1931, Goedellayed the foundation of Theoretical Computer Science 1920-30: He published the first universal formal language and showed that math itself is either flawed or allows for unprovable but true statements. ▪ In 1936, Turing reformulated Goedel’s result and Church’s extension thereof. ▪ In 1956, John McCarthy coined the term “Artificial Intelligence” as the topic of the Dartmouth Conference. ▪ In 1957, The General Problem Solver (GPS) demonstrated by Newell, Shaw & Simon. ▪ In 1958, John McCarthy (MIT) invented the LISP language. ▪ In 1959, Arthur Samuel (IBM) writes the first game-playing program, for checkers to achieve sufficient skill to challenge a world champion. ▪ In 1963, Ivan Sutherland’s MIT dissertation on Sketchpad introduced the ideas of interactive graphics into computing. ▪ In 1966, Ross Quillian (PhD dissertation, Carnegic Inst. Of Technology; now CMU) demonstrated semantic nets. ▪ In 1967, Doug Engelbart invernted the mouse at SRI. ▪ In 1968, Marvin Ministry & Seymour paper publish Perceptron’s demonstrating limits of simple neural nets. ▪ In 1972, Prolog developed by Alain Colmerauer. 4 NEP - 2020 DIGITAL FLUENCY NOTES ▪ In mid 80’s, Neural Networks become widely used with the Backpropagation algorithm. ▪ In 1990, Major advances in all areas of AI, with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games etc., ▪ In 1997, Deep Blue beats the World Chess Champion Kasparov. ▪ In 2002, iRobot, founded by researchers at the MIT Artificial Intelligence Lab, introduced Roomba, a vacuum cleaning robot. AI Technology Landscape: 1. Autonomous Systems: Autonomous systems are defined as systems that are able to accomplish a task, achieve a goal, or interact with its surroundings with minimal to no human involvement. It is also essential that these systems be able to predict, plan, and be aware of the world around them. For example, Autonomous Robots, Self-driving vehicles, Drones etc., 5 NEP - 2020 DIGITAL FLUENCY NOTES 2. Machine Learning: Algorithms that can learn from and make predictions on data. There are three types of Machine Learning. Those are 1. Supervised Learning 2. Unsupervised Learning 3. Reinforcement Learning 3. Deep Learning: A high-powered type of machine learning algorithms that uses a cascade of many computing layers. Each layer uses the input from the previous later as input. 4. Pattern Recognition: A branch of machine learning and deep learning which focusses on recognition of patterns in data. 5. Natural Language Processing: Technologies that enable computer systems to interact seamlessly with human languages. 6. Chat Bots: A software robot that interacts with humans online, receiving and sending conversational text with the aim of emulating the way a human communicates. 7. Real Time Emotion Analytics: The application of AI to analyze brain signals, voice and facial expression to detect human emotions. 8. Virtual Companion: Cloud connected, virtual reality-based avatars powered by AI engines that can behave and interact just as a human would. 9. Real Time Universal Translation: The application of natural language processing to enable two humans (with no common language) to understand each other in real-time. 10. Though Controlled Gaming: The application of AI, wearable technology, and brain computing interface technology to enable seamless interaction with social gaming environments in real-time, through avatars without the need for joystick type devices. 6 NEP - 2020 DIGITAL FLUENCY NOTES 11. Next Generation Cloud Robotics: Convergence of AI, big data, cloud and the as-a-service model will enable a cloud based robotic brain that robots can use for high powered intelligent and intuitive collaboration with humans. 12. Autonomous Surgical Robotics: Cloud based AI platforms can help robotic surgeons to perform precise surgeries by learning from large historical surgical data sets. 13. Robotic Personal Assistant: Cloud base AI learns from big data to enable human-like social robots that can perform usefully as personal assistants. 14. Cognitive Cyber Security: Cloud-based AI systems trained on historical cyber threat data, capable of mitigating real-time cyber threats. 15. Neuro-morphic Computing: Future generation computing hardware that mimics the function of the human brain in silicon chips. AI Trends in Healthcare: 1. AI and Machine Learning Offer Better way to Spot Diseases: AI and ML are also growing to offer new and innovative ways to identify disease, diagnose conditions, crowdsource and develop treatment plans, monitor health epidemics, create efficiencies in medical research and clinical trials, and make operations more efficient to handle increasing demand. 2. Robots in Healthcare can Conduct More Varied Tasks: Robots will be able to help doctors examine and treat patients in rural areas through telepresence, transporting medical supplies, disinfecting hospital rooms, helping patients with rehabilitation or with prosthetics, and automating labs and packaging medical devices. 3. Computer and Machine Vision Can Help Give Appropriate Care: There are various ways computers and machine vision are being used in medicine for diagnostics, viewing scans and medical images, surgery, and more. It is helping doctors to know exactly how much blood a woman loses while delivering in order to provide immediate care to reduce the mortality of mothers from post-partum hemorrhaging. 7 NEP - 2020 DIGITAL FLUENCY NOTES 4. Wearable Tech has More to Offer than Just Counting Steps: Wearable fitness technology is not only limited to tell people how many steps they walk each day. It possesses rather more opportunities for healthcare by monitoring heart rhythms, detecting atrial fibrillation and send reports to doctor, monitoring blood pressure and many more. 5. AI-Enabled Genomic can Determine Personalized Treatments: Artificial intelligence and machine learning help analyze a person’s genomic information to determine personalized treatment plans and clinical care. In pharmacology, oncology, infectious diseases, and more, genomic medicine is marking a great impact. 6. 3D Printing helps Doctors Replicate Patient-Specific Organs: 3D printing technology enables prototyping, customization, research, and manufacturing for healthcare. Surgeons can replicate patient-specific organs using the advancements of 3D printing. It helps them prepare for procedures. 7. Digital Twins Determine Possibility for Successful Outcomes: In healthcare, digital twin is a near real-time replica of life-long data record of an individual. It can help doctors in determining the possibilities for a successful outcome of a procedure. It also assists them in making better therapy decisions, and manage chronic diseases. 8. 5G can Support Organization in Transmission of Files: As the healthcare centers are extending their reach in remote or under-served areas through telemedicine, 5G technology will potentially increase, the quality and speed of the network and prove to be imperative for positive outcomes. The technology can better support healthcare organizations in transmission of large imaging files so specialists can review and advise on care. 9. AI Neural Network can Improve Healthcare Biometrics: Scientists are capable of analyzing the atypical risk factors that were too complicated to quantify, using AI neural networks. It helps develop the industry in various ways such as by enabling retinal scans, examining and recording skin colour changes, and many more. Its proficiency in finding patterns will enable the unlocking of new diagnostic methods and discover unknown risk factors. 8 NEP - 2020 DIGITAL FLUENCY NOTES AI in Business ▪ Virtual Agent: Facial recognition software, machine learning, and natural language enable virtual agents to greet you personally, anticipate orders and provide directions. ▪ Shopper’s Profile: Machine learning personalizes promotions to match shoppers profile. In store beacons send offers to their smart phones as they browse through the store. ▪ Identifies Articles: Computer vision with deep learning identifies articles bagged by shoppers; adding data from sensors. AI allows non-stop checkout and automatic payment. ▪ Last-mile Delivery: Autonomous drones using deep learning technology complete last-mile delivery, and are able to handle obstacles or absent recipient. ▪ Complementary Product: Interactive screens and tabletops enabled with computer vision and deep learning can identify articles and recommend complementary products and uses that fit shopper’s lifestyle profile. ▪ Home Delivery: An autonomous shopping cart follows you in the store, and can find its way to your vehicle or to a robot or drone for home delivery. ▪ Optimizes prices in Real Time: Store updates and optimize prices in real time, with machine learning leveraging data on competitor’s prices, weather, and inventory levels to maximize revenues. ▪ Track Inventory: AI enhanced robots continuously track inventory, recognize empty shelves, and replenish them; other robots fill the bags in the warehouse. AI Prediction for Future: 1. AI increasingly becomes a matter of International Politics: In the face of tariffs and export restrictions on goods and services used to create AI imposed by the US Government, China has stepped up its efforts to become self-reliant when it comes to research and development. 9 NEP - 2020 DIGITAL FLUENCY NOTES 2. A more towards transparent AI: Without knowing AI, the adoption of AI – particularly when it involves dealing with human data – is hindered by the “black box problem”. In 2018, IBM unveiled technology developed to improve the traceability of decisions into its AI OpenScale technology 3. AI & Automation drilling deeper into Business: Every business is as whole ready to move ahead with proven initiatives, moving from piloting and soft-launching to global deployment. AI has potential to rule in all industries like Financial, Retail, Manufacturing, Legal as well as support functions like HR hiring and firing, Optimizing supply chain where decisions around logistics. 4. More jobs will be created: Warehouse workers can often been replaced wholesale by automated technology. Retail cashiers can often be replaced by Robots. But when it comes to doctors and lawyers, AI can work alongside human professionals, assisting them with repetitive task 5. AI assistants will become truly useful: In coming years, more of us than ever will use an AI assistant like Alexa, Siri, Google Assistant to arrange our calendars, plan our journey and order a pizza MACHINE LEARNING What is Machine Learning? Machine learning is a specific subset of AI that trains a machine how to learn. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Methods of Machine Learning: Supervised Machine Learning Unsupervised Machine Learning Semi-supervised Machine Learning Reinforcement Machine Learning 10 NEP - 2020 DIGITAL FLUENCY NOTES 1. Supervised Machine Learning: Supervised Learning Algorithms are the ones that involve direct supervision (cue the title) of the operation. In this case, the developer labels sample data corpus and set strict boundaries upon which the algorithm operates. It is a spoon-fed version of machine learning: ▪ You select what kind of information output (samples) to “feed” the algorithm; ▪ What kind of results it is desired (for example “yes/no” or “true/false”) 2. Unsupervised Machine Learning: Unsupervised Learning is one that does not involve direct control of the developer. If the main point of supervised machine learning is that you know the results and need to sort out the data, then in the case of unsupervised machine learning algorithms the desired results are unknown and yet to be defined. Supervised learning uses labeled data exclusively, while unsupervised learning feeds on unlabeled data. 3. Semi Supervised Machine Learning: ▪ Semi-supervised learning algorithms represent a middle ground between supervised and unsupervised algorithms. ▪ In essence, the semi-supervised model combines some aspects of both into a thing of its own. ▪ A semi-supervised machine-learning algorithm uses a limited set of labeled sample data to shape the requirements of the operation (i.e., train itself). 4. Reinforcement Machine Learning: Reinforcement learning represents what is commonly understood as machine learning artificial intelligence. Reinforced ML uses the technique called exploration/exploitation. The mechanics are simple - the action takes place, the consequences are observed, and the next action considers the results of the first action 11 NEP - 2020 DIGITAL FLUENCY NOTES DEEP LEARNING What is Deep Learning? Deep learning is a machine learning method that takes in an input X, and uses it to predict an output of Y. As an example, given the stock prices of the past week as input, my deep learning algorithm will try to predict the stock price of the next day. Given a large dataset of input and output pairs, a deep learning algorithm will try to minimize the difference between its prediction and expected output. By doing this, it tries to learn the association/pattern between given inputs and outputs Deep Learning Example: Let’s say that inputs are images of dogs and cats, and outputs are labels for those images (i.e. is the input picture a dog or a cat). If an input has a label of a dog, but the deep learning algorithm predicts a cat, then my deep learning algorithm will learn that the features of my given image (e.g. sharp teeth, facial features) are going to be associated with a dog. How do Deep Learning Algorithm learns? Deep Learning Algorithms use something called a neural network to find associations between a set of inputs and outputs. A neural network is composed of input, hidden, and output layers — all of which are composed of “nodes”. Input layers take in a numerical representation of data (e.g. images with pixel specs), output layers output predictions, while hidden layers are correlated with most of the computation. 12 NEP - 2020 DIGITAL FLUENCY NOTES Neural Network: Let’s look inside the brain of our AI. Like animals, our estimator AI’s brain has neurons. They are represented by circles. These neurons are inter-connected. The neurons are grouped into three different types of layers: o Input Layer o Hidden Layer(s) o Output Layer The input layer receives input data. In our case, we have four neurons in the input layer: Origin Airport, Destination Airport, Departure Date, and Airline. The input layer passes the inputs to the first hidden layer. The hidden layers perform mathematical computations on our inputs. One of the challenges in creating neural networks is deciding the number of hidden layers, as well as the number of neurons for each layer. The “Deep” in Deep Learning refers to having more than one hidden layer. The output layer returns the output data. In our case, it gives us the price prediction. 13 NEP - 2020 DIGITAL FLUENCY NOTES Convolutional Neural Network: A convolutional neural network (CNN) is a type of neural network that is most often applied to image processing problems. You’ve probably seen them in action anywhere a computer is identifying objects in an image. But you can also use convolutional neural networks in natural language processing projects, too. BIG DATA ANALYTICS What is Data? Data is a collection of numbers, characters or symbols on which operators are performed by a computer, which may be stored and transmitted in the forms of electric signals and recorded on magnetic optical or mechanical media. In simple words, Data is a collection of facts and figures which can be stored in digital format. All the text, numbers, images, audios, videos stored in our phones and computers are some examples of data. What is Big Data? Big Data refers to copious amount of data which are too large to be processed and analysed traditional tools. For examples: 1. The amount of Big Data increases exponentially more than 500 terabytes of data are uploaded to Facebook’s database alone, in a single day. 2. Netflix collects user behaviour data from its more than 10o million customers. Based on the analysis it recommends movies and TV shows which the viewer will love to watch. As a result, the customer is happy because he is getting what he likes without even searching for it. It will result in higher customer retention. Big Data Examples: Credit Card companies collect and store the real-time data of when and where the credit cards are being swiped. Supposed a credit card is used at location A for the first time. Then after 2 hours the same card is being used at location B which is 5000 kms away from location A. Now it is practically impossible for a person to travel 5000 kms within two hours. 14 NEP - 2020 DIGITAL FLUENCY NOTES Big Data can be used in many applications like banking, communication, healthcare, media, advertisement, manufacturing, transportation, retail and business. Classification of Big Data: 1. Structured Data 2. Unstructured Data 3. Semi-structured Data Structured Data: Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and used the most in programming and computer related activities. There two sources of structured data – machines and humans Unstructured Data: Unstructured data is the opposite to structured data. They have no clear format in storage. The rest of data created, about 80% of the total account for unstructured big data. Unstructured data is also classified based on its source, machine-generated and human generated. Machine & Human generated Unstructured Data: Machine generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content Semi-structured Data: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured data at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi- structured data. 15 NEP - 2020 DIGITAL FLUENCY NOTES DATABASE MANAGEMENT SYSTEM What is Database? ▪ Database is any collection of electronic records that can be processed to produce useful information. ▪ The data can be accessed, modified, managed, controlled and organized to perform various data-processing operations. ▪ The data is typically indexed across rows, columns and tables that make workload processing and data querying efficient. Types of Databases: ▪ Hierarchical Database ▪ Network Database ▪ Relational Database ▪ Object-oriented Database ▪ Distributed Database What is DBMS? ▪ Database Management Systems (DBMS) refer to the technology solution used to optimize and manage the storage and retrieval of data from databases. ▪ DBMS offers a systematic approach to manage databases via an interface for users as well as workloads accessing the databases via apps Components of DBMS: ▪ Software: DBMS is primarily a software system that can be considered as a management console or an interface to interact with and manage databases. ▪ Data: DBMS contains operational data, access to database records and metadata as a resource to perform the necessary functionality. ▪ Procedures: While not a part of the DBMS software, procedures can be considered as instructions on using DBMS. ▪ Database languages: These are components of the DBMS used to access, modify, store, and retrieve data items from databases. Types of DBMS languages include Data Definition Language (DDL), Data Manipulation Language (DML), Database Access Language (DAL) and Data Control Language (DCL). 16 NEP - 2020 DIGITAL FLUENCY NOTES ▪ Query processor: As a fundamental component of the DBMS, the query processor acts as an intermediary between users and the DBMS data engine in order to communicate query requests. ▪ Database Manager: Database manager allows a set of commands to perform different DBMS operations that include creating, deleting, backup, restoring, cloning, and other database maintenance tasks. ▪ Database Engine: This is the core software component within the DBMS solution that performs the core functions associated with data storage and retrieval via applications. ▪ Reporting: The report generator extracts useful information from DBMS files and displays it in structured format based on defined specifications Benefits of DBMS: ▪ Data Security: DBMS allows organizations to enforce policies that enable compliance and security. The databases are available for appropriate users according to organizational policies. ▪ Data Sharing: Fast and efficient collaboration between users. ▪ Data Access and Auditing: Controlled access to databases. Logging associated access activities allows organizations to audit for security and compliance. ▪ Data Integration: Instead of operating island of database resources, a single interface is used to manage databases with logical and physical relationships. ▪ Abstraction and Independence: Organizations can change the physical schema of database systems without necessitating changes to the logical schema that govern database relationships. What is MySQL? ▪ A Swedish company called MySQL AB originally developed MySQL in 1994. ▪ The US tech company Sun Microsystems then took full ownership when they bought MySQL AB in 2008. ▪ US tech giant Oracle in 2010 acquired Sun Microsystems itself, and MySQL has been practically owned by Oracle since. ▪ MySQL is an open-source relational database management system (RDBMS) with a client-server model. ▪ RDBMS is a software or service used to create and manage databases based on a relational model 17 NEP - 2020 DIGITAL FLUENCY NOTES What is MongoDB? ▪ MongoDB is an open-source document-based database management tool that stores data in JSON-like formats. ▪ It is a highly scalable, flexible, and distributed NoSQL database. ▪ MongoDB Atlas is a cloud database solution for contemporary applications that is available globally. ▪ MongoDB Cloud is a unified data platform that includes a global cloud database, search, data lake, mobile, and application services INTERNET OF THINGS ▪ IoT is an evolution of mobile, home and embedded applications interconnected to each other. ▪ Using the internet and data analytics, billions of devices connected to each other create an intelligent system of systems. ▪ When these connected devices use cloud computing, analyze huge amounts of data and provide services. ▪ The Internet of Things (IoT) describes physical objects, that are embedded with sensors, processing ability, software, and other technologies using Internet. Google Nest – An example of IoT ▪ Google Nest is a home automation product includes: ▪ Internet connected security camera inside and outside the home ▪ Thermostat that use motion-detecting sensors to detect when the owners are about ▪ A camera-equipped doorbell ▪ A movement detecting alarm system and smoke detector ▪ Also gather data from other products – including cars, ovens, fitness trackers and evens sensor-equipped beds – to help “save energy and stay safe” 18 NEP - 2020 DIGITAL FLUENCY NOTES Examples of IoT Banking ▪ For organizations in retail banking, faster payments, improved operability and more responsive mobile services are the main points of focus for innovation. ▪ IoT offers retail banks an opportunity to gather more information on customers, offer more personalized experiences and improve efficiencies. ▪ Some examples of IoT in Retail Banking: o Wearables o Connected Cars o Banking at Home Wearables: ▪ Many banks now provide applications for popular wearables like Apple Watch and FitPay, which is already working with the Bank of America. ▪ Some banks have even launched their own devices, with Barclays unveiling bPay wearable contactless payment solutions and other wearable bands. Connected Cars: ▪ Connected cars not only have the potential to improve customer relationships, but also boost revenues. ▪ But smarter vehicles represent an opportunity for banks, too: for example, Idea Bank runs a fleet of cars, each customized with an integrated security deposit box and an ATM, which can visit the customer, rather than vice versa. ▪ The bank’s data suggests that the average deposit at one of its mobile, car-based ATMs is three times higher than at the branch. Banking at Home: ▪ With IoT, it is now possible to pay their bills through Amazon’s Alexa, Google Home, integrating its API with the smart speaker to enable users to carry out balance queries and payments through voice commands. IoT Architecture: ▪ The four stages of IoT Architecture are – 1. Sensors and Actuators – Sensing layer for Data Gathering 2. Internet gateways and Data Acquisition Systems – Network layer for Data Transmission 3. Edge IT - Data Analytics, Pre-processing 4. Data Center and Cloud – Apps & services 19 NEP - 2020 DIGITAL FLUENCY NOTES IoT Security: ▪ IoT Security is key for the secure development and secure operation of scalable IoT applications and services that connect the real and virtual worlds between objects, systems and people. ▪ IoT Security happens on four different levels o Delivery Security o Communication Security o Cloud Security o Lifecycle Management Security Future of IoT: ▪ Smart Home: Within next 2 years, smart devices will be filling our homes. ▪ Smart Cities: IoT helps to determine the roads, rails and bridges that need to undergo reconstruction, heavy traffics and locating empty parking spots, monitoring pollution levels, health monitoring of buildings etc., ▪ Driverless Cars: By 2020 75% of all the cars around the world will be connected to the internet along with driverless cars. ▪ Smart Offices: more efficient office operations, energy saving, comfortable work environment and increased employee productivity. Smart thermostat to determine the most effective workplace temperature, access control system instead of keys and locks, advanced digital assistance, smart lighting control. INDUSTRIAL INTERNET OF THINGS What is IIoT? ▪ Industrial Internet of Things (IIoT), is a vital element of Industry 4.0. IIoT harnesses the power of smart machines and real-time analysis to make better use of the data. ▪ The principal driver of IIoT is smart machines, for two reasons. 1. Smart machines capture and analyze data in real-time, which humans cannot. 2. Smart machines communicate their findings in a manner that is simple and fast, enabling faster and more accurate business decisions. 20 NEP - 2020 DIGITAL FLUENCY NOTES Examples of IIoT: ▪ As simple as a connected rat trap which relays information about catching a rat to a mobile phone. ▪ Slightly complex like a soil sensor which relays data about humidity and nutrient content to a system ▪ Complex system like smart parking and traffic management. ▪ A very complex set-up like a fully automated automobile assembly line relaying data in real-time to human supervisors. Comparison between IoT and IIoT: 21 NEP - 2020 DIGITAL FLUENCY NOTES Benefits of IIoT: ▪ Improvement of Operational Performance: through increased productivity, improved plant efficiency, asset uptime and quality, reduced operational risks, overhead costs and changeover times. ▪ Ensuring Safety and Compliance: through creating a naturally safer environment and abiding by health, safety and environment laws; complying with regulatory frameworks like energy, food and drug laws, labour laws etc. ▪ Increasing Flexibility and Agility: through updating and easy reprogramming of machines and robots to adhere to shifting and increasingly customized customer requirements. CLOUD COMPUTING What is Cloud Computing? ▪ The term cloud refers to a network or the internet. ▪ It is a technology that uses remote servers on the internet to store, manage, and access data online rather than local drives. ▪ The data can be anything such as files, images, documents, audio, video, and more. Operations of Cloud Computing: ▪ Developing new applications and services ▪ Storage, back up, and recovery of data ▪ Hosting blogs and websites ▪ Delivery of software on demand ▪ Analysis of data ▪ Streaming videos and audios Why Cloud Computing? ▪ IT companies, follow the traditional methods to provide the IT infrastructure. ▪ That means for any IT company, we need a Server Room with basic needs like a database server, mail server, networking, firewalls, routers, modem, switches, QPS, configurable system, high net speed, and the maintenance engineers. ▪ To establish such IT infrastructure, we need to spend lots of money. To overcome all these problems, Cloud Computing comes into existence. 22 NEP - 2020 DIGITAL FLUENCY NOTES Advantages of Cloud Computing: 1. Backup and Restore data: Once the data is stored in cloud, it is easier to get back-up and restore that data using the cloud. 2. Improved Collaborations: Cloud applications improve collaboration by allowing groups of people to quickly and easily share information in the cloud via shared storage. 3. Excellent accessibility: Cloud allows us to quickly and easily store and access information anytime, anywhere in the whole world, using an internet connection. 4. Low maintenance cost: Cloud computing reduces both hardware and software maintenance costs for organizations. 5. Mobility: Cloud computing allows us to easily access all cloud data via any device. 6. Services in the Pay-per-use Model: Cloud computing offers Application Programming Interfaces (APIs) to the users for access services on the cloud and pays the charges as per the usage of service. 7. Unlimited Storage: Cloud offers a huge amount of storing capacity for storing important data such as documents, images, audio, video, etc. in one place. 8. Data Security: Cloud offers many advanced features related to security and ensures that data is securely stored and handled. How does Cloud Computing works? ▪ Cloud Computing works by providing permission to the user to upload and download the information which stores. ▪ We can access the data from anywhere. A user will get the initial amount of storage at a very low price. ▪ Cloud Computing can be divided into two systems. One is front-end and the other is back-end. The two ends connect to each other with the help of an internet connection. ▪ The backend of the cloud is the system and the front end is a computer user or client. ▪ The front end of the system has the application, which is used to access the cloud system. ▪ The backend has various computers, hardware, servers, and data storage systems that make the cloud. 23 NEP - 2020 DIGITAL FLUENCY NOTES Cloud Service Models: There are three types of cloud service models. They are as follows: - ▪ Infrastructure as a Service (IaaS) ▪ Platform as a Service (PaaS) ▪ Software as a Service (SaaS) Infrastructure as a Service (IaaS): ▪ IaaS (Infrastructure as a service) is also known as Hardware as a Service (HaaS). ▪ It is a computing infrastructure managed over the internet. ▪ The main advantage of using IaaS is that it helps users to avoid the cost and complexity of purchasing and managing the physical servers. Platform as a Service: ▪ PaaS (Platform as a Service) cloud computing platform is created for the programmer to develop, test, run, and manage the applications. Software as a Service: ▪ SaaS (Software as a Service) is also known as “on-demand software”. It is software in which the applications are hosted by a cloud service provider. Users can access these applications with the help of an internet connection and a web browser. Types of Cloud: 1. Public Cloud: ▪ Public cloud is open to all to store and access information via the Internet using the pay-per-usage method. ▪ In public cloud, computing resources are managed and operated by the Cloud Service Provider (CSP). ▪ Example: Amazon elastic compute cloud (EC2), IBM SmartCloud Enterprise, Microsoft, Google App Engine, Windows Azure Services Platform. 2. Private Cloud: ▪ Private cloud is also known as an internal cloud or corporate cloud. ▪ It is used by organizations to build and manage their own data centers internally or by the third party. ▪ It can be deployed using Opensource tools such as Openstack and Eucalyptus. 24 NEP - 2020 DIGITAL FLUENCY NOTES 3. Hybrid Cloud: ▪ Hybrid Cloud is a combination of the public cloud and the private cloud. ▪ Hybrid cloud is partially secure because the services which are running on the public cloud can be accessed by anyone, while the services which are running on a private cloud can be accessed only by the organization's users. ▪ Example: Google Application Suite (Gmail, Google Apps, and Google Drive), Office 365 (MS Office on the Web and One Drive), Amazon Web Services. 4. Community Cloud: ▪ Community cloud allows systems and services to be accessible by a group of several organizations to share the information between the organization and a specific community. ▪ It is owned, managed, and operated by one or more organizations in the community, a third party, or a combination of them. ▪ Example: Health Care community cloud. CYBER SECURITY AND TYPES OF CYBER ATTACK What is Cyber Security? ▪ Cyber security is the application of technologies, processes and controls to protect systems, networks, programs, devices and data from cyber-attacks. ▪ It aims to reduce the risk of cyber-attacks and protect against the unauthorised exploitation of systems, networks and technologies. Types of Security: 1. Critical Infrastructure Security: Critical infrastructure security consists of the cyber-physical systems that modern societies rely on. Common examples of critical infrastructure: ▪ Electricity grid ▪ Water purification ▪ Traffic lights ▪ Shopping centres ▪ Hospitals Having the infrastructure of an electricity grid on the internet makes it vulnerable to cyber-attacks. 25 NEP - 2020 DIGITAL FLUENCY NOTES 2. Application Security: ▪ Application security uses software and hardware methods to tackle external threats that can arise in the development stage of an application. ▪ Applications are much more accessible over networks, causing the adoption of security measures during the development phase to be an imperative phase of the project. ▪ Types of application security: o Antivirus programs o Firewalls o Encryption programs 3. Network Security: ▪ As cyber security is concerned with outside threats, network security guards against unauthorized intrusion of your internal networks due to malicious intent. ▪ Common examples of network security implementation: o Extra logins o New passwords o Application security ✓ Antivirus programs ✓ Antispyware software ✓ Encryption ✓ Firewalls ✓ Monitored internet access 4. Cloud Security: ▪ Improved cyber security is one of the main reasons why the cloud is taking over. ▪ Cloud security is a software-based security tool that protects and monitors the data in your cloud resources. Cloud providers are constantly creating and implementing new security tools to help enterprise users better secure their data. ▪ Cloud computing security is similar to traditional on-premise data centres, only without the time and costs of maintaining huge data facilities, and the risk of security breaches is minimal. 26 NEP - 2020 DIGITAL FLUENCY NOTES 5. Internet of Things Security: ▪ IoT refers to a wide variety of critical and non-critical cyber physical systems, like appliances, sensors, televisions, Wi-Fi routers, printers, and security cameras. ▪ IoT devices are frequently sent in a vulnerable state and offer little to no security patching. This poses unique security challenges for all users. Tools for Cyber Security: 1. Password Managers: ▪ The need to keep private digital information protected is highlighted by the prevalence of growing cyber-attacks. ▪ Password managers are being used to keep track of and generate secure passwords. ▪ The user has to only remember one password, that of the password manager. ▪ Password managers like Lastpass, Dashlane, Sticky Password and KeepassX can be used. 2. Virtual Private Networks (VPN): ▪ A VPN connection establishes a secure connection between you and the internet. ▪ Via the VPN, all your data traffic is routed through an encrypted virtual tunnel. ▪ This disguises your IP address when you use the internet, making its location invisible to everyone. ▪ You can still access all online services using the VPN. ▪ VPNs offer the best protection available when it comes to your online security. Therefore, you should leave your VPN on at all times to protect from data leaks and cyberattacks. 3. Block Chain Technologies: ▪ Blockchain technology, a decentralized distributed ledger of transactions, offers the next level of cyber security. ▪ It can be used to create a cryptographic hash. ▪ Data stored in blockchain cannot be edited, stolen or replaced. ▪ Authenticate users and devices without using a password. ▪ Ensure privacy and security in chat rooms. 27 NEP - 2020 DIGITAL FLUENCY NOTES Common Types of Cyber Attacks: 1. Malware: ▪ The term “malware” encompasses various types of attacks including spyware, viruses, and worms. Malware uses a vulnerability to breach a network when a user clicks a “planted” dangerous link or email attachment, which is used to install malicious software inside the system. ▪ Malware and malicious files inside a computer system can: o Deny access to the critical components of the network o Obtain information by retrieving data from the hard drive o Disrupt the system or even rendering it inoperable 2. Phishing: Phishing attacks are extremely common and involve sending mass amounts of fraudulent emails to unsuspecting users, disguised as coming from a reliable source. The fraudulent emails often have the appearance of being legit, but link the recipient to a malicious file or script designed to grant attackers access to your device to control it or gather recon, install malicious scripts/files, or to extract data such as user information, financial info, and more. 3. Man-in-the-Middle Attacks: Occurs when an attacker intercepts a two-party transaction, inserting themselves in the middle. From there, cyber attackers can steal and manipulate data by interrupting traffic. 4. Denial-of-Service (DOS) Attack: DOS attacks work by flooding systems, servers, and/or networks with traffic to overload resources and bandwidth. This result is rendering the system unable to process and fulfill legitimate requests. In addition to denial-of-service (DoS) attacks, there are also distributed denial-of-service (DDoS) attacks. 5. SQL Injections: This occurs when an attacker inserts malicious code into a server using server query language (SQL) forcing the server to deliver protected information. This type of attack usually involves submitting malicious code into an unprotected website comment or search box. 28 NEP - 2020 DIGITAL FLUENCY NOTES 6. Zero-day Exploit: A Zero-day Exploit refers to exploiting a network vulnerability when it is new and recently announced before a patch is released and/or implemented. Zero-day attackers jump at the disclosed vulnerability in the small window of time where no solution/preventative measures exist. 7. Password Attack: Passwords are the most widespread method of authenticating access to a secure information system, making them an attractive target for cyber attackers. By accessing a person’s password, an attacker can gain entry to confidential or critical data and systems, including the ability to manipulate and control said data/systems. *** 29 NEP - 2020 DIGITAL FLUENCY NOTES Module II : Applications of Emerging Technologies Applications of Artificial Intelligence 1. Artificial Intelligence in Healthcare: With AI, natural language is a boon. It helps to respond to the questions that are asked for. It enables workflow assistants who screen the patients, getting preliminary information. This in turn helps the doctors to free up their schedules and also reduce the time and cost by streamlining processes. 2. Artificial Intelligence in Business: A business relies on real-time reporting, accuracy, and processing of large volumes of quantitative data to make crucial decisions. The adaptive intelligence, chatbots and automation helps to smoothen out the business process. 3. Artificial Intelligence in Education: It must be very tedious for a teacher to evaluate homework and tests for large lecture courses. A significant amount of time is consumed to interact with students, to prepare for class, or work on professional development. But, with AI in education, this will not be the case anymore. Though it can never replace human work, it is pretty close to it. So, with the automated grading system checking multiple-choice questions, fill-in-the-blank testing, grading of students can be done in no time. 4. Artificial Intelligence in Autonomous Vehicles: Long-range radar, cameras, and LIDAR, a lot of advancement has been made in the autonomous vehicle segment. These technologies are used in different capacities and each of them collects different pieces of information. Some of its usage in autonomous vehicles are: ▪ Directing the car to the fuel station or recharge station when it is running low on fuel. ▪ Adjust the trip’s directions based on known traffic conditions to find the quickest route. ▪ Incorporate speech recognition for advanced communication with passengers. ▪ Natural language interfaces and virtual assistance technologies. 30 NEP - 2020 DIGITAL FLUENCY NOTES 5. Artificial Intelligence in social media: Instagram, Snapchat, Facebook, Twitter, the world today is changing and everyone is using these social media apps to stay connected with the virtual world. Starting from notifications, to upgradations, everything is managed by AI. It considers all the past web searches, behaviours, interactions, and much more. So, while you visit these websites, your data is being stored and analysed and thus you are served with a personalized experience. 6. Artificial Intelligence for a Better World: It is the machines that are making the world a better place to live in. AI is helping us to prevent future damage. It understands the needs and addresses developmental needs while focusing on sustainability. Do you know that companies like Microsoft are using AI to study land-use patterns with terrain maps? This is helping in implementing proper preservation techniques. Scientists are using the information obtained to preserve biodiversity and the ecosystem. 7. Artificial Intelligence in Tourism: Competition in the travel and tourism industry is very high. You must have seen that prices keep on fluctuating and change often. You might have also booked a flight ticket in advance or have waited just before the departure date to find cheaper tickets. Everyone does that, but the struggle is minimized with AI. With predictive analytics driven by artificial intelligence, the price can be predicted. The application is able to predict price patterns and alert travellers when to buy the tickets. So, the cheapest rate can be known before you book the flights to your destination. APPLICATIONS OF BIG DATA ANALYTICS 1. Healthcare: Big data analytics have improved healthcare by providing personalized medicine and prescriptive analytics. Researchers are mining the data to see what treatments are more effective for particular conditions, identify patterns related to drug side effects, and gains other important information that can help patients and reduce costs. It’s possible to predict disease that will escalate in specific areas. Based on predictions, it’s easier to strategize diagnostics and plan for stocking serums and vaccines. 2. Media & Entertainment: Various companies in the media and entertainment industry are facing new business models, for the way they – create, market and distribute their content. 31 NEP - 2020 DIGITAL FLUENCY NOTES Big Data applications benefits media and entertainment industry by: ▪ Predicting what the audience wants ▪ Scheduling optimization ▪ Increasing acquisition and retention ▪ Ad targeting ▪ Content monetization and new product development Spotify, an on-demand music service, uses Hadoop Big Data analytics, to collect data from its millions of users worldwide and then uses the analysed data to give informed music recommendations to individual users. Amazon Prime, which is driven to provide a great customer experience by offering video, music, and Kindle books in a one-stop-shop, also heavily utilizes Big Data. 3. Traffic Optimization: Big Data helps in aggregating real-time traffic data gathered from road sensors, GPS devices and video cameras. The potential traffic problems in dense areas can be prevented by adjusting public transportation routes in real time. 4. Real-time Analytics to Optimize Flight Route: With each unsold seat of the aircraft, there is a loss of revenue. Route analysis is done to determine aircraft occupancy and route profitability. By analysing customers’ travel behaviour, airlines can optimize flight routes to provide services to maximum customers. 5. E-commerce Recommendation: By tracking customer spending habit, shopping behaviour, Big retails store provide a recommendation to the customer. E-commerce site like Amazon, Walmart, Flipkart does product recommendation. They track what product a customer is searching, based on that data they recommend that type of product to that customer. YouTube also shows recommend video based on user’s previous liked, watched video type. Based on the content of a video, the user is watching, relevant advertisement is shown during video running. 6. Big data applications in agriculture: Traditional tools are being replaced by sensor-equipped machines that can collect data from their environments to control their behaviour - such as thermostats for temperature regulation or algorithms for implementing crop protection strategies. Technology, combined with external big data sources like weather data, market data, or standards with other farms, is contributing to the rapid development of smart farming. 32 NEP - 2020 DIGITAL FLUENCY NOTES APPLICATIONS OF INTERNET OF THINGS 1. IoT Applications in Agriculture: For indoor planting, IoT makes monitoring and management of micro-climate conditions a reality, which in turn increases production. For outside planting, devices using IoT technology can sense soil moisture and nutrients, in conjunction with weather data, better control smart irrigation and fertilizer systems. If the sprinkler systems dispense water only when needed, for example, this prevents wasting a precious resource. A greenhouse with embedded devices not only makes it easier to be monitored but also, enables us to control the climate inside it. Sensors measure different parameters according to the plant requirement and send it to the cloud. It processes the data and applies a control action. 2. IoT Applications in Consumer Use: For the private citizen, IoT devices in the form of wearables and smart homes make life easier. Wearables cover accessories such as Fitbit, smartphones, Apple watches, health monitors, to name a few. These devices improve entertainment, network connectivity, health, and fitness. Smart homes take care of things like activating environmental controls so that your house is at peak comfort when you come home. Dinner that requires either an oven can be started remotely, so the food is ready when you arrive. Security is made more accessible as well, with the consumer having the ability to control appliances and lights remotely, as well as activating a smart lock to allow the appropriate people to enter the house even if they don’t have a key. 3. IoT Applications in Healthcare: First and foremost, wearable IoT devices let hospitals monitor their patients’ health at home, thereby reducing hospital stays while still providing up to the minute real-time information that could save lives. In hospitals, smart beds keep the staff informed as to the availability, thereby cutting wait time for free space. Putting IoT sensors on critical equipment means fewer breakdowns and increased reliability, which can mean the difference between life and death. One of the lesser-known wearables includes the Guardian glucose monitoring device. The device is developed to aid people suffering from diabetes. It detects glucose levels in the body, using a tiny electrode called glucose sensor placed under the skin and relays the information via Radio Frequency to a monitoring device. 33 NEP - 2020 DIGITAL FLUENCY NOTES 4. IoT Applications in Insurance: Even the insurance industry can benefit from the IoT revolution. Insurance companies can offer their policyholders discounts for IoT wearables such as Fitbit. By employing fitness tracking, the insurer can offer customized policies and encourage healthier habits. 5. IoT Applications in Manufacturing: The world of manufacturing and industrial automation uses IoT in a big way. RFID and GPS technology can help a manufacturer track a product from its start on the factory floor to its placement in the destination store, the whole supply chain from start to finish. Sensors attached to factory equipment can help identify bottlenecks in the production line, thereby reducing lost time and waste. Other sensors mounted on those same machines can also track the performance of the machine, predicting when the unit will require maintenance, thereby preventing costly breakdowns. 6. IoT Applications in Retail: IoT technology has a lot to offer the world of retail. Online and in-store shopping sales figures can control warehouse automation and robotics through information gathered from IoT sensors. IoT helps retailers target customers based on past purchases. Equipped with the information provided through IoT, a retailer could craft a personalized promotion for their loyal customers. Much of these promotions can be conducted through the customers’ smartphones, especially if they have an app for the appropriate store. 7. IoT Applications in Transportation: The GPS is another example of IoT. It is being utilized to help transportation companies plot faster and more efficient routes for trucks hauling freight, thereby speeding up delivery times. There’s already significant progress made in navigation, once again referring to a phone or car’s GPS. But city planners can also use that data to help determine traffic patterns, parking space demand, and road construction and maintenance. Maybe, an app can be developed that prevents the vehicle from starting if the driver is drunk. 8. IoT Applications in Utilities/Energy: IoT sensors can be employed to monitor environmental conditions such as humidity, temperature, and lighting. The information provided by IoT sensors can aid in the creation of algorithms that regulate energy usage and make the appropriate adjustment. With IoT- driven environmental control, businesses and private residences can experience significant energy savings. 34 NEP - 2020 DIGITAL FLUENCY NOTES APPLICATIONS OF CLOUD COMPUTING 1. Art Applications: Cloud computing offers various art applications for quickly and easily design attractive cards, booklets, and images. Applications: Vistaprint, Moo, Adobe Creative Cloud 2. Business Applications: Business applications are based on cloud service providers. Today, every organization requires the cloud business application to grow their business. It also ensures that business applications are 24*7 available to users. Applications: MailChimp, SalesForce, Chatter, QuickBooks. 3. Data Storage and Backup Applications: Cloud computing allows us to store information (data, files, images, audios, and videos) on the cloud and access this information using an internet connection. As the cloud provider is responsible for providing security, so they offer various backup recovery application for retrieving the lost data. Applications: Google G Suite, Box.com, Adobe Scanner 4. Education Applications: Cloud computing in the education sector becomes very popular. It offers various online distance learning platforms and student information portals to the students. The advantage of using cloud in the field of education is that it offers strong virtual classroom environments, Ease of accessibility, secure data storage, scalability, greater reach for the students, and minimal hardware requirements for the applications. Applications: Google Apps for Education, Chromebooks for Education 5. Entertainment Applications: Entertainment industries use a multi-cloud strategy to interact with the target audience. Cloud computing offers various entertainment applications such as online games and video conferencing. Applications: Online games, Video conferencing apps 6. Management Applications: Cloud computing offers various cloud management tools which help admins to manage all types of cloud activities, such as resource deployment, data integration, and disaster recovery. These management tools also provide administrative control over the platforms, applications, and infrastructure. Applications: EverNote, Toggl 7. Social Applications: Social cloud applications allow a large number of users to connect with each other using social networking applications. Applications: Facebook, Twitter, WhatsApp 35 NEP - 2020 DIGITAL FLUENCY NOTES APPLICATIONS OF CYBER SECURITY 1. Critical Infrastructure Security: This type of cybersecurity ensures that the digital infrastructure of our vital public systems remains preserved and protected from any form of malignant misuse such as keeping cyberattacks away from our hospitals, traffic lights, electricity grid, etc. 2. Application Security: Nowadays, when you have an app for nearly anything, it’s of vital importance to keep the space secure. Application security does just that by building in all the safety measures, such as encryption, firewalls, or anti-virus systems. 3. Network Security: Network security is an area of expertise primarily concerned with keeping the network integrity preserved. In practice, this means performing due diligence to ensure that the highest security standards and protocols protect networked data and systems. 4. Cloud Security: Even though cloud computing is usually considered the least secured space to store your data, experts suggest that clouds are safer than traditional IT infrastructures. There are nearly half as many attacks in the on-premise environments than in those serviced by a provider. Generally, these providers are the biggest generators of cloud security tools that keep the space safe. 5. Internet of Things (IoT) Security: The Internet of Things security is concerned with securing all the devices connected to the internet. From security cameras to the smallest home appliances, everything’s networked. Consequently, nearly everything can and should be clear of vulnerabilities and protected from potential cyber intrusions. *** 36 NEP - 2020