<h3 id="introduction"><br>Introduction</h3><p>With each passing day, technology is moving ahead. Every day we see new applications of the existing things or get the news about new technologies being created. We often ignore the news of emerging technologies until it's for our good.</p><p> If we start from the day when Graham Bell said hello for the first time to the discovery of touch screens and smartphones, we will realize that the constant efforts in the field of technology have never stopped. The same is the case today; from human robots to Quantum computers, technology is moving ahead day by day. </p><p>Two such integral parts of technology in today's world are IoT and blockchain.<br>In this article, we will talk about them and how they work efficiently in the field of security when combined.</p><h3 id="what-is-blockchain">What is Blockchain?</h3><p>Blockchain is a cutting-edge database technology that underpins almost all cryptocurrencies. Blockchain makes it exceedingly difficult to hack or cheat the system by spreading identical copies of a database across an entire network. While bitcoin is now the most prominent use for blockchain, the technology has the potential to serve a wide range of services.</p><p>The term blockchain isn't a coincidence: The digital ledger is sometimes referred to as a "chain" comprising discrete "blocks" of data. A new "block" is produced and joined to the "chain" as new data is regularly added to the network. This entails all nodes updating their blockchain ledgers to the same version.</p><p>Why blockchain is regarded as highly safe is due to how these new blocks are produced. Before a new block can be added to the ledger, a majority of nodes must check and validate the authenticity of the new data. They could include verifying that new transactions in a block aren't fraudulent or that the same currencies haven't been spent on more than one transaction.</p><h3 id="what-is-iot">What is IoT?<br></h3><p>The Internet of Things (IoT) is a term that describes the process of linking everyday physical items to the internet, ranging from lightbulbs to medical equipment to wearables, smart gadgets, and even smart cities.</p><p>Any physical device system that receives and transmits data via wireless networks with little human involvement is referred to as the Internet of Things (IoT). This is feasible because of the incorporation of computing devices into a wide range of items.</p><p>IoT and blockchain both cover a wide range of areas. From being used in our homes to the business sector, both are expanding rapidly. If we combine both of these technologies, we will come to know that blockchain in IoT amplifies the security system and puts maximum security questions at rest.</p><h3 id="when-iot-meets-blockchain">When IoT Meets Blockchain</h3><p>The Internet of Things (IoT) has substantially increased its reach over the last several decades, connecting a wide range of devices and networks in homes, offices, transportation systems, and even whole cities. Through its encrypted and distributed ledger meant to generate tamper-proof and real-time data, the decade-old blockchain, on the other hand, is expected to transform business structures. With the IoT and blockchain functioning together, the latter should be able to provide a verifiable and secure recording method for the former's devices and operations.</p><p>Every deletion or change of data is recorded in a distributed ledger as which blockchain works, and as additional entries, or blocks, are added to it, a longer chain of events is produced. A digital signature is attached to each transaction, which cannot be altered or erased. Because of its decentralised nature, blockchain, whether in a smart home or a smart factory, may theoretically prevent a susceptible device from sending misleading information and disrupting the network environment.</p><p>The combination of blockchain and IoT is intended to alleviate the issue of supervision as well. For example, in enterprises, transactions involving numerous sources may be managed through an immutable and transparent record that tracks data and physical items across the supply chain.</p><p>In light of recent high-profile IoT security problems, blockchain has the potential to minimize the danger of distributed denial-of-service (DDoS) attacks that affect numerous devices at the same time: A failure in one device should not influence the others.</p><h3 id="blockchain-and-iot-for-security">Blockchain and IoT For Security</h3><p>Blockchain, a decentralized distributed ledger, is a game-changing technology that might help solve IoT security issues. A blockchain-based approach to IoT networks might address many of the issues with the existing paradigm while also increasing security.</p><p>Blockchain provides the following weapons, which makes it an undefeatable form of technology when it comes to IoT cyber threats-</p><p><strong><strong><strong>Decentralization</strong></strong></strong></p><p>Data is kept on many nodes all around the world in a blockchain ledger, eliminating the single point of failure. Before any data is added to the network, it must be approved and verified by all nodes. As a result, no modification is permitted unless all network participants agree. This method is known as peer-to-peer communication, and it is designed to keep malevolent actors out of blockchain transactions. Because there is no single server, there is no risk of a man-in-the-middle attack, in which hackers intercept data transferred between a server and a device.</p><p><strong><strong><strong>Public Access </strong></strong></strong></p><p>Blockchain is open, which means that anybody in the network may access it. The shared history of stored blocks and transactions is visible to all network members, but viewing the information requires a private key. This provides total transparency to all processes while still keeping data secure. It is difficult to alter information after it has been saved on a blockchain.</p><p><strong><strong><strong>Data security</strong></strong></strong></p><p>Blockchain secures data using powerful encryption methods, making it more private. This is done largely to ensure that financial transactions are risk-free. IoT devices may use the blockchain paradigm to send and receive messages in the same manner that financial transactions do to enable secure data exchange between linked items.</p><h3 id="conclusion">Conclusion</h3><p>The combination of IoT and Blockchain can serve as a life-saving option for many sectors, intimidated by the IoT security threats. The use of blockchain in IoT security allows for direct information exchange between connected devices rather than communicating via a centralized network, reducing IoT susceptibility to cyber threats. The future for security by the means of IoT and blockchain is much brighter and shinier than them alone. We can even today, find some amazing examples of the same, like the use of them in the pharmaceutical sector or the transportation and energy sector. </p>

Utsav Mishra

Premium reporting on technology, startups, VC funding, and software from Techfastly tagged with security. Read articles on security here.

security

IoT and Blockchain for Security

<p>SpaceX. Heard about the name? Heard about the man behind it? You must have. And if you haven?t, just go and Google about upcoming Mars missions or Dogecoin or Elon Musk, or just keep an eye on every sentence of this article, because till you finish reading it, you will get acquainted with all of these. So, what shall we start with? Should I start with how one man?s dream of getting to Mars led us to a company that is now capable of sending people there? Or should I just explain the Musk phenomena? Let?s leave the rest for now and start with what an exceptional achievement SpaceX is.<br><br></p><h1 id="spacex">SpaceX<br></h1><p>SpaceX is a privately funded spaceflight business that launches satellites and, more recently, people to the International Space Station (ISS). It was the first commercial firm to deploy a cargo ship to the International Space Station, doing so in 2012. The firm sent its first two people to the International Space Station aboard the SpaceX Crew Dragon on May 30, 2020, and followed that test mission with the successful launch of four astronauts on Nov. 15, 2020. It is the only commercial spaceflight firm capable of transporting people to space as of early 2021.<br></p><p>Musk, a South African-born businessman and entrepreneur, created SpaceX. According to The New York Times, Musk acquired his initial fortune at the age of 30 by selling his two successful companies: Zip2, which he sold for $307 million in 1999, and PayPal, which was purchased by eBay for $1.5 billion in 2002. He determined that his next big project would be a privately funded space corporation.<br></p><p>Originally, Musk planned to send a greenhouse to Mars, named the Mars Oasis. His objective was to pique public interest in space travel while simultaneously establishing a research station on Mars. However, the cost proved to be prohibitively expensive, and Musk instead founded Space Exploration Technologies Corp., or SpaceX, which is currently located in the Los Angeles neighborhood of Hawthorne, California.<br></p><p>He paid $100 million, or one-third of his total fortune, to get SpaceX up and running. There was doubt about his ability to succeed, which lingered throughout SpaceX's early years.<br></p><p>After working privately on a spaceship for 18 months, SpaceX debuted it in 2006 as the Dragon. Musk allegedly called the spaceship "Puff, the Magic Dragon," a 1960s folk song by Peter, Paul, and Mary. He explained that he picked the term because detractors thought his spaceflight goals were unrealistic.<br></p><p>The first project of SpaceX was to develop the first-ever liquid-fueled private spacecraft that would make it into orbit. Musk called the spacecraft Falcon 1. <br></p><p>Falcon 1 flew twice successfully, on September 28, 2008, and July 14, 2009. The Malaysian RazakSat satellite was also sent into orbit during the 2009 launch.<br></p><h1 id="the-growth-of-spacex">The Growth of SpaceX<br></h1><p>SpaceX had intended to launch the spacecraft in 2008 or 2009, but the construction process took years longer than expected. The Falcon 9 made its first flight on June 4, 2010, with a simulated Dragon payload. The rocket successfully launched, however, the landing attempt failed due to a malfunctioning parachute. On December 8, 2010, SpaceX followed up by launching the Falcon 9 and Dragon spacecraft together. The launch was successful once more, satisfying NASA's COTS standards, however, the rocket's landing failed.<br></p><p>The delivery of the space station was the next and most important milestone. In May 2012, Dragon, flying a Falcon 9 rocket, carried its first cargo to the space station as part of a COTS test flight. The launch was postponed for a few days due to an engine malfunction, but the rocket successfully launched on the second attempt.<br></p><p>Spaceflight experts praised SpaceX's ability to deploy a cargo ship to the International Space Station. When the space station was designed in the 1980s and 1990s, private spaceflight was not even envisaged.<br></p><p>In October 2012, SpaceX completed the first of its scheduled commercial trips to the space station. Most of the flight's objectives were met, however, there was a partial rocket failure during launch. The failure stranded a satellite, Orbcomm-OG2, in an unusually low orbit, resulting in the mission's failure.<br></p><h1 id="musk-and-the-mars-dream">Musk and the Mars Dream<br></h1><p>Customers from the business sector, the military, and nonprofit organizations pay SpaceX to launch goods into orbit. Although SpaceX generates money by providing launch services, the business is equally focused on creating technologies for future space travel.<br></p><p>And Musk's ambitions to go to Mars remain unabated. He informed delegates at the American Institute of Aeronautics and Astronautics (AIAA) in San Diego in 2011 that he hoped to transport people to Mars within 10 to 15 years. Three years later, at the International Space Development Conference, he stated that the reusable rocket stage would be a step toward reaching Mars.<br></p><p>Musk presented his technical concept for Martian transportation in 2016, as part of his ambition to establish a self-sustaining Red Planet colony within the next 50 to 100 years. The rocket, known as the Interplanetary Transport System, is essentially a bigger version of the Falcon 9. The spaceship, on the other hand, will be far larger than the Dragon, carrying at least 100 people every voyage.<br><br></p><p>Musk stated in 2018 that Yusaku Maezawa, an artist and billionaire founder of the Japanese e-commerce company Zozo, and a group of artists will embark on a voyage around the moon aboard the BFR in the 2020s. Maezawa's payment for the trip was not disclosed by SpaceX.<br></p><p>In September 2019, Musk revealed an upgrade to his Mars ambitions, renaming the first BFR Starship Mk1 and replacing its exterior covering from costly carbon fiber to stainless steel. Photos of the gleaming, sci-fi-looking craft being built at SpaceX's South Texas facility in Boca Chica surfaced on the internet.<br><br></p><h1 id="future-plans-of-spacex">Future Plans of SpaceX<br></h1><p>SpaceX has many future goals and Musk has been really vocal about them since its founding mission. There are many future prospects on which SpaceX has been working. <br></p><p>A trip to another planet is a pretty logical next step in space research. Elon Musk has made it clear that he does not intend to die in an accident on Mars. SpaceX has been preparing for this step for years, not just by developing a thorough plan. Elon Musk also intends to ?warm-up Mars,' however it is unclear what technologies would be utilized.<br></p><p>The plan calls for the vehicle to be refueled twice. First, following launch, the spacecraft will be in Earth orbit, preparing for the voyage to Mars. The second refueling will take place on Mars, using native resources like water and carbon dioxide. This will allow the vehicle to return to Earth. The firm intends to land the first human on Mars by 2024.<br><br></p><p>Musk hopes to transport humans to Mars by 2026, however that timetable has been called into question. Over the next ten years, he aims to create 1,000 Starships, which are totally reusable transport systems capable of transporting 100 people to Mars. That equates to 100 Starships each year.<br></p><p>Musk intends to launch 12,000 internet satellites into orbit by 2027, where Starlink customers will be able to access broadband internet.<br></p><p>Tasmanian estimates that if SpaceX acquires 25 million Starlink customers, it will produce around $30 billion per year. This is ten times what the firm makes as a launch provider, according to the company.<br></p><p>The future plans of SpaceX don?t end here, but I guess I didn?t break my promise. The promise of making you get acquainted with Musk, his Mars dream, and the step towards it, i.e. SpaceX. <br></p><p>If the Mars dream of Musk comes true, then the future is going to be really exciting and we must note that part down. Till then, let us all hope for the best.</p>

SpaceX: The Future Lies Here

<p>Technology! What comes to your mind when you hear this word? You think either about flying cars or mobile houses and other things that were considered unrealistic a few years ago. Or you might think about drones, mobile applications that can control the electrical appliances around you, and things that we see around us every day. But every time a new technology steps in people are spectacle about it surviving in the long run. And this thing continues until that technology progresses enough to take the whole world by storm. A few years back while you were dialling a number on your Motorola handset, that had a few keys only and a game to keep you entertained, did you ever think of seeing the person you are talking to on the screen of a mobile phone? None of us did. But it happened and facetime or video call is a thing that we are thankful to technology for. Similarly, when Charles Babbage invented the first computer, no one thought of the attachments it would require after a few years. One such attachment was a printer and when the first-ever printer printed a piece of paper we didn?t dream of a 3D printer and we still can?t believe that it is actually a thing.<br></p><p>In this article, we will talk about 3D printing technology and the way it has revolutionized the spacecraft industry.</p><p>Let us first try to know what 3D printing actually is.</p><h3 id="what-is-3d-printing">What is 3D Printing?</h3><p>3D printing is a manufacturing method that turns a computer model file into a real thing. The technique works by layering material on top of the material to create a complete product.</p><p>The 3D printing method was invented in the 1980s and was initially referred to as "rapid prototyping." It allowed businesses to create prototypes more rapidly and precisely than previous approaches. After more than 30 years of development, its applications are considerably more diversified now.</p><p>Manufacturers, engineers, designers, educators, doctors, and amateurs all utilize the technology in a variety of ways.</p><h3 id="how-does-3d-printing-work">How Does 3D Printing Work?</h3><p>As previously said, 3D printing entails building up layer upon layer of molten plastic to construct an item. As each layer dries, the next layer is printed on top, and the thing is constructed.</p><p>A digital file that informs the 3D printer where to print the material is required to create a 3D print. G-code files are the most often used file type for this. This file effectively provides 'coordinates' that direct the printer's horizontal and vertical movements - commonly known as the X, Y, and Z axes.</p><p>The thickness of these layers, known as layer height, may be printed by 3D printers at varying thicknesses. More layers in a print, similar to pixels on a screen, will result in a higher resolution. But this will take more layers and subsequently more time.</p><h3 id="3d-printing-and-space">3D Printing and Space</h3><p>3D printing has a lot of applications, they vary from the food industry to the manufacturing of spare parts. But one application of it that is less talked about is its application in the space industry. 3D printing has revolutionized the space industry for decades to come. It is used in space stations for printing spare parts whenever they run out of them. It is used for printing in zero gravity zones.</p><p>NASA recently teamed with the Californian firm Made in Space to deploy a 3D printer to the International Space Station in 2014. This printer intended to conduct a series of zero-gravity tests. This experiment was designed, among other things, to assess if astronauts will be able to create required components in space, saving money in the future.</p><p>Apart from this large structures are printed in space itself. Made in Space, a California-based firm, revealed a few months ago that it will start on the Archinaut TDM project, which will include 3D printing huge structures in space-like settings.</p><p>The proposal calls for the use of a robotic arm capable of creating huge constructions. This will save transportation costs for heavy machinery and raw materials since the machine will utilize resources found in space, such as lunar dust. This is one of the initial stages toward the establishment of permanent human colonies in space.</p><p>These things are proof that the decision to bring 3D printers into space was a great one. Watching robots printing robots is a satisfying thing but you know what is more satisfying than this? Watching the printing of spacecraft in front of you in an era where space itself is a distant dream.</p><h3 id="3d-printing-of-spacecrafts">3D Printing of Spacecrafts</h3><p>3D printing is quickly becoming an interesting technique for the manufacture of spacecraft, particularly rockets. Both startups and established manufacturers are embracing 3D printing to produce rocket components with improved design and performance at a cheaper cost and in a shorter time frame.<br></p><h3 id="why-is-3d-printing-being-used-to-print-spacecrafts">Why is 3D Printing Being Used to Print Spacecrafts?</h3><p>The race to launch satellites into orbit is getting increasingly competitive.</p><p>As a result, spacecraft makers confront the task of speeding rocket research and manufacturing while simultaneously lowering costs and boosting efficiency.</p><p>However, in traditional rocket manufacturing, several development cycles and production phases lengthen and increase the cost of the process.</p><p>A typical combustion chamber, for example, can take between 10 and 14 months to build a structural rocket component where a mixture of fuel and compressed air is ignited.</p><p>Given these obstacles, major rocket makers are already using additive manufacturing to construct combustion chambers. A 3D-printed combustion chamber may be created in a matter of weeks once it has been developed, allowing manufacturers to drastically cut manufacturing times and costs.</p><p>Manufacturing gear for space launches necessitates extraordinary performance and precision. To guarantee a successful launch, every component must operate in unison.</p><p>When developing very complicated rocket parts, engineers encounter difficulties with traditional production. Engineers can overcome some of the constraints of traditional techniques by using metal 3D printing to create more intricate designs.</p><h3 id="3d-printing-processes-used-to-print-rockets-and-spacecrafts">3D Printing Processes Used to Print Rockets and Spacecrafts</h3><p>As of now, spacecraft manufacturers have largely relied on laser-based metal 3D printing technologies like Selective Laser Melting (SLM) to manufacture rocket engine components such as combustion chambers, injectors, nozzles, pumps, and valves. SLM fuses metal particles together by applying a strong, fine-tuned laser to a layer of metal powder. The procedure is done hundreds of times until a component is formed.</p><p>SLM has a high degree of accuracy (it can print layers as thin as 20 microns) and is designed to operate with a wide range of high-performance metals, from titanium to nickel alloys. Typically, this technique is utilized for smaller parts.</p><p>Some spacecraft manufacturers, in addition to SLM, employ Direct Energy Deposition (DED) technology to build massive components. DED 3D printers use a laser or an electron beam to melt metal material as it is placed through a nozzle onto the build platform.</p><p>DED machines often have high material deposition rates and can deal with metal materials in powder or wire form, resulting in extremely dense components with near-net forms.</p><h3 id="benefits-of-3d-printing-in-rockets">Benefits of 3D Printing in Rockets</h3><p>3D-printed rockets are beneficial in a lot of ways. The benefits of them are:</p><blockquote>Flexibility of design</blockquote><blockquote>Relatively less expensive</blockquote><blockquote>Simplified assembling<br></blockquote><h3 id="future-of-3d-printing-in-aerospace">Future of 3D Printing in Aerospace</h3><p>3D printing has changed the way rockets and rocket engine parts are designed and manufactured. Although the principles of rocket design have not altered, metal 3D printing has opened up new avenues for improving rocket performance and allowing for greater design freedom.</p><p>The technique has the potential to lower the cost of manufacturing engines, which are one of the most expensive components of a rocket. This is accomplished by decreasing the number of manufacturing stages and allowing for more frequent design revisions.</p><p>Although the possibilities of 3D printing beyond engine parts have yet to be verified, the technology's future potential for completely 3D-printed rockets is extremely intriguing.</p><p>3D printing has emerged as a critical manufacturing technique for spacecraft production, allowing engineers to develop more quickly and usher in the next phase of space flight.<br></p>

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Technology

3D Printing and Space: A Relationship to Cherish

<p><strong>By Utsav Mishra</strong></p><p>Meta Description: Quantum Computing is when classical computing combines with quantum. Learn about how it has brought a wave of change in machine learning. </p><p>Quantum computing has developed in recent years as a novel computing model that has the potential to take traditional computers to a whole new level. All technology-related media outlets have been publicizing all minor and potential advancements in this sector. Even though this is an exciting period for the field, the field itself remains a mystery.</p><p>The mystery behind quantum computing remained in the closet for many years until the day it was discovered. And since that day, everything has changed in the computing world. A lot of things became extremely easy, and the machine learning scenarios took a leap in just a few years.</p><p>Saying this wouldn't be wrong that quantum computing will revolutionize machine learning for years to come.</p><p>Let us begin with what quantum computing really is.<br><br></p><h1 id="what-is-quantum-computing">What is Quantum Computing?</h1><p>Quantum computing is a field of computing that focuses on developing computer technology based on quantum mechanics (which explains the behaviour of energy and material on the atomic and subatomic levels). Traditional computers can only encode/decode information in bits that have a value of 1 or 0, limiting their capabilities.</p><p>But this is where quantum computing distinguishes itself; it uses quantum bits, also known as qubits. The theory used here is that subatomic particles can exist in one or more states.</p><p>The assumption of quantum computing is that it can be useful in a variety of applications that are deemed critical in today's technological environment, ranging from cybersecurity to medical applications to machine learning. One of the primary reasons for the field's popularity is its broad range of applications.</p><p>Quantum computing is an exciting thing when we talk about machine learning (ML). It combines with ML and gives us Quantum Machine Learning as a product.</p><h1 id="what-is-quantum-machine-learning">What is Quantum Machine Learning?</h1><p>Let us go a little back in time and look at machine learning in a broader context. Machine learning is made up of two components: data and algorithms. So, when we talk about quantum machine learning, do we mean the data, algorithms, or both?</p><p>At points where classical machine learning raises its hands and refuses to help us, the above-mentioned quantum computers help us with quantum machine learning.</p><p>QML is an umbrella term for four kinds of scenarios. They are:</p><p>? Tensor network and de-quantized recommendation system algorithms are examples of quantum-inspired classical algorithms processing classical data.</p><p>? Classical methods, such as neural network-based quantum states and optimizing pulse sequences, are applied to quantum data.</p><p>? Quantum algorithms, such as quantum optimization algorithms and quantum classification of classical data, are applied to classical data.</p><p>? Quantum algorithms, such as quantum signal processing and quantum hardware modelling, are applied to quantum data.</p><p>Now that we are acquainted with quantum machine learning, let us look at how it can change the future, i.e., how it can improve the present machine learning scenarios to create a better future.</p><h1 id="the-future-is-all-quantum">The Future is All Quantum</h1><h2 id="quantum-computers-are-capable-of-quickly-resolving-complex-problems">Quantum computers are capable of quickly resolving complex problems</h2><p>If one of these industry giants succeed in developing a commercially viable quantum computer, these quantum computers would likely be able to finish computations that would take thousands of years to do using traditional computers. Google now claims to have a quantum computer that is 100 million times faster than any current machine. It will be important if we are to handle the massive amounts of data we create and solve extremely complicated issues. The key to success is converting our real-world problems into quantum language.</p><p>Our data sets' complexity and scale are expanding faster than our computer capabilities, putting significant pressure on the fabric of computers. While computers today struggle or are unable to tackle particular issues, the power of quantum computing is predicted to solve these same difficulties in seconds. It is expected that improvements in quantum computing technology would help artificial intelligence, particularly machine learning, and will continue to do so. Quantum computing methods enable us to improve on what machine learning can currently do.</p><h2 id="solutions-will-be-optimized-by-quantum-computers">Solutions will be optimized by quantum computers</h2><p>Another way quantum computing will assist usher in a revolution will be to sample data and optimize all types of issues we face; from portfolio analysis to the best delivery routes to help identify the best treatment and pharmaceutical routine for each individual.</p><p>With the rise of big data, we have modified our computer architecture, necessitating a novel computational method to deal with large data. It is not just greater in scope, but the challenges we attempt to tackle are extremely complex. Quantum computers are more suited to solving sequential problems. The ability they provide businesses and even consumers to make better decisions may be all that is required to persuade firms to invest in this emerging technology whenever it becomes available.</p><h2 id="quantum-computers-could-spot-patterns-in-large-data-sets">Quantum computers could spot patterns in large data sets</h2><p>Quantum computing is predicted to be capable of rapidly searching massive, unsorted data sets for patterns or abnormalities. Quantum computers may be able to examine all objects in your database at the same time and detect these abnormalities in seconds. While this is technically conceivable today, it requires a parallel computer to look at each record one by one, which takes an inordinate amount of time and may never happen depending on the size of the data collection.</p><h2 id="quantum-computers-might-aid-in-the-integration-of-data-from-various-data-sets">Quantum computers might aid in the integration of data from various data sets</h2><p>Furthermore, due to the integration of extremely diverse data sets, major advancements are predicted when quantum computers become accessible. Although this may be challenging initially without human interaction, human engagement will assist computers in learning how to integrate data in the future. So, if a research team wanted to compare various raw data sources with unique schema linked to them (terminology and column headings), a computer would need to grasp the relationship between the schemas before the data could be compared. Breakthroughs in the study of the semantics of the natural language are required to do this, which is really difficult in artificial intelligence. Humans, on the other hand, may provide input that trains the system for the future.</p><p>Quantum computers promise to enable rapid analysis and integration of massive data sets, improving and transforming machine learning and artificial intelligence capabilities.</p><h1 id="end-note">End-Note</h1><p>Quantum computing offers enormous promise for enhancing and altering the way technology works today. Despite the fact that quantum computing is not a new topic at all ? it is as ancient as computing itself ? it has garnered traction and a lot of attention in recent years.</p><p>However, quantum has recently progressed from pure theory to practice. That is the cause for the unexpected surge in media and scholarly interest. For the first time in quantum history, anybody may access and run tiny to moderate programs on quantum computers.</p><p>These signs are enough to provide the promise of a bright future that lies ahead of us in the field of quantum computing. Hope all these promises come true in the future.</p>

Quantum Computing and The Rise of Machine Learning

<h3 id="introduction">Introduction</h3><p>Imagine, you are working hard and very carefully while collecting important data. Suddenly, you come across some bad data in your calculation and the whole job gets awry. These errors are called ?outliers? in machine learning - something that everyone, especially data scientists finds very frustrating. Outliers in machine learning are harmful to the data collection process and can distort your observations. It is important to detect and get rid of these outliers beforehand. This is what we are going to learn in this article.</p><h3 id="what-are-outliers-in-machine-learning">What are Outliers in Machine Learning?</h3><p>An outlier is a data point that stands out from the rest. They reflect measurement mistakes, poor data collection, or simply variables that were not considered when collecting the data. According to Wikipedia, it is a ?distant observation location from other observations.' Outliers have the potential to distort your results and provide incorrect information.</p><h3 id="what-are-the-different-types-of-outlierspoint-outliers">What are the Different Types of Outliers?<br>Point Outliers</h3><p>Anomalies in comparison to the bulk of observations in a feature. In a nutshell, a data point is called a global outlier if its value falls significantly outside the bounds of the data collection in which it is discovered.</p><h3 id="contextual-outliers">Contextual Outliers</h3><p>Anomalies are observations that are deemed unusual in a certain situation. A data point is called a contextual outlier if its value deviates considerably from that of the other data points in the same context. It is important to note that the same result may not be deemed an outlier if it occurs in a different environment. Because time series data are records of a certain amount across time, the "context" is nearly always temporal. It should come as no surprise that contextual outliers are prevalent in time series data. Contextual Anomaly levels are not outside of the usual global range but are out of the seasonal rhythm.</p><h3 id="collective-outliers">Collective Outliers</h3><p>A group of anomalous observations that look near to one another due to their comparable abnormal value.</p><p>A subset of data points within a data set is deemed anomalous if their values as a group deviate considerably from the overall data set, but the individual data points' values are not abnormal in either a contextual or global sense. In time-series data, this might appear as typical peaks and valleys happening outside of a time period when the seasonal sequence is normal, or as a collection of time series that are in an outlier condition.</p><h3 id="what-causes-outliers-in-machine-learning">What Causes Outliers in Machine learning?</h3><p>The following are the most typical sources of outliers in a data set:</p><p><strong>Errors in data entry: </strong>Outliers in data can be produced by human mistakes such as those made during data collection, recording, or input.</p><p><strong>Error in measurement:</strong> Outliers are most commonly found in this context. This occurs when the measurement instrument employed is found to be defective.</p><p><strong>Errors in experimental design</strong> (data extraction or experiment planning/execution)</p><p><strong>On purpose </strong>(dummy outliers made to test detection methods)</p><p><strong>Errors in data processing</strong></p><p><strong>Errors in sampling</strong></p><p><strong>Natural outlier </strong>(data novelty, not an error): When an outlier is not caused by a human mistake, it is referred to as a natural outlier. This category contains the majority of real-world data.</p><h3 id="how-to-get-rid-of-outliers-in-machine-learning">How to Get Rid of Outliers in Machine Learning?</h3><p>Outliers cannot be identified when collecting data; you will not know which numbers are outliers until you begin analyzing the data. Many statistical tests are sensitive to outliers, thus being able to detect them is a crucial component of data analytics. The interpretability of an outlier model is critical because choices aimed at dealing with an outlier require some context or explanation.</p><p>In reality, outliers can be useful indications at times. Outlier analysis, for example, becomes essential in some data analytics applications, such as credit card fraud detection, since the analyst may be interested in the exception rather than the rule. Simply said, when you identify outliers, you have three options: accept them, rectify them, or remove them. If there is a probability that the outlier will not have a substantial impact on the outcome, you may choose to "accept" it. Otherwise, you have the option of ?correcting' it or deleting it. However, you should only delete data points that are clearly incorrect.</p><p>So, here are some ways in which outliers can be removed from machine learning:</p><h3 id="observation-deletion">Observation Deletion</h3><p>To avoid skewing your research, it is sometimes advisable to delete certain entries entirely from your dataset. We remove outlier values if they are the result of a data input error, a data processing error, or if the number of outlier observations is very tiny. Trimming at both ends can also be used to remove outliers. However, when we have a tiny dataset, removing the observation is not a smart idea.</p><h3 id="value-transformation">Value Transformation</h3><p>Outliers can be eliminated by transforming variables. The variance produced by extreme values is reduced by these modified values.<br><strong>Log transformation</strong><br><strong>Scaling</strong><br>B<strong>ox-transformation</strong><br><strong>Normalization of cube roots</strong><br><br>These approaches reduce the size of the dataset's values. If your data has too many extreme values or is skewed, this approach can help you normalise it. However, these methods do not always produce the greatest outcomes. These approaches result in no data loss. In all of these methods, the box cox transformation produces the best results.</p><h3 id="imputation">Imputation<br></h3><p>We can impute outliers in the same way that we may impute missing numbers. In this approach, we may utilise the mean, median, and zero value. There is no data loss because we are imputing. In this case, the median is suitable since it is unaffected by outliers.</p><h3 id="separately-treating">Separately Treating<br></h3><p>If there are a large number of outliers and the dataset is tiny, we should consider them individually in the statistical model. One method is to consider both groups as separate entities, construct distinct models for each, and then merge the results. However, when the dataset is huge, this approach becomes laborious.</p><h2 id="conclusion">Conclusion</h2><p>Getting a sudden error while writing or executing a code can be heavily frustrating. Outliers when show up, act like a bug for your hard drafted code. It can be a mood killer when you are coding for 6 or 7 hours straight and then they show up.</p><p> These were the ways how one can remove outliers from machine learning. Hope now you know the concept of outliers. Outliers are a part of every machine learning program, but they need to be identified  and removed. So, now that you know how to do the same, happy coding.<br></p>

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Machine Learning

What are Outliers in Machine Learning and How to Remove Them?

Quantum machine learning is a very new area with a lot of room for advancement. Learn about how QML could be used in data analytics. 

Use of Quantum Machine Learning in Data Analytics

<h3 id="introduction">Introduction</h3><p>Imagine sitting in front of your computer and finishing off a certain work by staying awake the whole night. The following day, when you wake up and open your tech news feed, you come across an article telling you how a particular new technology can help you do the same thing in half the time that you took to finish it off. You regret staying awake the whole night and not knowing about the new technology and its application in your life. The article you read might not be such a big thing, but it surely made you acquainted with something that can stay with you in the long run. Consider this as one such article which can make your life easier.</p><p>Today, our area of focus is quantum machine learning. QML is a new thing for the IT industry but a thing that can change the dimensions of the industry effectively. Let us first try to know what quantum machine learning is.</p><h3 id="what-is-quantum-machine-learning">What is Quantum Machine Learning?</h3><p>Traditional computers struggle to handle massive computations when data points are projected in greater and higher dimensions. Even if a traditional computer could do it, it would take too long.</p><p>Simply said, traditional machine learning techniques might be too demanding for traditional computers at times. Quantum computers, fortunately, have the computing capacity to tackle these difficult algorithms. They solve problems quicker than their classical equivalents by utilising strong principles such as superposition and entanglement.</p><p>Scientists can convert a conventional machine learning algorithm into a quantum circuit that can be performed effectively on a quantum computer using quantum machine learning.</p><p>So, in simple words, Quantum Machine Learning (Quantum ML) is an interdisciplinary field that brings together quantum physics and machine learning (ML). It's a symbiotic relationship, with quantum computing being used to create quantum versions of ML algorithms and traditional ML algorithms being used to study quantum systems.</p><p>Now that we know what quantum machine learning is, let us try to look at its applications.</p><h3 id="applications-of-quantum-machine-learning">Applications of Quantum Machine Learning</h3><p>Quantum machine learning is a very new area with a lot of room for advancement. However, we can already begin to forecast how it will affect our future!</p><p>Here are some of the areas where QML will have an impact:</p><ul><li>Understanding Nanoparticles </li><li>Molecular and atomic mapping is used to create novel materials</li><li>Medical research and molecular modeling to identify new medicines</li><li>Understanding the human body's inner workings</li><li>Pattern recognition and categorization have been improved</li><li>Expanding the scope of space exploration</li><li>By combining IoT and blockchain, we can achieve total linked security.</li><li>In the analysis of data</li></ul><p>QML will address more issues than we could have anticipated as more remarkable discoveries occur every day.</p><p>After looking at the applications, we can be assured of one thing, and i.e., if QML can help in such kinds of things in the early stages of its discovery, in a few years it can be the next big thing. In the applications above we came across a point that mentioned ?data analysis?. Watching the things that Big Data can do and how fast the data science industry is growing, anyone will be curious about this very application. So let us dive deeper into the ocean of quantum machine learning and search for the desired fish, data analysis. For this, we must start with what data analytics is.</p><h3 id="what-is-data-analytics">What is Data Analytics?</h3><p>The phrase "data analytics" refers to the act of analyzing datasets to make conclusions about the information contained within them. Data analysis techniques allow you to take raw data and derive important insights from it by uncovering patterns.</p><p>In their research, data scientists and analysts employ data analytics methodologies, and companies use them to influence their judgments. Data analysis may assist businesses in better understanding their consumers, evaluating their advertising efforts, personalizing content, developing content strategies, and developing new goods. Finally, firms may utilize data analytics to improve their bottom line and raise their performance.</p><h3 id="quantum-machine-learning-in-data-analytics">Quantum Machine Learning in Data Analytics</h3><p>While solving real-world problems, quantum machine learning algorithms work with existing datasets and provide a simple and better understanding of them. It makes the work easier for a data scientist who spends hours analyzing data. The rise of QML will benefit the ones in the industry and will also make them shed a lot of load from their already crowded head.</p><p>Researchers examine the topic of quantum advantage in machine learning in a recent paper published in Nature Communications titled "Power of data in quantum machine learning." Researchers attempted to quantify how the difficulty of an issue varies formally with the availability of data in the article, and how this might occasionally improve the competitiveness of classical learning models using quantum techniques. The data can raise classical models to rival quantum models, despite the fact that the quantum circuits generating the data are difficult to calculate classically.</p><p>To begin, they demonstrated how data-driven classical algorithms might match quantum output. They then established rigorous prediction error limits for training conventional and quantum machine learning algorithms to learn quantum mechanical models using kernel functions. Kernel techniques give verifiable guarantees and are also quite flexible in terms of the functions they can learn, therefore they took these steps.</p><p>Computational complexity classes are occasionally used to represent the idea of quantum advantage over a conventional computer. Bounded Quantum Polynomial (BQP) time tasks, such as factoring large numbers and modeling quantum systems, are predicted to be easier for quantum computers to handle than classical systems. Bounded probabilistic polynomial (BPP) problems, on the other hand, are simple to answer on conventional computers.</p><p>The researchers working on these showed that learning algorithms with input from a quantum process, such as fusion or chemical processes, generate a new class of problems known as BPP/Samp, which can effectively execute tasks that standard algorithms without data can't. It's a type of problem that can be solved quickly with polynomial-sized advice. This shows that understanding the quantum advantage for different machine learning tasks needs a data analysis as well.</p><p>Going deeper into this, we will realize that all the applications of QML are somewhere related to data analytics. From analyzing human behavior to pattern recognition and space exploration, QML uses data analytics to function smoothly and vice versa. </p><h3 id="conclusion">Conclusion</h3><p>In the near future when many new quantum machine learning algorithms will come up with the exploration of this field, more and more applications of it will show up too. Data analytics with time will get easier and easier for an industry in so much demand.</p><p>For the data science experts sitting out there, this was one such article that can make your lives a bit easier, all it will take is a pinch of quantum physics combined with machine learning.</p>

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