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Machine Learning Tools for Clinical Researchers: A Pragmatic Approach Event Series | Newsroom – UNC Health and UNC School of Medicine

April 20, 2022 | Author: admin

This virtual seminar series will provide a background in the use of machine learning tools to answer clinical questions, understand the strengths and limitations of these methods, and examine real-world examples of machine learning methodology in clinical research. The series is co-sponsored by the UNC Core Center for Clinical Research and the UNC Program for Precision Medicine in Health Care.

The UNC Core Center for Clinical Research and the UNC Program for Precision Medicine in Health Care are co-sponsoring a series of virtual, free events on machine learning tools for clinical researchers. Anyone interested in using machine learning as part of their own research is encouraged to attend, regardless of research background or experience with machine learning. The goal of the seminar series is to bring together researchers and clinicians across the UNC campus and catalyze new clinical research using machine learning.

Machine learning analysis methods offer the opportunity to integrate and learn from large amounts of biological, clinical and environmental data, and there is a growing interest in how these tools can be used to inform and individualize clinical decision making in a variety of disease areas.

Machine learning can offer different, yet often complementary, insights compared to traditional statistical analyses to better understand heterogeneity in patient presentation, prognoses, and treatment response, generating critical data for precision medicine research. These methods can allow integration across diverse data types and large feature sets, overcoming some limitations of traditional tools to answer clinical questions. However, many clinical researchers have little exposure to machine learning methods, presenting a barrier to utilization of these tools themselves and/or to effective collaboration with methodologists in their own research.

The event series will series provide a background/foundation of knowledge regarding the use of machine learning tools in clinical questions, help attendees understand the strengths and limitations of these methods, help attendees recognize some real-world examples of applied machine learning methodology in clinical research, and elucidate how machine learning can be used to advance precision medicine research.

On May 11, 2022, clinicians and researchers will discuss examples of how machine learning tools have been applied in arthritis and autoimmune disease. This session will feature an overview of machine learning and its application to identify clinical phenotypes of osteoarthritis and type 1 diabetes. Register online to attend.

On May 18, 2022, clinicians and researchers will explore the use of machine learning tools and precision medicine techniques in clinical research. This session will feature an overview of machine learning tools in the field of precision medicine and address how they may be used to inform decision support for peripheral artery disease and rare genetic diseases. Register online to attend.

On May 25, 2022, 1 3 p.m., a panel discussion will focus on how researchers and clinicians at UNC-Chapel Hill can integrate machine learning techniques into their own clinical research. Register online to attend.

Clinicians with ideas for how patient care could be improved with computational decision support tools can pitch their idea (5-10 minute overview) to assembled machine learning experts during the May 25 session. Attendees also will receive expert guidance and can compete for funding from the UNC Program for Precision Medicine in Health Care for analytical support to develop their projects. Participants can email precisionmedicine@med.unc.edu for more information about the pitch opportunity.

This series is jointly sponsored by the UNC Core Center for Clinical Research and the UNC Program for Precision Medicine in Health Care. All events will be virtual on Zoom and free of charge.

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What is Hybrid Machine Learning and How to Use it? – Analytics Insight

April 20, 2022 | Author: admin

Most of us have probably been including HML estimations in some designs without recognizing it. We might have used methodologies that are a blend of existing ones or got together with strategies that are imported from various fields. We try to a great extent to apply data change methods like principles component analysis (PCA) or simple linear correlation analysis to our data preceding passing them to a ML methodology. A couple of experts use extraordinary estimations to mechanize the headway of the limits of existing ML methodologies. HML estimations rely upon an ML plan that is hard and not exactly equivalent to the standard work process. We seem to have misjudged the ML estimations as we fundamentally use them ready to move, for the most part dismissing the nuances of how things fit together.

HML is a progress of the ML work process that perfectly unites different computations, processes, or procedures from equivalent or different spaces of data or areas of usage fully intended to enhance each other. As no single cap fits all heads, no single ML procedure is appropriate for all issues. A couple of strategies that are extraordinary in managing boisterous data anyway may not be prepared for dealing with high-layered input space. Some others could scale pretty well on high-layered input space anyway may not be good for managing sparse data. These conditions are a fair motivation to apply HML to enhance the contender procedures and use one to overcome the deficiency of the others.

The open doors for the hybridization of standard ML methodologies are ceaseless, and this ought to be workable for every single one to collect new combination models in different ways.

This kind of HML consistently consolidates the architecture of at least two customary algorithms, entirely or mostly, in an integral way to develop a more-hearty independent algorithm. The most ordinarily utilized model is Adaptive Neuro-Fluffy Interference System (ANFIS). ANFIS has been utilized for some time and is generally considered an independent customary ML strategy. It really is a blend of the standards of fluffy rationale and ANN. The engineering of ANFIS is made out of five layers. The initial three are taken from fuzzy logic, while the other two are from ANN.

This kind of cross hybrid advancement consistently joins information control cycles or systems with customary ML techniques with the goal of supplementing the last option with the result of the previous. The accompanying models are legitimate opportunities for this kind of crossover learning technique:

If an (FR) calculation is utilized to rank and preselect ideal highlights prior to applying the (SVM) calculation to the information, this can be called an FR-SVM hybrid model.

Assuming a PCA module is utilized to separate a submatrix of information that is adequate to make sense of the first information prior to applying a brain network to the information, we can call it a PCA-ANN hybrid model.

If an SVD calculation is utilized to lessen the dimensionality of an informational collection prior to applying an ELM model, then, at that point, we can call it an SVD-ELM hybrid model.

Hybrid techniques that we depend on include determination, a sort of information control process that looks to supplement the implicit model choice course of customary ML strategies, which have become normal. It is realized that every ML algorithm has an approach to choosing the best model in light of an ideal arrangement of info highlights.

It is realized that each conventional ML technique utilizes a specific improvement or search algorithm, for example, gradient descent or grid search to decide its ideal tuning boundaries. This sort of crossover learning tries to supplement or supplant the underlying boundary improvement strategy by utilizing specific progressed techniques that depend on developmental calculations. The potential outcomes are additionally huge here. Instances of such conceivable outcomes are:

1. Assuming the particular swam advancement (PSO) algorithm is utilized to upgrade the preparation boundaries of an ANN model, the last option turns into a PSO-ANN hybrid model.

2. At the point when generic calculation (GA) is utilized to streamline the preparation boundaries of the ANFIS technique, the last option turns into a GANFIS hybrid model.

3. The equivalent goes with other developmental streamlining calculations like Honey bee, Subterranean insect, Bat, and Fish State that are joined with customary ML techniques to shape their relating half, breed models.

An ordinary illustration of the component determination-based HML is the assessment of a specific supply property, for example, porosity utilizing coordinated rock physical science, geographical, drilling, and petrophysical informational collections. There could be in excess of 30 info highlights from the consolidated informational indexes. It will be a decent learning exercise and a commitment to the assortment of information to deliver a positioning and decide the general significance of the elements. Utilizing the main 5 or 10, for instance, may deliver comparative outcomes and subsequently decrease the computational intricacy of the proposed model. It might likewise help space specialists to fewer features in on the fewer highlights rather than the full arrangement of logs, most of which might be excess.

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Machine learning hiring levels in the mining industry rose to a year-high in March 2022 – Mining Technology

April 20, 2022 | Author: admin

The proportion of mining industry operations and technologies companies hiring for machine learning related positions rose in March 2022 compared with the equivalent month last year, with 25.8% of the companies included in our analysis recruiting for at least one such position.

This latest figure was higher than the 16.9% of companies that were hiring for machine learning related jobs a year ago and an increase compared to the figure of 18.8% in February 2022.

When it came to the rate of all job openings that were linked to machine learning, related job postings rose in March 2022, with 0.7% of newly posted job advertisements being linked to the topic.This latest figure was a decrease compared to the 1% of newly advertised jobs that were linked to machine learning in the equivalent month a year ago.

Machine learning is one of the topics that GlobalData, from which our data for this article is taken, has identified as being a key disruptive force facing companies in the coming years. Companies that excel and invest in these areas now are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.

Our analysis of the data shows that mining industry operations and technologies companies are currently hiring for machine learning jobs at a rate lower than the average for all companies within GlobalData's job analytics database. The average among all companies stood at 1.3% in March 2022.

GlobalData's job analytics database tracks the daily hiring patterns of thousands of companies across the world, drawing in jobs as they're posted and tagging them with additional layers of data on everything from the seniority of each position to whether a job is linked to wider industry trends.

You can keep track of the latest data from this database as it emerges by visiting our live dashboard here.

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The extensive use of AI and machine learning can do wonders in predictive analysis | eHealth Magazine eHealth Magazine – Elets

April 20, 2022 | Author: admin

Devanand K T, Regional Chief Executive Officer, Aster DM Healthcare shares his thoughts on strengthening the healthcare infrastructure in the country and the future of digital health. Edited excerpts:

Has Indias health care system embarked on a purely digital journey?

Yes, our country has taken the strategic decision of moving ahead with the digital health ecosystem as is evident from the recent budget. The allocation for the National Digital Health Mission is raised to Rs 200 crore from the existing Rs 30 crore. The mental health sector received an allocation of Rs 40 crore for the first time. The evolution of United Health Interface combined which focuses on the integration of various stakeholders of the eco system will rapidly accelerate the journey. The factors like rising growth of smartphone users, rapidly expanding healthcare IT infrastructure, people more conscious of the value of fitness and wellbeing, rise in volume of patients with chronic conditions, growing venture capital investments and economies of scale that bring down cost of digital health solutions will provide the necessary impetus to this drive.

How do you see the role of technology in pre-and post Covid healthcare?

Even pre-covid, patients were becoming more digitally savvy, and doctors were getting increasingly open to using digital platforms and tools. Covid has accelerated this trend leading to removal of many behavioural barriers to digital health adoption. There is a behavioural shift in regulatory and reimbursement trends that provide further opportunities for rise in digital health adoption. Telemedicine witnessed a spike in usage during covid that made inroads or at least made companies and users to place other components of digital health like AI, data mining, machine learning, biosensors, etc into high accelerated development mode. The effective combination of these technological tools will provide a digital ecosystem of comfort, improved quality, continuum of care, better outcomes, and lower healthcare costs.

What are the various technologies and tools you see making a difference in the healthcare segment in the coming times?

Tele-health has already made a huge difference in providing convenience to patients in accessing healthcare. The extensive use of AI and machine learning can do wonders in predictive analysis especially in areas like Oncology. Remote/Bio-sensing devices can provide ease of access to information and can provide vital real time data for better treatment outcomes by effective interventions.

How can technology be used to strengthen the health infrastructure in a country like ours?

The health infrastructure in our country is lagging, especially in rural areas. There are issues related to lack of accessibility, affordability, and awareness in these areas. The technology can provide the necessary impetus to overcome these challenges by providing services like Tele-ICU, Tele-Emergency, Tele-Radiology, etc. Cowin App was one such classical example of technology reaching the mass population in driving vaccination services. Such PPP models involving technological interventions can really help in addressing many concerns related to health infrastructure in a country like ours. Telehealth enabled Mobile Medical Services launched by Aster DM Healthcare is one such step taken by our organization to provide accessibility to the rural mass.

What are the policy initiatives you foresee in the Health IT space that will create a healthcare system of the future?

There should be clear policy guidelines on patient data privacy and security. The cyber frauds should be mitigated by enforcing some clear protocols. Universal Health Card unifying all government schemes can really help in mobilizing the entire population towards health coverage. This can help in reducing budget allocation to various Govt. schemes too. The steps taken by the Government of India in creating United Health Interface and higher allocation of budget to tele-health are clear indications towards this transformation. All healthcare providers to be incentivized for enabling healthcare digitalization. Linking various analytical/predictive tools using machine learning to this huge database can provide a plethora of information for Population Health Management.

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Machine Learning Applications in the Manufacturing Industry – IoT For All

April 20, 2022 | Author: admin

Manufacturers, to keep up with the latest changes in technology, need to explore one of the most critical elements driving factories forward into the future: machine learning. Lets talk about the most important applications and innovations that ML technology is providing in 2022.

Machine learning is a subfield of artificial intelligence, but not all AI technologies count as machine learning. There are various other types of AI that play a role in many industries, such as robotics, natural language processing, and computer vision. If youre curious about how these technologies affect the manufacturing industry, check out our review below.

Basically, machine learning algorithms utilize training data to power an algorithm that allows the software to solve a problem. This data may come from real-time IoT sensors on a factory floor, or it may come from other methods. Machine learning has a variety of methods such as neural networks and deep learning. Neural networks imitate biological neurons to discover patterns in a dataset to solve problems. Deep learning utilizes various layers of neural networks, where the first layer utilizes raw data input and passes processed information from one layer to the next.

Lets start by imagining a box with assembly robots, IoT sensors, and other automated machinery. At one end you supply the materials necessary to complete the product; at the other end, the product rolls off the assembly line. The only intervention needed for this device is routine maintenance of the equipment inside. This is the ideal future of manufacturing, and machine learning can help us understand the full picture of how to achieve this.

Aside from the advanced robotics necessary for automated assembly to work, machine learning can help ensure: quality assurance, NDT analysis, and localizing the causes of defects, among other things.

You can think of this factory in a box example as a way of simplifying a larger factory, but in some cases its quite literal.Nokiais utilizing portable manufacturing sites in the form of retrofitted shipping containers with advanced automated assembly equipment. You can use these portable containers in any location necessary, allowing manufacturers to assemble products on site instead of needing to transport the products longer distances.

Using neural networks, high optical resolution cameras, and powerfulGPUs, real-time video processing combined with machine learning and computer vision can complete visual inspection tasks better than humans can. This technology ensures that the factory in a box is working correctly and that unusable products are eliminated from the system.

In the past, machine learnings use in video analysis has been criticized for the quality of video used. This is because images can be blurry from frame to frame, and the inspection algorithm may be subject to more errors. With high-quality cameras and greater graphical processing power, however, neural networks can more efficiently search for defects in real-time without human intervention.

Using various IoT sensors, machine learning can help test the created products without damaging them. An algorithm can search for patterns in the real-time data that correlate with a defective version of the unit, enabling the system to flag potentially unwanted products.

Another way that we can detect defects in materials is through non-destructive testing. This involves measuring a materials stability and integrity without causing damage. For example, you can use an ultrasound machine to detect anomalies like cracks in a material. The machine can measure data that humans can analyze to look for these outliers by hand.

However, outlier detection algorithms, object detection algorithms, and segmentation algorithms can automate this process by analyzing the data for recognizable patterns that humans may not be able to see with much greater efficiency. Machine learning is also not subject to the same number of errors that humans are prone to make.

One of the core tenants of machine learnings role in manufacturing is predictive maintenance. PwCreportedthat predictive maintenance will be one of the largest growing machine learning technologies in manufacturing, having an increase of 38 percent in market value from 2020 to 2025.

With unscheduled maintenance having the potential to deeply cut into a businesss bottom line, predictive maintenance can enable factories to make appropriate adjustments and corrections before machinery can experience more costly failures. We want to make sure that our factory in a box will have as much uptime with the fewest delays possible, and predictive maintenance can make that happen.

Extensive IoT sensors that record vital information about the operating conditions and status of a machine make predictive maintenance possible. This may include humidity, temperature, and more.

A machine learning algorithm can analyze patterns in data collected over time and reasonably predict when the machine may need maintenance. There are several approaches to achieve this goal:

Thanks to the IoT sensors powering predictive maintenance, machine learning can analyze the patterns in the data to see what parts of the machine need to be maintained to prevent a failure. If certain patterns lead to a trend of defects, its possible that hardware or software behaviors can be identified as causes of those defects. From here, engineers can come up with solutions to correct the system to avoid those defects in the future. This enables us to reduce the margin of error of our factory in a box scenario.

Digital twins are a virtual recreation of the production process based on data from IoT sensors and real-time data. They can be created as an original hypothetical representation of a system that doesnt yet exist, or they could be a recreation of an existing system.

The digital twin is a sandbox for experimentation in which machine learning can be used to analyze patterns in a simulation to optimize the environment. This helps support quality assurance and predictive maintenance efforts as well. We can also use machine learning alongside digital twins for layout optimization. This works when planning the layout of a factory or for optimizing the existing layout.

If we want to optimize every part of the factory, we also need to pay attention to the energy that it requires. The most common way to do this is to use sequential data measurements, which can be analyzed by data scientists with machine learning algorithms powered by autoregressive models and deep neural networks.

Weve used machine learning to optimize the factorys production processes, but what about the product itself? BMWintroducedthe BMW iX Flow at CES 2022 with a special e-ink wrap that can allow it to change the color (or more accurately, the shade) of the car between black and white. BMW explained that Generative design processes are implemented to ensure the segments reflect the characteristic contours of the vehicle and the resulting variations in light and shadow.

Generative design is where machine learning is used to optimize the design of a product, whether it be an automobile, electronic device, toy, or other items. With data and a desired goal, machine learning can cycle through all possible arrangements to find the best design.

ML algorithms can be trained to optimize a design for weight, shape, durability, cost, strength, and even aesthetic parameters.

Generative design process can be based on these algorithms:

Lets step away from the factory in a box example for a bit and look at a broader picture of needs in manufacturing. Production is only one element. The supply chain roles from a manufacturing center are also being improved with machine learning technologies, such as logistics route optimization and warehouse inventory control. These make up a cognitive supply chain that continues to evolve in the manufacturing industry.

AI-powered logistics solutions use object detection models instead of barcode detection, thus replacing manual scanning. Computer vision systems can detect shortages and overstock. By identifying these patterns, managers can be made aware of actionable situations. Computers can even be left to take action automatically to optimize inventory storage.

At MobiDev, we have researched a use case of creating a system capable of detecting objects for logistics. Read more aboutobject detection using small datasetsfor automated items counting in logistics.

How much should a factory produce and ship out? This is a question that can be difficult to answer. However, with access to appropriate data, machine learning algorithms can help factories understand how much they should be making without overproducing. The future of machine learning in manufacturing depends on innovative decisions.

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New SEE Shell Mobile Application Uses Machine Learning to Help Tackle the Illegal Tortoiseshell Trade – PR Newswire

April 20, 2022 | Author: admin

PORTLAND, Ore., April 18, 2022 /PRNewswire/ --Conservation nonprofit SEE Turtles has launched an innovative mobile application that will address the illegal trade of hawksbill sea turtle shells. The beautiful shells of this critically endangered species, commonly referred to as "tortoiseshell," are used to create jewelry and ornamental souvenirs in many countries. The SEE Shell App employs machine learning to differentiate real and faux tortoiseshell products; it is the first mobile application to use artificial intelligence to combat the illegal wildlife trade. This novel technology will enable tourists, law enforcement, and wildlife officials to quickly identify products made of authentic tortoiseshell.

Despite international laws against the sale of tortoiseshell, this trade is active in at least 40 countries, according to SEE Turtles 2020 "Global Tortoiseshell Trade" report, and it remains the primary threat to hawksbill turtles. With an estimated 15,000 to 25,000 adult female hawksbills remaining in the wild, this groundbreaking mobile application will play a key role in bringing these animals back from the brink of extinction.

SEE Shell will help eliminate this confusion. This highly accurate application can now discern whether an item is made of real hawksbill shell or from faux tortoiseshell materials such as resin, horn, bone, seashells, or coconut shells with at least 94% accuracy by simply taking a photo. The mobile application utilizes deep learning technology that compares product photos taken by app users to a data library of more than 4,000 real and artificial tortoiseshell products. As images are stored in the catalog from locations around the globe, a clearer understanding of the size and location of the illegal tortoiseshell trade will emerge.

"Thanks to our conservation partners around the world who have contributed tortoiseshell photos, we have created a first in the wildlife trafficking field; an app that can help individual consumers identify and avoid endangered animal products," said Alexander Robillard, Computer Vision Engineer with SEE Turtles.

SEE Turtles has also partnered with the World Wildlife Fund for Nature, who is providing financial and technical support. As part of SEE Turtles' "Too Rare to Wear" campaign, partnering organizations in Indonesia and Latin America have helped to test the app in the field and will train local law enforcement officials on how to use the application to document the presence of tortoiseshell trade in their regions. Participating organizations include the Turtle Foundation (Indonesia), Fundacin Tortugas del Mar (Colombia), Latin American Sea Turtles (Costa Rica), The Leatherback Project (Panama), and Sos Nicaragua.

Media Contact:Brad Nahill800-215-0378[emailprotected]

SOURCE SEE Turtles

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Autonomous Drones use AI for infrastructure inspection with technologies from Auterion and Spleenlab – sUAS News

April 20, 2022 | Author: admin

Drones run AI on the edge and use machine learning to autonomously conduct infrastructure inspections and collision avoidance

MOORPARK, Calif. and ZURICH April 20, 2022 Auterion, the company building an open and software-defined future for enterprise drone fleets, is excited today to welcome Spleenlab to the growing Auterion ecosystem. Spleenlabs VISIONAIRYtechnology delivers AI-based safe perception software for drones to complete fully autonomous infrastructure detection, inspection, and collision avoidance.

Together with Spleenlab, were demonstrating that the future of robotics is now with our combined AI, machine learning and onboard edge technologies, says Markus Achtelik, vice president of Engineering at Auterion. This new partnership brings together the best technologiesAuterions Skynode and AI Node with Spleenlabs VISIONAIRY Perception Softwareto deliver the best possible autonomous solutions for enterprise drone users.

Spleenlabs VISIONAIRY AI, built to be safe from ground, together with Auterions AI Node, which is equipped with the worlds smallest AI supercomputer for embedded and edge systems, transforms Skynode itself into a supercomputer. The combination runs AI right at the edge onboard any drone. Software running on the Auterion ecosystem enables autonomous inspection of critical infrastructureempowering enterprise users to scale beyond single-pilot, single-drone operations.

Were excited to collaborate with Auterion to take this next step into an autonomous future, says Stefan Milz, Founder at Spleenlab. Advanced ML algorithms are computation-heavy and require appropriate horsepower to be deployed onboard mobile robots. With Auterions Skynode and AI Node, the VISIONAIRY AI software can easily be deployed and run onboard drones on a high safety level.

When Spleenlabs VISIONAIRYsoftware and ML algorithm are installed on Auterions AI Node, the combined technologies enable drones to:

Enterprise drones are able to understand their environment and predict safe landing spots in real time for package delivery, emergencies, and other situations. Risk estimation also includes detecting cooperative and non-cooperative air traffic, with up to 360 degree field of view and several kilometers range. The unique, combined capabilities move industries toward fully realized beyond-visual-line-of-sight (BVLOS) autonomous flight.

Learn more about the VISIONAIRYintegration with AI Nodehere.

About Spleenlab GmbH

Spleenlab GmbH is a highly specialized AI software company founded with the Idea to redefine Safety and AI. Since April 2018, the company has been primarily engaged in the development and distribution of safe Machine Learning algorithms for semi- and fully autonomous mobility, especially the flight of unmanned aerial vehicles (UAV), helicopters, Air Taxis, driving vehicles and beyond. The groundbreaking fusion of different sensors, such as cameras, lasers and radars by means of Machine Learning is the core business of the company. The generated SLAM (Simultaneous Localization and Mapping) enables completely new applications and products for any kind of autonomous mobility. The company is based in Jena (Saalburg), Germany.

Learn more athttps://spleenlab.com/

About Auterion

Auterion is building the worlds leading autonomous mobility platform for enterprise and government users to better capture data, carry out high-risk work remotely, and deliver goods with drones. Auterions open source based platform was nominated by the U.S. government as the standard for its future drone program. With 70+ employees across offices in California, Switzerland, and Germany, Auterions global customer base includes GE Aviation, Quantum-Systems, Freefly Systems, Avy, Watts Innovations, and the U.S. government.

Learn more athttps://auterion.com/

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Cryptocurrency crash: Will crypto continue its price fall and what to do? – Marca English

April 20, 2022 | Author: admin

The fall of cryptocurrencies such as Bitcoin and Ethereum that has been experiencing this 2022, especially in recent weeks, has generated fear among investors betting on digital tokens.

Just consider the drop of BTC in the last few hours, 3.12%, which so far this year amounts to 13.92%, while ETH has lost 2.59% of its value in the last few hours, while so far in 2022 it has lost 18.44%.

As expected, this negative effect has had consequences for other cryptocurrencies, such is the case of BNB, XRP, Solana, Cardano, Luna, and Avalanche, but the market drop does not seem to have reached its lowest point yet.

Due to its privileged position as the most valuable cryptocurrency in the market historically, Bitcoin's gains or losses are reflected in the rest of the digital tokens, which right now could be the worst possible news.

Crypto-investment experts do not rule out a drop from the average $40,000 level to the $30,000 level, a scenario that is alarming for all stakeholders in the subject.

"The signal for a break of the mild upward trend would be a consolidation below the $38,000 per bitcoin levels. If the bulls capitulate, the first cryptocurrency could be pushed into the $32,000 to $35,000 range without much resistance," Alex Kuptsikevich, FxPro senior market analyst, told Forbes.

Although there is no ideal formula to follow in case the cryptocurrency decline continues, there are some things you can do, such as diversify your investment portfolio, invest in more established cryptocurrencies, be informed of recent trends, keep liquid savings, and use a crypto exchange with low fees and simple trades.

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What is Web3? The future of the internet, cryptocurrency, and estate planning – Fast Company

April 20, 2022 | Author: admin

The term Web3 is creating quite the buzz lately, especially among those interested in technology, cryptocurrency, and venture capital.Heres an introduction to what some people are calling the internet of the future. No one really knows what it is yet, but from an estate planning standpoint, I have some predictions.

To understand Web3, its helpful to go back in time and review the history of the internet.

In the 1990s, we were calling the internet the information superhighway. Although it was confusing and overwhelming to most, many Americans began to dial up nonetheless. Children were being taught how to surf the web alongside adults who learned how to use email and conduct basic web searches for the first time. The internet was very much a blank slate with a steep learning curve. However, many people intrinsically knew this was the next big thing that would change our lives forever. This era of the internet is referred to as Web 1.0.

In the mid-2000s, we entered the age of Web 2.0. Major platforms like Google, Amazon, Facebook, and Twitter emerged. They began to make the internet a more convenient place to connect, interact, and transact. However, critics began to question whether these few companies were beginning to amass too much power. In recent years, we witnessed lawsuits in which tech giants were slammed with billion-dollar fines for harvesting personal data and breaching data privacy laws.

Although the public enjoys taking advantage of free services and centralized data, many are questioning the role of these tech giants and whether their control over data is too great. Should we democratize and create information exchange silos on the internet once again?

This is where Web3 comes in, and why its causing a brouhaha amongst tech speculators.

The internet would be decentralized sans tech giants in the future of Web3. We could be looking at the rise of new social platforms, search engines, and marketplaces built and owned by the collective, rather than by a corporation.

End users would reclaim their power and control over their own data. They could possibly move around from platform to platform on the internet using a single personalized account, rather than having to log into multiple different accounts for each respective company and platform.

We could be given tokens for participating, which can then be used to vote on decisions regarding how a platform should change or improve, or demand what features we want to be offered. These tokens could even accrue real value and build our wealth.

How will this be possible? How will all this be secure? Web3 will be built using blockchain technology. Yes, thats right. Its the same technology that is used by cryptocurrency right now. One of the reasons why cryptocurrency and decentralized finance are so popular today is because they cut the middleman out of determining value and executing trades on the internet.

Web3 may be the pathway to cutting the middleman out of the internet, data, and information exchange itself. The internet could become a place that is run by the collective, using digital tokens as its currency.

Although the idea of Web3 could make any average persons eyes glaze over, those who are ahead of the curve and understand crypto are getting excited and placing their bets on Web3.

Do you remember the very beginning of the internet? We would dial-up through America Online (now AOL) in the 1990s and the most we could really do was visit our friends Geocities pages. Everything felt clunky and awkward, and a lot of us didnt even really know what the internet was for. It turned out that the experts werent wrong, and today, the internet pretty much runs our lives.

Web3 could very much be a parallel to this. Its something that remains to be seen, and there is no universal understanding of what Web3 is, or what it will look like. We may be decades away from Web3 becoming our reality, but it behooves us to start thinking about how blockchain technology will revolutionize how we conduct our personal business.

One aspect of this is estate plans. Its entirely possible we could have blockchain-based estate plans. You could, in theory, set up smart contracts that dictate that your digital assets will be passed automatically to your spouse or beneficiary upon a specified trigger event, such as your death. These assets would not go through the probate court as they would today.

In other words, in the age of Web3, we could be looking at a possibility in which the effectuation of an estate plan could become decentralized.

Although we may be decades away from a new reality like Web3, the estate planning industry is already taking steps in this direction.

For instance, California has enabled the small-estate affidavit, which allows for estates valued under $166,250 to bypass probate. More states could follow suit. On a grander scale, more and more states are adopting laws that enable the digital execution of wills. This is made possible by legally recognizing e-signatures and e-notaries. These types of regulatory changes make it possible for wills to be effectuated automatically and digitally.

The digital age is here, and one thing you can do now is protect your assets (both physical and digital) with an estate plan. We are hearing too many jaw-dropping stories about hundreds of millions in cryptocurrency fortunes being lost in the ether due to a lack of proper estate planning. Crypto is popular amongst young adults, demonstrating the need for estate plans to be established earlier in life as well.

Have a plan in place for how to protect your digital assets, how you plan to pass them on if anything were to happen to you, and leave instructions for how they can be accessed and used. Estate planning is the key in this digital age.

Cody Barbo, Founder & CEO,Trust & Will

See more here:
What is Web3? The future of the internet, cryptocurrency, and estate planning - Fast Company

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Update on BlockMint’s Cryptocurrency Mining Operation in the USA – Yahoo Finance

April 20, 2022 | Author: admin

Vancouver, British Columbia--(Newsfile Corp. - April 20, 2022) - BlockMint Technologies Inc. (TSXV: BKMT) ("BlockMint" or the "Company") is pleased to provide an update on its Bitcoin mining operation which commenced on December 3, 2021 with fifty new S19 Antminers at a third-party hosting facility in Washington state. Since the start of mining operations, approximately 2.76 Bitcoins have been mined, equivalent to US$110,490 based on a Bitcoin price of US$40,000. For the most recent three month period (i.e. January 1st - March 31st 2022), the amount mined was approximately 2.07 Bitcoins. There is no guarantee of future mining or financial performance which is subject to a number of factors including Bitcoin price, mining difficulty rate, electricity prices, machine performance, etc.

BlockMint's CEO, Nelson Ijih, commented: "We are pleased with the early results of our US-based Bitcoin mining operation which is powered by clean, green hydro-power. This crypto mining operation is complimentary to our Minter browser which allows users to mine cryptocurrency to earn carbon credits or fractional interests in a NFT. The Company may scale-up its crypto-mining operations depending on future financial results and prevailing market conditions."

About BlockMint Technologies Inc.

BlockMint, through its wholly owned subsidiary, Blockmint (USA) Technologies Inc., operates a cryptocurrency mining operation in the state of Wahington, USA and develops distributed systems and networks that enable a more decentralized deployment of blockchain based applications such as cryptocurrency mining. The Company's Minter browser allows users to use their spare computing power to mine cryptocurrencies to earn either: (i) carbon credits to help offset their carbon footprint; or (ii) fractional ownership in a NFT. The current version of Minter is for use on desktops and laptops with a Windows operating system and is available at getminter.com.

On behalf ofBLOCKMINT TECHNOLOGIES INC.

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Nelson Ijih

Nelson Ijih, CEOinfo@blockmint.ai

Neither the TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.

CAUTIONARY STATEMENT REGARDING FORWARD-LOOKING INFORMATION: This news release includes certain "forward-looking statements" under applicable Canadian securities legislation. Forward-looking statements include, but are not limited to, statements with respect to the business strategy and objectives of BlockMint. Forward-looking statements are necessarily based upon a number of estimates and assumptions that, while considered reasonable, are subject to known and unknown risks, uncertainties, and other factors which may cause the actual results and future events to differ materially from those expressed or implied by such forward-looking statements. There can be no assurance that such statements will prove to be accurate, as actual results and future events could differ materially from those anticipated in such statements. There is further no assurance that the Company will be successful in profitably operating its expanded cryptocurrency mining business in the manner described. Accordingly, readers should not place undue reliance on forward-looking statements. The Company disclaims any intention or obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by law.

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/120936

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Update on BlockMint's Cryptocurrency Mining Operation in the USA - Yahoo Finance

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