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ML applications in the healthcare industry are evolving quickly and changing how doctors identify, treat, and prevent diseases. The potential uses of machine learning in the healthcare industry are numerous and varied, ranging from predicting disease outbreaks to finding intricate medical patterns and assisting researchers in developing targeted medicines.
ML algorithms have the potential to offer medical professionals previously unheard-of insights into patient health by analyzing massive datasets and seeing patterns that may not be obvious to the human eye. Machine learning has the potential to significantly transform healthcare, benefiting patient outcomes and the whole healthcare experience in this quickly developing industry. So, let us look at some of the machine learning applications in healthcare.Drug Creation and Production
Clinical applications for machine learning have great promise, especially in the early stages of the drug discovery process. This covers the development of alternate therapy strategies for complex disorders using next-generation sequencing and precision medicine. Currently, methods for unsupervised learning are utilized to find patterns in data without making predictions.Management of Health Records
Maintaining correct and current health data can be time-consuming and labor-intensive in the healthcare business. Although data input processes have been simplified by technology, many activities still take a lot of time and effort. A promising method for optimizing healthcare procedures and conserving time and money is machine learning.Disease Recognition
Machine learning is increasingly being used in healthcare to identify and diagnose difficult-to-detect diseases and maladies, such as cancer and genetic disorders. The ability to combine cognitive computing with genome-based tumor sequencing to allow IBM Watson Genomics best demonstrates quick and precise diagnostics.Clinical Trial Optimisation
Machine learning can significantly enhance clinical trials and research effectiveness and efficiency. Applying ML-based predictive analytics to find trial participants can help researchers access a wide range of data sources, such as previous doctor visits, social media activity, etc. Clinical trials are notoriously time- and money-consuming. Additionally, machine learning may choose the best sample size for testing, use electronic health records to reduce data-based errors, and enable real-time monitoring and data access for trial participants. These cutting-edge machine-learning algorithms can speed up drug discovery and enhance patient outcomes.Personalized Medicine
Predictive analytics is used in personalized medicine, a promising healthcare method, to match patient health data with customized treatment alternatives. Using machine learning, Personalized medicine can improve disease evaluation and increase treatment efficacy. Doctors may only choose diagnoses based on symptom history and genetic data.Predictive Modeling for Epidemics of Diseases
The monitoring and forecasting of epidemics on a global scale are becoming more dependent on machine learning and AI-based technology. Scientists may utilize artificial neural networks to gather information and forecast epidemics of everything from malaria to severe chronic infectious diseases because of the availability of large volumes of data from satellites, social media, and websites.Collection of Data Techniques
In the medical industry, crowdsourcing is a fast-spreading practice that gives researchers and practitioners access to a plethora of health data that individuals have voluntarily uploaded. This live health data will significantly impact the future of medicine. For instance, Apple’s ResearchKit uses interactive apps and facial recognition powered by machine learning to treat Asperger’s and Parkinson’s disease.
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Web scraping is used to aggregate travel, hotel, and airline data from multiple web sources using a web scraping tool or web scraping API. Scraped travel data enables businesses to monitor their competitors, optimize their pricing strategy, discover trending keywords in a specific topic, and personalize their customers’ journeys. Scraping travel data automates and improves the efficiency of many tasks in the travel and hospitality industries. However, companies face challenges in achieving desired business outcomes due to a lack of knowledge about how to benefit from scraped travel data.
This article discusses how a web scraper collects travel and tourism data from websites and the 5 common applications/use cases of web scraping in the travel industry.1. Scraping hotel reviews & pricing
After you enter the necessary information, such as check-in/out dates, the website will display the most popular hotels that match your preferences. The algorithms used by travel companies generally rank hotels based on user preferences, popularity, satisfaction, etc. It is critical to have a high number of positive reviews to be ranked higher. Web scraping helps hospitality companies:
Compare prices of hotel competitors to adjust their prices accordingly: Pricing strategy is influenced by strong competition, high taxes, unfavorable economic conditions, and the quality of products and services. If you want to adjust your prices based on your competitors’ prices. You need to collect publicly available information about your competitor’s prices. Web scraping bot enables companies to get room prices from different hotel pages.
Turn customer reviews into insight: You can scrape your company’s and competitors’ customer reviews from websites and web pages. Scraped web data allows companies to analyze sentiment in customers’ reviews ( see Figure 1). You can get insights about:
Your customers’ preferences and expectations.
Your pain points that need to be improved.
How you enhance your value proposition.
What/how your competitors do right.
Figure 1: Customers’ descriptive words for a specific product
Source: AIMultipleHow to collect hotel review & price data
Web scraping tools enable companies to collect publicly available hotel and tourism data, including hotel listings, prices, ratings, reviews, pictures, addresses, etc. A web scraping bot follows the steps below to collect public web data.
Web scraping bots help travel agencies in obtaining real-time data from multiple web sources or specific web pages. Product price and stock information are examples of data that are constantly changing.
Assume you want to track and observe hotel room prices in a specific area for a month. In that case, your dynamic data is “room price,” which is constantly changing. A web scraping bot will extract room pricing data on the first of the month. When the price of rooms changes on the website from which you extract data, your scraper also updates the price data. Thus, you will receive the most recent price data.
The web scraper will make numerous connection requests to the website to update the price. If you make a connection request with the same IP address, the website will identify your web scraper and block it to prevent data scraping. You can use a proxy server to scrape web data to avoid being detected and blocked by the website.
Check out our in-depth guide to proxy server types to decide which proxy server is best for you.
A quick tip: Residential and Internet Service Provider (ISP) proxy servers are excellent choices for large-scale web scraping projects. Because you must make multiple requests, IP anonymity and privacy are critical to avoid being identified as a scraping bot. If, on the other hand, your scraping project must be completed as soon as possible. Datacenter proxies are the best option for completing tasks quickly.
Bright Data’s Data Collector extracts publicly available data from various websites for travel and hospitality companies. It aggregates hotel, travel and airline data from different data sources, such as hotel listings, reviews, ticket prices, location data, customer data, social media trends, room and flight ticket availability (see Figure 2).
Figure 2: How Bright Data helps businesses in the travel sector3. Scraping airline/flight data
The airline industry implements various ticket pricing such as economy, premium economy, business class, etc. Ticket prices change according to:
The number of seats left,
Itinerary dependencies such as one-way flights,
Sales volume like off-peak times, etc.
Web scraping helps airline companies understand current airline market conditions, offer personalized prices to customers, and manage customers’ demands. Web scraping bots extract flight data, including flight number, flight duration, ticket price, plane name, arrival/destination time, airline name, etc. Web scraping is used by airlines for:
Price monitoring: Price monitoring is crucial to keep up with the competition in the airline industry. It helps airline companies to understand market demand and supply and improve demand management based on current market conditions. A web scraping tool can be used to analyze current market conditions.
Analyzing market share: Scraped flight data can be used to analyze the current airline market conditions. It allows airline companies to
Understand how their competitors are doing by analyzing their market share in the total airline industry share.
Pinpoint growing investment areas in the airline industry.
Analyze top players in the competition and understand how they differentiate their products and services.
Improve growth strategy to increase your company’s visibility.How to scrape flight price information from websites
Assume you want to get the most recent flight price information. Flight prices can be found using a travel booking platform or airline companies’ websites.
Select a date range to see all available flight tickets.
Copy the website’s URL.
Paste the URL into the web scraping bot and run it.
The bot will extract all the required information.
Depending on your needs, specify the scraping time, such as every 120 minutes, day, or week. The web scraping bot will refresh the scraped flight prices regularly.
Convert the extracted data into the desired format.4. Analyzing travel/tourism trends
Suppose you are in the travel and tourism industry. In that case, your competitors are most likely using social media channels effectively for campaigns, brand building, and other purposes to improve their online presence. Web scraping bots:
Extract trending topics and hashtags on social media platforms.
Aggregate travel news from blogs/travel sites.
Monitor competitors’ social media channels.How to scrape travel and tourism trends from websites
Let’s go over an example; assume you own a boutique hotel located in New Orleans. When you search “boutique hotels in New Orleans” many boutique hotels will appear in search results based on your search terms, reviews, ratings, and other factors (see Figure 3).
A quick tip: You can search for keywords that your hotel is trying to rank in Google search results, such as “boutique hotels for couples,” “boutique hotels for families,” “boutique hotels with child-friendly” etc.
Figure 3: Shows the search results for a “boutique hotels in New Orleans” query
According to Google, “near me” or “nearby” searches have increased by 150%. When people search for a restaurant, hotel, vacation rental, or flights they use search engine’ maps to find the places in their areas. They search for “restaurants near me” or “hotels near me,” for example. This makes search engine maps data critical for businesses.
When I search for “vacation rentals in Texas” Google Maps displays many vacation rentals in the searched area (see Figure 4). Results can also be sorted by rating. Google Maps data is an excellent resource for connecting customers with businesses. Web scraping bots collect information about real estate, hotels, restaurants, vacation rentals, etc. Companies can use Google Maps to scrape their competitors’ location details, customer ratings, and reviews.
Figure 4: Google Maps search result for “vacation rentals in Texas”
Source: GoogleHow to scrape travel & tourism data from Google Maps
Search for a specific keyword, such as “hotels in California”.
Scrape all business listings on each Google Map search result.
The web scraping bot scrapes each search result page individually.
On Google Map pages, the scraper extracts all available data by category, such as business names, website addresses, location details, customer ratings and reviews, service descriptions, and so on.
Download all scraped pages as CSV, XLSX, or other formats.Further Reading
For a comprehensive view of web scraping, how it works, use cases, and tools, download our in-depth whitepaper on the topic:
If you believe your company could benefit from a web scraping solution, look through our list of web crawlers to find the best vendor for you.
For guidance to choose the right tool, reach out to us:
Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.
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For many in the healthcare industry, the distinction between mobile device management (MDM) and enterprise mobility management (EMM) is unclear. When choosing the best solution provider, especially in terms of healthcare security, it’s important to understand the history and difference between the two practices.
A Blended History
Mobile device use isn’t new to healthcare — nurses, patients, doctors and everyone in-between are actively using mobile devices and solutions to address the challenges of the industry. According to HIT Infrastructure, until recently, MDM was a broad term, referring to both the segmented and general use of mobile devices.
That changed three years ago when Gartner proclaimed that MDM had evolved into EMM, a term that encompassed larger mobility management solutions that also included mobile content management (MCM), mobile application management (MAM) and identity and access management (IAM).
This structure means that every EMM solution includes an MDM component, but also that an MDM solution does not offer everything an EMM solution does.
Why MDM Isn’t Enough in an App-centered World
When the mobile environment was more simple, MDM solutions met the needs of most solutions providers. Today though, with every month bringing increased complexity to mobility, it’s becoming more apparent that EMM solutions will need to be a serious consideration for most facilities and organizations.
Many of MDM’s shortcomings were exposed by BYOD policies. As IT admins began integrating MDM into their BYOD strategies, they began to run across functionality limitations and bumps in user experience. These issues are most obvious in situations where employee-owned devices are used for work purposes. For example, since MDM functions at the device level, if a situation arises where an admin needs to destroy data in an application that’s used both privately and professionally, all the information needs to be wiped, regardless of classification.
In contrast, EMM offers a level of flexibility around applications where MDM does not. More specifically, EMM solutions allow IT departments to establish policies on both an application and information level.
Securing Healthcare Apps
Explore critical issues around mobile healthcare security and how to lessen the risks. Download Now
Those last two elements are non-negotiable in healthcare security environments where security depends on adoption and proper user behavior. EMM solutions offer IT departments the option of enabling privacy policies at multiple levels, including implementing single sign-on options where applicable.
Communicating With Vendors
When working with vendors and solution providers on building secure application environments, it will be essential to clearly understand the terminology they’re using.
The formal, 2023 Gartner definition of EMM is a broad solution that “enables organizations to integrate and manage mobile devices in their IT infrastructures.” Where things get tricky is that some vendors still use MDM to refer to EMM solutions, so it’s essential when working with vendors to understand organizational needs, where confusion can potentially take place, and ask questions to fully understand what’s being discussed and how terminology is used.
Here are four points to consider when working with a vendor to identify an EMM solution:
Support: Troubleshooting and support options in EMM offerings include inventory, analytics and remote actions.
Provisioning: Unlike MDM solutions, EMM solutions will configure applications and devices for enterprise deployment, as well as manage updates and assist with device upgrade and retirement.
Auditing, tracking and reporting: If a solution can’t track device inventory usage and settings to verify compliance with enterprise policies, it’s likely an MDM solution.
Enterprise data protection: EMM solutions offer mitigation against theft, employee termination, data loss and other incidents. They do this by adding controls for data access rights, data encryption, device lockdown and shared devices, as well as application wrapping and containment.
Ultimately, making the choice between MDM and EMM solutions will depend on your organization’s particular needs, as well as the path it takes in terms of application use, security and vulnerability. Regardless of choice, make sure to work with a vendor who understands the challenges around all those components as well as the particular challenges of healthcare security.
Are unpatched security vulnerabilities worth the risk? A recent report shows just how much known vulnerabilities can cost your business.
When Mark Pickett was a captain in the Marines, he knew he couldn’t be there to make every decision for his soldiers.
“You can’t rehearse every scenario, and there will be times when you can’t communicate,” he explained. “You want to groom your Marines to be able to rely on themselves and their unit.”
It’s not so different in the business world in this era of big data.
Now senior director for online analytics and business intelligence at Sears, Pickett has been an early champion of the so-called citizen data scientist movement, by which employees in multiple parts of an organization are empowered with the analytics tools and skills to get the answers they need from their data.
“The business understands the business more deeply than we ever could,” he said. “We’re trying to coach these people up and provide them with the data they need to craft their own reporting and do their own analyses.”
In Sears’ case, the motivation is particularly strong. Though a retail business overall, the company is in many ways a conglomeration of numerous vertical businesses, each focusing on different product types.
“We have a very multicategory sort of business, from lawn and garden to appliances to clothing and jewelry to mattresses,” Pickett said. “My team is built to support all of them, but we’ll never understand their businesses the way they do.”
By curating the right tools — in Sears’ case, Platfora’s big-data analytics platform for Hadoop — Pickett’s group aims to enable businesspeople to answer 80 percent of their data questions themselves. More than 300 trained citizen data scientists at the company are now using those tools to generate thousands of data-analysis reports each week without any assistance.
“The only reason we’d touch one is if someone had questions, or needed data added,” Pickett said.
A new generation of tools
Sears may have a particularly pressing need by virtue of the diverse nature of its business, but companies of all kinds are feeling the acute shortage of trained data scientists today. Even for those lucky enough to snag such a professional, “janitorial” tasks such as data preparation are still taking up an inordinate proportion of those workers’ time.
Empowering businesspeople to do much of the analysis themselves frees up highly trained data scientists to focus on the things that require their expertise — or so the thinking goes.
Part of what’s making it possible is the growing set of powerful self-service tools available on the market today, putting capabilities like artificial intelligence within reach for virtually anyone.
“Companies have more and more data,” said Lukas Biewald, CEO and founder at data-focused crowdsourcing site CrowdFlower.
Gartner predicts that the market for self-service data-preparation tools will reach $1 billion by 2023.
“Large enterprises are moving to data lakes, so all the data is in one place,” said Jason Zintak, Platfora’s president and CEO.
Next, companies need to help their employees make the most of it. Platfora bills its Hadoop-focused platform as a way to let anyone within a company run analyses across the entire organization’s data, including transactions, customer interactions and machine data.
‘They can build their own reports’
In many ways, the citizen data scientist represents an evolution of the traditional business analyst role.
“When I think about the traditional business analyst, they’d have a good understanding of the business but were not necessarily conversant with regard to the data,” Sears’ Pickett said.
Such professionals have often been focused on gleaning insights from Excel or other reporting tools without necessarily working knee-deep in the data, in other words.
“What I’m observing is that people who have a strong understanding of the business now have some capability in terms of the data,” he explained. “They can build their own reports, they know what attributes go together and they know what questions to ask not just from a business perspective but from a data perspective.”
Not everyone is sold on the citizen data scientist concept, however.
‘A recipe for disaster’
“I don’t like the ‘citizen data scientist’ term,” said Gregory Piatetsky-Shapiro, president of KDnuggets, an analytics and data-science consultancy.
For one thing, “the term implies that people without much training can do the work of a data scientist,” Piatetsky-Shapiro said.
It’s all too easy to discount the importance of education, in other words, even as big data is in many ways making it more important than ever before. With statistics at its core, data science often relies on an understanding of the assumptions underlying various statistical techniques, for example — factors that aren’t always apparent to those who haven’t formally learned about them.
“Would you trust your teeth to a ‘citizen dentist’ or fly in a plane piloted by ‘citizen pilot’?” Piatetsky-Shapiro asked. “Having untrained citizen data scientists analyze the data may be easy, but if they will be making decisions without proper training in data analysis and without an understanding of the business, it is a recipe for disaster.”
Platfora’s Zintak says built-in corporate governance structures can address that issue by controlling security and access levels, for example. At Sears, two weeks of training for the company’s 300+ citizen data scientists have helped as well.
‘Data is viral – everybody wants it’
Sears finalized its migration from a DB2 relational database management system to a Hadoop data lake in 2023. It had already adopted Platfora for a small group of specialists, but it wasn’t long before the need for broader availability became clear.
“Data is viral — everybody wants it,” Pickett said. “It quickly became apparent that we had to solve for the volume of data requested by people by enabling them to become self-sufficient.”
Focusing on the 300 or so people who handled many of the reporting needs for their teams, Sears’ own in-house experts conducted the training to bring those users up to speed. Topics covered included nomenclature and data-set manipulation, for example.
Today, those employees request data, not reports, he said: “That’s when we knew this was starting to take shape.”
Now freed up from the bulk of the company’s ad hoc reporting needs, Pickett’s team can focus on higher-level tasks such as data curation, model building and governance.
‘Start small and just do it’
Overall, Pickett touts decentralized decision-making as one of the chief benefits of the citizen data scientist model.
“It’s not just about reducing reliance on us,” he said. “It’s empowering people to become more capable with their own data, and that’s enabling them to think about their business in new ways.”
If Pickett had to do it all over again, he’d make the transition to the citizen data scientist model sooner, he said.
Leonardo.ai is an artificial intelligence platform that specializes in generating stunning and photorealistic images. Whether you’re an artist or just someone who wants to create amazing art with ease, this platform has something for everyone. The best part is that you don’t need to have any experience with AI or complex software to use it!
Leonardo AI is a cutting-edge tool that harnesses the power of artificial intelligence to create stunning game assets, including items, environments, helmets, buildings, and concept art. With its intuitive and artist-friendly interface, users can ideate and train their own AI models, resulting in unique production-ready assets that are perfect for video games.
Revolutionizing the way creative professionals, businesses, and individuals create high-quality visual content, Leonardo AI offers an exclusive early-access program that enables users to sign up and explore its features. Unlike other AI image generators, Leonardo AI focuses solely on generating assets for video games, making it an essential tool for game designers and developers.
Before you can start using chúng tôi you’ll need to sign up for a free account. The sign-up process can be a bit confusing, but we found a little workaround for immediate access. Here’s how it works:
Go to Leonardo.ai
To the top right, hit “Launch App”
First, sign up for early access
You will be taken to another screen
Hit back and go back to this screen, and then hit “Yes I’m whitelisted”
You will be taken to the Login page, where you can simply log in with Google or Microsoft, or you can hit sign up
Once you’ve completed, the first time you will be asked to select your interests
Once you’ve signed up, you’ll be taken to the home page, where you’ll find a list of featured models and a community feed showcasing recent creations.
Also read: How to Sign up and Use Leonardo AI
One of the most notable features of chúng tôi is the availability of pre-trained models that users can choose from. These models include photorealistic and artistic styles, vintage photography, magical creatures, and paper art. Our testing shows that these are fine-tuned models that produce specific and excellent outputs.
Another outstanding feature is the ability for users to create their own custom data sets and models by uploading photos. It’s relatively simple and fast to do this, which allows users to train chúng tôi in a specific style and get the results they want.
With Leonardo AI, you can design real-life ideas into spectacular art. Its AI-powered algorithms can generate staggering game resources, such as items, characters, caps, structures, and concept art. The result is a seamless integration of technology and creativity that allows users to bring their visions to life.
With Leonardo AI’s exclusive early-access program, users can sign up to explore the platform before its official launch. This program enables users to gain early access to new features, offer feedback, and be a part of the platform’s development process.
The user interface of chúng tôi is web-based and easy to navigate. The options available to users include the number of images they want to generate, the image dimensions, the guidance scale, and the tiling. On the right side of the screen, users have the ability to input their prompts, select the model and style they want to use, and include negative prompts if desired.
The interface has a clean and organized design, making navigation simple. The functions and features are presented clearly and do not obstruct the user. It is reminiscent of the layout of Midjourney’s account page.
Training a model in Leonardo AI is a crucial step in creating your desired outputs. There are several factors to consider to ensure a successful training run.
The characteristics of your dataset, specifically the balance between variation and consistency, will also play a role in the training process.
It is important to have a common theme or pattern between your images for the model to learn from. The elements that are consistent between images are what the model will learn and show up in the outputs.
Leonardo.ai features an AI canvas, a powerful, simple and easy-to-navigate editor that allows you to create and edit AI art images in new and innovative ways. If you have been previously intimidated by other Stable Diffusion models, this could be the solution for you.
You can either upload an image from your computer, a previous generation, or from the community. Once you have selected an image, you can copy the prompt and edit it in the canvas. The AI canvas provides a block-based interface that makes editing your prompts a breeze. You can move the blocks around, delete them, and add new ones with ease.
One of the most exciting features of the AI canvas is the ability to control the output of the generated images. You can modify the number of samples, the temperature, and the time taken to generate the output. Additionally, you can choose the type of output you want, such as still images or animations.
Once you have generated an image or animation that you are happy with, you can share it with the chúng tôi community. This is a great way to get feedback and see what other users are creating. You can also download the image or animation to your computer and use it as you wish.
Leonardo.ai is an exciting and innovative platform that allows users to generate AI art with ease. Its user interface is easy to navigate, and its features are well-designed and easy to use. The ability to create custom models and datasets is particularly impressive, as it allows users to create outputs that are unique and personalized. Overall, if you are interested in creating AI art, chúng tôi is definitely worth checking out.
Ethereum’s grip on DEX dominance is slipping, signaling a new era in decentralized trading.
However, Ethereum’s innovative Layer 2 solutions are recapturing lost traffic and solidifying its position as a dominant platform.
Ethereum emerged as a second-generation blockchain, revolutionizing the digital landscape by introducing smart contract functionality.
It ingeniously filled a void left by the Bitcoin network, which lacked this essential feature. Among its notable achievements, Ethereum solidified its position as the epicenter of Decentralized Exchanges (DEX).
However, Ethereum’s stronghold on the DEX throne is gradually slipping away, giving rise to a new era in decentralized trading.Is Ethereum lagging in DEX dominance?
Ethereum has long reigned supreme as the go-to network for Decentralized Applications (Dapps) and Decentralized Exchanges (DEX), with most smart contract platforms operating on its blockchain.
However, recent data from Messari suggested that Ethereum’s grip on DEX dominance was waning. This shift can be attributed to two factors.
Firstly, the decreasing dominance in DEX volumes could be attributed to the emergence of alternative Layer-1 (L1) DeFi ecosystems. Also, the strong bull market throughout 2023.
However, when market downturns hit in 2023, many large entities were wiped out. It also caused trading volumes to shift back to the mainnet.
Furthermore, this trend culminated in March 2023, during the USDC depeg. During this time its DEX volume dominance reached an impressive 80% – a level not seen since the beginning of 2023.
Secondly, users who migrate from the Ethereum mainnet to L2 DEXs are less likely to revert to their previous course. L2s inherit their security properties and base assets (ETH) from Ethereum.Ethereum L2s
In order to improve scalability and increase transaction throughput, ETH Layer 2 solutions have emerged as a potential solution. They exist to address the limitations of existing blockchain networks. These solutions are built on top of layer 1 networks to enhance performance.
One popular example of a Layer 2 solution on Ethereum is Polygon, which utilizes a side-chain approach. Another type of Layer 2 solution is rollups, which can be either Zero Knowledge (ZK) based, such as zkSync, or Optimistic Rollup, like Optimism.
These solutions allow for a higher volume of transactions to be processed while maintaining security and integrity.Total Value Locked of mainnet and L2s
According to data from L2 Beat, Ethereum rollups have been experiencing a notable upward trend in Total Value Locked (TVL). As of this writing, the TVL had surpassed the $9 billion mark, with Arbitrum and Optimism taking the lead in TVL. These leading Layer 2 (L2) solutions are categorized as Optimistic Rollups.
Furthermore, data from DefiLlama revealed that the TVL of Ethereum stood at an impressive $28.73 billion, at the time of writing. This represented over half of the total TVL in the market, which amounted to $49.09 billion.
How much are 1,10,100 ETHs worth today?
Although Ethereum’s DEX dominance may be diminishing, its Layer 2 (L2) solutions successfully recaptured the traffic it was losing.
While attention may have shifted away from the mainnet, it remains a dominant platform thanks to the adoption of side chains and rollups.
The platform’s innovative approach to scaling through side chains and rollups has allowed it to maintain prominence.
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