Trending February 2024 # Top 10 Edge Ai Trends To Watch Out For In 2023 # Suggested March 2024 # Top 10 Popular

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The top Edge AI trends in 2023  help increase efficiency, reduce cost, grow customer satisfaction

Many organisations see Artificial Intelligence as the solution to a lot of uncertainty like economic uncertainty, labour shortages, supply chain challenges, etc, bringing improved efficiency, differentiation, automation, and cost savings to airports, stores, and hospitals, among other places, which is why Edge AI trends have been accelerated.

Edge AI is AI that operates locally rather than in the cloud. Because of lightweight models and lower-cost high-performance GPUs, its implementation will become more accessible and less expensive in 2023. Edge AI enables the powering of scalable, mission-critical, and private AI applications. Because Edge AI is a new technology, many Edge AI applications are expected in the near future such as AI healthcare, Smart AI vision, Smart energy, and intelligent transportation system. According to Markets and Markets Research, the global Edge AI software market will grow from $590 million in 2023 to $1.83 trillion by 2026. Let’s take a look at the top 10 Edge AI Trends in 2023:

Focus on AI use cases with High ROI

    Machine learning with Automation

      Edge AI in Safety

      AI functional safety is related to the trend of human-machine collaboration. More companies are looking to use AI to add proactive and flexible safety measures to industrial environments, as seen in autonomous vehicles. The functional safety has been used in industrial settings in a binary fashion, with the primary role of the safety function being to immediately stop the equipment from causing any harm or damage when an event is triggered.

        AI in Cybersecurity

        The increasing use of AI in security operations is the next logical step in the evolution of automated defences against cyber threats. The use of artificial intelligence (AI) in cybersecurity extends beyond the capabilities of its forerunner, automation, and includes tasks like the routine storage and safeguarding of sensitive data.

          Edge AI picks up momentum

          AI was once considered experimental, but according to IBM research, 35% of companies today report using AI in their business, with an additional 42% exploring AI. Edge AI use cases can help improve efficiency and lower costs, making them an appealing place to direct new investments. Supermarkets and big box stores, for example, are investing heavily in AI at self-checkout machines to reduce loss due to theft and human error.

            Extensive use of AI in Process Discovery

              Increased growth of AI on 5G

              Edge AI along with new data processing and automation capabilities, supports a diverse ecosystem of evolving networks in ways that cloud-based solutions cannot. Furthermore, self-driving cars, virtual reality, and any other use case that requires real-time alerts require Edge AI and 5G for the fast processing it promises. As a result, 5G is promoting the Edge.

                IoT growth driving Edge AI

                Due to the limited data storage and computational power of these resource-constrained devices, performing deep learning in low-power IoT devices has always been difficult. Edge AI models are now cost-effective enough to operate at the edge, allowing devices to complete their own data processing and generate insights without relying on cloud-based AI.

                  Connecting Digital Twins to the Edge

                  The term “digital twin” refers to physically accurate virtual representations of real-world assets, processes, or environments that are perfectly synchronized. The explosion of IoT sensors and data that is driving both of these trends is what connects digital twins to the physical world and edge computing.

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                    Top 10 Best Ai Crypto Projects To Watch In 2023

                    The top 10 best AI crypto projects to watch in 2023 can help to create a dynamic atmosphere while also ensuring that operations are automatic and quicker. Another innovative technology is used by cryptos. Crypto technology has opened up marketplaces and established a trustless link between people, decreasing the amount of influence businesses have over various sectors.

                     AI crypto projects in 2023 have begun a new digital transformation by combining the characteristics of both of these technologies. This technical marvel has spread across various sectors, demonstrating the utility of these highly sophisticated digital tokens. Our guide will enlighten you on the technical marvels of artificial intelligence and cryptocurrency. Without further ado, here are the best top 10 AI crypto projects.

                    The Graph (GRT): The Graph makes it simple to create Dapps on Ether and IPFS using GraphQL. Numerous initiatives and people allegedly expressed interest. The Graph seeks to improve the overall Web 3 experience by outperforming the existing centralized options. Furthermore, this AI crypto initiative enables anyone to create and share APIs known as subgraphs. Over 3000 subgraphs have been released by coders at the moment this is written.

                    SingularityNET (AGIX): SingularityNET, the self-proclaimed next iteration of Decentralized AI, seeks to develop a decentralized, democratic, inclusive, and helpful Artificial General Intelligence (AGI). This AI crypto initiative will make it simple for anyone to develop, distribute, and sell AI services using their blockchain technology.

                    Render Token (RNDR): The Render network serves as a center, connecting users who want to perform render tasks with individuals who have spare Processors to process them. For example, GPU owners who are also node administrators will link to the Render Networks to run OctaneRender rendering tasks.

                    Fetch.ai (FET): chúng tôi describes itself as an Ethereum layer-1 network. Furthermore, it serves as an interchain gateway to the remainder of the blockchain universe. Its blockchain and artificial intelligence features enable anyone to link to and access private datasets while performing tasks autonomously. The chúng tôi ecosystem is also influenced by multi-gent Systems AI, which is suitable for multi-stakeholder settings. FET, the AI crypto project’s native currency, is used as the main way to exchange to pay for activities within its network.

                    The Oasis Network (ROSE): The Oasis Network is entirely dedicated to offering a private layer for Web3. With the simple-to-integrate, UX-friendly Oasis Privacy Layer, this network adds secrecy to current dApps on any EVM network. Intelligence was in high demand at the beginning of the new year. Artificial intelligence, on the other hand, could be dangerous if it offers biased information and breaches users’ privacy. To ensure that AI systems are secure and ethical, Oasis Network intends to use its privacy infrastructure to handle potential AI privacy issues.

                    Injective (INJ): Injective, a layer-1 blockchain network, was created primarily for DeFi apps and offers a “plug-and-play” financial system. Injective promises to be the initial blockchain to give auto-executing smart contracts by incorporating AI into its systems. Users will be able to quickly start dApps using Injective’s CosmWasm smart contract layer thanks to the automation feature.

                    Ocean Protocol (OCEAN):Ocean Protocol, a new AI crypto project, declared its goal to open data to the public, thereby decreasing the monopolistic control of organizations in the data and AI sectors. By enabling trades to take place with ERC-20 smart contract tokens, the Ocean Protocol can release the worth of data. Ocean Protocol guarantees open access to data, data control, and network development.

                    Exec RLC (RLC): iExec RLC concentrates on bridging the divide between resource providers and users to build the next iteration of the Internet. This AI crypto initiative also uses the capabilities of blockchain and secure computing to create a productive atmosphere for Requesters, Providers, and Developers. Blockchain establishes a market network in the iExec RLC environment where individuals earn rewards through processing capacity.

                    Artificial Liquid Intelligence (ALI): Althea’s Artificial Liquid Intelligence (ALI) token functions as the platform’s administration token, enabling token users to engage in decision-making that affects the platform. ALI also provides access to Althea AI’s innovative universe of innovation, enabling users to work across multiple initiatives. Furthermore, Business Insider named Althea the future rival of OpenAI, citing the community’s strong interest in artificial intelligence. Althea is well-known for producing Dapps such as Noah’s Ark and Character.

                    Numeraire (NMR): Numeraire is an Ethereum-based ecosystem that enables developers and data scientists to demonstrate more reliable machine-learning models. According to reports, this AI crypto initiative is the first hedge fund to debut cryptocurrency and use machine learning in its investment strategy, depending heavily on data and forecasts produced by Numerai Tournament participants. With their novel concept, Numeraire rewards participants with NMR tokens, the native currency of this AI crypto project, whose model performs well during the competition.

                    Top Time Series Forecasting Courses To Watch Out For In 2023

                    These time series forecasting courses are the best to study to follow a career in the time series field.

                    To have the ability to look into the future. Wouldn’t that be fantastic? We’ll undoubtedly get there someday, but time series forecasting can help you get there now. It enables you to “look” ahead of time and achieve success in your business. Time series forecasting is a The foundational knowledge needed to create and apply time series forecasting models in a range of business scenarios is provided in the Time Series Forecasting course. You’ll study the fundamentals of time series data and forecasting models, as well as a lot more. You’ll also learn how to use Alteryx, a data analytics program, to apply what you’ve learned in this course.  

                    This specialization will teach you how to use TensorFlow, a prominent open-source machine learning framework. In this fourth course, you’ll learn how to use TensorFlow to create time series models. To prepare time series data, you’ll first use best practices. You’ll also learn how to use RNNs and 1D ConvNets for prediction. Finally, you’ll put everything you’ve learned thus far into practice by creating a sunspot prediction model based on real-world data.  

                    This course will examine data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, agricultural commodity pricing, and so on. You’ll also look at a number of mathematical models that may be used to describe the processes that produce this type of data, as well as graphical representations that can help you understand your data. Finally, you’ll discover how to construct forecasts that accurately predict what you can expect in the future.  

                    This course covers additional Machine Learning techniques that supplement core tasks, such as forecasting and evaluating censored data. You’ll discover how to locate and analyze data having a time component, as well as censored data that requires outcome inference. You’ll learn a few Time Series Analysis and Survival Analysis approach. This course’s hands-on component focuses on recommended practices and testing assumptions generated from statistical learning.  

                    You will learn how to preprocess time series data, visualize time series data, and compare the time series predictions of four machine learning models in this 2-hour project-based course. You will use the Python programming language to develop time series analysis models to forecast daily deaths caused by SARS-CoV-19, or COVID-19. The following models will be created and trained: SARIMAX, Prophet, neural networks, and XGBOOST. You’ll use the matplotlib library to visualize data, extract features from a time series data set, and partition and normalize the data.  

                    By the completion of this project, you will have a solid understanding of the principles of time-series forecasting, which are used to anticipate web traffic flow in order to give useful business intelligence for operations, resource allocation, and opportunity identification. In Google Sheets, you’ll be able to forecast web traffic as well. To accomplish this, you’ll use the free Google Sheets software to explore trend forecasting and its applications.  

                    You will learn the fundamentals of time series analysis in R in this 2-hour project-based course. You will have created each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) using a real-world data set to anticipate the future by the end of this project.  

                    This project focuses on time series data analysis in Python for beginners. Only after conducting thorough exploratory research and gaining insight into the data set is model construction effective. The following are the goals: 1. Importing needed libraries and time-series data sets. 2. Review the summary of time-series data and obtain basic descriptive statistics. 3. Make inferences from time-series data visualization graphs 4. Examine how time series data behaves. 5. Convert non-stationary data to stationary data using transformation functions.  

                    On the basis of historical data, predictive models seek to forecast future value. You will analyze the global transmission of the Covid-19 virus and train a time-series model (fbprophet) to predict corona virus-related infections in the United States in this hands-on project.  

                    To have the ability to look into the future. Wouldn’t that be fantastic? We’ll undoubtedly get there someday, but time series forecasting can help you get there now. It enables you to “look” ahead of time and achieve success in your business. Time series forecasting is a machine learning technique that examines data and time sequences to forecast future events. Based on historical time-series data, this methodology delivers near-accurate predictions about future patterns. Today we have listed the top 10-time series forecasting courses to watch out for in 2023. If you are aspiring to carve a career path out of it, you may want to consider these courses The foundational knowledge needed to create and apply time series forecasting models in a range of business scenarios is provided in the Time Series Forecasting course. You’ll study the fundamentals of time series data and forecasting models, as well as a lot more. You’ll also learn how to use Alteryx, a data analytics program, to apply what you’ve learned in this chúng tôi specialization will teach you how to use TensorFlow, a prominent open-source machine learning framework. In this fourth course, you’ll learn how to use TensorFlow to create time series models. To prepare time series data, you’ll first use best practices. You’ll also learn how to use RNNs and 1D ConvNets for prediction. Finally, you’ll put everything you’ve learned thus far into practice by creating a sunspot prediction model based on real-world chúng tôi course will examine data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, agricultural commodity pricing, and so on. You’ll also look at a number of mathematical models that may be used to describe the processes that produce this type of data, as well as graphical representations that can help you understand your data. Finally, you’ll discover how to construct forecasts that accurately predict what you can expect in the chúng tôi course covers additional Machine Learning techniques that supplement core tasks, such as forecasting and evaluating censored data. You’ll discover how to locate and analyze data having a time component, as well as censored data that requires outcome inference. You’ll learn a few Time Series Analysis and Survival Analysis approach. This course’s hands-on component focuses on recommended practices and testing assumptions generated from statistical chúng tôi will learn how to preprocess time series data, visualize time series data, and compare the time series predictions of four machine learning models in this 2-hour project-based course. You will use the Python programming language to develop time series analysis models to forecast daily deaths caused by SARS-CoV-19, or COVID-19. The following models will be created and trained: SARIMAX, Prophet, neural networks, and XGBOOST. You’ll use the matplotlib library to visualize data, extract features from a time series data set, and partition and normalize the chúng tôi the completion of this project, you will have a solid understanding of the principles of time-series forecasting, which are used to anticipate web traffic flow in order to give useful business intelligence for operations, resource allocation, and opportunity identification. In Google Sheets, you’ll be able to forecast web traffic as well. To accomplish this, you’ll use the free Google Sheets software to explore trend forecasting and its chúng tôi will learn the fundamentals of time series analysis in R in this 2-hour project-based course. You will have created each of the major model types (Autoregressive, Moving Average, ARMA, ARIMA, and decomposition) using a real-world data set to anticipate the future by the end of this chúng tôi project focuses on time series data analysis in Python for beginners. Only after conducting thorough exploratory research and gaining insight into the data set is model construction effective. The following are the goals: 1. Importing needed libraries and time-series data sets. 2. Review the summary of time-series data and obtain basic descriptive statistics. 3. Make inferences from time-series data visualization graphs 4. Examine how time series data behaves. 5. Convert non-stationary data to stationary data using transformation chúng tôi the basis of historical data, predictive models seek to forecast future value. You will analyze the global transmission of the Covid-19 virus and train a time-series model (fbprophet) to predict corona virus-related infections in the United States in this hands-on chúng tôi specialization will go through basic predictive modeling approaches for estimating important parameter values, as well as optimization and simulation approaches for formulating judgments based on those parameter values and situational restrictions. The specialization will teach how to use predictive models, linear optimization, and simulation methods to model and solve decision-making problems.

                    Top 10 Affordable Ai Stocks To Buy In February 2023

                    If you want to be rich in no time, you must invest in these top AI stocks in February 2023

                    Currently, the rise of artificial intelligence (AI),

                    NVIDIA Corp

                    Stock Price Today: US$243.19 Market Cap: US$606.029B Nvidia’s data center business represents a steadily increasing share of the company’s total revenue. This segment isn’t all AI-related — Nvidia’s graphics cards are used to accelerate a wide variety of data center applications. But

                    International Business Machines Corp.

                    Stock Price Today: US$137.15 Market Cap: US$122.93B IBM’s strategy with AI is to apply the technology in ways that augment human intelligence, increase efficiency, or lower costs. In the healthcare industry, IBM’s AI technology is being used to create individualized care plans, accelerate the process of bringing new drugs to market, and improve the quality of care. In the financial services industry, via the company’s 2024 acquisition of Promontory Financial Group, IBM is using Artificial Intelligence to help clients with the daunting task of financial regulatory compliance.  

                    Alphabet Inc

                    Stock Price Today: US$2,860.32 Market Cap: US$1.893T Google and YouTube parent company Alphabet uses AI stocks and automation in virtually every facet of its business, from ad pricing to content promotion to email spam filters. Alphabet has AI and

                    Micron Technology, Inc

                    Stock Price Today: US$81.17 Market Cap: US$90.893B Micron Technology manufactures memory chips, including dynamic random-access memory (DRAM) and NAND flash memory found in solid-state storage drives. Most of what the company makes are commodity products, meaning that supply and demand dictate pricing. In the future, demand for memory chips will only grow, and that’s especially true in the Artificial Intelligence industry. Self-driving cars are a good example. All the sensors and cameras produce a lot of data around 1 GB per second, according to Micron estimates. Data centers running AI stocks processes need plenty of memory and so do smartphones that may be doing AI work.  

                    Microsoft Corp.

                    Stock Price Today: US$305.94 Market Cap: US$2.294T In 2023, Microsoft announced the construction of a new supercomputer hosted in Azure, Microsoft’s cloud computing network. The supercomputer was built in collaboration with OpenAI LP to train AI models with the ultimate goal of producing large AI models and related infrastructure for other organizations and developers. In late 2023, Microsoft also debuted Context IQ, an Artificial Intelligence application that can predict, seek and suggest information for employees.   Stock Price Today: US$305.94 Market Cap: US$2.294T Amazon uses artificial intelligence for everything from Alexa, its industry-leading voice-activated technology, to its cashier-less grocery stores, to Amazon Web Services Sagemaker, the cloud infrastructure tool that deploys high-quality

                    Meta Platforms Inc.

                    Stock Price Today: US$237.09 Market Cap: US$645.345B  

                    C3.ai Inc

                    Stock Price Today: US$25.18 Market Cap: US$2.645B C3.ai is a SaaS company whose software allows companies to deploy large AI stocks applications. The company’s tools help its customers accelerate software development and reduce cost and risk, and they have a wide variety of applications. For example, the U.S. Air Force uses C3 AI Readiness to predict aircraft systems failures, identify spare parts, and find new ways to increase mission capability. European utility company Engie (OTC: ENGIY) is using C3 AI to analyze energy consumption and reduce energy expenditures.  

                    NICE Ltd.

                    Stock Price Today: US$254.50 Market Cap: US$16.123B NICE is a leading provider of software applications that manage call center operations and customer interactions. NICE’s AI and

                    DocuSign Inc.

                    Stock Price Today: US$118.46 Market Cap: US$23.441B

                    Top 10 Programming Languages To Ace Ai Hackathons In 2023

                    To participate in hackathons, here are 10 programming languages that every security expert must know 

                    While security experts all need to learn a common foundation of security principles, the specific technologies including

                    programming languages

                    that each needs to understand can be very different. Regardless of whether you are a security aficionado, a future designer, or a veteran, the reality is that the tech landscape is steadily evolving. Because of this steadily evolving pattern, the cybersecurity career is popular among the youth. Therefore, it becomes essential for security experts to know and understand

                    programming languages

                    to participate in

                    AI hackathons

                    . Hackathons are events where people from different corners can come together under the name of the competition to sharpen their skills and learn more about their competitors. On that note, this article lists the

                    top 10 programming languages

                    to ace AI hackathons in 2023. 

                    HTML

                    HTML is significant because it is utilized by pretty much every other site. It is a markup language and is the most essential programming language of all. HTML is the sluggish stroll before figuring out how to walk. This programming language is utilized by 90.7% of the multitude of sites in the current tech scene.

                    JavaScript

                    JavaScript empowers designers to utilize any code when guests visit the site. This complements the core usefulness of the site. Despite what might be expected, it could create antagonistic usefulness covered by the guest. If the site gets controlled by the hacker, they can utilize malevolent codes to run a program. A wide understanding of JavaScript can assist you with getting the situation of JavaScript web a long time in the cybersecurity space.

                    Python

                    Python empowers software engineers to mechanize errands and manage malware research. Also, a major third-party library loaded with scripts is promptly available. If you know Python, a SOC support expert is one of the job roles. In this position, you will develop devices and scripts to get the site from cyber-attacks. You can likewise utilize data, logs, and artifacts to analyze the foundation of the issues.

                    C

                    C is best for reverse engineering and finding openings. This programming language has been utilized starting around 1970 and is still a famous decision since it is not difficult to run and learn. C empowers the developers to make low-level code. Security-cognizant experts will ensure that the site has no susceptibilities. Despite what is generally expected, programmers will utilize C to find openings for hampering the site.

                    PHP

                    If you are looking for a job that includes protecting a website, then PHP is everything that you need to know. It examines the data circulation from input parameters to prudent strategies in a web application. A PHP developer working on security subjects may use RIPS. A security-oriented PHP developer will inscribe a server-side web app logic.

                    C++

                    C++ is an augmented edition of C. This programming language is also aged like C. As both C and C++ are interconnected, most companies prefer applicants who have a broad understanding of these languages. A C++ developer builds mobile and desktop applications while coding professionals recognize and mitigate the samples of any exposure and bugs. 

                    Swift Ruby

                    Ruby is a general-purpose high-level language created and developed by Yukihiro Matsumoto in Japan. Since then, it has become one of the most popular programming languages in the world. Ruby has been widely used for sites including Airbnb, Hulu, Kickstarter, and Github. Ruby is one of the best programming languages for cybersecurity as it manages much of a machine’s complex information, making programs easier to develop and with less code.

                    SQL

                    Nearly every website breach that you hear about on the news that involves people’s details being stolen will involve attackers gaining access to a database, often via some sort of SQL injection. As cybersecurity professionals, being able to understand SQL queries and their impact and what they are accomplishing will go a long way to understanding the threat posed by a poorly protected database.

                    CSS

                    CSS is usually applied in conjunction with HTML and governs a site’s appearance. While HTML organizes site text into chunks, CSS is responsible for determining the size, color, and position of all page elements. The language is quite approachable, allowing beginners to dip their toes in the metaphorical coding pool.

                    More Trending Stories

                    5 Top Vulnerability Management Trends In 2023

                    Vulnerability management seeks to lower risk by identifying and dealing with any possible lines of incursion into a network by cybercriminals.

                    The field of vulnerability management includes automated scans, configuration management, regular penetration testing, patching, keeping track of various metrics, and reporting. The category has been evolving rapidly within cybersecurity, and here are some of the top trends in the vulnerability management market:

                    Vulnerability management is all about identifying, prioritizing, and remediating vulnerabilities in software.

                    As such, it encompasses far more than the running of vulnerability scans repeatedly to look for known weaknesses lurking within the infrastructure. Traditionally, vulnerability management also includes patch management and IT asset management. It addresses misconfiguration or code issues that could allow an attacker to exploit an environment as well as flaws or holes in device firmware, operating systems, and applications running on a wide range of devices.

                    “These vulnerabilities can be found in various parts of a system, from low-level device firmware to the operating system all the way through to software applications running on the device,” said Jeremy Linden, senior director of product management, Asimily.

                    See more: A holistic approach to vulnerability management solidifies cyber defenses

                    Some analysts and vendors stick strictly to the NIST definition when they’re talking about vulnerability management. Others include security information and event management (SIEM) with vulnerability management as part of larger suites. And a few combine it with threat intelligence, which prioritizes actions and helps IT to know what to do and in what order.

                    Gartner recently originated the new term attack surface management (ASM). The analyst defines ASM as the “combination of people, processes, technologies, and services deployed to continuously discover, inventory, and manage an organization’s assets.”

                    ASM tools are said to go beyond vulnerability management. The aim is to improve asset visibility, understand potential attack paths, provide audit compliance reporting, and offer actionable intelligence and metrics.

                    The as-a-service trend has invaded so many areas of IT, so it’s no wonder that vulnerability management as a service has emerged.

                    “With more than 20K vulnerabilities found and published in a single year, vulnerability management has become an enormous task,” said Michael Tremante, product manager, Cloudflare.

                    “This is made worse for large enterprises who also have the challenge of not necessarily knowing the full set of software components being used internally by the organization, potentially putting the company at risk. A big trend is adoption of managed services/SaaS environments, as they are externally managed, and offloading of vulnerability management to third parties.”

                    Thus, a growing set of products are hitting the market that help companies tackle vulnerability management via managed services of one kind or another.

                    See more: Vulnerability Management as a Service (VMaaS): Ultimate Guide

                    Containers and Kubernetes have become largely synonymous with modern DevOps methodologies, continuous delivery, deployment automation, and managing cloud-native applications and services.

                    However, the need to secure containerized applications at every layer of the underlying infrastructure — from bare-metal hardware to the network to the control plane of the orchestration platform itself — and at every stage of the development life cycle — from coding and testing to deployment and operations — means that container security must cover the whole spectrum of cybersecurity and then some, said KuppingerCole.

                    See more: Securing Container and Kubernetes Ecosystems

                    Due to the way the threat landscape is evolving, the way vulnerability management platforms are shifting, and the fast pace of innovation as evidenced by containerization, digitalization, and the cloud, a new approach is needed, according to Ashley Leonard, CEO, Syxsense.

                    “Businesses possess incredibly powerful processors inside storage equipment, servers, and desktops, which are underutilized in many cases” Leonard said.

                    For example, Syxsense has been incorporating more features into its vulnerability management tools. This includes more orchestration and automation capabilities, stronger endpoint capabilities, and mobile device management. These augment existing patch management, vulnerability scanning, remediation, and IT management capabilities.

                    See more: 12 Top Vulnerability Management Tools

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