Trending December 2023 # What Is Managed Detection And Response (Mdr)? Ultimate Guide # Suggested January 2024 # Top 17 Popular

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Managed detection and response (MDR) adds an additional layer of protection and elevates the security postures of organizations relying on legacy solutions.

Managed detection and response is becoming more popular as organizations look to outsource some elements of their cybersecurity approach. As bad actors become more adept at bypassing traditional network security platforms, managed services like MDR play an important – and growing – role in protecting the enterprise.

Organizations have varying tolerance levels for cyber risk, which should be reflected in the MDR service agreement. Some enterprises may prefer detailed analytical reports about network traffic, while others feel comfortable with a more hands-off approach.

Ultimately, the goal of MDR is to find and respond to threats before they cause damage. Core MDR functions include:

Offsite MDR partners configure their security platform to apply automated rules to help prioritize which risks are most urgent. Human analysts review alerts according to priority, sorting benign events and false positives from authentic threats.

Threat hunting is the primary function of MDR, through both automated, AI-driven methods and human analysis. An ideal MDR partner company includes cybersecurity experts who are at the top of their field — access to these high levels of expertise are one of the main draws to investing in MDR.

MDR usually includes detailed reporting about security events with additional context to help companies understand and mitigate vulnerabilities. With MDR, enterprises can better understand what happened, when, who was affected, and the extent of infiltration or damage or loss.

Remediation is the recovery support an organization can expect from an MDR provider. Arguably, this is the most critical component of an MDR partnership. After all, if remediation is not handled well, an organization’s entire investment in endpoint protection could be at risk.

MDR partners should be able to help an organization recover to a pre-attack state by removing malware, cleaning the registry, ejecting network intruders, and mitigating vulnerabilities throughout a network.

There are many benefits of MDR, but perhaps the chief benefit is that MDR offers protection that is both preventative and reactionary in nature. Not only does MDR provide insights into network behaviors that might develop into full blown problems, but it is also capable of quickly knocking down attacks that do occur. MDR platforms scan for possible breaches and eliminate issues as soon as they occur to minimize damages.

That’s where humans come in. The expert cybersecurity analysts who manage MDR platforms are experienced in dealing with a wide array of threats in multiple environments. That means they are well-equipped to help organizations bat down sophisticated attacks and to offer insight informed by real world experience. Automation is a must, but expert human analysis adds measurable value.

While MDR providers offer different levels of services, with some focusing more on the “right of boom” scenarios (after an attack has occurred), a comprehensive MDR platform is also proactive.

AI-enhanced MDR is especially well-equipped to monitor potentially problematic network behavior, since these platforms continuously review systems, searching for known threats as well as potential threats.

MDR-provided reports often reveal potential problem areas not only for cybersecurity worries, but compliance issues. Regulatory guidance for data management, especially, often dictates that companies have a comprehensive view of where data is stored, how it is accessed and used, and how it is protected. MDR reports can add insights to help enterprises make proactive decisions about regulatory compliance adherence.

While MDR providers often offer more than one level of service with associated features, these five features are typical to most MDR platforms.

Every MDR platform includes some version of intrusion detection and prevention. Some service providers use MDR platforms in addition to the network security in place at a given organization, while others provide almost complete network protection off-site.

Some MDR platforms can monitor a mix of endpoints, including cloud-based and connections to field sensors and other IoT devices. In any case, intrusion detection and prevention is a must.

As MDR hunts for threats across a network, it gathers data along the way. Information gathered includes insights into network behavior, including how employees are accessing the network; which endpoints are open and closed; where traffic originates and travels; and a whole host of other data points.

MDR providers offer different levels of data analytics as part of their service packages. Not only are these analytical reports helpful for improving an enterprise security posture, but they can help enterprises make more informed operational decisions.

Continuous monitoring is a key feature for MDR and a big reason many enterprises opt for these managed services. An off-site security team is on standby at all times, ready to jump in and tackle issues even when they occur outside normal business hours.

It can be challenging for SOCs to keep every element of a network security approach updated to the latest parameters. New viruses and malware emerge constantly. A managed approach takes away much of this burden, since part of MDR oversight agreements include keeping systems up-to-date.

For more information, also see: Why Firewalls are Important for Network Security

These 10 MDR providers are among the most popular and best rated.

Cynet

SecurityHQ

Rapid7

Cybereason

SentinelOne Vigilance

CrowdStrike

eSentire

Expel

Secureworks

Fidelis Cybersecurity

It’s become the norm that enterprises are dealing with sprawling, complex networks that include a mix of on-premises, cloud-based, IoT endpoints, and more. Outsourcing at least some of the oversight of these networks in terms of cybersecurity can help alleviate staffing concerns, connect enterprises with high-caliber cybersecurity professionals, and greatly increase network security.

A comprehensive MDR solution will include round-the-clock coverage, modern tech enhanced with AI, data analytics, and a mix of features that fit a given enterprise’s unique needs.

For more information, also see: Artificial Intelligence in Cybersecurity

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The Ultimate Guide To Intent Data: Benefits And How

What is intent data?

Intent data covers the purchasing intent of individuals or companies. This intent is determined by individuals’ or companies’ online or offline behavior. It allows vendors to determine which potential customers to reach and what messages to communicate to them.

Intent data can cover users’ interests and could predict users’ intent to complete actions other than buying (e.g. signing up to a newsletter etc.). However, the most common use of intent data is to estimate buyer intent, and therefore buyer intent data and intent data are used to mean the same thing in most cases.

What is B2B intent data?

Intent data is a fresh concept for B2B sales. In B2B organizations, intent data is mainly used to identify potential customers. Companies can use intent data also for sales enablement, ABM, and digital marketing assistance. For example, most B2B companies do some form of cold outreach via email or Linkedin. Using intent data makes this outreach more targeted, increasing conversion rates.

Where do companies get intent data from?

Companies’ own digital properties generate data on buyer intent but there are other sources as well and they can be used to improve a company’s intent estimates:

First-party intent data is generated on a company’s own digital properties. It can be detailed but it is limited to a company’s existing audience

Second-party intent data is first-party intent data obtained via a third-party provider. For example, a media company can shares its first-party intent data with other companies. From the perspective of other companies, this would be second-party intent data

Third-party intent data represents a global dataset purchased from data providers

Why is intent data important?

It drives sales and businesses are seeing benefits. As a result, most businesses are investing in intent data.

Gartner claims that more than 70% of B2B buyers will increase their use of third-party intent data by the end of 2023. As companies aim to expand their audience, they need to rely on second and third-party intent data for better targeting.

Source: Gartner

According to a study conducted by intent data provider TrustRadius in 2023, these companies are leveraging intent data:

40% of software companies

47% of midsize businesses

Gartner’s prediction and TrustRadius’ survey have quite different results but all agree that at least a significant share of companies are benefitting from intent data.

How do businesses collect intent data?

Websites record their user’s activities and it can generate intent data at the IP level as well as through user registration and cookies. IP databases are used to identify the company of the user in case of B2B intent data. Example recorded activities can be:

Intent data providers analyze this data and prepare a report or a list of firms you may target. These reports are reviewed by marketing and sales departments so they can be used for:

Preventing churn: If your customers are regularly checking out your competitors’ websites then it may be good idea to talk to them to understand why they are doing such research

Upselling: When different divisions or geographic units in one of your customers are researching your category, you can reach out to them to understand if they would like to try your solution. Providing their colleagues as a reference can shorten the sales cycle.

Improving targeting for outreach efforts/outbound sales

What is outbound sales?

Outbound sales is done via sales reps directly reaching out to potential buyers via email, cold calls or Linkedin et. to promote your brand, product, or service. Outbound sales has a very low success rate because it is hard to determine which companies are suitable for your product and whether they are ready to buy.

B2B Intent data helps vendors determine which companies are ready to buy

Firmographics and technographics help vendors determine which companies are suitable for their products.

Who are intent data providers?

There are 2 types of intent data providers:

Companies that have access to buyer intent information. This includes companies like us or review platforms like TrustRadius

Companies that aggregate intent data from brands like media companies and industry analysts. Companies like Bombora bring together intent data from many media companies.

The followings are the most frequent activities carried out by intent data providers:

Gathering of intent data

Data cleaning & quality assurance

Prediction of intent with machine learning models or rules based approaches

Integration of intent data to ABM or CRM tools

To explore the intent data landscape, feel free to check our data-driven lists for

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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The Ultimate Guide To Customer Engagement

Customer engagement refers to the level of enthusiasm your business generates among its customers.

Customer engagement is critical for driving repeat business and maintaining a strong brand reputation.

Maintaining customer engagement is an ongoing process that continues long after a lead converts into a paying customer.

This article is for small business owners who want to learn how to retain customers and encourage repeat business by improving engagement.

Engaging your customers is a critical element of running a successful business. Selling products or services isn’t enough to attract and retain loyal customers. It requires ongoing effort to genuinely connect with your audience and build lasting business relationships.

Customer engagement marketing isn’t a one-and-done process or a one-size-fits-all approach. We’ll explore tips for creating a unique customer engagement strategy and share tools for building and tracking audience engagement.

What is customer engagement?

Customer engagement means improving the customer experience through positive customer interactions and company efforts. You can engage customers through various channels, including social media business accounts and your company blog. However, engagement extends beyond attracting customers to build your brand; you must continuously maintain and strengthen customer relationships to cultivate a loyal, engaged audience.

Even though they might be the driving factor in the beginning, your products and services shouldn’t be the only value you offer customers. To improve customer satisfaction and loyalty, ensure you develop a content strategy that produces unique, quality materials that customers can freely access.

Tip

To improve communication with customers, address unhappy customers’ issues directly, always reply to customer queries promptly, and take responsibility for mistakes.

10 tips to improve customer engagement

Keep these points in mind when you’re developing your customer engagement strategy.

1. Maintain the customer life cycle.

A customer life cycle is the length and nature of a customer’s relationship with a brand or company. Marketers should aim to make the customer’s life cycle as long and prosperous as possible.

To cultivate a healthy customer life cycle, maintain and continuously improve the customer journey. Maintaining the cycle involves the following:

Acquiring customers.

Employing customer retention strategies to keep them interested.

Extending the relationship beyond a single purchase.

Key Takeaway

The best CRM software solutions include customer life cycle support. For example, Zendesk manages post-sale activities and renewals and is our pick for the best CRM for the complete customer life cycle. Read our Zendesk review to learn more.

Benefits of building a customer engagement strategy

Building a customer engagement marketing strategy is crucial for creating lasting relationships with your audience. Here are some of the benefits of building a customer engagement strategy:

You create deeper customer relationships. A customer engagement strategy helps create dedicated, loyal customers to support your brand. “Customer engagement aims to improve the customer experience to create a deep, personal relationship with the brand,” Rom said. “Customers need to feel appreciated and valued for more than just the money they spend. Brands who show they are dedicated to their customers’ needs will be rewarded with engaged, loyal customers.”

You set your business apart. Additionally, customer engagement helps differentiate your business from others in your industry. “Building a customer engagement strategy is of immense importance because this is what gives your brand the edge over the competitors,” Brenchley said. “You have to remember that your brand has a life of its own, and if it has a positive impact on your customers, then you’re likely to gain more business. For small companies, a positive customer engagement strategy can lead to higher referrals through word of mouth.”

Customer engagement examples

Consider the following examples of successful customer engagement campaigns. Use them as inspiration when you’re building your customer engagement strategy.

IKEA Kreativ. IKEA’s mixed-reality Kreativ tool lets customers use their iPhones to scan their living space and insert IKEA pieces into the image at scale. It’s a near-real-life, 3D picture of how IKEA furnishings look anywhere. This tool helps customers achieve a custom look and fit before they purchase an item, haul it home and assemble it. Kreativ can inspire customers to purchase and prevent returns (and ill will) while creating a seamless process for adding items to a cart or shopping list. And, of course, customers will share their results on social media.

Major League Baseball theme nights. MLB teams host Community Nights and Theme Nights, often with themes unrelated to baseball. Community Nights recognize community contributors such as teachers, veterans and first responders. Other Community Nights highlight current or past baseball stars. Theme Nights have included Shakespeare, boy bands, Margaritaville, Star Wars and other themes that draw diverse fans to the ballpark. These promotions often include giveaways that feature the team name and theme. They also sometimes include a postgame concert or other events. MLB’s promotions attract fans who may not otherwise attend a game, and they provide yet another chance for customers to post about a product and an experience.

Share a Coke. Who doesn’t remember #ShareACoke? This campaign replaced logos on Coke bottles with 250 names. Because the campaign felt so personalized to customers, it attracted the attention of people across the country. Customers bought bottles for their friends and loved ones, took photos to share online, and purchased drinks with their names to join the fun.

Key Takeaway

Big brands create customer engagement campaigns frequently. Although they don’t always directly drive sales, they usually build customer loyalty.

Javascript % Operator: An Ultimate Guide To Modulos

In mathematics, the modulo gives you the remainder in the division. To calculate the remainder in the division in JavaScript, use the modulo operator (%).

For example:

const r = 10 % 3 console.log(r)

Output:

You can interpret this as sharing 10 slices of pizza with 3 eaters. As a result of a fair share, there will be one leftover slice. This is the result of the modulo.

In JavaScript, the modulo has many practical use cases. The most common use cases include checking if a number is odd/even, or checking if a number is a prime number.

This guide teaches you all you need to know about the modulo operator in JavaScript.

Before jumping into calculating modulo in JavaScript, let’s take a look at modulos in maths.

Modulo in Mathematics

In mathematics, modulo describes the remainder in the division. In math, the modulo is commonly denoted with mod.

a mod b

Where:

a is called the dividend.

b is called the divisor.

The modulo returns the remainder in the division.

For example:

7 mod 3 = 1

To see why it works this way, think about sharing 7 apples with 3 persons fairly. In other words, each person should get the same number of apples.

To make this possible, you should share 2 apples with each person. This means you have shared 6 apples. Thus, there is one leftover apple. This is the remainder in the division.

Another example of modulos is a 12-hour clock. When you count the time, you count up to 12 and go back to 0.

For example, let’s figure out what time is it exactly 11 hours after 7:00.

To do this, you cannot add 11 to 7:00, because that would give 18:00. Instead, add 11 hours to 7:00 until you reach 12:00. Then the 6 leftover hours are added to the new round to make it to 6:00.

This is exactly how modulo works. In other words, an easier solution to the above example is by calculating 11 + 7 mod 12 which results in 6.

18 mod 12 = 6

So in a 12-hour clock, 18:00 and 6:00 are the same thing.

Mathematically this fact is denoted by:

18 ≡ 6 (mod 12)

Generally, in modular arithmetic, you can express these modular relationships by:

a ≡ b (mod n)

Which means “a and b are congruent to modulo n”.

That’s enough for the theory.

Now you understand what is modular arithmetic. Next, let’s move on to calculating modulos in JavaScript.

Modulo in JavaScript

In JavaScript, you can use the % operator to calculate modulo.

a % b

You can compute modulo with numeric types int and float and negative numbers.

Modulo with Integers in JavaScript

Usually, you calculate the modulo between two integers.

Here are some examples:

console.log(4 % 3) console.log(10 % 7) console.log(78 % 14) console.log(1000 % 10)

As you can see, the result of the modulo can also be 0. But taking a modulo with 0 is meaningless. The result is always NaN.

console.log(1000 % 0)

Now you have the understanding of how to calculate remainders in JavaScript using integers.

Next, let’s see some examples of using negative numbers and modulo.

Modulo with Negative Numbers

Calculating modulos of negative numbers is possible in JavaScript.

But notice that different programming languages calculate negative modulos differently. The reason is that it’s unclear whether the result should have the sign of the dividend or the divisor.

In JavaScript, the result of modulo takes the sign of the dividend (the value on the left).

For example, the result of this calculation is positive, even though the divisor is negative:

console.log(7 % -4)

But in Python, the same calculation yields a different result that has the sign of the divisor:

-1

But why are the results not the same?

It boils down to how the modulo is calculated under the hood.

As it turns out, the modulo is calculated differently in JavaScript as opposed to Python.

Here is how a % b is calculated behind the scenes:

javascript: r = a - (b * trunc(a / b)) python: r = a - (b * floor(a / b))

Take a look at the last term in both of these equations. There is a difference. In JavaScript, the last term is trunc(a / b). In Python, it is floor(a / b).

trunc(a / b) means a truncated division. This rounds a negative number toward 0.

floor(a / b) means floor division. This rounds a negative number away from 0.

Here is an illustration of both of these functions:

But what about positive values?

Both floor() and trunc() round positive values down to the nearest integer value. In other words, they work the same way with positive numbers.

This causes the differences with modulos using negative values.

Let’s use the above equation to manually calculate 7 % -4 in JavaScript and Python.

Here is the JavaScript version:

r = a - (b * trunc(a / b)) a = 7 b = -4 r = 7 - (-4 * trunc(7 / -4)) = 7 - (-4 * trunc(-1.75)) = 7 - (-4 * -1) = 7 - 4 = 3

And here is Python’s version:

r = a - (b * floor(a / b)) a = 7 b = -4 r = 7 - (-4 * floor(7 / -4)) = 7 - (-4 * floor(-1.75)) = 7 - (-4 * -2) = 7 - 8 = -1

As you can see, the JS version gives 3 but Python gives -1.

To recap, negative modulos might give different values in different languages. They are calculated in a different way due to the unclarity of how to handle the negative sign.

Next, let’s take a look at modulos between floating-point numbers.

Modulo with Floats

Similar to how you can use integers to calculate modulos, you can use floats.

Here are some examples:

console.log(10.5 % 4.5) console.log(10 % 1.5) console.log(12.5 % 3.5) console.log(10.0 % 3.0)

Similar to other arithmetic operations in JavaScript, you may encounter floating-point issues with modulos.

For example:

console.log(10.0 % 3.1)

Now you have taken a complete overview of how to calculate remainders in JavaScript.

Next, let’s discuss the operator precedence in calculations that involve other operators.

Operator Precedence with Modulos

Operator precedence describes what operations take place first when combined in the same equation.

In JavaScript, the modulo operator has the same precedence level as:

Multiplication (*)

Division (/)

Floor division (//).

For example, if you multiply first and then take a modulo, the multiplication takes place first.

But if you add two numbers and take a modulo, the modulo precedes.

For instance:

console.log(3 * 4 % 5 - 6)

To better understand how JavaScript calculates this, place parenthesis around the terms.

((3 * 4) % 5) - 6

JavaScript evaluates this as follows:

((3 * 4) % 5) – 6

(12 % 5) – 6

2 – 6

-4

Now you have a good understanding of the modulo in general. Let’s take a look at some common applications next.

Common Use Cases of Modulo in JavaScript

There are many use cases for modulo in JavaScript.

A common example is to check whether a number is odd or even. Another popular task is to check if a number is a prime number.

Let’s see these and a couple of more examples next.

Use Modulo to Deal with Periodicity

Modulo helps you when there is periodicity in your code.

Think about a game character. It runs out of the screen on the right side and pops back in on the left side. The code that makes this possible defines the player’s x-position modulo screen width.

In layman’s terms, when the player’s x position exceeds the screen width, the modulo operation turns it back to 0.

x_pos = x_pos % screen_width

Let’s see another example using the 12-hour clock we talked about earlier.

Even though there are 24 hours in a day, a 12-hour clock wraps around itself 12 hours before the day is over.

But it is still a perfectly valid way to track time.

This is possible because for example 15:00 on a 24-hour clock is 3:00. The only problem is the time points same spot twice a day. But because we can clearly distinguish day from night, this is not an issue.

Let’s simulate a 12-hour clock in JavaScript.

To display the hours of the day in a 12-hour clock, take a modulo between the hour of the day and 12.

Here is how it looks in code:

function wallclock(hour) { const result = hour % 12 console.log(`${hour}:00 is ${result}:00 on a 12-hour clock`) } const hours = [...Array(25).keys()] for (hour of hours) { wallclock(hour) }

Output:

0:00 is 0:00 on a 12-hour clock 1:00 is 1:00 on a 12-hour clock 2:00 is 2:00 on a 12-hour clock 3:00 is 3:00 on a 12-hour clock 4:00 is 4:00 on a 12-hour clock 5:00 is 5:00 on a 12-hour clock 6:00 is 6:00 on a 12-hour clock 7:00 is 7:00 on a 12-hour clock 8:00 is 8:00 on a 12-hour clock 9:00 is 9:00 on a 12-hour clock 10:00 is 10:00 on a 12-hour clock 11:00 is 11:00 on a 12-hour clock 12:00 is 0:00 on a 12-hour clock 13:00 is 1:00 on a 12-hour clock 14:00 is 2:00 on a 12-hour clock 15:00 is 3:00 on a 12-hour clock 16:00 is 4:00 on a 12-hour clock 17:00 is 5:00 on a 12-hour clock 18:00 is 6:00 on a 12-hour clock 19:00 is 7:00 on a 12-hour clock 20:00 is 8:00 on a 12-hour clock 21:00 is 9:00 on a 12-hour clock 22:00 is 10:00 on a 12-hour clock 23:00 is 11:00 on a 12-hour clock 24:00 is 0:00 on a 12-hour clock Odd or Even?

To check if a number is odd or even, use the modulo.

If the number is even, it is evenly divisible by 2. In other words, number mod 2 yields 0.

If the number is not even, it is odd.

For instance:

function isEven(number) { return number % 2 == 0 } console.log(isEven(10)) console.log(isEven(7))

Output:

true false

Similarly, to find out if a number is odd, you can either use the isEven() function with negation:

function isOdd(number) { return !isEven(number) }

Or you can use the fact that any odd number modulo 2 gives a remainder of division of 1:

function isOdd(number) { return number % 2 == 1 } Repeat Code in Intervals

Sometimes, when running a loop, you don’t want to run code at each iteration. Instead, you specify an interval on how often a code should be run.

To run code in intervals, check if the current iteration index is evenly divisible by the interval. In other words, use modulo.

For example, let’s print every 3rd number in an array of numbers:

const numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] var i = 0 const interval = 3 while (i < numbers.length) { if (i % interval == 0) { console.log(i) } i += 1 }

Output:

0 3 6 9 12 Conclusion

Today you learned how to calculate remainders in JavaScript.

To recap, in maths, a mod b calculates the remainder in the division between a and b. In other words, it calculates what is left after an even division.

For example, 7 mod 3 represents sharing 7 apples with 3 workers evenly. The result is 1. In other words, 1 apple is left over after sharing 7 apples with 3 persons evenly.

To calculate modulo in JavaScript, use the modulo operator %.

Modulos also work for negative numbers in JavaScript. But the way negative modulos are calculated differs among programming languages.

There are many use cases for modulo in JavaScript.

Thanks for reading.

Happy coding!

Beginner’s Guide To Object Detection For Computer Vision Project

Object detection can be defined as a computer vision technique which aims to identify and locate objects on an image or a video. Computers might be able to process information way faster than humans, however, it is still difficult for computers to detect various objects on an image or video. The reason for this is that the computer interprets the majority of the outputs in the binary language only. This article aims to briefly discuss:

The basics of object detection

The object detection models

The benefits of object detection

The challenges and solutions

Before we get to the points above, we need to understand the difference between image classification and object detection. Beginners tend to confuse these two.

Difference between Object Detection and Image Classification

Let us break down these techniques, to know the difference between them. When you look at a picture of a dog you can instantly say it’s an image of an animal i.e. tell what the image is about. This is what image classification is all about. As long as there is only one object,

Object Detection Models

Now that we are clear with the definition of Object Detection, let’s have a look at some popular Object Detection models.

R-CNN, Faster R-CNN, Mask R-CNN

The most popular object detection models belong to the family of regional based CNN models. This model has revolutionized the way the world of Object Detection used to work. In the past few years, they’ve not only become more accurate but more efficient too.

SSD and YOLO

There are a plethora of models belonging to the single shot detector family which were published in 2023. Although SSDs are faster than CNN models, their accuracy rate is much lower than that of the CNNs. YOLO or you only look once, is quite different from region-based algorithms. Just like SDDs, yolo is faster than R-CNNs but lags behind because of low accuracy. For mobile or embedded devices, SDDs are the perfect choice.

CenterNet

In recent years, these object detection models are gaining more popularity. CentreNet follows a key point-based approach for object detection. When compared with SSD or R-CNN approaches, this model proves to be more efficient and as well as more accurate. The only drawback of this method is slow training process.

Benefits of Object detection to Real-world

Object detection is completely inter-linked with other similar computer vision techniques such as image segmentation and image recognition that assist us to understand and analyze the scenes in videos and images. Nowadays, several real-world use cases are implemented in the market of object detection which make a tremendous impact on different industries. Here we’ll specifically examine how object detection applications have impacted in the following areas.

Self-driving cars

The primary reason behind the success of autonomous vehicles is real-time object detection artificial intelligence based models. These systems allow us to locate, identify and track the objects around them, for the purpose of safety and efficiency.

Video Surveillance

Real-time object detection and tracking the movements of objects allow video surveillance cameras to track the record of scenes of a particular location such as an airport. This state-of-the-art technique accurately recognizes and locates several instances of a given object in the video. In real-time, as the object moves through a given scene or across the particular frame, the system stores the information with real-time tracking feeds.

Crowd Counting

For heavily populated areas such as shopping malls, airports, city squares and theme parks, this application performs unbelievably well. Generally, this object detection application proves to be helpful to large enterprises and municipalities for tracking road traffic, violation of laws and number of vehicles passing in a particular time frame.

Anomaly detection

There are several anomaly detection applications available for different industries which use object detection. For instance, in agriculture, object detection models can accurately recognize and find the potential instances of plant disease. With the help of this, farmers will get notified and they will be able to prevent their crops from such threats. As another example, this model has been used to identify the skin infections and symptomatic lesions. Some applications are already built for skin care and acne treatment using object detection models. Keep in mind, there are some problems encountered while creating any kind of object detection model. However, solutions are also available to limit the challenges.

Challenges and Solutions of Object detection Modelling Dual Synchronization

The first challenge for object detection is to classify the image and position of the object, which is known as

Solution: Regional based Convolutional neural networks displays one class of object detection framework that consist of region generation proposals where objects are likely to be located, followed by CNN models processing to classify and rectify the object locations. Fast-R CNN model can improve the initial results with R-CNN. As its name denotes, this Fast R-CNN model provides tremendous speed, but accuracy also improves only because the localization and object classification tasks are optimized using a multi-task loss function.

Real-time detection speed

Fast speed of object detection algorithms has always been a major problem to classify and localize the crucial objects accurately at same time to meet the real-time video processing. Over the years, several algorithms improved the test time from 0.02 frames per second to 155 fps.

Solution: Faster R-CNN and Fast R-CNN models aim to speed up the original speed of R-CNN approach. Because R-CNN uses the selective search to produce 2000 candidate regions of interest and passes through each CNN based model individually, that may cause a heavy bottleneck since the model processing gets down. Whereas, Fast R-CNN model transmits the whole image through CNN base once and then matches the ROIs created with selective search to feature map, considering 20-fold reduction in processing time.

Multiple aspects ratios and spatial scales

For several object detection applications, items of interest may appear in huge range of aspect ratios and sizes. Researchers proved numerous methods to ensure the detection algorithms which are able to recognize different objects at different views and scales.

Solution: Rather than selective search, faster R-CNN has been updated with a region proposal network that uses a small sliding window over the picture’s convolutional feature map to produce candidate regions of interest. Several regions of Interests can be predicted at different positions and described relative to reference anchor boxes. The size and shape of these anchor boxes are selected to span a range of aspect ratios and different scales. It lets several types of objects identify with a hope that bounding box coordinates do not need to be adjusted during the localization task.

Limited data

One of the undeniable facts to be considered is the limited amount of annotated data which becomes a hurdle to build an application. These datasets are specifically containing ground truth examples for dozens to hundreds of objects, while image classification datasets include approximately 100,000 different classes.

Final thought

What Is Rose Ai: The Ultimate Cloud Data Platform For Smarter Machines

Are you looking for a comprehensive cloud data platform that can help you find, engage, visualize, and share data with ease? Look no further than Rose AI! Founded in 2023, Rose AI is a private company with three executive team members. The platform uses natural language processing and open-source LLMs to parse and integrate data from external and internal sources, providing infrastructure tools to clean, analyze, and visualize data in one centralized solution. Let’s dive deeper into what Rose AI can do for you.

Important: Rose AI is a cloud platform that simplifies finding and sharing data using natural language processing and AI. It integrates with top providers, is user-friendly, and has social media presence. It’s also funded by investors.

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Rose AI is a cloud data platform designed to help users find, engage, visualize, and share data. It offers an integrated, mutually reinforcing data workspace, analytics engine, and marketplace platform that allows transforming, sharing, and monetizing vetted and quality-controlled data. The platform also enables users to permission data for internal teams or third parties.

With Rose AI, you can easily find the data you need and make data-driven decisions with confidence. The platform leverages generative AI to help you find, visualize, and share data, providing smarter machines for beautiful data. Let’s take a closer look at how Rose AI works and what it can do for you.

Rose AI uses natural language processing to parse and understand user queries. This allows users to interact with the platform using natural language, making it more user-friendly and efficient. Natural language processing is a key field in artificial intelligence and enables seamless interaction with computers.

By leveraging natural language processing, Rose AI is able to provide a more intuitive and user-friendly experience for its users. You can simply ask the platform a question, and it will provide you with the data you need. This eliminates the need to sift through large amounts of data manually, saving you time and effort.

There are several benefits to using natural language processing in Rose AI, including:

Understanding and Parsing User Queries: Natural language processing allows Rose AI to understand and parse user queries, making the platform more user-friendly and efficient. This means that you can quickly find the data you need without having to spend hours manually sifting through data.

Extraction of Data and Information from Text-Based Documents: Natural language processing enables the extraction of data and information from text-based documents, which can improve complex analytics tasks like sentiment analysis. This means that you can get more insights from your data, making it easier to make data-driven decisions.

Seamless Interaction with Computers and Robots: The use of natural language processing in artificial intelligence and robotics enables seamless interaction with computers and robots. This means that Rose AI can provide smarter machines for beautiful data, making it easier for you to find, visualize, and share data.

By leveraging natural language processing, Rose AI is able to provide a more intuitive and user-friendly experience for its users, making it easier to find, visualize, and share data.

Rose AI’s main function is to provide a cloud data platform that helps users find, engage, visualize, and share data. It enables integration of external and internal data, with the ability to permission data for internal teams or third parties. The platform also provides infrastructure tools to clean, analyze, and visualize data.

With Rose AI, you can quickly find the data you need and make data-driven decisions with confidence. The platform is designed to be user-friendly and efficient, making it easy for you to interact with it using natural language.

According to PitchBook, Rose AI has received $5.5 million in seed funding as of September 2023. The search results do not provide information on which companies have invested in Rose AI.