Automated Network Management

Businesses nowadays are understanding the value of adding artificial intelligence and automation to network management. This has come out in the new market research conducted by Enterprise Management Associates (EMA). Recently in relation to this, a technological term that is getting popular in the industry is AIOps. It is short for Artificial Intelligence for IT Operations which is an umbrella term for the combination of big data, machine learning, and other advanced analytics to automate IT management processes.

Primarily AIOps is all about analyzing and correlating different sets of IT data, recognizing patterns, retrieving insights from data, and presenting those insights to the IT personnel. In a recent survey, it was found out that 90% of the networking pros are in favor of applying AIOps to network management. They believe that it will lead to better business outcomes for an organization. Especially those who had already experienced the benefits of AIOps were strongly in agreement that it will provide even more benefits with a network management system.

We can say that with AI automation-driven network monitoring systems businesses can perform better while enjoying improved customer experience, better employee productivity, and high revenue generation. But how AIOps is actually doing all this?

What are the network management benefits of AIOps?

In the EMA survey, when the experts are asked about the potential benefits of an AIOps- driven network management system, then they stated the below-given points:
Network optimization
Improved security/compliance
Operational efficiency
Analyzing the current network management toolsets Networking experts were asked why they are showing a sudden interest in AIOps driven applications; if it’s because of any shortcomings of the existing networking management system. About 91% of them were hoping that AIOps would resolve various issues with the existing tools. The below-given opportunities were called out by the research respondents:

Role of Gamification in Digital Marketing

Many brands are just there for the market. On their social media handle, there is no movement at all. Engagements or reach are not there. That is affecting the value of their brand. According to statistics, there are over one billion active users on Instagram. It is a vast platform that will allow companies to get the best out of digital marketing. With the reach of this kind, companies can usher in growth and stability in their business. There are two categories of digital marketing. Organic and inorganic.

Organic marketing and in-organic marketing both need gamification. With the help of these, companies will grow in the market for sure. Digital marketing company in Kolkata caters to this need of the companies to excel in the market.

Benefits with examples
For digital marketing, Instagram is the key to the heart of consumers. On Instagram, there are video ads that are grinding out all the perks of the market. Through these video ads, consumers will be able to link up with the creators. Aspirational ads are there for the audience to find their desired product. Digital marketing company in Kolkata is using these ads to get the benefits of digital marketing. Here are some of the factors why gamification through video ads is required.

The first thing to consider is the duration. Instagram ads are not more than 30 seconds. Within these ads, there is numerous content.
The second important thing is the target audience. Nowadays, the attention span is very low as per quality is concerned. So if companies need to have the attention, the ads have to be thought-provoking.
The last thing about these ads is they are not static. Consumers cannot skip them like regular posts. These are in-stream video advertisements that will cater to the need of companies. It will convey the message and create the best branding for the product.
How to create an impactful advertisement
A digital marketing company in Kolkata caters to the need of the clients. Digital marketing is the key to achieving growth in the modern market. Here are some of the techniques to create impactful video ads for the campaign.

The first ‘three’ seconds
The reason gamification hasn’t been used in the digital market is because of the ‘three’ seconds rule. Companies held it on a pedestal because it started its magic instantly. The three seconds phenomenon is natural. Because it is a video and not a static picture, it will hold the consumers’ attention. Brands know this very well. The content is the main part of any medium. To engage the audience in just three seconds, the content must be thought-provoking. When the emotional bond deepens, then the customers will be hooked up with the video for sure.

Uses And Future Of Polyline Annotation

Artificial intelligence promises to transform the way people travel in the coming years. AI is already present in numerous products and is being increasingly adopted by large corporations. There are significant challenges to the use of automated vehicles for consumers across the globe, but serious progress is being made.

AI algorithms that drive automated vehicles are able to interpret and navigate their environment because they are trained with annotated video and image data. Human operators label roads and traffic in real-time, allowing car AI systems to operate better and drive policies. This blog will focus on one practical and very important type of annotation that’s used in automated vehicles: polyline annotation.

What is polyline annotation?

A technique that is used to define linear structures in images and video. Our roads trace the outline of roads, and our train tracks trace the outline of train tracks, using perpendicular lines connected to vertices. Annotators use these lines or even place them within images using online tools. For video training data, you must pick specific areas out of each frame.

Different ways through which polyline annotation supports automated vehicles

Using polyline annotation, you can teach a machine learning model the exact scale and space relationship of the different objects the vehicle travels over. The technique enables the following functionalities:

Lane detection is a core capability for all autonomous vehicles due to its importance in keeping autonomous vehicles centered and in the correct position in the lane. AI is also a super-powerful technology, one of the most advanced, which then helps computer vision models know what to do next when navigating across multi-lane highways. To build models that accurately predict driving behavior, those polylines must be properly marked.
It helps in navigating traffic laws as different road markings indicate different traffic laws. If the driverless car is to stay out of emergency situations, like getting stuck in a parking lot. It’s possible to train a system that highlights the appropriate road markings and important information on them.
Lane lines are crucial for vehicle safety. It can help autonomous cars avoid collisions and not be a road hazard by making them safe in designated lanes. Drivers must never stray from their assigned lanes or lanes that are clearly marked for passing.

The Core Tech Solution For Connected Cars

Connected cars aren’t a futuristic dream anymore – any 2020, 2019, or even 2018 car has elements of this tech. And rightly so: if there’s a chance to make vehicles safer and more convenient, then why not? Especially nowadays, when car lovers are into virtual assistants, top-notch computers, and other cutting-edge technologies.

The Audi 8, for instance, prepares itself for an unavoidable crash thanks to an advanced view of its surroundings. This car prepares airbags and stiffens seatbelts when you need them. What is more, in half of a second, the Audi 8 grows 8 centimeters to save its most vulnerable parts from damage. MBUX has an upscale infotainment system that allows the vehicle to adjust to the driver’s needs, answer questions and update over the air. As for XAIN and Porsche, these companies enable clients to record traffic data, lock/unlock cars remotely. Quite impressive examples, aren’t they?

Today, we are going to tell you about seven promising technologies that give us the above-mentioned opportunities. And, consequently, bring us closer to a driverless age. So let us begin!

IoT – the core tech solution for connected cars
Through the internet, modern vehicles can connect to lots of devices. So they receive data from various sources, such as beacons on traffic signals and line markers, cars’ sensors and radar units, cloud services, and smart home devices. All that makes vehicles data- and software-centric: when they connect to data platforms, they automatically receive new software updates and features. Thanks to IoT devices, connected cars receive notifications and warning signals, which leads to enhanced safety.

Using DSRC & 5G for dealing with the connectivity issues
It is crucial to use communication technologies when talking and listening to other road users in real-time. Dedicated Short-Range Communications is a robust protocol that enables vehicles to communicate with infrastructure wirelessly and with high speed. It has become a viable alternative to 4G LTE and Wi-Fi that aren’t seamless enough for ensuring cars’ safety. We cannot wait for 5G to send data promptly, even before drivers find out about it. According to Ford, V2X cars with 5G will be released in 2022.

The Significant Role of Machine

The Fintech sector has progressed beyond imagination. Just a few years ago, it took several weeks to get loans approved, but today, everything is processed online and it takes barely a day. Likewise, financial frauds used to occur very often and the financial safety of the user was a big concern worldwide. However in recent times, such fraudulent transactions have reduced considerably, though, online transactions have increased immensely.

How did this happen? What changed?

The mobile revolution and the emergence of trending technologies like machine learning and artificial intelligence have brought a paradigm shift in the fintech industry. Machine learning technology has undoubtedly transformed financial services in a big way.

The algorithm used by Machine Learning solutions is used to identify correlations and work patterns within the large amount of data used in sequences, operations, and events. Hence it is used successfully in process automation, customer support optimization, financial monitoring, portfolio management, algorithmic trading, etc., and much more!

So, in this blog, we will outline how machine learning services has impacted the fintech sector! Rather, we will have a glimpse at the machine learning applications in this sector. Let’s commence.

Significant Role of Machine Learning Solutions in the Fintech Sector!

Risk Management

In the Fintech industry, risk management refers to the process of identifying, analyzing, and migrating or accepting uncertainties i.e. potential risks while making any investment. It also includes taking precautionary steps to reduce such financial risk. Machine learning solutions along with AI and deep learning can help in making informed decisions about financial reports or loan applications. The predictive analysis used in Machine learning can predict potential risks from this unstructured data, and curb financial fraud to some extent.

Use Big Data To Mitigate Credit Card Fraud

When it comes to implementing and using high-end ultra-modern technology, it seems like fraud and cybercriminal activities are never going to end. Malicious activities are increasing with each passing day with the rise of cutting-edge technology, as it has become easier to get credit card details.

Nowadays the counterfeit transactions are rising, as most credit card companies are striving to find a robust solution to the credit card problem. Several credit card firms have a great interest in recognizing fraudulent financial transactions.

As per sources, the citizens in the United States paid off 26.2 billion in 2012 using credit cards, and the approximate loss accounted for that year was $6.1 billion due to several non-authorized transactions. And by the end of 2020, the United States witnessed approximately $11 billion in losses due to credit card fraud.

Therefore to put an end to such criminal activities and mitigate the loss of billions, several credit card companies and banks combined forces to leverage the big data tech as it is the best way to fight credit card fraud. Before we go into how big data can help evade credit card fraud let’s understand the basics.

Topics to cover

Introduction to Credit Card Fraud
Big Data: A Boon for Credit Card Companies
How can Big Data Tech Identify Credit Card Frauds?
Challenges faced by Credit Card Companies
The Bottom Line
Introduction to Credit Card Fraud

To put it simply, credit card fraud can be defined as using credit cards or debit cards without any authorization with malicious intentions to acquire funds. And several players in this process can be the potential victim to fraudsters, such as:

Card issuers
Cardholders
Payment gateway providers
Banks
Credit card payment systems
Payment processing firms, etc.
So, it is clear how credit card fraud affects consumers, issuers, and merchants as its economic cost goes way ahead of the cost of illicitly bought merchandise. Companies spend millions to secure themselves from scams.

What is a smart classroom?

A smart classroom integrates interactive and engaging digital content into everyday teaching and learning activities to support students’ cognitive development and fosters creativity, critical thinking, problem-solving skills and 21st-century skills. Though there has been some concern about whether or not these types of classrooms actually work for children’s development, research has shown mixed results; either showing no significant statistical difference between those who take classes in such a setting versus those who do not; or proving that such an environment does indeed have benefits for student achievement, engagement, motivation and creativity. Smart Classroom solutions in Sri Lanka are also available now through reputed vendors and are gradually being implemented through various organisations.

Classrooms that are described as “smart” usually have technology integrated into the learning environment in some way. This can include using interactive whiteboards or projectors to display content on a screen for the entire class to see, providing laptops or tablets for each student to use, or having computers and other devices hooked up to internet-enabled screens around the room. Smart classrooms may also have microphones and cameras installed so that teachers can call on students remotely and monitor their work from anywhere in the room. Additionally, many smart classrooms make use of various software programs that allow for different types of interactivities between students and teachers or among students themselves.

The advantages of having a smart classroom include:

The ability for the teacher to call on any student in the room without having to leave their desk.
The ability for students to work collaboratively on projects from anywhere in the room.
The ability for teachers to track individual student progress and provide help or support as needed.
The disadvantages of having a smart classroom include:

Teachers may feel overwhelmed by all of the technology at their disposal and not be sure how to use it effectively.
Students who are not comfortable with computers or who do not have access to devices outside of school may fall behind their peers in terms of technological skills.
There is potential for cyberbullying if proper precautions are not taken.

Let AI-based Virtual Assistants Transform

From the smallest local business to the biggest worldwide players, I believe every organization must embrace the AI revolution, and recognize how AI (Artificial Intelligence) will have the greatest effect on their business.

Yet, before you can develop a robust AI strategy – in which you work out how best to utilize AI to drive business achievement – you first need to comprehend what’s conceivable with AI. To put it another way, how are different organizations utilizing AI to drive success?

So let’s first understand that what are AI-Based Virtual Assistants:
An AI Assistant is a program or an application that is capable of performing various tasks after receiving appropriate voice commands. Connecting with them is actually similar to how we communicate with another individual to get things done. These things generally include tasks that require human intelligence that the AI framework learns, understands contextually and improvises over time.

The best part about AI-based Virtual Assistant is that it improves with time and works on itself with each communication. It can even recall insights concerning your preference for various tasks.

Presently, how does a voice assistant work?

We can understand that by taking a look at how we interact with Google Assistant or Siri. We trigger the application on the smartphone or the AI Assistant by giving a basic voice command for instance “Hello Google, What’s the Height of Eiffel Tower” as input.

Just after we input the voice command to our AI Assistant, it utilizes the Natural Language Processing (NLP) to understand the voice command along with the context. It will utilize that data to handle information, look for relevant information and yield the outcome in sound just as outwardly on the screen.

Now imagine the same context in a business situation. We have an AI Assistant that works on conversational AI technology for business with all the data fed to it. We can ask tons of meaningful questions from conversational AI-based virtual assistants and complete things that would somehow expect hours to manually generate and look at numerous reports.

There are basically 3 business areas where organizations are utilizing artificial intelligence:
1. Improving Business Processes

In order, AI could be worked into any part of a business: manufacturing, HR, marketing, sales, supply chain and logistics, client administrations, quality control, IT, finance and so on.

From automated machinery and vehicles to customer support chatbots and algorithms that detect customer fraud, AI solutions and technologies are being incorporated into a wide range of business functions in order to maximize efficiency, save money and further develop business performance.

Machine Learning Used To Detect Cyber Attacks

The digital world is growing exponentially as time passes. And as it is expanding, we are discovering its true potential and value. But the yin and yang concept is also prevalent in the internet domain. So, there’s evil in good here too, as cyberattacks loom over everything digital.

Where machine learning was utilized in cyber security to spot similar malware and malicious links, instead with cyber crime it’s wont to evade filters, bypass CAPTCHA checks, and generate targeted phishing emails. When comparing the 2 , cyber security appears to possess far more consolidated uses for machine learning. But future trends towards evasive malware and phishing may pose a significant threat to the cyber security industry.

Cyberattacks have become a growing threat and are problematic for the government, business organizations, as well as individuals. According to a 2019 report, malware attacks were approximately 10 billion in the year 2018. What’s even scarier is that as technology strengthens, cybercriminals develop with it.

As a result, they get to enhance their skills and trick you seamlessly. Cyberattacks have the potential to not only disturb businesses but also inflict serious damages on one’s technological resources. But how does machine learning detect cyber attacks? And how are these two related?

Well, the traditional network monitoring tools do not work anymore. Hence, it is essential to look for new and advanced methods to detect as well as prevent cyber attacks. This is precisely where machine learning kicks in!

So basically machine learning algorithms have the ability to classify unseen data as well as predict the future of that data, which means that it has a variety of uses in cybersecurity. However, the same features of machine learning can also be used in malicious contexts.

Let’s Begin with What Machine Learning Is?

Machine learning is essentially a method of data analysis that automates analytical model building. It can also be defined as a branch of artificial intelligence that stems from the notion that systems have the potential to identify patterns, learn from data as well as make decisions with minimum human intervention.

In Machine learning, computer algorithms are studied to ameliorate the use of data and experience. Moreover, machine learning is also known as part of Artificial Intelligence.

The Modes to Uplift Prolificness

If we talk about the technology industry, we have noticed that Artificial Intelligence is growing rapidly. However, AI is grabbing the middle phase at discussions and showcasing the potential across several industries like manufacturing and retail. Therefore, one should thank the invention of cloud computing and data storage that AI is progressing in enhancing efficiency for better performance. The annual growth rate of Artificial Intelligence will rise by 60 percent in the forthcoming years. The user can acquire better performance with the increase in productivity and efficiency. AI also helps organizations make better decisions by delivering vital data.

For some businesses, using Artificial Intelligence entails examining specific aspects of their operations in order to develop AI use cases. While this method can assist you in quickly following trends, it is not the path to AI authority. You may need to reimagine procedures and human-machine interactions within your organization to become a genuine AI-fueled organization.

In order to implement insight-driven decision-making across the enterprise, top-level executives need to be vested in the idea of applying machine learning and other cognitive technologies across the organization’s essential processes.

How Can Artificial Intelligence Uplift Productivity in Your Organization?

Here are the best ways which describe the importance of AI and help in boosting overall productivity in businesses.

Sales Prediction

Businesses can use AI to focus on prospects that have the best chance of succeeding. How? In practice, the concept is straightforward, but sifting through massive datasets necessitates the use of powerful algorithms and AI-powered solutions. It is no secret that the average company amasses vast amounts of customer and behavioral data. Putting this data to good use can mean the difference between gaining a competitive advantage and continuing to fail.

The agency uses AI to find patterns and trends in this data, picture, and object recognition. Further, it is used to identify clients who are more likely to support the company’s products or services. However, it is the best technology that lets you know whether a buyer will purchase or not. Even they will enter the store or pay a visit to an online business.

Perhaps, all the information is further analyzed like market data, customers, previous sales details, behavioral trends, and more. After it, the Artificial Intelligence technology will create customer profiles to forecast what a client or customer will do in the future.