How Data Science And Machine Learning Works To Counter Cyber Attacks

We are all aware of the heinous cyber-attack that took down more than 200,000 systems in 150 countries in only a few days in May 2017. This was found by the National Security Agency (NSA) and was nicknamed “WannaCry,” which exploited a vulnerability and stole important resources before being distributed online. 

After successfully accessing the computer, it encrypted the machine’s contents and rendered them unreadable. Now, victims of the assault were informed they needed to acquire special decryption software to retrieve their stolen material. Furthermore, the attackers marketed this software.

This ransomware outbreak targeted both people and big organizations, including the United Kingdom’s National Health Business, Russian banks, Chinese schools, the Spanish telecommunications company Telefonica, and the US-based transportation service FedEx. 

The overall losses were estimated at $4 billion. Other forms of cyber intrusions, such as crypto jacking, which are more subtle and less destructive but costly, are on the rise. Even high-profile firms with sophisticated cybersecurity processes are vulnerable. 

A recent panic at Tesla in 2018 was averted owing to a diligent third-party team of cybersecurity specialists. As a result, there were over 11 billion malware infections in 2018. That is a major problem that cannot be solved solely by humans.

Fortunately, this is where machine learning may come in handy.

How Machine Learning Helps to Boost Cybersecurity? 

Machine learning is a subset of artificial intelligence that makes assumptions about a computer’s behavior by using algorithms from prior datasets and statistical analysis. It allows the computer to modify its operations and even execute functions for which it was not expressly intended. Thus, the role of ML and AI in cybersecurity has been increasing. 

Machine learning is increasing in popularity to detect risks and automatically eliminate them before they can wreak mayhem. It can filter through millions of files and detect potentially dangerous ones. This was accomplished by Microsoft’s software in early 2018.

According to the firm, hackers utilized Trojan spyware to infiltrate hundreds of thousands of systems and run rogue cryptocurrency miners. Microsoft’s Windows Defender, a software that utilizes many layers of machine learning to identify and block potential threats, effectively blocked this attack. 

As a result, the business was able to shut off the crypto miners as soon as they began digging. Machine learning is used to search for network vulnerabilities and automate actions, in addition to detecting early threats. Machine learning excels at some tasks, such as swiftly scanning vast volumes of data and evaluating it with statistics. Cybersecurity systems create massive amounts of data, so it’s no surprise that this technology is so beneficial. As a result, in the domain of cybersecurity, this is proving to be a big benefit.

Microsoft, Chronicle, Splunk, Sqrrl, BlackBerry, Demisto, and other big corporations are utilizing machine learning to strengthen their cybersecurity systems.

How Modern Data Science Powered by AI Identifies and FIxed IT Vulnerabilities

Here is how data science helps identify and resolve IT vulnerabilities:

1- Improve the Usage of Technologies

Modern Data Science has the potential to both improve and simplify the usage of such technologies. A machine-learning algorithm may be fed both current and historical data through data science. So that the system can detect possible problems accurately over time.

This allows the system to be more precise since it can predict assaults and identify potential vulnerabilities. 

2- Use Encryption

A data breach or assault can cause severe damage to your organization in terms of the loss of important data and information.

This is where data science comes in handy since it uses very sophisticated signatures or encryption to prevent anyone from delving into a dataset. 

3- Create Protocols

Data science has the potential to create impenetrable protocols. By examining the history of your cyber-attacks, you may create algorithms to detect the most often targeted pieces of data. Data science programs may assist you in harnessing the potential of data science to empower networks powered by self-improving algorithms.

Why Should Companies Hire Qualified Professionals?

Thus, the above points indicate the importance of data science and qualified data science professionals in your firm. Focus on hiring professionals who have a master’s degree in engineering in data science and the knowledge of how to decode big data.

We have access to a massive amount of data, and the data is typically telling a narrative. You should be able to identify deviations from the norm if you understand how to analyze data. and such variations can occasionally signal a threat. And, owing to the usage and advancements achieved in machine learning, dangers may now be appropriately countered in a wide range of industries. It is used for image recognition and speech recognition applications.

Even though cybersecurity has improved as a result of this process, humans remain critical. Some individuals believe that you can learn everything from data, but this is just not true. An over-reliance on AI might lead to a false sense of security. 

However, without a doubt, artificial intelligence will become increasingly widespread in maintaining security. It’s maturing, and it’s a feature, not a business. It will play a part in resolving a certain issue. 

Final Thoughts

However, AI cannot address every problem. It will be a tool in the toolbox. At the end of the day, humans are the overlords. 

As a result, in addition to carefully deployed algorithms, cybersecurity specialists, data scientists, and psychologists will play an important role. Human efforts, like those of all existing artificial intelligence and machine learning supplements, augment rather than replace them.

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What is data preparation?

Good data preparation gives efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. It has also gotten easier with the self-service data preparation tool that enables users to cleanse and qualify on their own.

Data preparation:

In simple terms, data collection can be termed as collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis. In more technical terms, it can be termed as the process of gathering, combining, structuring, and organizing data to be used in business intelligence (BI), analytics, and data visualization applications. Data preparation is also referred to as data prep.

Importance of data preparation

Fix errors quickly – Data Preparation process helps to catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.

Top-quality data – Data Cleansing and reformatting datasets ensure that all data used in the analysis will be high quality.

Better business decision – Higher quality data can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.

Superior scalability – Cloud data preparation can grow at the pace of the business.

Future proof – Cloud data preparation automatically upgrades so that new capabilities or bug fixes can be triggered as soon as they are released. This allows organizations to stay ahead of the future betterment without risking delays or additional costs.

Accelerated data usage and collaboration – Doing data preparation in the cloud is always on, does not require any technical installation, and lets teams collaborate on the work for faster results.

Now, The Self-service Data Preparation process has become faster and more accessible to a wider variety of users.

To learn more about data preparation, Schedule a demo.

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Smart Waste Management: AI Leads the Way

The amount of waste generated in the world amounted to billions (metric tons) in quantity at the end of 2020. With the growing pressure of the human population, it is set to escalate further. In the developed nation, waste management is a profit-making market vertical. Innovative methodologies and techniques are helping make waste management seem an opportunity to bank upon. The sector is steadily progressing at a CAGR of 5.5% annually. Meanwhile, the global waste management market is set to reach USD 2.3 trillion in the upcoming five to six years.

Waste management scenario in the world

In terms of waste management, the sector encompasses – waste dumping, recycling, and minimization. The main categories are segmented as municipal waste, industrial waste, e-waste, plastic waste, biomedical waste, and others, across the world. As per the World Bank report, the regions producing waste across the world are East Asia and the Pacific region. Often, changes in incremental income in low-income levels have resulted in the production of more solid waste and other types of waste; meanwhile, waste production has also been associated with factors like high income and population to add to the numbers.

Here is a snapshot of trends in waste production, especially solid waste category in the past few years and what the leading trends look like, region-wise.

Image credit: datatopics.worldbank.org

Several entities in the waste management domain have emerged in the past few years. By making use of comprehensive processes to deal with escalating waste piles, many firms belonging to North America and European regions especially, are developing techniques to process the waste and are diligently working on waste minimization procedures. In this goal, technology has become integral to handling the burden posed by categories of waste. Some of the leading nations to work towards developing innovative ways for waste handling and minimization are – Germany, Switzerland, Austria, Sweden, Singapore, among others.

Solving waste management concerns

The primary methods used for waste management include landfill, incineration, composting, and recycling. Out of these, incineration and composting help in reducing the volume of waste to a considerable extent. Other methods to tackle waste include disposal at compost areas, volume reductions plants, borrow pit reclamation areas, and processing locations. While landfilling or decomposition contributes to GHG or greenhouse emissions that cause maximum negative effects than harmful carbon dioxide or CO2. As an active contributor in producing GHG, waste decomposition is far more harmful than carbon dioxide for the environment. Starting from open waste dumps to waste decomposition, the main reason why waste management is mandatory is the deterioration of the environment and human surroundings. Today, it is being helmed as a leading cause of climate change and creating various health risks. Additionally, waste in various forms is posing a great health risk to health workers who are involved in the collection and dumping of the waste on a day-to-day basis.

Although, things are changing fast now with the adoption of practices that are carried out through technological intervention. Technology-led initiatives in waste minimization are influencing the way waste is collected, transported, and sent for recycling. With the onset of internet-of-things or IoT, the possibilities and methods to recycle, upcycle and decomposition processes have become more streamlined and attainable.

The AI-enabled smart waste management

In terms of waste management, traditional waste management techniques have proven to be complex, labor-intensive, and often pose a risk to the life of sanitation workers and staff. On the other hand, a connected ecosystem inspired by IoT has paved the way for the application of AI and Machine Learning models for channeling multiple elements for better Urban Planning and smart cities or cities of the future.

Several developed nations have successfully implemented the AI-enabled waste management infrastructure to reduce waste and process recyclable material. Smart Bins equipped with scanners can scan each and every object discarded by an individual and save the data for transfer remotely through a sensor. The bins can segregate different types of waste like metal, paper, glass, plastic, organic, etc while it gets detected as a frozen inference graph through a camera attached inside with the processing unit. AI programs powered by Machine learning and accurate computer vision training data help in classifying different types of waste images and help in the detection of their categories. Post this, an embedded ultrasonic sensor device also checks the filling level and notifies the owner of the usage. Once the trash bins are filled, the sensors notify centralized waste management systems, which then turn up to collect the waste.

Further, once the collection of trash is done, the Smart Bins are taken to waste processing facilities. Herein, the waste processing facilities working with Artificial Intelligence-based programs, identify the types of waste material and start the segregation on the basis of inference graph data. The segregated waste is sent for the next level of processing for various other methods of waste recycling. Items like metal, cardboard, plastic, wood, and electronic equipment are recycled and made contamination-free for the production of goods.

Last word

The never-ending cycle of waste production and disposal has crippled the existing infrastructure and over-pressurized manpower for a long time. AI-enabled smart waste management systems are a viable answer to the health risks posed, time and energy costs involved in the collection and disposal of waste. With the burden of the growing population and exhaustion of landfill sites, smart waste management has become an imperative and a must-have option to live on a waste-free planet. Not merely this, it will help in tackling waste disposal issues but will also contribute to the creation of a healthier environment. Rather than following decades-old techniques, Smart Waste Management-focused AI applications have opened up new facets to tackle the persistent problem of waste management, especially in countries with swelling populations.

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Industry 4.0 in CNC Machine Monitoring

The demand for Computer Numerical Control (CNC) equipment is gradually increasing and performing to expect a huge growth over the coming years. For this an annual growth rate of more than six percent. CNC machining plays a major role in present manufacturing and helps us create a diverse range of products in several industries, from agriculture, automotive, and aerospace to Semiconductor and circuit boards.

Nowadays, machining has developed rapidly in periods of processing complexity, precision, machine scale, and automation level. In the development of processing quality and efficiency, CNC machine tools play a vital role.

Faststream Technologies has implemented the IoT-enabled CNC machine monitoring solutions, which creates machine-to-machine interaction resulting in automated operations and less manual intervention.

Embedded the IoT sensors on CNC machines that can measure various parameters and send them to a platform from where the state and operation of the machines can be fully supervised. Furthermore, CNC machines can scrutinize the data collected from sensors to perpetually replace tools, change the degree of freedom, or perform any other action.

ADVANTAGES:

An Enterprise can leverage the following advantages by coalescence of Industry 4.0 and CNC.

Predictive Maintenance:

CNC Machine operators and handlers embrace the Industrial IoT which allows them to appropriately interconnect with their CNC machines in many ways through smartphones or tablets. Therefore the operators can monitor the condition of machines at all times remotely using Faststream’s IoT-based CNC machine monitoring.

This remote and real-time monitoring aids the machine operating person to program a CNC for a checkup or restore.

On the other hand, these can also arrange their CNC machines to send alerts or notifications to operators whenever machines deem themselves due for tuning or maintenance. In another term, the machine will raise red flags about complications such as a rise in temperature, increased vibrations, or tool damage.

Reducing Downtime and Efficient Machine Monitoring :

Digital Transformation in CNC Machine solutions has broad competence and is not restricted to distant control and programmed maintenance for CNC machines.

Reduce machine downtime and escalate overall equipment effectiveness by using our IoT system and grasping its real-time alert features. The Alerts received from machines can be used to do predictive measures and unexpected breakdown of tools or any other element of a CNC machine.

Faststream Technologies similar solutions to its clients by arranging the IoT energy management solution for their CNC machines. Pre-executing these solutions, the client was facing difficulties with the future breakdown of their machines. Faststream’s IoT solution guided them to retain a clear insight into the running hours of their CNC machines, which in turn gave them exact thoughts of how they were maintaining their production run-time.

Machine downtime reducing solutions can be utilized for a chain of CNC machines to not only ameliorate their processing but also to boost the machine synchronization process in industrial inception and realize the operational eminence.

Less manual effort and Worker Safety:

For the bigger enactment, the technology of Industrial IoT can also be implemented to bring down manual efforts, or in other terms, mitigate the possibility of workers’ injury in the factory operation process.

From this action, machine-to-machine synchronization and interrelation come into the picture. The synergy between machines will result in more interpretation between various electromechanical devices, which will lead to automated operations in a Manufacturing unit.

Many companies are already working towards the development of smart robots and machines that can.

Several Companies that perform on smart robots and machine development can work on pre-programmed tasks and retaliation to the existing needs of CNC machines for bringing down the extra strain of quality operation from the manual workforce. All these robots can perform confined and elegant work like opening & close the slab of a CNC machine or reform the tool whenever sharpness is required.

Apart from the lowering injuries in the workshop, our Industry 4.0 in CNC Machine also helps in lowering material wastage and betterment the efficiency of CNC machines, which will help in the rise in production of exact elements in a shorter time frame.

CONCLUSION:

CNC machines are electromechanical devices that can operate tools on a different range of axes with more accuracy to generate a small part as per command put through a computer program. These can run faster than any other non-automated machine as well as can generate further objects with high accuracy from any type of design.

Using the technology of the Industrial Internet of Things(IIOT), the competence of a company can be boosted even further, though CNC machines are themselves proficient in uplifting a machine to a new peak.

Faststream Technologies is a cutting-edge IoT solution provider that assists factories and workshops to integrate their CNC machines with Industry 4.0 solutions.

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S2, A next generation data science toolbox

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COVID-19 Drives AI in Medical Imaging

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A Simple Regression Problem

This article is part of a new series featuring problems with solution, to help you hone your machine learning and pattern recognition skills. Try to solve this problem by yourself first, before looking at the solution. Today’s problem also has an intriguing mathematical appeal and solution: this allows you to check if your solution found using machine learning techniques, is correct or not. The level is for beginners. 

The problem is as follows. Let X1, X2, X3 and so on be a sequence recursively defined by Xn+1 = Stdev(X1, …, Xn). Here X1, the initial condition, is a positive real number or random variable. Thus,

It is clear that Xn = An X1, where An is a number that does not depend on X1. So we can assume, without loss of generality, that X1 = 1. For instance, A1 = 1 and A2 = 0. The purpose here is to study the behavior of An (for large n) using simple model fitting techniques. I plotted the first few values of An, below. In the figure below, the X-axis represents n, and the Y-axis represents An. The question is: how to approximate An as a simple function of n? Of course, a linear regression won’t work. What about a polynomial regression?

The first 600 values of An are available here, as a text file.

Solution

A tool as basic as Excel is good enough to find the solution. However, if you use Excel, the built-in function Stdev has a correcting factor that needs to be taken care of. But you can just use the values of An available in my text file mentioned above, to avoid this problem.

If you use Excel, you can try various types of trend lines to approximate the blue curve, and even compute the regression coefficients and the R-squared for each tested model. You will find very quickly that the power trend line is the best model by far, that is, An is very well approximated (for large values of n) by An = b n^c. Here n^c stands for n at power c; also, b and c are the regression coefficients. In other words, log An = log b + c log n (approximately). 

What is very interesting, is that using some mathematics, you can actually compute the exact value of c. Indeed, c is solution of the equation c^2 = (2c + 1) (c + 1)^2, see here. This is a polynomial equation of degree 3, so the exact value of c can be computed. The approximation is c = -0.3522011. It is however very hard to get the exact value of b. 

It would interesting to plot the residual error for each estimated value of An, and see if it shows some pattern. This could lead to a better approximation: An = b n^c (1 + d / n), with three parameters: b, c (unchanged) and d.

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DSC Weekly Digest 27 July 2021

There are fashions in technology that are every bit as ephemeral as fashions in the garment industry. For a while, all data was BIG DATA, then data warehouses were cool, then data lakes became the gotta-have look for the year. Data science had its heyday, and everyone had to stock up on PhDs, then knowledge graphs gained a brief bit of currency, like a particularly frilly collar or gold chains. DevOps was hot and everyone wanted to be a DevOps tech, then machine learning was hot and everyone became a machine learning guru. Yesterday we were arguing about whether R or Python was the next big thing, and today it’s shifted to AutoMLOps vs. AIOps. 

Everyone is currently chasing the holy grail of being data-driven companies, often with at best only a very faint idea about what that actually means. Every so often, it is worth stepping off the carousel and letting the brass ring go past,

In general, data can be thought of as records of the events that take place around a person or an organization as they take place Some of this information is a record of the events themselves, such as sales transactions. Some of the data is contextual metadata that puts the events into perspective.

It’s worth noting that some of this data has no relevance to you or your organization, which we refer to as noise, while other data does have relevance, which can be referred to as signal. Unfortunately, there is no explicit guide about what is noise and what is signal until you have a question or query to ask, and typically the biggest problem that most organizations face is that they tend to hold on to transactional data preferentially to metadata, despite the fact that it is frequently the latter that holds the answer to the queries, simply because transactional data is easiest to capture.

Data analytics, at its core, is the art of knowing how to ask the right questions. Not surprisingly, data analytics is stochastic or probabilistic in nature because it is based upon the assumption that people and organizations that act a certain way in the past will continue to do so into the future. This is true, so long as the conditions that applied in the past also continue into the future, and because people’s behaviors have a certain degree of momentum, it is even somewhat true when the conditions change, at least for a little while. However, the future is notoriously fuzzy around inflection points, where events change in dramatic ways, and in those times a good data scientist is worth their Ph.D.-enhanced salaries.

A data-driven organization then is one that both practices good “data hygiene” in the acquisition and preparation of data (typically by attempting to determine semantics or meaning in that data independent of the form of that data) as well as utilizes that data in order to not only read the tea leaves but also to change the behavior of that organization in response to changes in data. Failure to change when the model indicates that change is warranted makes everything else that happens in the data process moot – it is an exercise in adding process without using that process for something positive.

In many respects, the goal of being data-driven, then, is to make the organization become aware in the same way that an animal is aware of its surroundings and can react when those surroundings change, or the way that a seasoned captain aboard a sailing ship can read the sky and know whether to unfurl the sails to catch favorable winds or to furl them to protect the ship from storms.

A data-driven organization is one that is capable of discerning the signal from the noise and acting in response. All else is marketing.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

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The Tao of Tau

class Tau {     

        static TAU = 2 * Math.PI

        static TAU_2 = this.TAU/2

        static TAU_4 = this.TAU/4

        static TAU_6 = this.TAU/6

        static TAU_8 = this.TAU/8

        static TAU_12 = this.TAU/12

        constructor(){

            // This consists solely of static methods and constants

        }

        // converts a revolution to degrees

        static toDegrees(rev){return rev * 360}

        // converts a revolution to radians

        static toRadians(rev){return rev * this.TAU}

        // converts from degrees to revolutions

        static fromDegrees(deg){return deg / 360}

        // converts from radians to revolutions

        static fromRadians(rad){return rad / this.TAU}

        // returns the sine value of the given revolution

        static sin(rev){

            return Math.sin(rev * this.TAU)

        }

        // returns the cosine value of the given revolution

        static cos(rev){

            return Math.cos(rev * this.TAU)

        }

        // returns the tangent value of the given revolution

        static tan(rev){

            return Math.tan(rev * this.TAU)

        }

        // returns the arcsine value of the given revolution

        static asin(rev){

            return this.fromRadians(Math.asin(rev))

        }

        // returns the arccosine value of the given revolution

        static acos(rev){

            return this.fromRadians(Math.acos(rev))

        }

        // For a given x,y value, returns the corresponding revolution from -0.5 to 0.5.

        static atan(x,y){

            return this.fromRadians(Math.atan2(y,x))    }

    

    }

    

    class TauComplex{

        // Indicates the number of significant digits complex numbers are displayed using.

        static SIGDIGITS = 5;

        constructor(x,y){

            this.x = x

            this.y = y

            return this

        }

        // toString() generates a complex number of the form “a+bi” for string output

        toString(){

            let minX = Math.abs(this.x)/span>1e-5?0:TauComplex.trim(this.x);

            let minY = Math.abs(this.y)/span>1e-5?0:TauComplex.trim(this.y);

            return `${minX} ${Math.sign(this.y)>=0?‘+’:‘-‘} ${Math.abs(minY)}i`

        }

        // generates the length of the complex number vector

        get modulus(){

            return Math.sqrt(this.x*this.x + this.y*this.y);

        }

        // generates the square of the length of the complex number vector. This avoids the need to take the square root

        get modsquare(){

            return this.x*this.x + this.y*this.y;

        }

        // retrieves the angle relative to the positive x axis of the complex number, in revolutions

        get theta(){

            let angle = Tau.atan(this.x,this.y);

            let ySgn = Math.sign(this.y);

            let adjAngle = ySgn/span>0?1+angle:angle;

            return adjAngle;

        }

        // retrieves the complex conjugate (a-bi) of the complex number (a+bi)

        get conjugate(){

            return new TauComplex(this.x,-this.y)

        }

        // retrieves the complex inverse of the number (a+bi).

        get inverse(){

            return (this.conjugate).scale(1/this.modsquare)

        }

        // rotates the complex number through the angle, expressed in revolutions.

        rotate(angle){

            let newX = this.x * Tau.cos(angle) – this.y * Tau.sin(angle);

            let newY = this.x * Tau.sin(angle) + this.y * Tau.cos(angle)

            return new TauComplex(newX,newY)

        }

        // Multiplies the complex number by a scalar value (or values if two arguments are supplied)

        scale(x,y=x){

            let newX = this.x * x;

            let newY = this.y * y;

            return new TauComplex(newX,newY)

        }

        // translates the complex number by the given amount. Equivalent to adding two complex numbers

        translate(x,y=x){

            let newX = this.x + x;

            let newY = this.y + y;

            return new TauComplex(newX,newY)

        }

        // Adds two or more complex numbers together.

        static sum(…c){

            let reducer = (acc, cur) => new TauComplex(acc.x+cur.x,acc.y+cur.y)

            return c.reduce(reducer)

        }

        // Multiples two or more complex numbers together.

        static mult(…c){

            let reducer = (acc, cur) => new TauComplex(acc.x*cur.x–acc.y*cur.y,acc.x*cur.y+acc.y*cur.x)

            return c.reduce(reducer)

        }

        // Divides the first complex number by the second

        static div(c1,c2){

            return TauComplex.mult(c1,c2.inverse)

        }

        // Takes the complex number to the given power. Power MUST be a non-negative integer.

        pow(power){

            let arr = [];

            for (var index=0;index!=power;index++){

                arr.push(this);

            }

            if (arr.length>0) {

                return TauComplex.mult(…arr)

            }

            else {

                return new TauComplex(1,0);

            }

        }

        // Returns the real portion of a complex number

        get re(){

            return this.x

        }

        // Returns the imaginary portion of a complex number

        get im(){

            return this.y

        }

        // Returns the complex number associated with a unit vector rotated by the revolution amount

        static tau(rev){

            return new TauComplex(Tau.cos(rev),Tau.sin(rev));

        }

        // Returns the complex exponent of the given complex number

        get exp(){

            return TauComplex.tau(this.y).scale(Math.exp(this.x))

        }

        // Creates a string representation of a number to the given significant digits, default being 5.

        static trim(value,sigDigits=this.SIGDIGITS){

            return value.toLocaleString(“en-us”,{maximumSignificantDigits:sigDigits})

        }

        static array(…arr){

            return arr.map((subArr,index)=>new TauComplex(…subArr))

        }

    }

    const _TauComplex = TauComplex;

    exports.TauComplex = _TauComplex;

    const _Tau = Tau;

    exports.Tau = _Tau;

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Seven Ways that AI in Telecom is Transforming the Enterprises

The telecom sector is no longer limited to delivering basic phone and internet service. In the Internet of Things (IoT) era, mobile and broadband services are driving technological innovation. Telcos are utilizing AI to handle and analyze these massive amounts of Big Data to extract meaningful insights and improve customer experience, operations, and revenue through new products and services. With Gartner predicting that 20.4 billion connected devices would be in use globally by 2020, more service providers can see the benefit of AI in the telecom industry, including optimization, maintenance, client targeting, and more.

1. Network Optimization

To strengthen their infrastructure, 63.5 percent of operators are investing in AI systems. In the telecom industry, artificial intelligence is critical for CSPs to develop self-optimizing networks (SONs), which allow operators to autonomously adjust network quality based on traffic data by region and time zone. Artificial intelligence in the telecom industry utilizes powerful algorithms to seek patterns in data. It allows telcos to discover, forecast network anomalies, and proactively address problems before customers are harmed.

2. Detecting and preventing fraud

ML algorithms cut down fraudulent activities happening in the telecom industry, such as fake profiles, illegal access, etc. With the aid of advanced ML algorithms, the system can detect the irregularities occurring on a real-time basis, which is more effective than what human analysts can perform.

3. Enabling predictive analytics

By combining data, complex algorithms, and machine learning approaches to anticipate future results based on historical data, AI-driven predictive analytics assists telcos in providing better services. It means that operators may use data-driven insights to track the health of equipment, predict failure based on patterns, and prevent problems with communications hardware like cell towers, power lines, data center servers, and even set-top boxes in consumers’ homes. Through the process of Predictive Analytics, CSPs can make efficient and effective business decisions. Technologies such as network automation and intelligence will allow for more accurate root cause investigation and issue prediction. With technologies such as AI/ML, it will support more strategic aims like creating new consumer experiences and efficiently dealing with evolving company needs.

4. Optimizing Service Quality

Machine learning and artificial intelligence in telecom can assist you in improving the quality of your service. You can apply Machine Learning techniques to forecast how your network’s consumption will change over time across the different geographies it serves. In order to improve optimization, a variety of criteria can be considered, including time zone, hour, weather, national or regional holidays, and more.

5. Improve Customer Service

Another advantage of AI in telecom is through the automation of the customer service mechanism. It can help telcos reinvent customer relationships through personalized, intelligent, and persistent two-way conversations at scale. Conversational AI systems are another use-case of AI in telecom. According to Juniper Research, virtual assistants have learned to automate and scale one-on-one conversations so effectively that they are expected to save businesses up to $8 billion annually by 2022. The large volume of support requests for installation, set up, troubleshooting, and maintenance, which often overwhelm customer service centers, has led telcos to turn to virtual assistants for assistance. Operators can add self-service capabilities that show customers how to install and run their own devices using data science, AI, and machine learning.

6. Preventing Malicious Activity

Machine Learning can effectively protect your network from dangerous behaviours such as DDoS attacks. Using AI in telecom, the network can be trained to recognize a large number of similar requests that are inundating it.  At the same time, it lets them decide whether to deny these requests outright or shunt them to a less busy data center to be handled manually by your staff.

7. Foster innovation and drive new business

One of the promises of 5G is to bring Industry 4.0 use cases to fruition by enabling high speed, low latency, and dense deployment of endpoints such as sensors, robots, and video cameras. It opens up new business opportunities for telcos to not only outsourced IT services to the enterprises but also offer innovative services driven by AI at the Edge. New innovative AI-driven services are geared to address many new business segments, for which telecom operators will be one of the beneficiaries.

AI technology plays an essential role in digital transformation across all industries and verticals. The crucial integration of AI in the telecom industry will help assist and guide CSPs in delivering, managing, and optimizing the telecom infrastructure and networks.

Do you agree artificial intelligence is changing the telecom industry? If yes, how will it benefit an operator? Feel free to share your thoughts in the comments section.

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