how does computer vision works

How Does Computer Vision Work – Detailed Guide

How Does Computer Vision Work?

Muhammad Imran


December 30, 2019

how does computer vision works

Artificial Intelligence (AI) is extensively used in our everyday life. Its introduction has revolutionized the execution of a variety of processes that were known for their complexity. Computer vision is also a sub-branch of Artificial Intelligence. It is not about image processing, as often people misunderstand how does computer vision work? It is far different from image processing

Computer vision involves techniques enabling computers to see and interpret visual content for the automation of various processes. Computer vision has revolutionized plenty of industrial processes including manufacturing.

What are Computer Vision Goals and Tasks?

As the use of visual content is increasing day by day. More than half of consumers want to observe more visual content from the brands they like, as conceived by HubSpot. It has become immensely essential to make use of computer vision to handle information from visual content, including images and videos as well.

Simply put, computer vision is employed to mimic natural processes done by a human brain like retrieving information through visual content, handling the data, and interpreting the results for further actions. Though computers are still way behind the capabilities of a human brain in processing visual processing, computer vision is a great step in the right direction.

Computer vision works to perform multiple tasks. These tasks include image classification, semantic segmentation, classification, localization, object detection, object tracking, instance segmentation, action recognition, and image enhancement. These tasks of computer vision work pretty well in combination with each to bring comprehensive results.

How does Computer Vision Method Work in Today`s World?

In this section, we will try to elaborate on how computer vision works in various industries and processes for automation and saving the human effort required for the operation otherwise. Here is a list of some use cases for your reference:

Robotic Manufacturing Processes

As the industry is going through a revolutionary phase while transforming the manufacturing work from manual operations to automated solutions using robotics, computer vision has a lot to do for this cause.    

Robotic machines which are employed to handle the manufacturing process and eliminate the need for human effort are using computer vision method. Robots deployed to work in the manufacturing process need to see what is around them for accurate operations. 

Robotic machines often inspect entire assembly lines to figure out what is coming for them, this helps them in working at the required pace to perform required operations on every item in assembly tolerance timely.

Check out our Robotic process automation services.

Computer Vision for Vehicles

People are turning towards autonomous solutions in almost every task in their everyday life. Similar is the case with vehicles. After some successful results in testing of self-driving vehicles, almost all the vehicle manufacturers around the world have started their campaign to introduce autonomous vehicles with the capability of self-driving. Leaders in this autonomous vehicle campaign are Tesla and Mercedes. 

Tesla is known to be the leader in introducing self-driving vehicles. Here is a link to give more details about self-driving vehicles by Tesla. Mercedes, a renowned vehicle manufacturer, is known for its innovative approach throughout its history, is also introducing self-driving vehicles to have the edge over its competitors, especially BMW. 

In addition to self-driving, vehicles are using computer vision as driver assistance systems. The vehicles can assist in the control of speed, steering, and lane switch with the use of computer vision. There is more to come with the passage of time, but we are sure computer vision is helping a lot in working towards totally autonomous vehicles.

Health and Medicine:

The use of computer vision in the medicine industry is already bringing ideal results. Computer vision is used to pipeline cell segmentation in such a way that it can process an image at an individual pixel level. In addition to processing, computer vision is also capable of interpreting the results and identify the spot where rogue cells are present. Computer vision also helps in coloring multiple elements in an object in different colors for more straightforward interpretation.

Update: We have highlighted some greate uses cases of Predictive analytics in healthcare 

It is immensely useful in identifying and precisely detecting the root cause of a disease like tumors. Using a conventional way of pathology and medical scanning was quite complicated. It was also observed that medical experts agreed on the diagnosis in less than 48% of the medical cases. However, with the help of AI and computer vision, results are highly accurate. Therefore, medical experts don’t need much effort in brainstorming to diagnose the disease. This ultimately helps them in focusing more on the treatment to eliminate the ailment of patients.

Predictive Maintenance:

Predictive maintenance is a crucial part of the manufacturing process. The failure of machinery in the middle of this process could bring a lot of embarrassment and bad reputation to the company. This could be a disaster for any company. Therefore, many companies are turning towards Robotic Process Automation (RPA) for this process, which works hand in hand with the computer vision for accurate working and almost zero chance of failure.   

Machine learning-powered smart devices use computer vision to monitor incoming data from machinery with the help of sensors. This enables smart devices to identify signals and take proactive actions to avoid any manufacturing disaster.

Inspection of Packages:

Computer vision could also be used for inspection of packages to control the quality of the manufacturing process. This is especially a very beneficial tool for pharmaceutical companies to ensure the right number of tablets and capsules in packing. It could also be a great tool for companies manufacturing spare parts and bearings. 

The photos of packages moving through the production line are taken through advanced cameras and sensors. These cameras then deliver these snaps to main control unit which checks the package and counts the tally of items in the package. If an irregularity is identified, the particular package is eliminated from checkout line. This technology could also be used for inspection of packages at airports and ports by the customs department to avoid the delivery of any prohibited item in the country. 

Object Detection and Tracking in Sports:

Machine learning and computer vision have become an essential element in sports. Umpires and referees take the help of these technologies during decision referral systems for object detection and object tracking. 

The technology also helps in getting knowledge about the performances of players and athletes while performing actions. Computer vision also helps in the post-game analysis as well.

3D Computer Vision:

The 3D computer vision could be employed to analyze the performances of athletes and predict the actions of athletes during a game. 3D vision also allows the building of a 3D point cloud, which is a representation of a 2D image in 3D format. Computers can trace the location and shape of the object after building a 3D point cloud. This technology could also help a lot in forensics.

3D vision is also used in retail for monitoring items without any barcodes scanning process by Amazon. It also helps in healthcare. The process of surgery of patients could be observed in real-time through this technology.

Processing of Visuals for Agriculture:

Precision farming is getting popular day by day. Farmers are finding livestock management solution convenient for various agriculture processes. Many farming industries are making use of satellite images for analysis through computer vision to get precise information about conditions of crops and lands. 

AI-led computer vision solutions are also employed in the process of winemaking to ensure the production of finest and disease-free wine and monitor vineyards. Automated drones with high-quality cameras are also deployed to inspect the are to figure out any problem through computer vision.

Computer Vision Powered AR:

Augmented reality has become an essential element in advertising campaigns and industrial procedures. AR made possible through computer vision could help out vehicle manufacturers and various other industries to get a boost in the maintenance and assembly process because of the use of AR.

AR enables industries to make use of the option to implement the application of real-time data integrated with real objects easily. 

How Does Folio3 Computer Vision Solution Help Businesses?

Folio3 is known for its progressive approach and helping businesses from various verticals of the industry to bring automation in their industrial processes. The implementation of computer vision in industrial processes by Folio3 is not different. Here are some use cases made possible by our efficient computer vision implementation team for your reference:

Road Traffic Analysis

We took up a challenge to develop a solution for road traffic analysis by incorporating computer vision in the development to craft an excellent road traffic analysis proprietary product. We developed this product after in-depth research to analyze the road and traffic situation with the help of AI. 

Our solution is capable of distinguishing various types of vehicles and classify them through AI and deep learning. The road analysis system developed by Folio3 makes use of surveillance cameras and specialized software to manage the cameras & visual data through intellectual analysis. It is also capable of interacting with other existing systems already being used for the purpose of traffic management.

Some key functionalities of our traffic analysis and management system are the capability of road supervision by observing the movement of traffic, capable of making fast decisions, and acting in real-time to call relevant authorities like police and ambulance in case of an accident or traffic violation.  

It is also capable of monitoring traffic in real-time that is useful in taking actions proactively to save time in critical situations. It also enables centralized management for traffic control and management operations from the central office. 

Our intelligent solution has proved to be a key element in saving lives and improve the situation of traffic for better and safer roads. We have used technologies like Yolo, SSD, and OpenCV in the development of this efficient solution.

Automated Authentication for Drive-Thrus

Our customer, named ‘Dashcode’, consulted us for the development of an intelligent solution for automation of the drive-thru process. We did substantial research to come up with a smart solution incorporating AI and image analytics to increase workflow efficiency and avoid the time-consuming drive-thru activities. 

The manual process of drive-thru was time-consuming and had many choke-points that were responsible for the slow working of drive-thrus. We applied deep learning technology to change the working method of specific points in the drive-thru process to automate the processes and create a quality enterprise solution to ensure an enhanced drive-thru experience. 

Our state-of-the-art product includes many highlights. Some of the significant features are discussed here. Automated authentication assists in confirming customer identity increase the pace of traffic through the line.  A deep learning method helps the system to accurately identify the customers. Their vehicle’s make and model are also determined to enhance the ease in the identification process.

Our system performs automated transactions through analysis of live visuals. This helps in quick order-taking and avoiding the delay in transmissions to reduce time. Time efficiency is what keeps business running and customers satisfied. It also enables the system to process automated and secure payments in real-time. This, in turn, enables businesses to streamline order accuracy.

The technologies used in our drive-thru automation system includes TensorFlow, scikit-image. AM Turk, and various small tools and libraries. 

Facial Recognition System

Our innovative approach urges us to develop solutions for complex challenges, and that’s why we have developed a highly accurate real-time facial recognition solution. Our system provides real-time results by using HOG (Histogram of Oriented Gradients) and Convolutional Neural Network (CNN). It also makes use of dLib for face recognition and object-oriented detection, our system is capable of accurately showcase results in real-time. The face recognition system offers a feasible option for biometric security. Another advantage of using face recognition is there is no need for making contact with a person who needs to be identified through the biometric process. 

Some highlights of the face recognition system developed by Folio3 are discussed briefly here. Face searching enables the system to perform searches that would help in locating a specific person directly through the entries in the database by matching the specimen’s face.

Data management allows the system to share information with other systems by importing the JPEG format of generic photo data. It can be later used for face searching. Specific faces can be imported in advance to alert relevant authorities when the system observes them through surveillance cameras or any other source of visuals. 

The system would automatically notify users through a pop up if it discovers the face of a specific person. It would also produce warning sounds and flashing the camera on the map. The system is handy in identifying the faces on its own and notify the users for proactive action. The technologies used in the development of this smart solution are CNN, HOG, DLIB, and OpenCV.

How Does Computer Vision Work FAQs

1) What is vision input system?

It incorporates the use of a computer vision system that acts as a sensor and delivers high-value information about what is around.

2) How does object tracking work in computer vision?

Object tracking tends to track objects as they move through a series of video frames. It is a fast-paced system in computer vision.

3) How does computer vision work in the new amazon go store?

Computer vision is used to implement the 3D vision that is used by Amazon technology in retail to monitor items without any need to scan barcodes.

Drawing the Line

AI has brought a revolution in human lifestyle and industrial processes. Computer vision is also a sub-section of AI. Computer vision is proving to be helpful in various industrial and retail processes. It also brings automation to processes that require manual operation otherwise. We hope that after reading this blog, you would get plenty of information about what is computer vision and how does computer vision works? Computer vision is used in many processes, which ultimately bring comfort to our everyday life and our living experience, all thanks to AI!

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pros and cons of artificial intelligence

Pros and Cons of Artificial Intelligence in 2020

Pros and Cons of Artificial Intelligence in 2020 - Detailed Insights

Muhammad Imran


December 25, 2019

pros and cons of artificial intelligence

Artificial intelligence, more commonly known as AI has been in the news for decades. However, only in the recent past, has it been integrated with all our electronic devices. Machine learning solution has the capability to think and behave like humans. It has the potential to take over most of the tasks performed by humans, that produces cost, time and effort savings. Below we have discussed the pros and cons of artificial intelligence:

Best Pros of Artificial Intelligence in 2020

1) Integration with Other Technologies

The most positive impact utilizing AI can be achieved by integration with other technologies. For instance, both IoT and AI need to work in conjunction to enable self-driving cars. Where IoT is responsible for activating and regulating sensors in the car that collect real time data, vehicle relies on AI for decision making. Similarly, integration Blockchain with would enable better security and scalability.

2) Media and Entertainment Penetration

It is predicted that AI will penetrate the creative industry in the coming year. Even though gaming and movie production has a high cost, only few make it big. This means that  AI can prove cost effective when its comes to developing stories, scriptwriting, production and even acting.

3) Powering Cybersecurity Systems

With more sophisticated and frequent cyber attacks, the existing defence measures are becoming ineffective. Where in the face of this adversity, humans are of no match, AI enables cyber-threat hunting to proactively detect malicious attackers to prevent criminal activities. Plus, Machine Learning as a service can also be helpful in identifying security breaches.

4) Less Reliance on Traditional Industries

As more and more companies realise the benefits of AI and incorporate it into their operations, the traditional industries should become less relevant. For instance, with self-driving cars, the traditional players would either have to adapt or become irrelevant.

5) Enabling Real-time Customer Interactions

With more AI driven real-time data and activities, customer interactions across channels will become truly become real-time.  AI marketing will help with customer retention, winning customers back and engaging first timers: enabled by targeting social media and platform campaigning.

6) Automation of Processes

AI has been drastically adopted to automate key processes across sectors such as retail, banking, manufacturing. And it is predicted that more will follow.

7) Utilization of AI Assistants 

AI assistants have been successfully installed to streamline and automate customer service and sales tasks. Some of the most common ones are: Siri, Cortana, Alexa and Google Assistant. And it is expected that more companies will opt for AI assistants to handle basic tasks. According to ComScore, by 2020, an estimated fifty per cent of all searches are going to be carried out through voice.

8) Elimination of Biased Data

Organisations have relied on Machine Learning models for critical decisions like hiring or loan approvals. Since biased data is an inherent risk with Machine Learning, AI based applications are going to be useful in making more informed decisions.

9) Facial Recognition Adoption

One way to ensure biometric authentication is through facial recognition. Investments and researches have been directed at improving AI applications accuracy and readability. In 2020, it is expected that AI facial recognition technology will be used widely.

10) Enhancing Privacy and Policy

In 2020, like the former year, safeguarding individual privacy would remain a high priority. Some concerns have been triggered by the latest developments in AI, as not many companies know how to use their information. This has been further complicated by newly introduced AI laws.  But as companies start adopting tips, tricks and tools, the issues can be resolved utilizing privacy control features.

Worst Cons of Artificial Intelligence in Upcoming Years

1) Investment Required

This enticing technology has many benefits but it can be very expensive and not a lot of companies, especially SMEs can afford it. Cost is one of the most debated cons of AI, as not just the technology but installation and maintenance costs, along with repairs that boosts the total cost of this technology.

2) Reduction in Human Jobs

It can not be argued that machines are taking over human jobs at a rapid pace and more manual workers are going out of work. This fast-paced automation is not just a threat to human jobs but is also reducing the efficiency of humans to solve problems.

3) Increased Dependency

As scientists make breakthroughs with this groundbreaking technology, ever industry would want to embed the same to optimize its operations. One of the most widely experienced advantages is automation, which means elimination of workers that performed those tasks. This will make people more dependent on AI and there will be a time when they will lose their capacity for solving problems.

4) Absence of Creativity

One of the major cons of AI is the lack of innovation and creativity. Moreover, since AI depends on past experiences to make decisions, it can prove inaccurate if the situation has changed, as the system does not employ analytical decision making.

5) Faster Changes in Processes

With constant updates and increased usage of AI in every process, the way things work is constantly changing. While sometimes the changes in AI requires the workforce to be trained to update their skills for using AI-enabled processes. Often, the change reaches to an extent that there is no human intervention, causing unemployed in at least one sector.

6) Unexpected Results

Softwares do, exactly what they are programmed to. However, there are times when programmers do not get it right; bugs in systems exist not because there is something wrong with the computer but as a result of human error. However, problems with AI software can really prove to be more complex, subtle and can often produce terrifying results. 

Like any other software, AI software programs are also vulnerable to attacks. The only difference is that with ‘intelligence’ involved things can become more complicated. For instance, this One group have designed glasses that tricks AI facial recognition into thinking you are someone else.

7) Disastrous Results

Some have even predicted that AI can drag the world into war as autonomous weapons can locate and engage targets with any human interaction.  Most of this technology already exists and is ascribed to third revolution in warfare; the other two are gunpowder and nuclear weapons. These weapons can take the form of a quadcopter or a drone; these are increasingly becoming more available, cheaper and smarter. 

8) Invasion of Privacy

These smart technologies have access to all our personal information,  this poses a problem for business, as they need to convince their customers of how the benefits of artificial intelligence outweigh the risks and the invasion of their privacy. A few decades ago, people were reluctant to share their personal information but now most have given up their privacy in exchange for convenience. 

9) Humans as Commodities

There is one outcome that is feared by many, as it is, many businesses are already treating customers and workers as commodities. And if start perceiving each other  as commodities and educate the AI the same, the outcomes can be very unpredictable. 

10) Limited Capabilities

As opposed to popular belief, AI available for now has very limited scope. It‘s easy to overestimate the abilities of an AI based on how it applies their intelligence to solve one problem. Organizations, looking to automate their processes should ideally be aware of such limitations.

Pros and Cons of Artificial Intelligence - Quora Review has real authority on any topic. If you want to find legitimate answers to your queries, Quora is your only destination after google. Check this thread where influencers are giving their thoughs on advantages and disadvantages of artificial intelligence:

Advantages and Disadvantages of Artificial Intelligence - Reddit Review

A good conversation is going around the on said topic.

Advantages and Disadvantages of Artificial Intelligence - Reddit Review

Source: Reddit

Pros and Cons of Artificial Intelligence FAQs

Will Artificial intelligence take over humans in the future?

AI is like any other tool, for instance a calculator an accountant uses or a spreadsheet that allows a banker to present information. The way an accountant relies on the calculator to simplify complex tasks; AI functions in the same manner. It is just a ‘must have’ technology for today’s connected world. 

AI has become ingrained in our daily lives and it is taking over the business world, but not how they show it in movies. Where it is not likely that AI robots will replace humans but it may prove as an excellent replacement for human interactions in the business world. For instance, more AI enabled customer experiences.

Advantages of Artificial Intelligence in accounting?

AI enhances productivity, as it processes data at lightning speed. It facilitates complex decision-making, whereby eliminating the risk of human error and bias. If properly coded, it allows incredible accuracy, speed and precision; even detect fraud. Several softwares have been developed to automate and streamline bookkeeping tasks; allowing repetitive, tedious accounting tasks to be easily automated. With predictive capabilities, AI can recommend and direct actions without human involvement.  

Advantages and Disadvantages of Artificial Intelligence - Final Verdict

AI is undoubtedly a must-have technology that is becoming an essential element of everyday life.   It enables savings in time, money and effort; whereby automating tasks and even initiating actions. As the world becomes more digitized and automated, AI can expose businesses to tremendous possibilities.

However, its usage raises some ethical questions like is it ok for technology to replace humans in the business world? And then what of the jobs that are lost due to AI. 

AI has unlimited storage, however it can not make connections the way humans do. The tasks carried out by AI can become better overtime but how do we justify it being better than humans - is something we should ponder on. Can they ever be as creative or wise as humans? 

More importantly, the scope of work is defined, hence we can not expect them to work outside of what they were programmed for. Again, in some roles, emotions or common sense are important, such as in the case of being a nurse. With robots replacing jobs, there are chances that a large number of people will become unemployed, which would require government level intervention.

We have already seen how technology has made humans dependent, whereby it is likely that they would lose their mental capacities to perform tasks. Moreover, AI technology can be very destructive, if it is used in that manner. Some also believe that AI as robots can supercede humans and eventually lead to enslaving us. 

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data mining vs machine learning

Data Mining vs Machine Learning – 15 Best Things Compared And Reviewed

Data Mining vs Machine Learning - 15 Best Things Compared And Reviewed

Muhammad Imran


December 09, 2019


Recent technological developments have enabled the automated extraction of hidden predictive information from databases. To drive greater value from data, companies across the globe are taking more interested in learning about technologies such as Statistics, Machine Learning, Artificial Intelligence, Data Mining, and pattern recognition. Most of our Machine Learning as a service clients shows a great deal of interest in learning about Data Mining vs Machine Learning. 

Data Mining enables the extraction of information from a large pool of data. This technique is employed to discover different patterns inherited in a given set of data to generate new, precise and useful data. Data Mining is similar to experimental studies and works as an extension to business analytics. 

Machine Learning on the other hand, includes algorithms that can automatically improve through data-based experience. With experience, it finds new algorithms and enables the study of an algorithm that can automatically extract the data. Machine Learning solutions employ Data Mining techniques and other learning algorithms to construct models of how information is being generated to predict future results.

Data Mining and Machine Learning are often combined, have overlapping properties and influenced by each other in some ways, however individually they have different ends.

Machine Learning vs Data Mining Trend in 2020

data mining vs machine learning trend

Just in the last month, 160 people searched for Data Mining Vs Machine Learning. Most of the searches for Data Mining vs Machine Learning were from India. South and West US seem to be taking a lot of interest in these technologies as well. Overall, people have been consistently looking for information relating to these technologies and given the upward, it is likely that it will get more traction in the coming days. 

Source: Google Trends

Data Mining vs Machine Learning - Core Comparison Overview

Basic ComparisonData MiningMachine Learning
MeaningKnowledge extraction from a large pool of dataIntroduce new algorithms from data, based on experience
HistoryIntroduced in 1930 as knowledge discovery in databasesIntroduced in 1950 through Samuel’s checker-playing program
ResponsibilityData Mining extracts the rules from the existing dataMachine Learning facilitates computers to learn and understand the given rules
OriginTraditional databases with unstructured dataExisting data as well as algorithms
ImplementationData Mining techniques can be employed on different modelsMachine Learning algorithm can be utilized in the decision tree, computer vision,  neural networks and some areas of AI
NatureRequires manual human interference Designed as an automated process
ApplicationUsed in cluster analysisUsed in web search, spam filter, credit scoring, fraud detection, cognitive services and computer design
AbstractionData Mining abstract from the data warehouseMachine Learning reads machines
Techniques involveData Mining takes a research-based approachSelf-learned and trains system to do the intelligent task
ScopeLimited area applicationCan be used in a vast area
AlgorithmData Mining employs many algorithms such as a statistically based method, Machine Learning based method, classification algorithms, neural network and many others.Machine Learning also utilizes  many algorithms such as supervised Machine Learning algorithms, unsupervised Machine Learning algorithms, semi-supervised learning algorithm, clustering algorithms, regression, Bayesian algorithm and many more.
Pattern RecognitionData Mining uncovers hidden patterns by using classification and sequence analysis.Machine Learning, uses the same concept but in a different way. Check out these predictive analytics solutions
Open Source ToolsFor Data Mining, open source tools are Rapid Miner; KNIME and  Rattle are used. Machine Learning open source tools are Shogun, Theano, Keras, Microsoft Cognitive Toolkit (CNTK), 
TechnologySome of the most sought-after software for Data Mining on the market are: Sisense, Oracle, Microsoft SharePoint, Dundas BI and WEKA.Poplar software  for developing Machine Learning models are: Google Cloud ML Engine, Amazon Machine Learning and Apache Singa. 
IndustriesData Mining projects are those where numerous data is available such as medical science, banking  and research. Machine Learning can be used in identifying product bundles, sentiment analysis of social media, music recommendation system, sales prediction, and many more.

Is Data Mining Better than Machine Learning?

Data Mining and Machine Learning share a foundation in data science and there is an overlap between the two. Data Mining can utilize Machine Learning algorithms to improve the accuracy and depth of analysis. While Machine Learning can employ mined data as its foundation, in order to refine the dataset to achieve better results. It can be argued that Data Mining and Machine Learning are similar when it comes to extracting meaningful information from a given set of data. However, the differences lie in the way in which they achieve this end and their applications.

The most obvious difference is their approach to data analysis. Data Mining allows analysts to combine and study vast amounts of structured or unstructured data, without driving any processes by itself. Machine Learning, on the other hand, has capabilities to learn from new data and become more intelligent with experience, without being programmed.

For instance, Data Mining is utilized by e-commerce retailers to identify which products are frequently bought together, enabling them to make recommendations accordingly. Machine Learning, on the other hand, has powers to go deeper and learn from customers’ buying habits to improve its ability to recommend products; whereby becoming better over time.

Another important difference between Data Mining and Machine Learning is the purpose they fulfill. Where, Data Mining is widely used in retail to identify sales trends and customer purchase patterns, to allow companies create better marketing campaigns and forecast sales; it is also used for identifying investment opportunities, detecting fraud and financial planning. While Machine Learning offers more accurate insights, often in real time, It facilitates revolutionizing sales and marketing by enabling customized shopping experiences based on purchase history. 

Data Mining and Machine Learning have differences in their applications to enterprise too. Data Mining can be integrated with any given ERP application and can work with diverse processes. To this end, a Machine Learning project would require considerable resources. From assembling the training and test data to feature extraction and selection, project managers need to have everything in place. Moreover, with Data Mining activities can kick-off with a quick sign-off, while Machine Learning projects go through complex forms of buy-in from various stakeholders. 

Machine Learning can be one of the steps of a Data Mining, if you are interested in developing algorithms. So if you are interested in developing algorithms that create models then you will pick Machine Learning but if your aim is to investigate data and create models by using existing algorithms, then Data Mining will have to be employed.

When it comes to understanding Machine Learning vs artificial intelligence vs Data Mining, in simplest terms Artificial Intelligence is the study to create intelligent machines that can come up with solutions to problems based on their learning. Data mining forms part of the programming codes with the necessary information and data AI systems. A large part of Artificial Intelligence falls under Machine Learning. AI uses Machine Learning algorithms for intelligent behavior. 

The data universe is growing at a rapid scale; creating greater demand for advanced Data Mining and Machine Learning techniques in order for the industry to keep evolving. There is likely to be more overlap between the two techniques as the two intersect to improve the usability and predictive capabilities of large amounts of data for analytics purposes.

While Data Mining is drawing unparalleled capabilities for predictive analysis, only the surface of Machine Learning has been scratched.  With billions of machines becoming connected and human’s generating vast amounts of data on a daily basis, we should not be looking at Machine Learning vs Data Mining or Machine Learning vs artificial intelligence vs Data Mining but at how these technologies can be used to add further value to businesses. 

Data Mining vs Machine Learning FAQs

What is meant by Machine Learning in Data Mining?

Data Mining is a cross-disciplinary field that focuses on finding properties of data sets. Whereas, Machine Learning is a subfield of data science that focuses on designing algorithms that can make predictions and learn from the data. Machine Learning in Data Mining is when results of Machine Learning are used in Data Mining. Data Mining can employ other techniques besides or on top of Machine Learning.

What's the core difference between Big Data and Machine Learning?

Big data analytics involves the analysis of big data to discover hidden patterns and extracting information. Whereas, Machine Learning, is a technique that employs Machine Learning models to respond to unknown inputs and give desirable outputs. Even though both big data and Machine Learning can be used to find specific types of data & parameters, Big data can not identify relationships between existing pieces of data with the same depth as Machine Learning. While Machine Learning is a part of Data Science, Big data has got more to do with High-Performance Computing.

It is often the case that Big data analytics is used to analyze and transform data to extract information, which then goes through a Machine Learning system for further analysis to predict output results.

Machine Learning performs tasks without the need for human interaction. Whereas, Big data analysis gives structure and models the data for humans to make more informed decisions.

What's the Core Difference Between Data Mining vs Statistics?

Both Data Mining and Statistics are tools that extract information from data by discovering and identifying structures. Statistics are similar to Data Mining, as they both are used for data-analysis to facilitate decision-making. However, individually they are very different techniques that require different skills. Statistics employs tools to find relevant properties of data, whereas Data Mining builds models to detect patterns and relationships in a given set of data.

Data Mining requires the application of various methods of statistics, data analysis and Machine Learning to study and analyze large data sets in order to drive meaningful information and make accurate predictions. Whereas, Statistics is at the core of Data Mining, whereby utilizing identification models designed for inference about the relationships between variables that can further be analyzed to identify differences between random noise and significant findings — such as theories for establishing probabilities of predictions.

Is Machine Learning better than Statistics at all?

Machine Learning algorithms are designed to work with large datasets whereas statistical models work well with smaller sets of data with clear features. Machine Learning beats statistics, when it comes to large datasets, especially when the data lacks describable features. Statistics on the other hand may prove better than Machine Learning when there is a need to identify  relationships between data points to gain better insight into a given problem domain.

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Predictive Analytics in Healthcare – Use Cases, Applications And Examples

Predictive Analytics in Healthcare - Use Cases, Applications And Examples

Muhammad Imran


December 01, 2019


Predictive Analytics in Healthcare is a huge leap forward towards the betterment of medicine and healthcare. Predictive analytics is supposed to tentatively judge the probability of a happening in the future on the basis of patterns analyzed from the existing data. You can also observe the examples of predictive analytics used in various industries.

Similarly, Predictive modeling in healthcare is capable of analyzing medical results of individual patients assessing historical data and predict the probability of a patient to get affected by a disease in the future by going through historical data and identifying a disease pattern by comparing the pathological results of a patient. 

Data analytics in healthcare means doing certain functions through machine learning solutions, which physicians and clinicians were doing for long but with excellent precision and on a large scale. This technology enables physicians and clinicians to know about any potential medical threat to the patient before it happens, so they could take preemptive measures to decline the chances of possible ailment.  

In the medical field, being one step ahead is essential because the lives of patients are on the stake and there is no time to waste, so if surgeons or physicians already know the potential threat to a patient’s life, they can make quick and valuable decisions in real-time to restrict the threats. This is how predictive analytics in healthcare is proving to be a fortune for the medical and healthcare domain. It can actually save the lives of patients because of its timely predictions and identification of potential ailment using machine learning as a service

How We See Predictive Analytics in Healthcare 2020?

The healthcare domain has certainly turned their head toward predictive analytics and is trying to implement this technique in the treatment and other essential operations required in healthcare. 

The capability of predictive analytics to estimate the probability of unforeseen medical threats has certainly helped physicians, surgeons, and clinicians a lot to stay one step ahead in medical treatment. This is why the use of predictive analytics is becoming an essential component of healthcare.

The year 2020 would see a massive rise in the use of predictive modeling in health sciences because of its efficiency and accuracy. The world has already started noticing the advantages of using this effective technology in the healthcare domain. Many medical research organizations are going hand in hand with tech organizations to advanced predictive analytics solutions in health care.

There is a lot of buzz and hype created for data analytics in healthcare, and rightly so, because of its effectiveness. People around the world are taking an interest in predictive analytics for the health care domain and want to know more about this technology.

Google trends also reflect the interest of people by showing stats of search queries from users residing in various parts of the world. The healthcare predictive analysis is a hot search topic on Google as you can see in the image given below:

How we see predictive analytics in healthcare

Source: Google Trends

So, it is quite visible that this technology would be implemented in large numbers to make the lives of people safer.

Predictive Analytics in Healthcare Use Cases for 2020

Predictive Analytics has found multiple uses in the healthcare domain. To highlight its importance, we have picked some significant use cases of predictive analytics in healthcare. These use cases are being used in multiple healthcare facilities, and the popularity of these use cases would rise in the year 2020. These use cases are as follows:

Preventing Patients from Self-Harming

Some people get so depressed due to some reasons that they are more likely to attempt suicide or harm themselves. There could be a massive benefit if doctors and other healthcare staff somehow identify it early and start providing mental healthcare to patients with stress or suicidal thoughts to save them from hurting themselves.

Using electronic healthcare data, including mental health stats, suicidal attempts in the past, use of psychiatric medications, high depression scores, the predictive algorithms can identify the persons who are likely to attempt suicide or hurt themselves. 

This is how healthcare predictive analytics is saving the lives of patients who are going through depression or mental trauma and process suicidal thoughts in their minds. These early notifications help physicians or healthcare professionals to take preemptive measures before any incident and save the lives of such patients.

Chronic diseases and Population Health Risk Scoring

Both the prediction and prevention work in close relation to each other. Healthcare facilities with the ability to identify individual patients with a high risk of developing chronic conditions when the disease is in its initial stages are more likely to help patients in avoiding health issues for a longer duration. Treating these chronic health issues in matured stages are more difficult to treat and can prove costly.

Predictive modeling imports and analyzes various types of data like lab test results, biometric data, claims data, Patient’s medical status, and social elements for health to create risk scores and identify the individuals who are more likely to get poor health outcomes. This helps healthcare providers in taking insights into the health of patients and avoid a probable complicated health outcome.

These predictive analytics also helps physicians in determining the needs of advanced healthcare efforts for particular individuals. This helps healthcare facilities to manage risks and improve proactive intervention of chronic diseases.

Avoiding Short-term Hospital Re-Admissions

Health-giving facilities are penalized under the law if a patient gets re-admitted in the hospital under 30 days of discharge from the facility. To handle this situation, a predictive analysis technique is applied to identify the patients who are likely to get re-admitted within the span of 30 days through assessment of risk factors of the patients.

This helps healthcare givers in enhancing the medical treatment of particular patients and extending their stay in the hospital to completely resolve the health issues with the patient and neutralize the likelihood of the same patient to get readmitted. 

This also helps staff to focus resources for better care of the patient and designing a proper strategy for the discharge of the patients in such a way that it could prevent their early returns to the healthcare facility and avoid the risk of being subjected to penalties.

Taking Care of Patient Deterioration

Patients staying in the hospital face a variety of potential threats to their health. There is a probability of a patient developing a disease like sepsis, complicated infection, or a sudden downfall in their health because of existing medication during their stay in the hospital. 

Predictive modeling could be useful in this kind of scenario to help medical caregivers to respond as soon as possible to alteration in patient’s wellbeing and become able to recognize a potential deterioration before the symptoms become visible. 

The data analytics technique is an excellent fit for predicting clinical outcomes during the stay of the patient in the hospital. These outcomes may include acute kidney injury or sepsis. This ability of machine learning predictive algorithms could help considerably in reducing the death rates due to the deterioration of patients in the hospital.

Limiting the Appointment And No-shows

Some patients don’t come to the hospital for a checkup even after finalizing an appointment with a clinician. This type of unexpected gaps could ruin the whole workflow of healthcare facilities and stalls the chances for a checkup of other patients. 

Recognizing the patients with the likelihood to skip the appointments without prior notice is possible through data analytics and computer vision. This could help in improving the consistent workflow of healthcare, providing the facility, decrease the losses in revenue, and the likelihood to check other patients in their spare time.

This also helps health providers for additional reminders to patients with a probability of No-show. The facility can also provide transportation and other services to enhance the workflow without any unforeseen gap. 

Predicting Patterns for Patient Management

Predictive health analytics can also notify the staff of a health care facility about their busy schedule ahead. Hospitals have to go through multiple challenges, sometimes the number of patients become so massive that hospital resources and staff are not enough to fully manage the influx of patients. 

If somehow, the providers get early notifications about the busy schedule ahead, they could manage the unexpected incoming of patients by getting additional resources and staff from other work shifts to handle the workload. This won’t only help providers but also the patients because of reduced waiting time. This would also help the healthcare staff to provide more care to the inpatients. 

Managing Supply Chain

The supply chain is one of the costliest expenses for any healthcare facility. If this could be managed efficiently, it would help in reducing unnecessary spending. Predictive tools are a great help in this scenario. These predictive analytics would help in decreasing the number of variations, getting more information about ordering patterns, and proper utilization of supply.

Using predictive analytics to manage the supply chain would help executives to make proactive and data-driven decisions. These decisions would eventually help healthcare facilities to save up to $10 million yearly. This would also help in negotiating the prices and improving the order patterns.

Strong Data Security

Predictive analytics could also be anticipated to play an important role in cybersecurity. Analytics tools can be used to look over the data access, sharing, and utilization patterns would help healthcare organizations to recognize any potential threat to their data and limit the activity of any intruder. 

This could be a significant step towards the protection of healthcare and other types of data, which could be vital for the routine working of health organizations. The deployment of these solutions for defense of data in healthcare organization leave minimal chances of cyber-attacks because this cyber defense system becomes way beyond the capability of average hackers.

Development of Precision Medicine and New Therapies

Predictive analysis is also proving to be a handful in supplementing traditional clinical trials and drug research techniques. The new method of testing named “In Silico” testing is proving to be an efficient technique, which helps in avoiding multiple complications. It also declines the need for recruitment of patients for the complicated and expensive clinical trials.

With the help of machine learning, modeling and simulations methods could be devised to incorporate characteristics of physiology and genetics in enzymes used for drug-metabolizing processes. This process can be further used to identify various patients that require dose adjustments, whether it needs to be increased or decreased. 

This is how clinical decision support tools and predictive analytics in healthcare are playing an essential role in transforming newly introduced or discovered drugs into precision medication or therapy. 

A Mean for Patient Engagement and Satisfaction

Although predictive analytics have found multiple uses in the healthcare domain, the main goal is to create a comfortable environment in healthcare facilities for various types of patients. Consumer relationship management and cognitive services plays a crucial role in the satisfaction of consumers. Patients as consumers of healthcare facilities should be engaged in all the aspects of their health recovery process in the hospital. 

The main goal is to design a service-oriented, and consumer-oriented working environment that could work better for patients and providers. Profiles of individual patients are created through data analytics tools to improvise the customized healthcare services and devise treatment strategies for individual patients for their well-being.

ATM Cash Forecasting for Patients

Predictive analytics is being used to analyze the patterns of atm cash withdrawal on hospital premises. ATM Cash forecasting could also be applied in healthcare facilities to ensure there is continuous availability of cash for patients admitted to the hospital in case they need it.

This would also help in minimizing the out of stock and overflow situations. This would not only benefit the patients but healthcare organizations as well.

Customer Churn Prediction for Predictive Modeling in Healthcare

This solution could be really beneficial to create a great bond between the consumers and providers in the healthcare domain. This solution could be applied to identify the behavior of consumers and serving or responding to them accordingly.

The churn prediction solution posses the capability of judging the mental state and behavior of consumers and handling their issues in accordance with their behavior to provide better and enhanced services. This solution could be deployed to focus on delivering enhanced services to the individual patients according to their behavior and mental state.

Predictive analytics in Healthcare- More Examples

There are various other uses of predictive analytics in the healthcare industry to enhance the working of health care facilities, some of these uses are listed below:

  1. Analyzing medical images efficiently
  2. Reducing risk chances in prescription
  3. Improving the accuracy of diagnostics
  4. Identifying the cure of potentially lethal diseases
  5. Enhancing patient’s safety in ICU
  6. Devising multiple treatment plans with detailed costs
  7. Usage of speech to text software for all in-house video training.

There are various other uses of predictive modeling for the healthcare industry, but we have listed some of the significant ones here. The year 2020 would see a huge rise in the use of healthcare predictive analytics.

FAQs Regarding Healthcare Predictive Analytics

How is predictive analytics used in healthcare?

Predictive analytics in healthcare is used to manage chronic disease risks, reduce waiting times, precision medicine, enhanced data security, and for various other purposes.

How does IoT improve predictive analytics in healthcare with more data?

The IoT-powered devices help in collecting and storing a massive amount of additional healthcare data, which could be further analyzed the predictive analytics for better working. This is how IoT improves predictive modeling in healthcare with more data.

 Why is predictive analytics important in healthcare?

Since healthcare is a serious industry with multiple lives on stake, even the smallest errors could be a reason for the loss of a number of lives. The predictive analytics helps in reducing the chances of error and thus proves to be a handful in saving lives. Therefore, it plays a very important role in healthcare.

What are the downsides to predictive analytics in healthcare?

The downsides to predictive analytics in healthcare are limitations of deployed solutions due to advancements in technology and impacts on decision processing, moral threats and any error due to human interventions with automated systems, and lack of regulations and biases of algorithms.

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Best Machine Learning Applications in Finance – The Ultimate Guide

Best Machine Learning Applications in Finance - The Ultimate Guide

Muhammad Imran


December 03, 2019

machine learning applications in finance

Machine learning in finance has seen a massive rise in popularity during recent years. In simple words, machine learning in finance is all about importing large amounts of data and learning to perform particular tasks by learning from the data. It is making use of various techniques to manage massive volumes of information in the form of data. It also uses statistical models to get insights and make predictions. Head on to this link to get more info about machine learning solutions.

What is Machine Learning in Finance?

Management of massive data volumes through computer systems is also known as data science. There are many applications of data science in finance, like carrying out credit scores, managing assets, and analyzing risk. Machine learning has been a great fit for the financial industry because of its capability of handling large amounts of data with historical financial records.

Because of this capability, top banks and companies dealing in financial services have deployed AI and machine learning as a service. The reason is, of course, the automation in the various process which were sluggish and prone to error when performed manually. It has also helped finance-related businesses to decrease underlying risk and underwrite loans.

Advantages of Machine Learning Applications in Finance

There are various advantages of machine learning applications in finance. Many businesses and companies are using AI and Machine Learning in full throttle to get the maximum out of their business using these technologies. Some advantages are listed below:

  1. Process automation
  2. Reduced operational costs
  3. Almost zero chance of an error
  4. Enhanced Productivity
  5. Better user experience using computer vision
  6. Improved compliance
  7. Reinforced security

In addition to these significant advantages, there are many benefits of implementing machine learning in the finance industry. There is a massive number of machine learning algorithms and tools which are compatible with financial records. Due to the stability of finance companies, there is no restriction of funds to set-up high-quality infrastructure of hardware for enhanced functionality and efficiency. 

As we all know, the finance industry is quantitative in nature. Finance experts have a great focus on maintaining and saving large amounts of historical financial data, which is a great fortune in the case of machine learning. Machine learning technique is deployed through computer systems to learn the process using previous data. 

As there are large amounts of financial data available, this means systems backed with machine learning algorithms would get better with time. In the case of machine learning, the more, the better is a fact. More data means more learning of the system, which would ultimately give advantage to the organizations. It would also help in enhancing many factors and processes in the financial domain.

Many companies have taken note of this development and are working to implement machine learning applications in finance. They are investing massive amounts of money in research and development for machine learning to make it more useable for the finance industry. And for those who are still not interested in it, they would find their businesses in hot waters after a few years.

9 Best Use Cases of Machine Learning and Data Sciences in Finance

To highlight the purpose of using data science in finance, we have selected some use cases to discuss in detail and elaborate on how machine learning is turning out to be a fortune for the finance industry. These use cases are listed below:

Fraud Detection

One of the most significant responsibilities of financial services providers is to decline any fraudulent move against their clients. They have to bear more than 250% of the cost lost due to fraudulent activities against their clients in terms of recovery and relevant charges. To avoid such huge loses, these organizations can use machine learning software for fraud detection.

They won’t win their campaign against financial frauds using outdated and obsolete techniques and approaches. However, it is possible by incorporating machine learning applications in finance domain. They can use sophisticated software solutions backed with machine learning to identify and prevent fraudulent transactions.

These solutions are capable of analyzing massive volumes of data. This analysis enables software systems to recognize patterns and process predictive analysis. Thus, machine learning algorithms used in these solutions can restrict fraudulent transactions with a high accuracy, which won’t be possible by using AI only. 

Many companies are using machine learning to reduce the losses due to financial frauds, while others are working fast to implement it in their systems to take advantage.

Risk Management

Risk management is also an essential responsibility of financial institutions, and service providers are supposed to do risk management. They depend on accurate predictions for the success of their businesses. Therefore, it is absolutely necessary for financial institutions to process current data in order to identify trends and accurately forecast emerging risks.

Conventional software systems used in the financial domain are capable of predicting creditworthiness on the basis of static data imported from loan applications and recent financial reports. However, machine learning technology is far more advanced, with a whole lot of possibilities. Machine learning algorithms can recognize live trends and relevant factors that could influence the ability of the client to make the payment.

Risk management is also connected with the prevention of financial fraud and crisis prediction. Machine learning financial services are capable of addressing these and many other relevant issues in order to manage financial risks. That is why a considerable number of financial institutions were already emphasizing on the implementation of machine learning-enabled solutions in their existing systems.

Investment Predictions

The hedge funds have diverted from old-school prediction methods these days. The use of machine learning in predictions of trends of the fund has seen a huge rise. Hedge fund managers can easily recognize market inclination probably a lot earlier than it was possible with conventional investment analysis models. 

Major financial institutions have taken the potential of machine learning to interfere with the investment banking industry, and therefore, they are working to develop automated investment advisors or Robo-advisors backed with machine learning technology. JP Morgan, Bank of America, and Morgan Stanley have achieved considerable success in this venture. Other companies are also likely to follow the footsteps of leaders. 

Network Security

The security of financial data has been a huge concern for financial institutions. The number of security breaches has also increased considerably in recent years. The task of identifying modern cyber-attacks can’t be restricted using obsolete security software. 

This is a challenging situation and requires advanced counter technology. The security solutions backed with machine learning are amazingly capable of serving the purpose of security of high-value financial data. These solutions have the ability of intelligent pattern analysis in combination with big data operations. 

This gives machine learning security technology an upper hand over conventional security software solutions. This is why a lot of companies are investing in advanced technology machine learning that enables data security solutions to make their valuable data safe from cyber-attacks.  

Loan and Insurance Underwriting

A considerable number of insurance companies are turning their heads towards machine learning to identify risks and set premiums. Machine learning is capable of making predictions on the basis of historical patterns and on-going trends, that is why it is the perfect tool for insurance companies to enhance their revenue and profits.

The banking sector is also getting a huge advantage through the use of machine learning technology. Financial organizations that offer insurance products and loans to their clients are also getting benefits because of machine learning in a similar way. Regardless of the insurance product, whether it is loan protection, health, mortgage, or life insurance. Machine learning is able to cut the chances of underwriting risks.

Algorithmic Trading

Algorithmic trading is supposed to automate the process of trading by performing trading action in accordance with existing criteria defined by the user, which could be a trader or fund manager. In short, algorithmic trade is capable of executing purchase or sale of a stock quantity whenever price-per reaches an ideal or particular value.

With the incorporation of machine learning in algorithmic trading, various new tools are available to make algorithmic trading more than just an automated process. It turns algorithmic trading into intelligent trading. 

The machine learning algorithms are designed in such a way that they are capable of analyzing historical behavior of markets, figure out an ideal market strategy, making trade forecasts, and much more. Even AI is not capable of giving such value without machine learning.

Money-Laundering Prevention

According to recent estimations, it was found that around 2% to 5% of the Global GDP was laundered annually. Banks are not capable of winning the battle against this unethical and immoral activity.

This problem could be addressed with the help of advanced machine learning technology. It has the ability to identify patterns that are closely associated with money-laundering practices. Machine learning applications in finance are proved to be a great help in the detection of money laundering patterns, reducing the number of false positives, and easier compliance with regulatory authorities.

Commerzbank is working on automating 80% of its compliance checklist processes through machine learning till the year 2020. The process will be done through shifting the focus of AI towards money laundering. 

Customer Services

Financial consumers often make complaints against poor customer services.  They want accurate information and fast processing for the solution to their problems regardless of whom they are talking with, whether it is a virtual assistant or a human operator.

AI chatbots are being used for long for customer services, but customers aren’t satisfied. A lot of consumers complain that it doesn’t look like their problems are being understood while talking to chatbots. 

Machine learning brings a whole new era of virtual assistants and cognitive services who are capable of learning instead of following a predefined set of instructions. Chatbots powered with machine learning adapts their serving strategy in accordance with the behavior of individual customers. This ultimately gives a whole new experience of customer services to consumers, which is enhanced and more comfortable.

Check out this free speech to text software

Trade Settlements

The process of payment transactions and purchased security following a stock trade is termed as trade settlement. Electronic transactions are an instant way to complete trade settlements and are being used for long, but the trade isn’t always like it should be. A number of factors could limit the accomplishment of trade.

The use of modern trading platforms and regulatory requirements have considerably reduced the number of trade failures. But while handling high trade volumes, trade failures can still influence the efficiency of the trading system. A challenging task is to resolve failed settlements manually, which takes a considerable amount of time.

However, with the use of machine learning solutions, the cause for failure of a trade could be identified instantly. The machine learning applications in finance are also capable of providing solutions in a matter of seconds. Machine learning technology can even predict the trades which are likely to fail. So, machine learning is a great way to handle failed trades in a fraction of seconds. 

Folio3 Machine Learning Financial Services

Folio3 always do intense research and take up challenges to meet the requirement of clients and cater to the needs of various verticals of the industry. Here are some applications of our machine learning financial services for industry:

ATM Cash Forecasting

A multinational bank that is also regarded as the largest bank in Pakistan on the basis of assets is a client of Folio3. Our valuable client approached us to address their problem regarding ATMs (Automatic Teller Machines). The bank operates over 2000 ATMs across the globe. They wanted to get a solution with the capability to predict the cash-flow management for such a large number of ATMs. 

They asked us to develop a state-of-the-art atm cash forecasting solution for them to address the issue. Our talented developers worked on the requirements and suggestions of our client to come up with a cutting-edge solution. This intelligent solution helped the bank in managing the cash flow for ATMs and raised the profits by 6%.

The highlights of our sophisticated ATM cash flow management solution are the optimization of ATM cash management, which helps the bank to avoid situations like out of cash and overstock. Our solution provides automation, automated analysis of past transactions enables our system to predict the required cash in individual ATMs. It also provides timely reports and notifications to predict the pattern of cash withdrawal.

Our system is capable of forecasting patterns of cash flow and real-time data, which ensures the availability of cash and, ultimately, customer satisfaction. The technology used explicitly in the development of this sophisticated solution is scikit-learn.

Customer Churn Prediction   

A leading tech company in Pakistan is our client. This company deals in high-end services for clients in various countries. They needed a solution capable of predicting and understanding the behaviors of customers. After analyzing the needs and suggestions of our client, we took the responsibility to develop a high-end predictive learning system. The solution was able to recognize the customers who were not willing to pay dues and the ones who won’t renew the subscription of SaaS solutions. In addition to working on user’s data, the system is aimed to work in cooperation with owners and marketing team to understand various underlying factors which could help in designing better campaigns.

Our churn prediction solution helps our client in quantifying the loyalty of their customers while facilitating the reduction of churn with the help of data. The highlights of our solution are customer segmentation through advance data science techniques for dynamic user segments and evolved client base. 

These highlights also include predictive attrition for an enhanced customer retention rate and improved data management activities. The statistical analysis helps our client in recognizing the underlying factors and designing better campaigns. Through these highlights, our sophisticated customer churn prediction solution improves customer retention.

We used technologies like Apache Kafka, Yarn, Spark, and Zeppelin to develop this cutting-edge solution for our client. To get more detail about this solution, head on to the given link

3 Best Machine Learning Applications in Finance

In this section, we will discuss some great machine learning applications in finance, which have made various aspects of the finance domain better. These applications are listed below:

Streamlined Claim Handling Process

There are multiple challenges for the insurance industry. One of the limiting factors in the insurance field of the finance domain is the longer time span taken to process the claims of the insured person or organization. As the insurance company’s personnel gets involved in the claim process to investigate and assess the situation for claim processes.

This sluggish process could be a huge roadblock for an insurance company and the whole industry. To avoid complications, machine learning and artificial intelligence algorithms are deployed to streamline the claim processes in the insurance industry.

Whether it is a simple car accident or destruction of crops due to drought, the aftermath of an insurance claim could take weeks otherwise. However, machine learning algorithms can be employed to examine the situation and make quick rational decisions about the early payouts, which would surely satisfy the consumers. Thus, eventually creating a trusted and strong bond between insurance companies and consumers.

Internal Workflow Automation

Inspecting massive data written in hard form in a short time is almost impossible. Loan and Insurance organizations don’t have much time to engage their employees in extracting data from papers and then transfer it again on papers.

This could be a massive roadblock for financial organizations. However, going through the data of consumers is also necessary to gain substantial information about the consumers for a secure business.

Therefore, machine learning algorithms are deployed to automate the internal workflow of financial organizations and to engage the staff in more productive work. Machine learning algorithms have the capability of analyzing the data written in hard form and converting it in soft form for further use. These algorithms also predict the risk rate while analyzing the data extracted and aid in making quick decisions.

Behavioral Finance

There could be various factors to drive the performance of the stock market. One of these factors is the behavior of investors and how they respond to rumors or news floating in the market. AI machine learning algorithms deployed by various trading agencies import massive data about historical transactions and analyze the current situation while keeping in view the various types of news influencing the market to predict the behavior of traders and investors in the stock market.

There could be news like natural disasters which could impact the market on a larger scale, the machine learning algorithms deployed to analyze behavioral finance predicts the future outcomes and helps trading organizations to act accordingly to get benefit from the market and avoid incurring any loss due to these situations.

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AI and ML QA Testing Automation

Why AI & ML Will Boost Software Testing

Why AI & ML Will Boost Software Testing

Machine learning and artificial intelligence are unquestionably emerging as important essentials in software testing and quality assurance domain. Because they can speed up the manual testing,automate the testing process,removed overlooked bugs and decrease unnoticed bugs possibility.


Ali Zain


December 30, 2019

AI and ML QA Testing Automation

Most of us have heard of trending advancements in Machine Learning and AI.

Will AI be able to replace the human when it comes to QA automation? What role will AI play from test automation to the end to end functional testing ? Below is a complete demonstration.

Breaking, Reconstruction and Regression: The Problems of Existing Testing Methodology

On every stage when the developers add in new code, they bring out new cases. And manual regression testing can consume a lot of time.

The existing QA process is built on the technique of examining the set of normal and simple errands that form a complete project. At the commencement of the development, testing can normally drive at the same time with growing features, but the more multifaceted a product becomes, the more exciting it becomes to assure it has complete test coverage and analysis.

AI and ML Bringing up an Evolutionary Aspect to Software Testing

What is Artificial Intelligence in Actual?

It characterizes a computer science territory that explains the making of intelligent types of machinery working like humans.

ML rises from the study of pattern detection and computational learning methodology in AI. The key determination is to enable machines to make decisions without being explicitly programmed. This science grips loads of difficult data and classifies analytical schemes. 

Four Reasons Why AI & ML can boost up Software Testing

1.  Speed Up Manual Testing and Whole Processes

Testing good-sized software may take several days or maybe several weeks to complete. And it is costly either we talk about time or money.

AI can help by examining log files which reduces the time and improves accuracy in the program immensely. The potential product will provide testers with a comprehensive sight of the changes that they should execute.

2.  Automate the Testing Process

Whenever a new change is introduced in the application, we the testers must have to revise the test scripts. Mostly it turns out much of the struggle of automation swiftly turns into smooth maintenance with some little alterations in further coverage.

Artificial intelligence bots keep themselves updated after modifications in the scripts. As the bots are not completely programmed, they adjust and absorb to find any fresh application tasks themselves. And when artificial intelligence recognizes alterations, it automatically evaluates them to choose whether it’s a new feature or flaws of the current release. Therefore, hardcoded test scripts are outdated and need manual modifications whenever any alterations exist in the code, but the artificial intelligence supported bots change themselves during the whole procedure.

3.  Removing Overlooked Bugs

Artificial intelligence can discover hidden flaws in code. If required, QA engineers can adapt this knowledge to choose their coding methodology like “Test driven development”, pair programming etc. Artificial intelligence in the QA domain can handle continuing inquiry of issues .

4.  Decrease Unnoticed Bugs Possibility

The issue of overlooked and unnoticed bugs is much differing and bears undesirable penalties. If we don’t dedicate adequate consideration to data management, later, as a consequence, we will accept an entire group of unnoticed bugs.

At this stage we are near to endorse and accept a machine learning technique, which will purpose extra trustworthy results as compared to existing testing does. And the time we want to execute a test cycle, we see bugs shrinking.

The final thoughts: What Is the Future of Software Testing in the time of AI and ML?

So in this fast paced industry, apparently it’s clear what the modern phase testing is! Machine learning and artificial intelligence are unquestionably emerging as important essentials in software testing and quality assurance domain. AI will improve accuracy, provide higher income and minor charges for entire QA processes. Furthermore, AI assists in to recognize errors earlier and faster. The QA engineers can stop thinking and upsetting about losing their employment or career and just concentrate on creating healthier procedures. So basically there is no purpose to panic about AI. Alternately, we must consider potential ways to integrate it into our daily tasks.

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