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Category: Analytics

Autonomous Cyber AI A New Defense System in Cybersecurity!

Posted on by Imarticus Learning

Artificial Intelligence (AI) is being used nowadays to enhance cybersecurity. Security tools embedded with AI analyze data from various cyber incidents/threats and use them to identify potential threats. Anomaly detection can also be automated with the use of AI in cybersecurity. Threat actors are conducting data breaches in firms with new tools and ways. There are numerous types of attacks evolving every day.

To tackle the evolving data breaches & to enhance cybersecurity, firms require a fully automated security system embedded with AI. Autonomous cyber AI is predicted to be a revolutionary asset with a lot of firms adopting it quickly. Let us see more details about autonomous cyber AI.

 

Autonomous Cyber AI

 

Autonomous cyber AI is a defense system that can handle the complexity & variety of cyber-attacks. It has automated security protocols and is activated at the time of any threat. It is believed that threat actors are using AI-driven attacks where the AI algorithm can manipulate any machine’s decision. To counter possible AI attacks & various other types of complex attacks, firms require secure algorithms and automated defense systems.

 

The data generated by firms is also huge and to manage this big data, we need AI to reduce human labor and increase accuracy. Cybersecurity experts also use other technologies with AI like machine learning, deep learning, etc. to create an autonomous cyber AI. Autonomous cyber AI is capable of identifying data outliers or anomalies which are hazardous for business data. Autonomous cyber AI immediately identifies any foreign element in the business data and takes measures to protect the system/data.

 

Humans cannot identify new attacks in time, which leads to data theft/breaches. It is expected that we can see machines fighting each other in the future because of the rise of AI-driven attacks. More than 3000 organizations/firms around the globe have already adopted autonomous cyber AI to tackle cyber-attacks. One can know more about AI by opting for an Artificial Intelligence Course from a trusted source like Imarticus Learning.

 

Benefits of AI Cybersecurity

 

The benefits of AI cybersecurity to firms/businesses are:

 

• Management of big data can be easily done with less human labor. Large volumes of data can be processed in less time.

 

• New security attacks can be identified by AI.

 

• Unknown/possible security threats can be known and fixed in time.

 

• 24*7 autonomous protection without any human intervention.

 

• It will help in cost optimization as it is a long-lasting solution for cybersecurity.

• Authentication system can be strengthened via AI where only a limited number of people are given access to security details.

 

• The response time after an attack is decreased as autonomous cyber AI acts quickly.

 

Conclusion

 

Cybersecurity is very necessary for firms to protect their data and digital ecosystem. AI is being used to develop smart algorithms that can control the movement of data. One should learn about autonomous cyber AI if he/she is looking to build a successful Artificial Intelligence Career as many companies are adopting it in recent times. Start your AI course now!

Why is IIT- Roorkee, Supply Chain Management Program the Best?

Posted on by Imarticus Learning

The supply chain encompasses all of the actions, people, organizations, information, and resources required to transport a product from conception to client. In the consumer goods industry, this is likely to include raw materials, production, packaging, shipping, warehousing, delivery, and retailing. The end goal is straightforward: fulfill the customer’s desire. 

Further, supply chain management is the process of combining supply and managing demand not only within the firm but also across all of the supply chain’s innumerable units and channels so that they function together quickly and productively.

 Supply chain management is critical for any firm since it can introduce various benefits. But, inadequate supply chain management can result in very expensive delays, quality concerns, or reputation issues. Poor supply chain management can also lead to legal concerns in some circumstances if vendors or processes are not compliant. Technology advancements have opened enormous possibilities for supply chain management, allowing supply chain managers to collaborate intimately and in real-time with supply chain participants.

 A certified supply chain analyst has a very important role in every industry that involves the supply chain. A supply chain management career can guarantee you a high-salary job in various sectors. The IIT Supply Chain Program is the best and comes with some exclusive benefits. This 6-month supply chain management online course has been specially prepared by IIT faculty and industry professionals to assist you in learning. 

Supply chain management courses by IIT RoorkeeWith the ever-increasing trend of e-commerce, the number of products in transit has also increased. The number of supply chain management employees has increased disproportionately across industries. This training will prepare you to capitalize on this opportunity.

Why Pursue IIT Supply Chain Management Course?

The benefits of IIT certified supply chain analyst course are as follows:

  1. Discover supply chain management knowledge and much-required analytical skills with this program built by IIT Roorkee’s DoMS & E-Learning Centre. This course will assist you to become more data-centric and improve supply chain selection by using the strength of Python.
  2. This supply chain management online course will assist you to learn from one of India’s greatest faculties via live online sessions and conversations. IIT faculty workshops will help you construct essential concepts in SCM, while industry specialists will provide hands-on learning for SCM processes, tools, and strategies. This course will teach you how to construct a solid SCM foundation and use analytics through engaging live sessions with renowned trainers and industry professionals.
  3. Participate in a three-day campus immersion session to tour the IIT Roorkee campus. During the campus immersion, you will connect with your peers and learn from IIT academics. You will get the opportunity to go to the Delhi or Noida campus and work on the Capstone Project with the help of expert mentors.
  4. Tackle tough real-world challenges that necessitate a full understanding of principles, mastery of skills, and comprehension of the business context. With these real-world SCM projects, you can boost your resume and stand out. You will be able to create your own project portfolio and dazzle potential employers. Through rigorous in-class exercises and SCM projects, industry experts teach you crucial job-relevant skills.
  5. Massive volumes of data are generated by supply chains – Big Data, to be more explicit. Prepare to participate in the creation of smarter, more strategic, and tactical global supply chains of the future by merging traditional supply chain methodologies with data-driven judgment.

 It is very evident that the demand for a certified supply chain analyst is increasing by the day. In such a situation, certification can give your career the right boost and help you bag your dream job in your desired company. Enrolling in this course can be a game-changer for you!

Why is the AI course the best machine learning and artificial intelligence course by E&ICT Academy, IIT Guwahati?

Posted on by Imarticus Learning

The most important question to any student these days would be what to study that will tremendously benefit his/her career, and where to study it from. Numerous courses are offered by several institutions, and choosing the best option for yourself in such a scenario can be very confusing. This is why we are here to shed light on possibly the most relevant course right now.

That is, of course, the artificial intelligence and machine learning course. It is one of the most versatile courses out there that lets you work in almost any field you want. That is because all the major sectors now need the help of artificial intelligence and machine learning to optimize their business and keep the customer and employee-friendly. 

artificial intelligence and machine learning coursesA lot of institutes in India provide AI ML courses. Imarticus Learning is one of the best in this field with its certificate course.

However, if we are to talk about the best institute to learn an artificial intelligence and machine learning course from, then it would undoubtedly be an IIT. Here, we are going to take a look at why an AI and ML course might be one of the most relevant courses out there that will benefit your career. And, why an AI and ML course from an IIT is the best.

Benefits of an AI and ML course

Data analytics basically uses numerous tools to extract and analyze data in a way that helps to detect patterns from past records. It also analyses where the company is now and predicts where it can go from here. All of it is done through analyzing market trends, the company’s financial condition as well as the customer’s online habits. The main benefits of this course are:

  • It is one of those jobs that is applicable in any given field, from the health sector to finance to marketing. This means that you can land your dream job from the get-go or if you feel like it, then you can even change your sector without much thought.
  • It is one of the highest-paying jobs in the country right now, which, of course, means a stable future.
  • Expert reports state that in the near future, there are going to be even more positions opening up in all corporate sectors.

Why IIT is the best choice

As we all know, IIT is an unparalleled choice when it comes to courses in any sector of business. There are a few reasons for that, such as:

  • It teaches you deep skills that are most popularly used in AI and ML.
  • The opportunity to learn from actual corporate cases, that too from the top-level industry professionals of AI and ML.
  • Overall excellent vocational training, as students experience hands-on learning with lab-based cases related to the most high-level industry problems.
  • Another thing that IITs are most known for is excellent industrial exposure.
  • The opportunity to learn AI and ML from any IIT will immediately put you leagues beyond your peers.
  • An excellent package right from the beginning in your preferred sector.

Conclusion
The opportunity to learn AI ML courses from an IIT is the best thing that can happen to your career. It is an academic investment that will be paying off throughout your life. So, prepare hard enough to give yourself that edge over others. Also, do check out Imarticus Learning’s AI and ML certificate course as we have one of the best-planned courses in this field.

Related Article:

Best Artificial Intelligence and Machine Learning courses by E&ICT Academy, IIT Guwahati

Why problem-solving using Data Analytics needs new thinking?

Posted on by Imarticus Learning

Data analytics helps organizations examine all sorts of data, be its real-time, historical, unstructured, structured, or qualitative data to uncover trends and develop insights that may be used to guide and, in some circumstances, automate decisions, thus connecting intelligence and action.

best data analytics certification courses in IndiaThe best solutions now enable the entire analytical process, from data access, preparation, and analysis through analytics operationalization and evaluating results. Hence, there is a rise in data analytics courses in India.

Data analytics enables firms to digitally transform their company and culture, allowing them to make more innovative and forward-thinking decisions. Algorithm-driven firms are the emerging innovators and business leaders, going beyond typical KPI monitoring and reporting to uncover hidden patterns in data.

In the present scenario, many firms have had to adjust their emphasis, review their existing timeframes, and rethink how they function. Companies that prioritized the digitalization of their business, particularly investments in analytics and automation, have fared the best during this crisis. Nonetheless, AI continues to be a source of consternation for many business leaders across industries.

Why is data analytics used to solve business problems?

Businesses sometimes overlook the fact that the most complicated business issues can be solved using analytics by utilizing the most recent data analytics solutions. It is critical to understand that the analyst spends approximately 70% – 80% of his or her time generating analytical files.

Only the remaining 20-30% is spent on developing a solution. Predictive models or simple business rules are used in this approach to solution creation. A corporation can use analytics tools to extract simple structured data and reduce its effort in retrieving unstructured and semi-structured information.

The rising need for an analytical solution has compelled businesses to invest in analytics solutions that will allow business users and employees across the enterprise to get the answers they need. Businesses can use these powerful tools to perform complex analytics without the need for programming assistance from data scientists. As a result, businesses will gain a new competitive advantage and the ability to unearth previously undetected patterns, propelling them to a position of influence.

Today’s problem solving necessitates a new outlook in order to obtain these insights quickly and efficiently, and there is a need for a cohesive technological change to displace the conventional data analytics tools and dissolve the walls between business groups.

Technology behemoths have demonstrated what can be accomplished when data and analytics are at the core of a business model. Needless to mention, the world’s most successful firms are all data-driven, with their focus on leveraging data to understand, market to, and increase income from their customers. This cultural shift of democratization of access to data and analytics across an organization has allowed these organizations to capitalize on the data economy and drive digital transformation.

Analytic Process Automation, or APA, is a new category that could be the key to capturing the best of man and machine at scale. APA automates business processes and provides immediate self-service access to business-critical data insights to even the most inexperienced knowledge workers. In reality, this implies that more employees will be able to use as well as gain from data with hardly any training.

This, in turn, alleviates the long-standing friction between data specialists and business managers, the latter of whom has relied on the former’s access to critical information. APA de-emphasizes data analytics at a level that the business sector has never seen before.

Conclusion

Data Science as we all know is emerging as an extremely important profession in the present scenario and it is important to have a data analytics certification to be an expert in this field. There are good data analytics courses in India that provide data analytics certification and will also increase your career prospects if you wish to pursue it further.

How Long-Term Modelling of Our Future Energy System Can Be Mapped With Artificial Intelligence and Machine Learning?

Posted on by Imarticus Learning

Today, technology and sustainability are the main axes of development. To secure the planet and continue the growth of industry, we are engaged in a global energy transition. Most countries have become aware that measures must be taken to address a problem that, if not curbed, will have catastrophic consequences for the environment and, of course, for human beings themselves.

ai and ml courses by E&ICT Academy, IIT GuwahatiHowever, such a transformation requires the support of technology and, because of the enormous amount of data, artificial intelligence and machine learning courses are the basis to ensure the advancement of the energy sector.

At Imarticus you can join the postgraduate program in data analytics & machine learning (AIML). 

Technology as a tool

Changing the energy paradigm of the last century will be an arduous and complicated task. That is why new technologies have a lot to say as tools to facilitate evolution. The Internet of Things, machine learning, artificial intelligence, and Big Data will be key to making the processes of change as effective as possible. Massive data analysis must become a fundamental pillar for transforming how energy is generated, transmitted, and distributed.

Artificial Intelligence allows us to handle enormous quantities and analyze them logically and reasonably. About energy, in particular, we have data on meteorology, health, or the behavior of the people involved in the system: who generates electricity, who transports and distributes it, and who consumes it. Data that, when properly analyzed, can provide a tailor-made understanding of the sector.

The development and implementation of intelligent systems must not only facilitate the massive introduction of alternative energy sources but will also have the task of achieving rationalized storage of this energy, as well as providing greater flexibility for the demand, i.e. the people who use it.

Three levels of analytics can be applied: descriptive, to know what information is available and where to apply intelligence, predictive analytics, to anticipate production or demand, and prescriptive analytics. With the data, we work on predicting production, including renewable energies and demand, with the implementation of smart meters.

In addition, technical and non-technical incidents, such as energy fraud, are detected. All of this is aimed at optimizing the energy model, with the resulting economic and environmental benefits. We will see a huge take-off in the number of professionals who will choose to pursue a machine learning career.

Tools for the consumer

In this scenario, smart meters and internet-enabled sensors will be commonplace, which will improve our energy use while at the same time making it possible to bring costs in line with what each individual actually consumes. Thus, machine learning will automate processes, while artificial intelligence will make it possible for devices to work automatically and learn from consumers’ habits. This will also be possible on a large scale, so that the operation of future solar or wind power plants, to give just two examples, will be more effective in a shorter space of time.

In this respect, we should note that although everyone is involved in the energy transition and awareness must start in every household, the technology will be geared towards people having little to do in terms of reducing consumption and costs.

Artificial intelligence-based models and predictions facilitate and will continue to be a major advantage in mapping energy systems. What is most surprising is that this is just one of the many applications of these technologies. If you want to contribute to the change, you can sign up for AI and ML courses by E&ICT Academy, IIT Guwahati.

Big Data Engineer Salary: How Much Can You Earn as a Big Data Engineer?

Posted on by Imarticus Learning

Who is a Data Engineer?

As businesses across the globe are enthusiastically adapting the data-driven strategies to optimize their decisions, the demand of highly skilled Data Engineers has increased manifold. A skilled person who is able to convert the raw data into a self-explanatory form to analyze the trends by developing requisite algorithms is a Data Engineer.

The entire task of Data Mining, maintaining and extracting trends from different data sets in an organization is completed by a team of Data Engineers. Ultimately, the Data Engineers provide reliable infrastructure to maintain big data.

Skills required to be a Data Engineer

A Data Engineer must have deep understanding of SQL, Extract Transform Load, Apache Hadoop, in depth knowledge of Python, Java, Scala, Kafka, hive, storm and many more.

Big Data EngineerEnterprises now a days prefer the employees with the experience of working on the cloud platforms like Amazon Web Services etc. Sound knowledge of Data warehousing and Data modelling is also given a lot of preference these days.

The required skills and preferences may affect the salary of an Data Engineer by 10%-15%.

A Data Engineer deals in Big Data, the person should be proficient in the documentation skills and must also be good in his/her verbal and Non-verbal communication skills.

How to Become a Data Engineer?

Applied Mathematicians, Engineers, People holding Bachelor’s degree in Computer Sciences or related IT field find it easier to become a Data Engineer. The aspiring candidates then go for a Big Data certification course to have in depth understanding of required technological skills to be a Data Engineer.

Roles and Responsibilities of a Data Engineer

The generic tasks that a Data Engineer has to perform include:

  • Aggregation and Analysis of given data sets
  • Development of Dashboards and reports
  • Development of tools for business professionals
  • Providing improved techniques to access the Big Data

Three main domains in which a Data Engineer works are: Generalist, Pipeline centric, Database-Centric Generalists are the Data Engineers who processes, manages and analyses the data.

Big Data EngineerPipe-line centric Data Engineers work in coherence with Data Scientists to utilize their collected Data. Database-centric Data Engineers manages the Data-flow and database analytics.

Along with the technical skills, a Data Engineers must have some soft skills as well to communicate their analysis. Some of the key responsibilities are:

  • Acquisition of Data
  • To match their development constantly with the business requirements
  • Consistent improvement in the data reliability, efficiency and Data Quality
  • Development of predictive and prescriptive modelling

The key responsibilities vary from organization to organization.

Data Engineer: Employers and Salaries

Some of the top companies where Data Engineers are highly paid are:

  • com Inc
  • Tata Consultancy Services Limited
  • IBM Private Limited
  • General Electric (GE) Co
  • Hewlett-Packard
  • Facebook

Factors affecting Salaries of Data Engineers 

Experience:

Average Experience as a Data Engineer Average Pay-Scale based only on Experience
Entry level ₹400,000 approx.
1-4 years ₹739,916 based on 317 salaries
5-9 years ₹1,227,921 based on 179 salaries
10-19 years ₹1,525,827 based on 49 salaries

Job Location:

The Data Engineers working in the prime locations like Gurgaon (Haryana) earns 27.3% more average salary, in Hyderabad (Andhra Pradesh) 13.7% more average salary, in Bangalore (Karnataka) 12.5% more average salary than in locations across the nation.

The average salary of a Data Engineer in Mumbai, New Delhi and Chennai are relatively lesser than average salary across the nation.

What Is Distributed Computing Training in Machine Learning?

Posted on by Imarticus Learning

The traditional machine learning approaches rely on using open-source tools for data analysis and prediction making. This approach does not work out well when the data is large. The RAM on the system gets damaged when large files like these are involved. We need to use an approach that not only helps us build the machine learning models successful but also ensures that the system is not burdened or damaged while an operation is being performed. Hence, we need to learn Distributed Computing in Machine Learning.

What is distributed computing?

An approach to improve the system performance, resolve scalability issues and increase the system efficiency by dividing the task being performed on a single machine to different systems is called distributed computing.

Distributed computing has many applications such as the world wide web, global financial systems, machine learning and much more. Here we concentrate basically on the concepts of Machine Learning Training with distributed computing.

Distributed computing training 

The main purpose of this training in machine learning is to help an individual master the skills in machine learning and resource allocation and management. Distributed computing came up as a technique to resolve the scalability associated with machine learning algorithms. It developed on a massive scale in recent years to provide large-scale operations such as big data analysis efficiently.

When we talk about distributed computing, there are two main approaches:

  1. Horizontal fragmentation- It uses an approach to store the selected portions of the available instance at different sites.
  2. Vertical fragmentation- Storing of the selected attributes of the subsets of the instances comprises of vertical fragmentation.

The data involved in machine learning is very massive if a real-time problem is involved. A situation might be encountered where the machine learning model needs to be trained again and again without disrupting the ongoing parallel task. In this situation, distributed computing serves as a boon by resolving the issues.

The training in distributed computing also highlights the importance of applying these techniques in fields such as medical computing where huge amounts of data are uploaded at every instance of the given time and need to be analyzed for relevant purposes.

Distributed machine learning platforms

Training in distributed computing for machine learning also provides information about the platforms that been developed to do so. Some of these platforms are listed below:

  • H2O- Developed by H2O.ai, H2O is an open-source platform for distributed computing in machine learning with in-memory support. It also provides support for traditional machine learning algorithms and includes AutoML functionalities.
  • TensorFlow- Distributed TensorFlow provides different servers each of which is considered to be a cluster and each process is made to run on an executive search engine.
  • DMTK- It stands for distributed ML toolkit and is developed by Microsoft to provide highly efficient techniques for performing a machine learning task.

Apart from the frameworks mentioned above, there are other frameworks such as Apache Spark Mlib and Apache Mount that assists in the machine learning applications as well.

Conclusion

Most of the problems that we encounter today are voluminous and very hard to process for machine learning tasks. Distributed computing left its footprints in the field of machine learning by solving one of the major issues that are big data handling. It has gained a lot of popularity in recent years because of its high degree of scalability, efficiency, and performance. It has not only helped in performing large-scale computations but has also helped in the optimization of the operating systems. To be accurate, it has revolutionized the world of machine learning training and computations.

How Managing Supply Chain Effectively Can Help You Reduce Overhead Costs

Posted on by Imarticus Learning

Effective supply chain management can help a business to grow amidst high competition. From buying raw materials to selling products, businesses can reduce the errors that hamper the ROI (Return on Investment). Effective supply chain management can reduce overhead costs and boost revenue. The day-to-day operational cost of a company is known as the overhead cost. Read on to know how effective supply chain management can help in slashing overhead costs.

 

Understanding supply chain management

 

Supply chain management includes the flow of products/services from the manufacturer to the customers. Every business should know the flow of products/services in the supply chain for better results. With the help of supply chain data, businesses can reduce operational issues. The objective of supply chain management is to slash overhead/inventory costs and reduce operational errors. Many businesses invest in supply chain software to know more about the flow of products and services. With effective supply chain management, you can fulfill the customer requirements and build better relationships with distributors/vendors. At present, businesses are hiring employees that have a certification in supply chain management for slashing operational costs.

 

Slashing energy costs with supply chain management

 

Businesses try to consume limited environmental resources to slash their overhead costs. Warehouses and outlets use lighting and coolers all day long. Businesses spend a large amount on maintaining the lights and other devices that utilize energy. With effective supply chain management, you can uncover the energy costs involved in your day-to-day operations. The lights and coolers can be replaced with other devices that are energy efficient. You can get to know which areas of your supply chain are consuming more energy than others.

 

Reduce the cost of office supplies

 

Office supplies may not seem costly at prima facie but can add a lot to your overhead costs. With effective supply chain management, you can get to know the cost of supplies used in your offices every month. Many businesses go paperless to cut the cost of ink and paper. Paperless documentation is easy and cheaper than traditional paper documentation. Even a small adjustment in your supply chain can save a lot in the long run.

 

Cost tracking with effective supply chain management

 

Operational costs can be slashed when the business knows about all the costs. Effective supply chain management includes documentation of all types of costs. You can study the record of your costs and can compare the records of different months. If any changes in the costs have occurred, you will quickly identify them. Once you have maintained an effective supply chain, you will spend less on retaining it. You will only fix those areas of the supply chain that need improvement.

 

How to learn more about supply chain management?

 

A supply chain management course can help in learning the required skills. You can go for the Professional Certification in Supply Chain Management & Analytics offered by Imarticus Learning. This course is provided by Imarticus in partnership with IIT Roorkee. You can learn job-relevant skills from the IIT faculty via this online course. One-to-one mentoring sessions will be provided by industry experts during the course. This course will also include a 3-day immersion program that allows students to interact with peers in IIT Roorkee. You will work on various real-life projects related to supply chain management in this online course.

 

Conclusion

 

The supply chain management online course offered by Imarticus follows an industry-oriented curriculum for a better learning curve. From career support to supply chain assignments, Imarticus will offer it all via their online course. Start your supply chain online course now!

How Providers Can Use AI to Improve the Payment Integrity Process

Posted on by Imarticus Learning

Nowadays AI is utilized successfully and has proven to be an efficient, cost-effective, and reliable solution to cut down inappropriate payment claims worth a million dollars every year. The anomalies and patterns can be detected in less than a minute which helps to decrease fraud, system abuse, and future wastes.

From the provider’s point of view, they can be educated well to ensure evidence-based and high-quality alternatives. Learn more to know how the AIML program by Imarticus uses AI to improve the payment integrity process.

AI and Payment Integrity

A huge data volume from the providers, facilities, labs, etc. is integrated with AI-based computer power systems. This recognizes patterns in the data in a very effective and automatic way and helps to identify false claims. However, the billing behavior of the providers is difficult to detect as they are usually dealing directly with third-party enterprises for handling billing and coding issues.

This outsourcing may result in missing clarity and inconsistent processes which can ultimately lead to upcoding errors and fraudulent claims.

Thanks to the AI certification course, the identification of errors and fraud is a quick procedure with high precision and accuracy and the errors can be avoided drastically.

artificial intelligence and machine learning coursesInteroperability, APIs, and NLP Efficiency

The real innovation lies in the fact that the medical records of the patients can be directly obtained from the providers of EHRs with firm signed contracts.

This kind of interoperability helps in making the tasks work automatically like pre-authorization of the requests as per the need. This saves the manual working hours and makes the entire system run fluidly.

AI-based natural language processing (NLP) can further accelerate the time-saving process by around 40 percent when used on unfiltered data in the review stages. This helps in the augmentation of the staff efficiency and reduction of the costly human resources like nurses.

Integrating technologies like AI, NLP, robotic processing, and machine learning courses can give the payers the advantage of controlling the expenditure. Furthermore, it gives a helping hand to the providers to better manage the revenue systems to have a more unified and fluid cash flow within the system.

Prepayment cost avoidance model

One of the emerging trends of the industry is a significant shift to a prepayment from a post-payment cost avoidance model. It results in cost reduction related to reprocessing, reworking, and claim recoveries. But, the payers have to be super cautious when adopting this method as it is not yet well demonstrated and proven. Payment integrity based on AI is positioned very uniquely and this prepayment cost reduction model is close to becoming a reality in the industry soon.

Educating the providers

To overcome overutilization and fraud claims another approach that can be employed is their pre-detection by the providers themselves even before the claim submission. During the overpayment or appeal recovery process, the providers can be educated about the non-compliance, errors, overpayment issues, or the reasons for service rejection. This can increase the cooperation from the providers and helps decrease the number of appeals made.

On the same lines, AI-based technologies can analyze the data sets and send responses to the doctors, and list all the factors causing the denial of the claim and also about the unnecessary medical care as mentioned in the health plans.

Conclusion

Finally, analytics and solutions based on AI can ensure to cut down inappropriate claims significantly by identifying the wrong claims and acting upon them. Learn AI and improve the healthcare systems by making proper and efficient use of AI-based algorithms and methods.

How Has Data Science Given Rise to Smart Logistics?

Posted on by Imarticus Learning

How Has Data Science Given Rise to Smart Logistics?

Every day, billions of packages are delivered to customers by the logistics industry. At every supply chain node, a large quantity of data is generated. Customer data and delivery data are collected by the logistics firms every day. Data science plays a crucial role in supply chain management and many other logistics processes.

Businesses are relying on data science to reduce waste, forecast demand cycles, manage delivery routes, and many other processes. Young enthusiasts can learn data science to earn a lucrative job offer in the logistics industry. Read on to know how data science is affecting the logistics industry.

 Autonomous vehicles for logistics 

With the growing population, businesses have to cater to the growing needs of the customers. Also, e-commerce sites are growing in number that has generated more online customers. Delivery teams now have to cover remote areas for delivering the packages to customers. Even the top logistics companies in the world are facing driver shortages. It is why many experts are suggesting the use of autonomous vehicles for delivering packages. It may seem like a far-fetched thought but, autonomous vehicles are already available in the market.

AI and ML algorithms are used for designing better autonomous vehicles. As a data scientist, one should be familiar with AI and ML. If autonomous vehicles disrupt the services of traditional vehicles in the future, data scientists will be in huge demand. You can learn data science now to make your skillset futureproof and earn a lucrative job offer.

Smart warehouses 

For storing different types of products, logistics firms need many warehouses. Some products need to be stored under specific temperatures. For example, meat products need to be stored in cold temperatures. The temperature requirements may differ from one product to another in a warehouse. With the help of data science and ML, smart warehouses can be created. Smart warehouses help you set automatic alarms for any temperature failure. All the products can be stored in ideal conditions with the least manual interruption. It will prevent the product damages that occur in warehouses.

Market forecasting with data science 

Data science can help in analyzing customer data and better supply chain management. With data science, you can forecast market demands and supplies. Many times, warehouses have to bear a loss due to oversupply or undersupply. Data science can help in designing smart algorithms that can predict supply and demand trends. Logistics firms can track their supply following the demands of the customers.

Reverse logistics with data science

 Data science algorithms can identify the geographic locations that are prone to return the products. Based on that, you could target geographic locations accordingly. Fewer customers will return your product and you can save the cost for reverse logistics. You can build a successful data scientist career if you can help businesses to slash operational costs.

How to learn data science for logistics? 

An online data science course in India can help in learning industry practices. Imarticus Learning is a reliable EdTech platform that can help in learning data science for logistics. The PG Program in Data Analytics & ML offered by Imarticus can make you job-ready.

best data science courses in IndiaWith an industry-designed curriculum, you can learn about the use cases of data science in the logistics industry. From logistic regression to programming languages, this course will cover them all.

 Conclusion 

 The course offered by Imarticus will help you in learning via 25 real-life projects related to data science. A data science online course can help in kickstarting a data science career or getting a raise. Start learning data science for logistics now!