What could be better than knowing what future lies ahead us? Predictive Analysis is one such branch of data analytics which can be used to make predictions of future unknown events and is growing with a rapid pace. On the other hand, innovation is an ongoing process which finds its application in almost every field. Without innovation, we would not have reached the platform at which we are now. A number of technological achievements have improved our lives.

These days, Innovation has found a guide in Predictive Analytics that helps to walk towards success.  Many innovations are made but majority of them never succeeds. Predictive Analytics is going to play an important role aiming towards new products ensuring greater economic stability and progress in coming years. 

To know more about how predictive analysis can help in innovation read https://www.smartdatacollective.com/predictive-analytics-methods-make-innovation-successful/

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

To know more visit: https://www-cio-com.cdn.ampproject.org/c/s/www.cio.com/article/3273114/predictive-analytics/what-is-predictive-analytics-transforming-data-into-future-insights.amp.html

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit: https://hackernoon.com/stop-feeding-garbage-to-your-model-the-6-biggest-mistakes-with-datasets-and-how-to-avoid-them-3cb7532ad3b7

Destroying traditional jobs but creating new ones, technical innovations have changed the course of work over the years. The Industrial Revolution of the 18^th century marked the transition to new manufacturing processes, effectively increasing the output levels and discovering the modern industrial marvels. With AI improving the standard of living, the current and future generations are likely to witness taxing employment pattern changes.

AI would change the future of work by bringing about the following changes:

1)      Create new jobs: Tasks requiring the least of the human cognitive mind would be dealt with the application of modern AI powered robotics allowing individuals to devote their time to community services, volunteering etc.

2)      Bring Automation: A research carried out by Carl Benedikt Frey and Michael Osborne of Oxford University in 2013 reported that approximately 47% of jobs would be automated in the next few decades with non-routine jobs and tasks requiring  high cognitive and good social skills having the lowest probability of being automated compared to a greater probability involved in automation of manual jobs and routine jobs like data entry, production logistics etc.

3)      Increase the gap between the owner and the worker: AI is likely to widen the gap between high skilled and low skilled workers and also increase the persistent inequality between the owner and the workers by laying off workers that would inflate the profit margin of the owners as robots and chat-bots would not demand overtime allowances.

Gartner, the global research and advisory firm, reported that AI is creating more jobs than it is destroying by bringing about a net increase of nearly 2 million jobs by 2025. The core objective of AI should be to make human workers more efficient without laying them off. AI coupled with human intelligence is all set to revolutionize the economy we inhabit.

Read More at: https://www.analyticsinsight.net/is-artificial-intelligence-a-threat-to-your-job/

Artificial Intelligence, big data and machine learning have been ruling the industry in recent times. Starting from Amazon to Google, indulgence in predictive modelling is indispensible. When it comes to the human body, well, artificial intelligence plays a pivotal role in saving lives. Rampant use of AI is involved in CT scans in cases of stroke or brain injuries. Radiologists have a backlog of cases which might delay the detection of the criticality involved in a particular case. To the rescue comes AI, which by streamlining the CT scan interpretation workflow by triage process and automation of the initial screening process, radically reduces the time lapse in detection and diagnosis of time sensitive cases. To detect abnormalities demanding urgent attention such as intracranial haemorrhage, cranial fractures, midline shifts etc, Qure.ai has provided automated deep learning algorithms to assist physicians. The algorithms’ accuracy is equal to that of a physician and classification algorithms are used in radiology itself. TITAN X of NVIDIA, cuDNN and GeForce GTX 1080 GPUs were used that achieved almost 95% accuracy rate as compared to that of 97% by radiologists. Such AI algorithms tend to become a life saver in a world where there is an acute shortage of specialized radiologists.

Read More at: https://www.analyticsinsight.net/how-artificial-intelligence-predicts-life-threatening-brain-disorders/

Business Intelligence and Business Analytics are being used interchangeably nowadays in almost every field of businesses worldwide involving, in particular, leveraging the data of a company in order to evolve and grow. In addition to other sectors, predictive analytics greatly benefit the construction business categorizing information with relevance and accuracy. Predictive Analytics assists in the following ways:

1)      Leveraging work packages: Predictive Analytics helps in task breakdown matching the right people for the right job, scans past project documents, including the Work Breakdown Structure (WBS), and assess the fallacies in project execution. With such technical know-how, businesses can scientifically cut on resourcing costs without compromising on potential.

2)      Prescribe, Predict and Describe: Descriptive Analytics creates a database containing the failures and their severity which is followed by predictive analytics analyzing their recurrence. Finally, prescriptive analytics explores options that can prevent such fallacies in future work.

3)      Scanning risks: The construction space advocates vociferously for the health and safety of the crew and this purpose could be served by predictive analysis in conjunction with prescriptive analytics. Pinpointing disaster zones to nth degree accuracy and using pedometer analytics to measure the distance the crew covers, predictive analysis places heavy-duty equipments at various access points improving visibility in low lying areas and also alerts about the resources that demand servicing. It ensures both the project’s progress and the business’s adherence to the crew’s safety standards.

4)      Lowered production costs: Manual monitoring methods are laborious and entail a cost on the business’s profits. GE’s Kimberlite Survey reported that businesses using predictive approach using retrofit sensors and cloud computing experienced approximately 40% less unplanned downtime.

5)      Immersive Insight: Predictive analysis converts dormant data into actionable analysis and prevents any information from lying unanalyzed.

Read More at: https://www.analyticsinsight.net/5-benefits-of-business-analytics-for-the-construction-business/

Data has become a central part of the economy and applications in analytics have been proliferating fast from private to the public sector. Data collection and analysis are at the root of critical economic decision-making which makes the socio-economic issues easy to interpret and comprehend, thus playing a pivotal role in economic battles. Big data analytics help the government in infusing transparency into the system, combat fraudulence and deliver public services effectively and efficiently.

The year 2017 will always be known to have triggered off this big data journey with Demonetization and GST being the two notable data-driven policies injected into the system coupled with the shift in focus to the macroeconomic issues like Aadhar data collection which gained an edge to bring economic reforms. Using big data analytics, the following few untapped areas can positively impact the government:

1)      Tax and Welfare: The ‘Project Insight’ rolled out by the Indian govt. used data mining techniques to counter tax evasion in 2017. It also helped in tracking down deregistered firms and gathered information about black money potholes in existence.

2)      National Security:  The uncertainties faced by national security officers with regards to the unpredictable security situation can be overcome by the use of analytics thus enabling them to combat crime attacks easily.

3)      Healthcare: Healthcare system in India has the opportunity to leverage big data analytics on the data emanating from biometric, patient records and thus provide actionable insights with greater prediction power contributing to effective public health.

4)      Education:  Ranking second in terms of student enrollment,  the titanic amount of student data can be analyzed to predict statistical figures and would help in efficient budget allocation.

In addition to these, analytics has also entered the farming sector where the concept of geo-tagging the entire agriculture infrastructure was implemented. Although the entire process is still in its infancy, the outcomes that big data analytics present to the Indian economy are much more effective. Though big data analytics have not been used in policymaking yet, the budget allocation hints at a widespread adoption of artificial intelligence and big data analytics in the Indian economy.

Read more at: https://www.analyticsinsight.net/how-indian-government-is-using-big-data-analytics-to-improve-economy-and-public-policy/

Fifteen years ago it was only fair for the airlines companies to keep record of the ingoing and outgoing flights only. But now let’s imagine a sudden storm hitting the East Coast today. This would imply that several flights will be delayed. Hence keeping up to their standards and adding a value to the every penny the passengers have paid would involve several entailing jobs like determining the flights to be connected with the airline, baggage transfer time, the number of transferring passengers, the flights that are coming from and so on. It naturally means a proliferating amount of big data sifting and shifting from a constellation of different sources. A Boeing 787 alone creates a half a terabyte of data every day. Big data in aviation are useful in many cases such as:

·         Fuel Efficiency- Fuel is the second highest expense for airlines and estimating power has developed to a point where airlines can congregate and process the enormous amounts of data they need to analyze on a per-trip basis. It is hoped that data mining will produce actionable intelligence around decisions such as adding or subtracting flights to routes, setting fuel loads for each aircraft, and selling additional passenger tickets. 

·         Smart Maintenance- The big data, including mechanic write-ups, shop findings and in-flight measurements, helps the airline company to plan equipment maintenance with minimal disturbance to flights.

·         Airline Safety- A data collection and analysis program named Data4Safety has been recently launched by The European Aviation Safety Agency (EASA)  to detect risks using a amalgamation of safety reports, in-flight telemetry data, air traffic surveillance information, weather data and so on.

Read more at: https://hortonworks.com/article/how-big-data-in-aviation-is-transforming-the-industry/

The use of predictive analysis is widespread including in connecting car industry which provides both opportunities and challenges for the automotive industry. By using Telematics and infotainment systems, connected cars increasingly stream data into the cloud. Each connected vehicle is expected to generate more than 25 gigabytes per hour as the dizzying array of smart IoT sensors are coming into the picture. Predictive analysis in automotive industries is enabling connected cars to stay more on roads rather than in shops. Some of the ways in which predictive automotive data analysis is driving the growth of connected car industry are listed as follows:

·         Predictive Maintenance-Predictive data analysis can spot maintenance issues before they occur by leveraging data from warranty repairs with existing vehicle sensor data. This is done by pulling in data from virtually every vehicle, comparing the information with warranty repair trends and finding meaningful correlation which is otherwise impossible to be discovered by humans.

·         Predictive Collision Prevention-By utilizing big and fast data, latest sensors and car-to-car connectivity, predictive analytics technology may completely eliminate the possibility of accidents in the future.

·         Connected Car Cyber Security- Predictive analytics is efficient in securing connected cars in the sense that it is able to identify patterns of attackers’ behavior. It not only looks for an intruder to repeat the same behavior as pervious attackers but also searches for a combination of behaviors that are inconsistent with what would be expected of an authorized user.

Read more at: https://igniteoutsourcing.com/publications/predictive-analytics-in-connected-car-industry/

The power of machine learning is growing exponentially. Almost no industry domain is remaining untouched with the wonders and powers of machine learning.  Machine learning is just an application of artificial intelligence whose algorithms helps to analyze the historic experience without being explicitly programmed to predict the future affairs. Before jumping into the world of machine learning, it’s important to know which languages are being used to analyze the data and predict the future. Here are those 5 languages which are being using for machine learning: 

1. Python

2. R Programming

3. LISP

4. Prolog

5. javaScript

why and how are these languages being used for machine learning? For detailed information,

https://www.analyticsinsight.net/top-5-machine-learning-programming-languages-you-should-master/

Although cloud storage has benefited the businesses in intelligence enterprises but still it shouldn’t be trusted blindly. Cloud storage techniques have helped digital data storage and the changes are remarkable.

The 2 challenges that are connected with cloud storage, cloud lock-in and management complexity are:

• Cloud lock-in: Failure to transfer data from one cloud storage facility to other.

• Management complexity: The inability to perform proper management of available storage environments.

Following are the solutions to these problems that may be seen as unsolvable.

1. Gateway device: It behaves as an agent between the on-premise storage and the cloud storage.

2. Hybrid cloud: With the help of hybrid cloud, cloud storage acts as an extension to on-premise data storage.

3. Multicolored controller: It permits the data to be seen at the same time.

For more information go to,

https://www.analyticsinsight.net/how-to-solve-the-challenges-of-data-management-on-cloud/

Nowadays businesses have opened new gates for certain innovations using cloud computing technology with a proper and selective approach. Most of the IT executives are now entirely focused on how to make cloud a way to achieve their business goals and their entire focus is on cost optimization instead of cost management. In terms of transformation, the cloud has been a central to many organizations. Big data technology has allowed storing and recapturing of the vast amount of information. Regardless of any sector, most of the organizations have transferred all their data to the cloud. But still, most of the executive’s are reluctant to adopt cloud due to security concerns.

Businesses can achieve maximum growth only by accessing, controlling and analyzing all flaws present in the cloud network. That is why multi cloud is more preferred.

Due to digital transformation most of the companies have enforced cloud services to become more profitable.

For more information, go to:

https://www.analyticsinsight.net/how-innovation-in-cloud-computing-drives-exceptional-business-growth/

Nowadays for every organization it has became essential to be associated with cloud as a platform, infrastructure and service. Cloud computing is a very helpful tool as it can be used to create new revenue opportunities for the organization. This era of cloud computing has expanded the efficiency of the computing by reinforcing memory, processing, bandwidth and storage. If you haven’t dispersed your organization to the cloud yet, these can be the right footsteps to follow:

Step 1: create an assessment

Step 2: choose a right cloud environment for your business

Step 3: decide your cloud architecture

Step 4: choose the right cloud computing provider

Step 5: make a strategy for risk mitigation

Step 6: make a plan for mitigation  

Step 7: execute your computing plans

Step 8: examine the implementation

Need a better understanding of these steps?  visit:

https://www.analyticsinsight.net/the-right-approach-to-integrating-cloud-computing-into-your-organization/

Of course we have achieved 3D printing, but somehow we are still not able to produce a metal object which is capable of replacing the real world articles. Now implementing machine learning with 3D printing we have the capability to have real world objects replaced by objects produced by 3D printers. In the world of manufacturing researchers are planning to produce self correcting and repairing machines. There can be multiple approaches to have self-correcting machines. What are they? 

For more information, visit:

https://www.analyticsinsight.net/the-confluence-of-machine-learning-and-3d-printing-will-revolutionize-manufacturing/

Whenever it comes to complications, we all know human brain is the most unpredictable and complicated organ of the human body. Any brain injury leads to damage of millions of cells due to lack of oxygen in the body. Such damages require immediate attention of the doctors. But somehow, making out and analyzing those reports results to the latency which more often comes out as life threatening news for the patient.   

So, how AI can contribute its role here? For increasing the efficiency of the workflow some AI algorithms has been applied to the machine which are now capable of detecting the abnormalities requiring urgent attention of the doctor.

Want to know more about how actually AI and deep learning is applied to radiology? 

Go to:

https://www.analyticsinsight.net/how-artificial-intelligence-predicts-life-threatening-brain-disorders/

Autism being a spectrum or developmental disorder characterized by lack of social skills and repetitive behavior is being diagnosed using several methods in order to identify the onset of this disease. One such method is used by Autism Diagnostic Observation Schedule which inspects videotapes on the basis of an assessment between an examiner and a child for understanding the child’s behavior. According to a paper published in Science Translational Machine by researchers from the University of North Carolina at Chapel Hill and Washington University School of Medicine, doctors could precisely forecast which child might develop autism before hitting 24 months and with a 96 percent accuracy rate. A fully cross-validated machine learning was developed which used the scans of the 6-month-old infants. 59 high risk brain scans were taken over 230 regions and the whole brain was mapped creating matrices of functional connectivity from each child’s MRI data.  The algorithm further analyzed the brain scans of the 6-month-old infants and it properly predicted 9 out of 11 infants had the symptoms of autism at 24 months, with a sensitivity of 81.8%. Such data-driven approach is a good indicator of predictive measure that suggests that AI and machine learning could someday possibly recognize diseases with accurateness and extend treatments for the mass and maybe halt the headway of the disorders themselves.

Read more at: https://www.analyticsinsight.net/artificial-intelligence-machine-learning-can-be-used-to-predict-autism-in-children/

Google has been dominating the search engine industry over the years, though it has been frequently criticized of not providing search results in non-English languages. To cater to the problem, it has resorted to semantic indexing thereby becoming proficient at providing multilingual search results. The spectrum of search contents have been widening with time thus hinting at an expanding and trending macro environment. The search engines use algorithms which are solely based on Artificial Intelligence which would be rather simpler with limited pre-defined inputs. In its quest to understand the true meaning of different search queries, the algorithms are required to understand the contextual meaning behind various pairs of words which is attributable to deep learning. Despite capturing 70% of the search engine market globally, certain discrepancies arise due to regulatory policies. However, according to Shout Agency, the core problem is not the structure of algorithms as Google can make educated assumptions indexing any language but discrepancies in search results persist. The crux of the matter entirely stems from the fact that Google has had limited opportunities to conduct deep learning in some language than others. A potential risk is involved due to smaller user base and fewer Google employees that can understand the language enough to determine the worth of the content which lowers the chance of Google to conduct manual penalties for content. This could lead to greater pervasiveness of spun content throwing away algorithms dependent on deep learning.

Read more at:  https://www.smartdatacollective.com/google-search-algorithms-use-big-data-multilingual-latent-semantic-indexing/