The arrival of Artificial Intelligence and its practical implementation in industries has paved the way for unlimited opportunities.

The term artificial intelligence (AI) first came up in 1965. AI has become the latest technology, incorporated in different industries across the globe. This technology, in turn, is a great combination when accompanied with techniques to enhance business operations.

Artificial Intelligence essentially means the development, theory, and execution of computer-based electronic systems.

These systems boost the ability of machines in the execution of tasks; tasks otherwise requiring human intervention. AI brings a boost to the profitability of businesses within a range of 38 percentage points.

Artificial Intelligence enhances efficiency and leads to a reduction in the overall costs. The primary objective of Artificial Intelligence techniques is to facilitate the machines to perform intellect-heavy tasks performed by humans.

AI technology is getting increasingly accepted in several areas with the common objective of achieving a reduction in redundant tasks, and attain a better level of performance.

AI Update:-

With time, AI has made its presence felt in all essential areas of work. With the pace AI adoption is happening in different fields, AI can rightly take up more positions of interest several times.

AI is present in self-driven cars, human resource planning, store management, and in the areas of medicine and science. AI has provided great results for all these jobs.

AI-Enabled Retail Outlet

The incorporation of an AI-based retail store in Kochi, India, dramatically shows the impact of AI. In this store, Artificial Intelligence is utilized to cut down repetitive jobs, demanded otherwise from the employer.

There are a lot of tasks monotonous in nature, having the potential to hamper the efficiency and cost utilization ability of human resource. This is a massive reason as to why AI has been such a significant influence on a global scale.

Benefits of this AI update:-

• The AI-based retail store in Kochi is entirely devoid of human interruption while the customers indulge themselves in shopping.
• The involvement of AI in this system sees automation to such an extent that the full payment process concludes by e-wallet. The entire store is cashier-free, making the whole shopping experience automated for the customers.
• The fact that a salesperson is absent in the store led to an experience of lesser interruption for the customers.

The purchase amount gets automatically deducted from the customer’s e-wallet. Here, a significant benefit that surfaces is related to the ease of purchase and reduction in time consumption.

The use of AI in retail stores has led to a noticeable decrease in the time invested in shopping. Long queues for payment get substantially avoided as the amount has a direct deduction from the e-wallets.

Customer Manual:-

• The customer is required to download the “Watasale” app, through which a QR code gets produced and scanned while a customer will enter the store.
• The store works with sophisticated camera technology and facial recognition. The shelves of the store have embedded cameras for keeping track of the items picked up.
• The algorithms then take care of the payment procedure based on the information transmitted through the cameras.
• All the customer has to do is download an app, scan the QR code, go in the store, shop on their own, and that is all because the store is free from cashiers and has hi-tech cameras working in tandem with the whole e-wallet, the needful gets automatically done at the time of payment.

AI has brought specific projections for the upcoming trends of retail in India for estimation through store visitor analytics. The store visitor analytics use the concepts of AI and machine learning to make a note of footfalls in the store.

AI For Forecasting Wind Farm Output

With the increasing need for renewable sources of energy, the world is making efforts for obtaining reliable sources of energy generation.  Wind farms are a great source of such renewable energy. However, there is a significant issue that wind farms have been facing lately.

Google has included Artificial Intelligence into this matter. In this case, AI is used to make accurate predictions and detailed calculations. The accuracy of these forecasts is an essential determinant for operators to meet the requirements.

The Benefits of AI in Wind Farm Output Calculation

• Future demands of wind power generation require assessment and analysis much before for staying prepared with the essential strategies and workload. AI is serving as a prospective solution to this issue by ensuring quick calculations and predictions.
• The system devised by Google can make accurate 36-hour predictions within the final output. This is considerably impressive, without a doubt.
• The system is beneficial as energy sources can be scheduled to produce extraordinary power at a particular time. Also, machine learning, when included in this system, aids in the enhancement of the value of wind energy generated.

Amidst the continuous optimization of the model, Google is claiming to have increased the value of wind energy by around twenty per cent so far. The upcoming optimization and work done on the system are targeting even higher returns in coming time.

Artificial Intelligence and The Future

Artificial Intelligence is a concept on the brink to reform every industry and the working and structure of every business. Spheres like inventory management, medical examinations, and prior predictions also get streamlined beautifully with AI.

Also, the involvement of AI-powered robots in retail stores is a prospective boon, expected soon by the store owners in retail management.

Besides this, bot chats, blockchain, and machine learning are some of the related areas that are under works.

Furthermore, right from the management of inventory, workforce, movement of materials in the organization to technical analysis, experts are working on making AI useful in every possible way.

Artificial Intelligence and the various concepts surmounting it are indeed capable of reforming a large part of our daily lives and businesses.

If there is any idea you have in mind, or any assistance required, do get in touch with us.

The concept of data visualization plays a very essential role when it comes to assisting the users in question to comprehend the critical ideas quite easily. For instance, identifying the data patterns and trends quickly, and getting the maximum output from the presented data as well.

As a matter of fact, with the increase of data escalation in terms of generating data, finding a way to extract, processing and visualize those data to enhance data interpretation is growing rapidly. In addition to that, if interactive capabilities and data visualization are combined together then it will be easy for the users to dive into the finest details of graphs, charts, maps, dashboard, etc, and helps them to preserve the important data analyses and insights.

On the contrary, with the help of JavaScript programming, the developers can come up with interactive and attractive charts quite easily, directly from the chart libraries. No matter what, be it the requirement for an open source library, a paid-for, or any other types. You can surely find the required one to uplift your visualization skills. There are a number of charting libraries, that includes, Google chart, AnyChart, D3, and Highcharts are the one that worth mentioning. The creating process for all of these is in most of the cases quite similar. Hence, mastering one of then give you the privilege to use the other libraries. Certainly, when you want to add any specific characteristics for any of them.

For the purpose of JavaScript tutorial, let us take the examples from the AnyChart library. This is easy to use and quite flexible as well. It has exhaustive documentation, a wide range of supported chart types verity, and also a code playground, that allows you to test the codes, etc.

Therefore, let’s get into the process of how AnyChart helps in meeting the need for data visualization.

Here we would be addressing the three easy steps that can be used to create a basic bar chart in JavaScript. You can later integrate the same into your website or even in application. These are:
– Data Preparation
– Go to the JavaScript charts library
– Create or enter codes

Now, let’s talk about each one of them in detail.

Data Preparation

In case the data type is unstructured, then you need to prepare the same for the purpose of easy loading into the chart library. According to the format of the library you choose, you need to process the data in the format that the library accepts. However, AnyCharts supports a verity of formats for data including the other ones mentioned above.

For almost all of the charts you create, all you need to do, just put the values for both X and Y axes. Again, for the purpose of making Bar Charts, you will be required to put the values for Y-axis followed by an index number or any item number. This number will be considered as the value for the X-axis.

For instance, let us consider the data in array format, then X will be the item number and data values will be Y axis.

Again, when the data is on JSON format, it will look like this.

Connect the Chart Library

In order to create a connection to the preferred JavaScript chart library, you need to download the relevant package and install the same locally. You can also use the CDN service. In most of the cases, CDN service is the most preferred one. As it will give you the privilege to load data from server Library’s files to the users directly. That in turn as well increase the page load time and enhance the experiences.

Furthermore, AnyChart has a module-based system which allows you to connect to the specific chart type and the features that are essential for your project. It will also help you shrink the code sizes that are running on the application.

Such as, when creating a bar chart using the AnyChart JavaScript, you will be required to add below-mentioned core and basic Cartesian modules.

Writing the Codes

To create a JavaScript Bar Chart, you need to follow the steps mentioned below

To start with let us start with creating a container on the HTML page, it will refer the bar chart.
Then put all the previously processed data on the above-mentioned step.
Let’s create the type of charts that we are going to create from the applicable chart constructor function.
Create a title for the chart and axes
Now at this point create a bar series and put the data
Point the chart to the container the created earlier and start outlining the bar chart
Hence, you can easily understand that creating a bar chart using AnyChart is pretty much straight forward and simple.

Now let’s concentrate on the factors to enhance the looks and feel of the chart, by using some advanced level steps.

Create Advanced JavaScript Bar Charts

From the above discussion, the process of making the bar chart using AnyChart is clear. Thus, dive into other possibilities, when we have to perform a bit complex data visualization job to do. Of course, JavaScript chat creating is not at all complicated for smart web developers.

Lets, go to the advanced method that you can use from the same library.

Create Multi-Series Bar Chart

Besides the single series bar chart, AnyChart JS will allow you to create a multi-series bar chart, that enables to show the multiple sets of data on charts plot. Series referred to the single data set in AnyChat that will also display in chart’s plot. Now with the help of multiple series chart, it is easy to visualize the detailed information with the insights to the audience.

To build a Multi serious bar chart using AnyChart, it is essential to add data adopter module. It will help you load the HTML data in the work environment.

Parse data from the HTML table

Now create the data and specify the data source

Add a legend if you want your audience to understand and read the meaning of the values

Upon running the codes, it will create the multi-series bar chart displaying the composition of the values and understanding of the data as well.

Stacked Bar Chart

It is quite easy, without making any huge changes to the multi-series bar chart you can create a stacked bar chart. There are two ways, value stacking, and percent stacking. You can choose any one by setting the scale of the stack mode method to the “value” and “percent”.

Heres how to set:

This is how to set the mode back to the 100% stacked bar chart

Set Interactivity to Charts

All the charts prepared by using AnyChart are interactive by default. Some of the default chart behaviors are highlighting and points when hovered over, directing to hovered tooltips point, etc. In addition, you can tailor the chart’s interactivity to match the specified requirements.

Final Thoughts

Considering all the point it is clear that, creating the interactive JavaScript (HTML5) charts is quite easy with the appropriate JS library. So, here in this guide, we have just touched the surface. You can also visit the AnyChart’s documents and learn other factors.

For any queries, or specific instructions do let us know in the comment section below.

Artificial Intelligence to diagnose Alzheimer’s disease.

Alzheimer’s disease. The diagnosis and treatment. Artificial Intelligence. The first two phrases are directly associated with each other. Artificial Intelligence or AI, however, is closing the gap of difference pretty quickly to emerge as the technology that can detect Alzheimer’s well before its diagnosis.

Before we delve into how AI is helping out, it is essential to first know about this disease itself.  We will help you do just that.

UNDERSTANDING ALZHEIMER’S

Alzheimer’s is a dominant reason for the occurrence of dementia, which is a layman term for memory loss and various other cognitive issues severe enough to hinder a routine life. The disease is not a standard parcel that comes with aging but has a high risk of developing at an old age. A considerable part of people with Alzheimer’s belongs to a very young age group too.

The fact that Alzheimer’s is a progressive disease that does not make matters easier. The dementia symptoms gradually take a turn for the worse with time. Initially, the memory loss is pretty mild, but as more time passes, people even lose the ability to have a simple conversation and react to their surroundings.

Currently, the disease has no cure. The present treatments can only help in slowing down the progress of Alzheimer’s, but it is only temporary as the situation worsens over time.

The earliest symptom that an individual has Alzheimer’s is when there are problems faced to remember the recently learned information. It is because the disease initially affects that part of the brain which governs learning. As it grows, the symptoms become more severe like disorientation, mood swings, increasing confusion about time and events, and trouble in talking too.

Alzheimer’s disease is a focal point in today’s biomedical research. The researchers are making constant efforts to detect as many facets of Alzheimer’s and other types of dementia as possible. The most fantastic progress has provided information as to how it affects the brain cells.

ARTIFICIAL INTELLIGENCE AND ALZHEIMER’S

Some recent studies have conclusively demonstrated how AI can lead to an improvement in brain imaging to predict the earliest stages of Alzheimer’s disease. According to them, AI will be able to detect the disease in patients about six years before a confirmed diagnosis comes to the fore. It will lead to incorporating the changes in lifestyle, preparation, and methods of treatment well in advance.

Thanks to more and more innovations which help out in the early stages of Alzheimer’s, early diagnosis will mean that the patients will have more time in financially, personally and legally prepare themselves for their treatment.

With more research conducted every day, newer ways for diagnosing Alzheimer’s and dementia forms are getting tested. From brain imaging to blood tests, the hunt to find some of the most affordable ways to diagnose is on- much before even the symptoms start to show.

By the use of a usual form of brain scan, researchers were able to programme a machine learning algorithm for diagnosing early stages of Alzheimer’s as much as six years in advance before a clinical diagnosis, which will give the doctors a possible chance, to begin with, the treatment.

While no permanent cure for Alzheimer’s disease is available quite yet, some promising drugs have come into existence since the past few years which can help to stem the progress of this condition. These treatments, however, need to be administered early in the course of the disease to do some good. The race against the flow of time has motivated scientists to search for ways to help diagnose this condition much earlier.

Positron emission tomography (PET) scans, which can measure the level of particular molecules like glucose in a brain, have been analyzed as a useful tool for diagnosing Alzheimer’s disease even before the symptoms tend to get severe. And, this is a revolution in Healthcare Industry.

Glucose is like a driving fuel for the brain cells, and the more active a brain is, the more glucose gets used up. As these brain cells die out, they use less and finally, no glucose at all. Other kinds of PET scans look out for proteins that are mainly related to Alzheimer’s disease, but the glucose PET scans are cheaper and more common. This is mostly true for smaller health care facilities and also developing countries, as they also help for the process of cancer staging.

Radiologists have used the PET scans to try and detect Alzheimer’s disease by having a look at the glucose levels through the brain, particularly in the areas of frontal and parietal lobes of the brain. But because it is slow and progressive, the changes in glucose level are pretty subtle, making it difficult to spot with a naked eye.

To sort out this issue, the machine learning algorithm was applied to the PET scans to help with the diagnosis of early-stage Alzheimer’s disease with more accuracy.

For training the algorithm, images from Alzheimer’s Disease Neuroimaging Initiative (ADNI) served as the input. ADNI is a substantial public dataset of numerous PET scans from the patients who were diagnosed either with Alzheimer’s, a mild cognitive problem or no kind of disorder.

After a point of time, the algorithm started to learn on its own about the features which were considered necessary for prediction of the diagnosis of Alzheimer’s and which were not.

Once the algorithm became trained on a vast number of scans, the scientists tested in on two kinds of datasets for an evaluation of its performance. It passed the assessment very successfully, with an estimated 92% of patients who had developed Alzheimer’s identified correctly.

At such impressive statistics, this algorithm has a lot of potential to be clinically relevant. If it can perform well in such kind of tests, the algorithm can then be of use when a neurosurgeon looks at a patient in a clinic as a diagnostic and predictive tool for Alzheimer’s, proving very integral to get the patients a treatment which they require much sooner.

**We at GoodWorkLabs help Healthcare Companies develop essential Apps, Web, and Software Solutions using AI!

Want help in building a technology for your healthcare business needs? Work with GoodWorkLabs who understand and have expertise in this space. Contact us to get a free quote for your project.

Dating apps are increasingly taking the help of AI!

It is apparent that you will have used a dating app at least once, even if you never dared to admit it openly in your social circle. The premise of most dating apps is the same; take a look at the picture visible with a little information and then decide to take a swipe left or right. These swipes determine your rejection or interest to the profile of a particular person respectively.

During development stages, these dating apps were a little cluttered and confusing to move through. Today, however, you can just bid a farewell to hours of mindless swiping through numerous profiles. Thanks to Artificial Intelligence.

Dating apps are increasingly taking the help of AI to help users suggest places to go for a first date, indicating the initial remarks that can be said to the person at the other end. To make the matter all the more intriguing, these apps even assist you in finding a partner who resembles your favorite celebrity.

Until very recently, smartphone dating apps like Tinder left the task of asking someone out and making a date go well to people who were using the app. Gradually, this led to fatigue in the users who had to keep searching through a lot of profiles without too much success.

This is why the online dating sector turned over to take the help of Artificial Intelligence and get people to arrange dates in their real lives, acting more like a dating coach of sorts.

These newly found utilities of Artificial Intelligence, where the computers are programmed to develop human processes like thinking or decision-making have been highlighted time and again, signifying its importance.

Uses of Artificial Intelligence for Dating Apps

If anything, dating websites and applications have established themselves as the new benchmarks when it boils down to getting the first date for yourself. This is why as we mentioned above, many websites and app owners are trying to use something different on the lines of AI to ensure and provide the users with a fantastic overall experience.

Here we look at how AI is improving the dating lives of users along with the user experience of a dating app or website as a whole-

1. Help find better matches

Being the most obvious use, of course, AI for dating apps helps to improve the matching of people with their potential dates. There are two pretty remarkable methods through which this is happening. The dating app Hinge has recently been observed testing a feature which they call Most Compatible that takes the help of machine learning in finding better matches.

The feature monitors how people behave on the app. This behavior involves the kind of content a user has previously liked. The function aspires to serve as a matchmaker to find you, people, with whom you matched with on the platform prior.

The dating sites presently are as good as the data they have. Keeping that in mind, the dating sites are increasingly making use of technology and suitable data to filter out the matches for their users. There are many cues like emotion in communication, revert times and the size of profiles too.

2. Keeps things in moderation

Keeping things moderate on dating apps is very important for two essential reasons. It is evident that you wish that people have an overall positive user experience. If people have to continuously swipe with the fear of accidentally getting a fake account, they will ultimately switch over to some other app.

Moderation has also become essential to protect the app company itself. Many authorities are taking down any web platform which is not severe for sex trafficking and related crimes.

This has left with moderation not being an option anymore for brands, effectively going them with two options- manual moderation or automation enabled by computer vision (CV) moderation. Only one method out of the two helps a dating app scale and moderate more content at lower costs, and that method is computer vision.

3. Prevents security concerns

For any user of dating apps, security is one of the prime concerns. One negative experience is more than enough to turn people away from a specific app permanently. It is essential that dating apps take this very seriously and invest in measures to make their platforms secure to the maximum possible extent.

Getting every individual with enough help for a date is going to be impossible, and this is why companies will have to depend on AI to take care of this issue. An app called Hily gives the users a “risk score” that provides a user with passing ID verification, past complaints, the extent of conversation with other users and time spent on the app.

An individual with a high-risk score can be blocked on the app by the other users from sending their private information to the particular profile. The app can also detect when a photo has been tampered and then blocks such users too.

4. Provides great & useful user content

The final use of dating apps for the dating scene in 2019. Many factors make a dating app interactive and user-friendly where they can move to have a good time. Selfie images and information related to the profile of an individual are part of the content which is available on such apps.

AI can be used to provide better advice to users as to what they could do to improve their dating profile and visibility. For instance, online dating coach Greg Schwartz used face recognition model Clarifai to create an app which could recognize the standard errors that people tend to make in the photos they use on certain dating apps like using the images of fancy cars and bikes to get an impressive looking profile.

While not everyone has the same opinion that Artificial Intelligence is going to help them out in finding the love of their lives, the trend is currently on the rise, and it will be fascinating to see how things further unfold within this year.  

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