Location Intelligence – The Way to a Smarter Future

Location-Intelligence_1

A smart society is one that foresees the changing requirements of its people in every phase of their lives by taking into account new trends, technologies, resources, people and industries and provides the most effective solutions. In this fast developing world of technology, what seemed brand new just yesterday seems ordinary today and would eventually be outdated. Until recently, the world was raving about technology-based connectivity in lines of telephonic communication, real-time visual conversations and transferring of videos that are based on the Internet of things (IoT). Today the conversations are about Artificial Intelligence which will have a prevailing effect on all aspects of life that includes communication and staying connected. One of the biggest hurdles in staying connected is distance. Location, as said to be the heart of everything, is also the heart of doing things with a modern touch. Conquering locations in a faster, more accurate and efficient manner can become the focal point of a smarter society. Today the world has moved on to developing technology-based support systems along with the use of machines to not only stay connected but also to cover distances and geographies in a fast and accurate manner.

“If you think that the internet has changed your life, think again. The Internet of Things is about to change it all over again!”Brendan O’Brien

Cloud capabilities have powered not only the location data, based on which we so confidently commute from one place to another, it has also brought millions of connected devices together, organized traffic information and accurately synchronized global maps right on to our mobile phones. Some call this the fourth industrial revolution of modern history. This technology is Cloud-based, AI-powered and can relate geographic contexts to business data to develop insights for multiple business purposes. Such tools draw on a variety of data sources, such as geographic information systems (GIS), aerial maps, demographic information and the database of the organization. Location intelligence is important for businesses across industries for their marketing, revenue and growth strategies. From the womb of the Internet of Things and with a partnership of cloud, machine learning, and Artificial Intelligence has given birth to the Location of Things (LoT). Three things work in tandem here viz. cloud, machine learning, and artificial intelligence. Machines receive large amounts of data in a regular and increasing stream. They then recognize patterns, form deeper insights and are able to contextualize or in simple terms make sense of their surroundings. This data comes from millions of sensors and is, therefore, both real-time as well as meticulously detailed.

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The question that still comes to a layman’s mind is – how does it work? Thousands of smartphone users are contributing to this database from a particular location at a given point in time and this data is continuously collected, sorted and analyzed to convert to accurate and precise information.

What benefits does this offer to both the business and the consumer?

From a consumer perspective, information on products, services, localities, and geographies can be found easier and faster. Comparison of timing, weather, and distance becomes possible. This makes both commute and connectivity simple.

From a business point of view, location intelligence can provide one’s firm with the information of the latest trending places, businesses and localities which would help them recommend and advice their customers. This is in general for both brick and mortar as well as brick and click businesses. In particular, location intelligence can help different industries in different ways. For example – Service firms such as travel companies can connect with reviewing platforms such as Trip Advisor whereas credit card companies find it easy to connect with their merchants. With this technology, a business can stay connected with its customers both online and offline as well as via mobile. This was all about connectivity, but how about the combination? When a business combines the location intelligence information with data such as customer profiles, interesting insights can be generated in areas such as which offers made a customer move to an offline store after viewing the options online and vice-versa! These possibilities can offer great opportunities for a business to target the right customer at the right time. Location-based customer engagement makes it possible for firms to measure customer activity as he moves from one location to another, identify key anchor points of contact with the customer and design communication or advertisements in such a manner that the customer would be most likely to avail the offer made to him. This technology also makes it possible to gather and monitor data from different websites, blogs, social media platforms and analyze this data based on various metrics such as time spent on the page, click through rate, content sharing, comments, and inputs, etc. These insights provide inputs on the positive and negative sentiments that the brand is generating online and this information can then be used for designing effective marketing strategies.

“The data fabric is the next middleware”Todd Papaioannou

However, not everything is as smooth as silk. A few aspects need to be kept in mind when strategizing with the use of location intelligence. What is the probability that all customer data that we have available is accurate and updated? What is the possibility that a customer for whom a business has worked so hard in customizing the advertisement will certainly look at the advertisement and not choose “Skip Ad” option? With the amount of information overload that customers have today and options to install ad-blocking software, this is not a surprise. What should be done then? Critics say that while customization is the key, so is patience. There is no assurance that a perfectly planned advertisement is going to impress the already occupied mind of a busy customer.

“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – Angela Ahrendts

Geolocation data is useful if used efficiently along with other information and tools. It can’t be used in isolation and needs the right software and analyses as support. Both artificial intelligence and human intuition with logic become necessary for effective strategy design for business.

– Team iView

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Machine Learning into Action V1 – The Journey of an Android Developer

Machine-Learning

The author of this article is Mayur Kanojia, An Adro-Geek (Android Developer) of our team “iView Crafters”, presenting his fine R&D on “MACHINE LEARNING”.

The first impression of hearing the word “Machine Learning” I was almost imagining myself as a teacher and 50-60 machines will come to my class for learning.

Jokes apart but Machine Learning in terms of words  is defined  as follows:

Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed

  – Arthur Samuel (1959).

Machine Learning was never a thought in my head but at iView you need to be extraordinarily skillful to uplift your performance curve ahead than any average programmer. Challenges drive me! I always wanted to work with Algorithms.After discussing with our CTO, we decided to hands-on towards Machine learning. Hence I vividly started my journey wondering it to be as cool as I mentioned in the very first line.

The biggest question was how to kick-start? Perhaps I did exactly what all the Techies love to – “Google Baba”. Google Baba has a lot to say about Machine Learning. Someone said it so true THE WORLD IS ROUND and so is Google. Every theory of Machine Learning at the end had only one term and that was “Gradient Decent”. While hands-on to the Internet I learned about everybody referring to  Prof. Andrew N.G’s examples about ML. There are several online portals offering specialization in Machine Learning such as Coursera, Udemy, Udacity, Pyimagesearch, Pythonprogramming.net. So I decided to go for Coursera course for ML by Andrew N. G. Equivalently, Pyimagesearch and Pythonprogramming.Net  improved my knowledge in ML by coding perspective.

Prof. Andrew N. G had started the way of using math  (vectorization) in coding to optimise code and also running algorithm faster. Whow!! that amazed me. Here is the sample example.

Screenshot_2017-10-16-00-25-08-062_com.google.android.apps.docs

Now after learning basics of Linear Regression and Logistic Regression I came across to Neural Network and this thing had confused my head a lot between Forward Propagation and Backward Propagation.  It was like I have to do FW BW FW FW BW BW BW FW FW FW BW.

I was tired of doing FW and BW and then my life savior was  YouTube, it’s my entertainment buddy. One day accidentally I searched for how Neural Network works 😛 and then youtube took me into a deep ocean with the amazing example of Neural Network like Mario Game completed by ML  V O.o. Also, it took me into the Vista of my whole childhood hours that I spent on Mario Game and was just completed within minutes in front of my eyes. In Future we the “iView Crafters” are keen to take up this R&D into reality by training Neural Network on how to drive a car in GTA V.

These are the links :

https://youtu.be/qv6UVOQ0F44

https://youtu.be/QVyu9oVyh9Q

So I thought we can train Neural Network to play this game which is outdated nowadays. But this video had proved me wrong.  This guy had trained a  neural network on to how to drive a car in GTA V and this guy’s material helped a lot in understanding the neural network.

https://youtu.be/edWI4ZnWUGg

If you are a beginner like me then you will face following words in your journey of ML – the cost function, Gradient Decent, Linear Regression, Logical Regression, Regularisation, KNN, Neural Network,  training data, testing data,  cross-validation,  pipeline, loss, epoch and Blah Blah Blah

 

At iView Labs, We don’t believe in theories we believe in results.

Knowledge is of no value unless you put it into practice.

                                                                                                                     – Anton Chekhov

So to convert my knowledge into a practical solution, initially I started learning python from pythonprogramming.net and trust me will learn basics of python by just watching 15-16 videos, if you know programming concepts well. After learning python, I started developing my 1st ML program by just converting my Coursera exercise into python code. (You can check this blog to understand your Coursera basics into python)

This exercise is about Linear Regression where I have to predict Boston House prices by the data given to me. I created one method which gives me the total value of cost function.

J = (h(x)  –  yi) ^2

Also, One Gradient Decent algorithm which reduces my cost function.

θj= θj – α * (Ə J(θ)/ Əθj)

where α is learning rate

In Linear Regression,  You have to plot data first to see how actually your data is. From the curve of your data you can decide whether you are suffering from high bias(under fitting)  or high variance (overfitting)  according to this you can understand that your model needs more training data or tuning learning rate high or low etc. You can find my code for practice here.

This way I created basic Linear Regression Machine Learning program, also I had done same code in Octave. The doubt to strike your head would be then why I choose python to code? One of my teammate in iView guided me that python has some awesome libraries for machine learning where you don’t have to worry about these basics. You have to just remember algorithm’s name that you have to choose for your program. The best ML Libraries are Tensorflow, sklearn, keras. I coded one program in python to predict stock price by using the sklearn library. The example is here.

Meanwhile, our CTO was super enthusiastic about the conversion of my learning to the real-time execution. So we decided to implement the concept of Pipeline from my ML theory into one of our Live projects. We buckled up our shoes and started R&D on “Bill Scanner” – An OCR that recognizes characters from Bill images.

The empathy is that during ML Training, Handwritten Digits scanning with MNIST Dataset is said to be “Hello World” level program of Machine Learning. Hence super excitedly we started the task and as the days went off my cognition was wrong. Before predictions, we have to do lots of tasks, image processing etc.

Pipeline :

  • Object Detection
  • Text Extraction
  • Character Segmentation
  • Character Prediction

1. Object Detection

To detect object successfully, we will use tensorflow object_detection API. To use this API, we have to make model according to our number of class. For object detection, we have collected chunks of images to train our model. After days of training, we got 99% results.

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2.Text Extraction

To extract text from bill images we had used Computer vision API by Open CV, we processed the image with lots of filters and extracted the texts.

3. Character Segmentation

After extracting text data we got this output for each text area.

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4. Character Prediction

After all the steps above, you will get separate characters, now you have to apply the Neural network to predict character from an image.

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This is #83rd Day of our 120 Day Machine learning Project and the quantum of implementation of Pipeline in our Project “Bill Scanner”  shall be briefed in Volume – II .

 

 

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