Town Hall hosted by Sequoia on Conventional Approached to Unconventional Challenges

The event was moderated by Priya Samant and the speakers were from different industries . The speaker list is included below

Dr Frank-Jürgen Richter, Founder and Chairman, Horasis: The Global Visions Community .

Gopal Goswami, Research Scholar and Social Entrepreneur .

Arushi Nishank, Environmentalist, Entrepreneur and Acclaimed Kathak Dancer.

Madhur Bhandarkar – Acclaimed National Award Winning Filmmaker .

Brahmanand Singh, Director and 2 times National Award Winner .

Mehmood Ali, Founder Don Cinema & Pen N Camera International .

Harshal Pradhan, Political Strategist .

Vikkas Chopra Business Head for Films, Pen Studios .

Dr Christoph Nabzdyk, MD. Asst. Professor of Anesthesiology, Mayo Clinic School of Medicine .

Aju Kuriakose, CEO, Sequoia Applied Technologies .

Leena Pradhan-Nabzdyk, PhD, MBA Assistant Professor of Surgery, Harvard Medical School.

The event was made possible only because of Virendra Rawat – CEO Green Mentors and Geo Murickan – CEO Transfinnovation.

SequoiaAT in 10 Best IoT Solutions Providers of 2019

Sequoia AT is pleased to announce that they are on the list of the CIO bulletin’s 10 Best IoT solution providers for 2019. Speaking on the occasion, COO of the company, KR Gopinath says “I am glad that they recognized what we do here in Sequoia. Our team’s outside the box thinking and persistence for making our customers products better is why we were recognized by the CIO Bulletin.”

SequoiaAT currently has two development centers in Santa Clara (USA) and Trivandrum (India). SequoiaAT is planning on expansion of their development offices in Santa Clara (USA)  & setting up new office in Kochi (India).

Working with passion is the internal theme at Sequoia. And this recognition is a proof of what every Sequoiaan believes in. Ram Mohan (Director) says that “At SequoiaAT the quality starts by ensuring that we hire for our culture. We hire only individuals who are extremely passionate about their work. This enables us to go beyond our customer expectations.”

SequoiaAT was named perviously named in the Top 100 Tech companies founded by Indians in Silicon India Magazine

Complete link to this article in CIO Bulletin can be found at this link 

How AI is changing healthcare

AI in Healthcare
AI in Healthcare

AI is the next big wave which will change we know the world for generations to come.  AI has attracted over $17 billion in investments since 2009  and will add over 15 trillion to world economy by 2030  as per estimates.

The term AI was coined in 1956 and even thought of by ancient philosophers, but Some of the early work in this space was done at Stanford University for treating blood infections. Till about early 2000’s most of the work in AI was limited to universities like MIT, Stanford, Rutgers etc.

 

One of the domains which stands to benefit the most from AI is healthcare. The healthcare industry is advancing in discoveries daily as technology advances in major ways. We have done amazing things in the last few years and currently Artificial Intelligence has been dominating as the main point of interest. AI is being harnessed to increase longevity and health of the human race.

As an example we all know one problem with hospitals is wait times. As a hospital, doctors need to make every second count. With help of AI hospitals can assign beds to patients faster and more effectively. While this may seem like a useless task it prevents having employees do this job, and little by little, it saves a lot of time. In the John Hopkins Hospital, this has been able to see and predict future requests for beds, and even plan for future unavailabilities. As per the recent article in HBR, It decreased wait times, and even allowed them to accept over 50% more new patients from other hospitals. AI can also do the paperwork that takes doctors a significant amount of time, giving them more time to engage with their patients. Every second that AI saves is another second for doctors to save a life.

Besides preparation, AI directly uses Brain Computer Interfaces. This can be used to decode neural activity. Potentially it could be used to help the many people with ALS and strokes, as well as the half a million people yearly that have spinal cord injuries. Neurological problems have been extremely difficult, if not impossible to solve. AI is helping in ways unimaginable 10 years ago. When AI is allowed to look at all the data from patients, it can notice patterns and analyze them in ways that would be humanly unachievable. AI will make sense of data allowing them to predict things that will happen to specific patients with incredible accuracy. AI could take all of the unstructured data and classify them, and this is especially useful as we are expected to double medical data every 73 days from 2020, according to IBM.

Even selfies can be used to find diseases. An algorithm can find the subjects facial features, and predict facial feature abnormalities. Just in a few pictures the AI can analyze things that we would need expensive equipment and preparation to find out. AI with expensive tools such as x-rays and MRI scans, can find out all problems instantaneously. AI is highly useful in predicting patterns. This can be used to predict problems and also patient recovery time. With the right data sets, AI will be able to foresee diseases like seizures and sepsis.

At SequoiaAT we have started taking small steps towards AI in medicine by collaborating with companies in life-sciences and medical domains. We have been working with them on solutions which further this goal.

AI will do everything that humans can do in a fraction of the time, in all helping and curing more people. AI will save unbelievable amounts of money, and even more time, making every second count.

Visualization frameworks for Bio-Informatics

By Anu P

With the advent of fast genome sequencing techniques, biological datasets worldwide have exploded to tremendous sizes today. For instance, a single patient’s sample after sequencing and several stages of data processing and analysis could run into over a Terra byte! Raw sequencing data that comes out of the sequencing machine is at an abstract level of potentially useful information, requiring significant processing to be converted into meaningful form to drive genomics research.

Some of the data conversion steps being highly computation intensive and/or requiring specialized bioinformatics algorithms, a large portion of the bio-informatics data processing pipeline is implemented in the cloud today. However, as the data resident in the “genomics cloud” reaches the hands of the researcher, it is only as good for research as the analytics and visualization capabilities.

Visualization is a graphical representation of data intended to provide the user a qualitative understanding of information. Data visualization techniques greatly enhance the user’s understanding and interpretation of these massive data sets. A visualization-integrated bio-informatics pipeline provides researchers with the ability to explore genomics data and enables them to progressively iterate, backtrack or zero-in on their analysis steps, thereby enabling them to infer high-impact conclusions with an improved degree of confidence within a reasonable time.

The two essential attributes of a successful data visualization framework are:

1)   High interactivity

2)   Performance at the speed of analysis

Interactivity implies the ability to manipulate graphical entities to derive intuitive data representations. Interactive graphics involves the detection, measurement and comparison between points, lines, shapes and images being represented for the effectiveness of user interpretation, accuracy of quantitative evaluation, aesthetics and adaptability. Enhancing data interpretation by varying the views, labelling to retrieve the original data, zooming in to focus the clarity of data, exploring the neighboring points and a user adjustable mapping can create a good data exploration experience to the user.

Consequently, as the user continuously manipulates data (applies filters, adjusts thresholds, tunes parameters like scale and dynamic range of values) to make “research sense” out of the data, the visualization framework should permit

1) Discrete or continuously variable settings with user-friendly controls like text boxes, selection drop-downs, sliders, knobs etc. and

2) Quick redrawing of the updated graphical representation after every change is made in user settings.

General-purpose and traditional analytics software packages that have been adopted in bio-informatics often come with add-on packages for interactive visualization to a basic level of utility for research. With an easy non-programmer model that appeals very much to researchers, these packages provide interactive graphs and plots. Having an in-built web server eliminates the need to install any client applications, all that the user needs is a browser and an URL to point it to.

However, when it comes to enormous datasets that range millions of data points, these in-built/add-on visualization frameworks are found to be incapable of giving the user an acceptable (sub 1-second?) performance each time a user setting is changed. Therefore, guaranteeing an analysis-continuum to the users remains challenging. Besides the rendering stability of these in-built/add-on packages is often found problematic when large data sets are thrown at them, with statistical methods applied on the data. Rendering inaccuracies including gross misrepresentations of data are frequently encountered that expose the limitations of their scalability.

Here comes the need for evaluating, piloting and implementing visualization frameworks based on customized graphical libraries that leverage fast rendering techniques in a browser environment. As was proven by our experiments with multiple fast-visualization techniques, a customized visualization framework for bio-informatics is the sole solution to match the user’s speed of analysis to provide an enhanced time-to-insights experience.

In conclusion, bio-informatics visualization framework needs to be highly interactive and lightning fast to handle data sets in the millions. Further, from the bioinformatics pipeline provider’s perspective, scalability for a large number of concurrent users and security of data are the other key attributes to be satisfied by the visualization framework, as is applicable to the other modules like data transformation and analytics modules in the pipeline.

Cross-platform vs. Native Mobile App Development: Which one to choose

 
Aruna R S
Aruna R S

Today, 99.6% of all smartphones run on either IOS or Android. Increasingly mobile apps have gained significance as way to not only conduct business but also for raising brand awareness. There are hundreds of new applications being launched on a daily basis. In the last few years, the concept of cross-platform mobile app development has taken off in a big way. It allows the developer to write the code once and employ it across all platforms – Android, IOS or Windows. Some of the advantages of developing Cross Platform apps.

Cross-platform vs Native apps:

Native apps

Native apps are written in languages that the platform accepts natively. For example, Swift or Objective-C is used to write native IOS apps, Java is used to write native Android apps, and C# for the most part for Windows Phone apps.

Apple and Google offer app developers their own development tools, interface elements and standardized SDK; XCode and Android Studio. This allows any professional developer to develop a native app relatively easily.

Advantages

  • Since native apps work with the device’s built-in features, they are easier to work with and also perform faster on the device.
  • Native apps get full support from the concerned app stores and marketplaces. Users can easily find and download apps of their choice from these stores.
  • Because these apps have to get the approval of the app store they are intended for, the user can be assured of complete safety and security of the app.
  • Native apps work out better for developers, who are provided the SDK and all other tools to create the app with much more ease.

Cross-platform apps

While cross-platform development is somewhat an umbrella term for any mobile app project that targets multiple platforms, hybrid is a subtype that implies the use of a specific development model. Legitimate representatives of hybrid development tools are Cordova and Phone Gap. Both allow to create apps that are web/native ‘hybrids’, with the code being written in HTML, CSS or JavaScript, and later wrapped in an invisible native WebView browser.

Cross-platform development tools that do not use WebView and communicate with the platform directly aren’t united in any subgroup. Existing under the general term of cross-platform development, they are sometimes called native development tools, which just makes it all even more confusing. For the sake of convenience, we’ll refer to these tools as ‘near-native’ here and will explain why they deserve such a praise.

In ideal scenario, cross-platform apps work on multiple operating systems with a single code base. There are 2 types of cross-platform apps:

  1. Native Cross-Platform Apps
  2. Hybrid ‘HTML 5’ Cross-Platform Apps

Native Cross-platform Apps

Native cross-platform apps are created when you use APIs that are provided by the Apple or Android SDK but implement them in other programming languages that aren’t supported by the operating system vendor. Generally, a third-party vendor provides an integrated development environment that handles the process of creating the native application bundle for iOS and Android from a single cross-platform codebase. In this case, the final product is an app that still uses native APIs, and cross-platform native apps can achieve almost native performance without any lag visible to the user. Native Script, Xamarin, and React Native are the most common examples native cross-platform languages.

Hybrid HTML 5 cross-platform apps

Although mobile applications are designed for smartphones and tablets, it is back end servers (either on-prem or Cloud-based) that handle application logic. Since both IOS and Android SDKs feature advanced web components, skilled software engineers often utilize Web View to create parts of an application’s GUI (Graphical User Interface) with HTML 5, CSS and JavaScript. The most popular hybrid app development framework is Apache Cordova (formerly known as PhoneGap).

Mobile app development tools

Xamarin:

Xamarin apps are built with standard, native user interface controls. Built with #C and .NET, Xamarin allows developers to re-use code and simplifies the process of creating dynamic layouts in iOS.Apps not only look the way the end user expects, they behave that way too. Xamarin apps have access to the full spectrum of functionality exposed by the underlying platform and device, including platform-specific capabilities like iBeacons and Android Fragments. Xamarin apps leverage platform-specific hardware acceleration and are compiled for native performance. This can’t be achieved with solutions that interpret code at runtime.

Apache Cordova

Apache Cordova is an open-source mobile development framework. It allows you to use standard web technologies – HTML5, CSS3, and JavaScript for cross-platform development. Applications execute within wrappers targeted to each platform, and rely on standards-compliant API bindings to access each device’s capabilities such as sensors, data, network status, etc. Cordova has no limitations in relation to natively developed applications. What you get with Cordova is simply a JavaScript API, which serves as a wrapper for native code and is consistent across devices. You can consider Cordova to be an application container with a web view, which covers the entire screen of the device. The web view used by Cordova is the same web view used by the native operating system. On IOS, this is the Objective-C UIWebView class; on Android, this is android.webkit.WebView.

Apache Cordova comes with a set of pre-developed plugins which provide access to the device’s camera, GPS, file system etc. As mobile devices evolve, adding support for additional hardware is simply a matter of developing new plugins.

React Native

The React Native framework was created by Facebook, and its development started as a result of a hackathon back in 2013. React is an example of a technology that the developer community created for itself when developers were looking for a tool that would combine the good things about mobile development with the power and agility of the native React environment. React Native’s genesis resulted in a huge enthusiastic community investing into the framework’s development, and there are catalogs of freely available components that go with it.

React Native uses various UI blocks to compose rich mobile apps for both IOS and Android using a common JavaScript codebase. React Native also allows developers to see their code and its implementation on real mobile screens next to each other in real time.

React Native provides development tools for debugging and application packaging, which saves time.

Which One to Choose

So, if you want to impress users with a lightning fast interface, rich functionality, and overall performance, native apps are what you need. In addition, you get better security and stability. The price for this is that you’ll most likely need to hire two dedicated teams for each platform. Small business may not be able to afford develop an application for both platforms.

Cross platform apps, on the other hand, can be developed for both IOS and Android. Plus, cross platform apps are much easier in terms of maintenance and deployment, so you can spend more time and money on marketing and attracting new customers. However, their biggest disadvantage is lower performance, which may be especially crucial if you’re developing an application with features that require deep hardware integration.