Data Analytics in Revolutionizing Senior Living Facilities Role of Algorithms &Data Analytics in Rail Travel
Predicting the main character of Game of Thrones
OCTOBER 2017
VOLUME 08 ISSUE 08
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Editor’s Note Article Submission and Readers write to: newsletter.quantinuum@gmail.com
Editorial Team: Rupal Doshi Amod Kulkarni Aditya Gupta Chaitanya Agarwal Aditya Sharma VVNS Anudeep Kapil Gupta Samoshri Mitra Parvinder Singh Khushbu Mehta Designing Team: Shreyas Kulkarni Ashish Mahadik
Welcome to the latest issue of Quriosity, the monthly newsletter of Quantinuum! Many current data analysis techniques are beyond the reach of most people. Obscure maths and daunting algorithms have created a chasm for problem solvers and decision makers. Quriosity is trying to bridge these gaps by giving appropriate inputs to our students and readers who are the future managers. The objective of Quriosity is to publish up-to-date articles on data analytics, alongside relevant and insightful news. This way the magazine aspires to be vibrant, engaging and accessible, and at the same time integrative. The cover story by Ashish Soni is based on how data analytics can revolutionize senior living facilities. The other story by Yatharth Jaiswal is based on how algorithms and data analytics increase the speed of rail travel and save money. We have an article on how data analytics can be used to predict the main character of Game of Thrones, by Sonika Aneja. Any articles that you wish to submit, either individually or collaboratively, are much appreciated and will make a substantial contribution to the development and success of the magazine.
Thank you and Happy Reading! Editorial Team Quantinuum@SIMSR
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Contents TOPIC
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Cover Story on Data Analytics – Revolutionizing Senior Living Facilities by
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Ashish Soni
Role of Algorithms and Data Analytics in Rail Travel by Yatharth Jaiswal
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Quant Guru – Subramanyan Chandrasekhar
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by Dropad Saxena
Intern Diaries – Air India by Maheshwaran Kumar
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Curiosity Updates by Kapil Gupta
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News Digest by Akshay Nagpal
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Quantertainment by Sonika Aneja
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Quant Fun
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Quant Connect
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Cover Story Data Analytics – Revolutionizing Senior Living Facilities We exist in a world where data impacts nearly every aspect of our daily lives. Senior care is no exception. This is an age where toddlers and seniors alike have access to and use tablets daily. As the growth of wearable adoption goes down the same path, there now exists a huge amount of data that can be analyzed to develop enhanced senior care that’s more personalized than ever before. The ability to analyze larger data sets can lead to more innovation in a positive feedback loop. The more diverse sets of data a facility collects, the more it can comb through that information to flag correspondence with adverse events. Senior care communities can then act to mitigate the effects of those events or, in some cases, avoid them entirely. Technology and analytic tools also help communities to improve staffing and workflow. Trends that suggest specific resource use at certain times, in certain areas or around certain residents, for example, can lead to better care coordination and more efficiency.
Prevention of impending health issues At the end of the day, care providers – no matter how talented – are only human. It’s impossible for providers to be everywhere at once, which means they are physically incapable of monitoring and engaging with each resident at every hour of the day. With a more comprehensive look at and deeper understanding of data and analytics, a handful of the most common health issues like urinary tract infections (UTIs), and slips or falls, become avoidable.
These risks, which are expensive to treat, and unfortunately can prove to be fatal for the senior population, are often predictable with the right data – it’s as simple as observing data for abnormalities in activity levels and patient mobility. Q U R I O S I T Y - V O L - 8, I S S U E - 8
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Individualization of the senior care experience It is not uncommon for seniors, especially when a family member cannot be present, to be held responsible for communicating their health improvements or setbacks directly to a physician or nurse. And bridging this gap can be difficult if visits are far apart — leaving room for critical details to be overlooked. But, with the use of analytics, this information is not only easy to uncover but more accessible and dependable for caregivers rather than relying on the patient to fully track and communicate overall health improvements or setbacks.
Time-savings for caregivers On average, individuals experience a lifetime loss of $304,000 because of providing care to a senior family member. The monetary impact plus the significant amount of time caregivers dedicate to ‘checking in on Mom/Dad’ lends itself to increased stress levels among these family member caregivers. It’s this stress that often leads to seniors being placed in senior living communities earlier than necessary. With the use of analytics and data, family members and professional caregivers alike are now able to analyze a senior’s health compared to their common level of activity –enabling informed decision making and better time prioritization. For concerned relatives, friends and the caregiver’s family, this access to objective insights into a senior’s health and activity is invaluable. The senior living industry must prioritize balancing the safety and independence of residents. Technology and analytics hold the potential to transform how senior care providers achieve that goal.
Source: http://hitconsultant.net https://healthtechmagazine.net
By Ashish Soni PGDM (2017-19)
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Sub Article Algorithms and Data Analytics in Rail Travel
The Indian Railways carries more than 23 million passengers over a route of 66,000 km, passing through more than 7,100 railways stations and employing over 1.3 million people the numbers show why human intervention alone is not enough. The need is to apply analytics to the Big Data churned out encompassing all aspects — millions travelling long distance at any given time, ticket reservations, passengers commuting over shorter distances in the millions (the Mumbai rail network, for example), sales points of various items in stations; locomotives, passenger and freight cars, maintenance and service, weighing, loading, dispatching and unloading freight, vendor management, hundreds of thousands of staff at work across the country! If this is not a scenario that cries out for using analytics, it is hard to imagine any other, anywhere in the world that does. Trains and railways have an old-timey nostalgia attached to them; we typically associate train travel with the simpler times of our grandparents. However, the reality of the railway transportation system is far from slow and simple. Just like many other industries, railroad companies have integrated big data into many different aspects of their operations. Certain elements of the railway system are predictable. The staff, cars, schedule, etc. are predetermined before a single car is moving. The real data generation magic begins once the trains start moving. So, where is all this big data coming from? Here are just a few of the many data sources utilized today: • Maintenance logs • GPS units combined with weather data can be used to ensure train safety • Handheld field tablets • GPS units that record speed, distance between trains, arrival time and location • Visual and acoustic sensors in brakes, rails, switches and other hardware These data sources provide rich analytics that can quickly influence both automated and human decision-making. There are global examples of the likes of Siemens and Burlington Northern Santa Fe Corporation, among others, that use for everything from being on time to predicting failures. In the case of Siemens, it uses and re-uses existing data, creating what it calls an ‘Internet of Trains’. Towards the end, Siemens analyses sensor data in near real time, which means they can react very quickly, ensuring uninterrupted customer transport service. Q U R I O S I T Y - V O L - 8, I S S U E - 8
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In the case of Spanish train operator RENFE, which uses Siemens high-speed train, a train developing abnormal patterns is sent for inspection to prevent failure on the track, helping keep RENFE services unbelievably reliable. Only one of 2,300 journeys has been noticeably delayed (by 5 minutes). Passengers are reimbursed fully, if a delay is over 15 minutes. And it allows the train to compete with flights on routes between Madrid, Barcelona and others. This is an existing scenario made possible by analytics and there is no reason why we cannot have the same in India! A pilot project done in the UK by Siemens analysed a data set of one million sensor-log readings, taken in five-minute intervals over one year. Analysts measured variables such as component temperature and pressure from 300 different sensors and this data was overlaid with many thousands of corresponding reports of failures and fixes. Then the team combined data sources, defined the most relevant engine problems, and divided the data into sections. They used analytic tools to evaluate the combined data from different perspectives, which helped predict engine problems and identify failed elements that triggered the malfunction of other components. For example, many a time Siemens found that when the engine temperature dropped from mid to low then rose to mid value again, an engine failed three days later! Such are the uses of technology that are available for the Railways too! There are various aspects that the Railways have expressed interest in growing, such as e-catering (by the IRCTC). Food safety, availability and choice are at the centre of this and sheer passenger numbers and the ubiquity of social media will mean near-instant customer feedback. Apart from internet of trains IR would require technologies and strategies to counter theft on trains. Overloading of wagons and parcel vans are rampant, which have been highlighted by CAG as well. Railways failure to provide adequate weigh bridges and dependence on privately maintained weigh bridges cause revenue loss as overloaded wagons escape penalties stipulated. Data analytics shall have systems for real time recordings of the arrival of wagons for loading and unloading in private sidings as well as times of departure for correct accountable and collection of demurrage charges for detention of wagons. Biometric machines for offices need to be installed in locos and diesel sheds to guard against pilferage of fuel. Railways shall place their fuel consumption statistics in relation to verifiable performance factors in public domain to have a public watch on their relative fuel efficiencies and costs Trainline uses predictive analytics to help rail passengers save money. According to Trainline, rail passengers can save an average of 49% on prices if they pay when they first search for the journey. For example, an advanced single ticket for a trip from London Euston to Manchester Piccadilly costs £32 around 80 days before departure but rises to £87 two days before. Even IRCTC has started a similar pricing strategy under “premium tatkal” where the prices go up to 5 times the actual cost depending on the demand. The decision announced in the budget, that the Railways is working on an IT vision is gratifying; while the total investments planned by it over the next five years is a cumulative Rs. 8.5 lakh crore, a minute percentage of it will have to be in analytics tools, a fraction of a fraction of this figure — but, with the ability to tangibly beneficially impact the entire railway organisation and its patrons for the better. Rails will not just improve the speed of rail travel and save money but will also save lives that are lost in the mishap where rail failures go undetected. Sources: https://analyticsindiamag.com/railyatri-using-analytics-make-intelligent-travel-predictions/
http://www.computerweekly.com/news/450425988/Trainline-uses-predictive-analytics-to-help-rail-passengers-save-money By Yatharth Jaiswal PGDM (2017-19)
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QuantGuru Subrahmanyan Chandrasekhar (1910-1995) Subrahmanyan Chandrasekhar was one of the greatest scientists of the 20th century. He did commendable work in astrophysics, physics and applied mathematics. Chandrasekhar was awarded the Nobel Prize in Physics in 1983. Chandrasekhar was born on October 19, 1910 in Lahore. C.V. Raman, the first Indian to get Nobel Prize in science was the younger brother of Chandrasekhar's father. Till the age of 12, Chandrasekhar had his education at home under his parents and private tutors. In 1922, at the age of 12, he attended the Hindu High School. He joined the Madras Presidency College in 1925. He passed his Bachelor's degree, B.Sc. (Hon.), in physics in June 1930. In July 1930, he was awarded a Government of India scholarship for graduate studies in Cambridge, England. Chandrasekhar completed his Ph.D. degree at Cambridge in the summer of 1933. In October 1933, he was elected to a Prize Fellowship at Trinity College for the period 1933-37. In 1936, while on a short visit to Harvard University, Chandrasekhar, was offered a position as a Research Associate at the University of Chicago and remained there ever since. In September 1936, Chandrasekhar married Lomita Doraiswamy, who was his junior at the Presidency College. Chandrasekhar is best known for his discovery of Chandrasekhar Limit. He showed that there is a maximum mass which can be supported against gravity by pressure made up of electrons and atomic nuclei. The value of this limit is about 1.44 times a solar mass. The Chandrasekhar Limit plays a crucial role in understanding the stellar evolution. If the mass of a star exceeded this limit, the star would not become a white dwarf. It would continue to collapse under the extreme pressure of gravitational forces. The formulation of the Chandrasekhar Limit led to the discovery of neutron stars and black holes. Depending on the mass there are three possible final stages of a star white dwarf, neutron star and black hole. Apart from discovery of Chandrasekhar Limit, major work done by Chandrasekhar includes: theory of Brownian motion (1938-1943); theory of the illumination and the polarization of the sunlit sky (1943-1950); theory of the illumination and the polarization of the sunlit sky (1943-1950); the equilibrium and the stability of ellipsoidal figures of equilibrium, partly in collaboration with Norman R. Lebovitz (1961-1968); the general theory of relativity and relativistic astrophysics (1962-1971); and the mathematical theory of black holes (1974- 1983). Subrahmanyan Chandrasekhar was awarded (jointly with the nuclear astrophysicist W.A. Fowler) the Nobel Prize in Physics in 1983. He died on August 21, 1995. Source: https://www.youtube.com/watch?v=n-lJjR7pM7k https://www.nobelprize.org/nobel_prizes/physics/laureates/1983/chandrasekhar-bio.html
By Dropad Saxena PGDM (2017-19)
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Internship Diaries Air India I did my summer internship at Air India’s Headquarters in Delhi and Chennai one of its regional stations. Air India is the national Air carrier and it has both international and domestic operations. The objective of the project was to develop a prediction model which can predict flight delays and to carry out analysis of delays, ground time, block time to get any insights which can be helpful in planning of schedule for the flights. The methodology includes analysis and comparison by basic statistical and graphical techniques of critical parameters like the ground time, block time among the various players in the industry to understand the difference in the corporate strategy and the operational strategy of Air India and other players using primary and secondary research. Multitude of tools and techniques like various regression models (Logistic Regression, Non-Linear Regression, etc.) were also studied thoroughly and applied for building the model and comparison of the parameters among the various players in the industry to come to conclusions and make interpretations based on the results statistically. Key Learnings from the internship were as follows: -
• • • •
Understanding about the operations of Airline Services. Getting an in-depth understanding about market competition and dynamics. Exposure to various Regression models and statistical techniques. Importance of corporate strategy and operational efficiency. By Maheshwaran Kumar PGDM (2016-18)
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Curiosity Updates NASA Photos from Mars Curiosity Rover Reveal Red Planet is also Green and Purple NASA’s rover has been roaming around Mars since 2012, but the photos it sent back to Earth this week just changed the view. Long known as the Red Planet, it turns out Mars has some other colors to show off. Through special lenses on its camera-eyes, the rover sent back photos of purple contrasting with green tracking one mineral in particular: hematite. The cameras on Curiosity take photographs at different wavelengths of lights. Since rocks reflect and absorb light differently, the camera (specifically the Mast Camera) can determine what type of minerals make-up Mars’ surface. Scientists noticed the variations in rocks from spectrometer observations from orbit years ago and have been planning to tackle the Vera Rubin Ridge on the lower part of Mount Sharp since. “This is the juiciest part of the traverse,” said Jeffrey Johnson, a planetary geologist of John Hopkins University’s applied physics laboratory. Since Curiosity's landing in 2012, it has found simple organic compounds (though it is unclear if they were formed from human contamination or are native to Mars), evidence of water, and photographs from points of interest such as Mount Sharp, the Murray Buttes, Hidden Valley, Yellowknife Bay Formation, and Rocknest. The latest images from the rover are showing a different perspective of what Mars looks like—and what it may have looked like in the past. Collecting data on these rocks can answer questions about factors of the water that may have once passed through, such as its acidity, according to Johnson. Those answers could tell scientists what types of lifeforms would have been able to survive and potentially thrive. Source: http://www.newsweek.com/nasa-photos-mars-curiosity-rover-reveals-red-planet-also-green-and-purple-699184
By Kapil Gupta PGDM (2017-19)
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News Digest AWS and Microsoft Team Up on Deep Learning with Gluon, A Simplified ML Model Builder The power of machine learning that two arch rivals, Amazon’s AWS and Microsoft have together announced Gluon, a new open source deep learning interface, which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon is one of the big steps ahead in taking out some of the grunt work in developing AI systems by bringing together training algorithms and neural network models, two of the key components in a deep learning system. This is not the first time the two have collaborated on AI initiatives. The two have worked together in the Cloud Native Computing Foundation.
Ola Raises $1.1B In Funding; To Make Investments in AI and ML Ola, Uber’s biggest rival in India, has raised $1.1 billion from China’s Tencent Holdings Ltd and existing investor SoftBank Group Corp. of Japan. The Bengaluru-based ride-hailing company, which has raised around $4 billion since its founding in 2010, is also in “advanced talks” with other investors for an additional $1 billion. With the latest round of funding, Ola plans to make strategic investments in its supply chain and technology, while also making significant investments in technologies such as artificial intelligence and machine learning. Ola will also invest in increasing its fleet of vehicles and presence in Indian cities.
AI-Powered Fraud Prevention Start-up Third Watch Raises Angel Funding Artificial Intelligence-powered fraud prevention start-up Third Watch has raised an undisclosed amount of angel funding from Indian Angel Network (IAN). The one-and-a-half-year old Gurgaon-based start-up’s key product is Mitra, an AI-based programme which evaluates and flags every transaction as ‘genuine’ or ‘fraudulent’ in real time. It works with the help of a Trust Score which is generated with the help of machine learning algorithms, browsing behaviour analysis, device fingerprinting, location profiles and other evaluation parameters which determines the transactions’ authenticity.
Capillary Launches ‘Visitor Metrix’, An Artificial Intelligence-based Visitor Counter for Retail Stores With its new AI based, computer vision and machine learning powered product called “Visitor Metrix”, Capillary Technologies has become the recent company to vouch by the power of artificial intelligence. The new product by Capillary, is a perfect product for retail stores to maintain a visitor count and analyse customer behaviour in the shop floor. The company believes that this would help in drastically improving the store efficiencies, conversions and campaign effectiveness.
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Multipoint Capital Invests $2 Million On AI Startup, ParellelDots ParallelDots, a Gurugram-based AI and deep learning start-up has secured a funding of $2 million from US-based investment firm Multipoint Capital. The firm had earlier invested $600K on the start-up, in 2016. With a total funding of $2 Mn, a 2014 founded company plans to utilize the funds to expand its technology team and support its global expansion plan along with taking care of general operational expenses.
Alien: Covenant Follow-Up to Focus on Artificial Intelligence The ‘monsters-in-space’ horror movie, directed by Ridley Scott is about the creation and end of the world. The shift in focus is set to be carried into the future of the series, as Scott has revealed the upcoming sequel will focus more on AI than the aliens. The Blade Runner director is currently acting as executive producer on the scorching sequel Blade Runner: 2049. Alien: Awakening is set to start filming next year, ready for a 2019 release.
Indian Cops Have Big Plans on Using Big Data to Prevent and Solve Crimes Now, like in every police procedural sitcom ever, the Indian cops will be seen fighting, in fact, stopping crime with the help of high-tech gadgets and gizmos, all thanks to Big Data. Reportedly, the National Crime Records Bureau (NCRB) is trying to use crime data analytics software to enable predictive policing so that crime can be stopped even before it takes place. Media reports suggest that a special software is being developed by the Hyderabadbased Advanced Data Research Institute (ADRIN) so that law enforcement agencies can map crime patterns and predictive analysis. Source: http://analyticsindiamag.com/
By Akshay Nagpal PGDM (2017-19)
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Quantertainment Predicting the main character of Game of Thrones “Game of Thrones” is the most popular show on TV. For much of its seven-year run, it’s been one of a select handful of shows to generate widespread audience conversation and engagement. Since the series’ debut in 2011, viewership has only grown. The average viewership per season went from 3.3 million in the first season to 10.6 million last seasons, never suffering a weak-season misstep. From the storyline to the characters to all the fan theories, Game of Thrones has gone where no show has gone before. Its popularity has been amazing, especially considering its massive cast. Hundreds of characters have appeared on screen, with weird made-up names which most viewers forget as soon as they’ve heard them. As no such official dataset available from HBO about Game of Thrones, we have decided to record the screen time of each character by watching each episode scene by scene and noting the time to create the dataset. The list consists of 200 characters in terms of screen time, but we selected only the top 100 characters for analysis and then prepared the charts to visualize the screen time of the characters and their houses to fulfil the objective of the analysis. Four charts are prepared for the visualization and results of the analysis:
Screen time of the top 100 characters Analysis: - Jon Snow and Tyrion Lannister are the two most important characters of the epic fantasy series as they have spent the maximum total time on screen. After them are the titanic trio Daenerys Targaryen, Sansa Stark and Cersei Lannister with their impressive screen time, which shouldn’t really come as a surprise. The average screen time of the top 100 characters stands at 7 minutes and 45 seconds, but the top five go way beyond that. What’s interesting about this list is that Ned Stark (played by Sean Bean) is among in the top 15 after seven seasons, even though he’s killed off in the first season, in episode 9.
Top 10 characters in terms of screen time, by season Analysis: - In the graph below we have shown the top 10 characters screen time season wise. After reading the season wise screen time we can say that Jon snow is the strong contender as he is always in first or second place except for season 2. In season 2 Tyrion Lannister and Theon Greyjoy are top 2 and Jon snow is not even close. Jon snow’s screen time has only grown since then and evolved to become one of the most important characters.
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Screen time of the nine primary houses by season Analysis: - The charts show each house screen time season wise and by the visualization we can see that Starks got the most screen time in all the season excluding season 4 and 5 where Lannister took the top slot. It is interesting to note that the last season revolves around only five houses.
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CONCLUSION By the study we can say that Jon Snow has been the primary character all along and the house Stark has enjoyed most of the screen time in comparison to other houses. Therefore, as the data says Jon Snow and house Stark are the main characters of the Game of Thrones. Source: Data collected from Ishita Doshi, after obtaining the necessary permissions.
By Sonika Aneja PGDM (2017-19)
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Quant Fun Sudoku:
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Puzzles: 1. A farmer wants to divide his 17 horses among his three sons. According to the farmer, the oldest son should get half of the horses, the middle son should get one third of the horses and the youngest son should get one ninth of the horses. When their father died they were not able to divide the horses as the result was coming in fractions. As the sons were fighting on how to divide the horses, a travelling mathematician came and heard their problem. He proposed a solution with which all the sons got their share in the property without harming any animal. What was the advice given and how the group of horses were divided?
2. A Petri dish hosts a healthy colony of bacteria. Once a minute every bacterium divides into two. The colony was founded by a single cell at noon. At exactly 12:43 (43 minutes later) the Petri dish was half full. At what time will the dish be full?
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Quantinuum, the Quant and Analytics committee of KJ Somaiya Institute of Management Studies and Research aims to empower students and professionals alike to organize and understand numbers and, in turn, to make good and rational decisions as future managers. The newsletter published monthly consists of a gamut of articles for readers ranging from beginners to advanced learners so as to further enrich the young minds understand the contributions made to the field of mathematics along with a couple of brain- racking sections of Sudoku to tickle the gray cells. For any further queries and feedback, please contact the following address: KJ Somaiya Institute of Management Studies and Research, Vidya Nagar, VidyaVihar, Ghatkopar East, Mumbai -400077 or drop us a mail at newsletter.quantinuum@gmail.com Mentor:
Prof. N.S.Nilakantan (+919820680741)
Email – nilakantan@somaiya.edu Team Leaders:
Vaibhav M (+917708521382) Maheshwaran Kumar (+919566173411) Rishita Shah (+919867290018)
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