Ảnh đại diện của KrishnaPG
@KrishnaPG
Cờ của India Mumbai, India
Thành viên từ May, 2005
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Gopalakrishna Palem

Trực tuyến Ngoại tuyến
Gopalakrishna, widely referred to as GK, is a Technology Management & Strategy Consultant specialized in Big data, Cloud and Distributed Predictive Analytics. (Web: ) Recent Big-data Predictive Analytics solutions by Gopalakrishna: • Fleet management with Predictive Maintenance • Fire Spread Models for Public Safety • Epidemic Outbreak Detection for Healthcare • Sentiment Analysis for social media • Text-mining health-care documents for HIE For more than a decade GK is being actively engaged by companies like Microsoft, Oracle and Symphony on various tasks ranging from applying Machine Learning to predict component failure and machine reliability, to designing RESTful Single-Sign-On (SSO) architectures for Oracle IDM Fusion Middleware, distributed CRM systems on Windows Azure for high-availability, low-cost Render Farm solutions on Hadoop for high-scalability and so on. Gopalakrishna has proven track record in various research fields ranging from Computational Automata Theory, Artificial Intelligence, Advanced Analytics, to Systems Control and Simulation & Modeling. His contributions to Open-source community created multiple innovative solutions such as CarMusTy, CFugue, PhTranslator, .Net Obfuscator so on. Not to mention, Gopalakrishna's business strategy helped many customers build their product portflios strong and setup Center-of-Excellences. More about his work can be found at:
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Medicare Healthcare Charge Disparity Analysis

Transparency in administration and effective corporate governance leads to huge volumes of public data that when processed with analytical procedures yield meaningful insights into the nature of the data and the business operations generating that data. A case in point being the recent public release of United States Medicare charges for healthcare system. In an initiative dubbed Obamacare, United States government released data for hospital charges for top 100 Diagnosis-Related Groups (DRG). The data inclu

Condition-Based Maintenance using Sensor Arrays and Telematics

Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities. Though the technology itself is not new, its application has been quite limited until now. Sensor based telematics technologies generate volumes of data that are orders of magnitude larger than what operators have dealt with previously. Real-time big data computation capabilities have opened the flood gates for creating new predictive analytics capabilities into an otherwise simple data log systems, enablin

The Practice of Predictive Analytics in Healthcare

Problems such as inaccurate diagnoses and poor drug-adherence pose challenges to individual health and safety. These challenges are now being alleviated with big data analytics using personalized drug regimes, follow-up alerts and real-time diagnosis monitoring. In this paper, you will learn how predictive analytics is helping healthcare industry with possibilites such as clinical decision support systems, medical text analysis and Electronic Health Records.

M2M Telematics & Predictive Analytics

As the global energy usage continues to surge, the confluence of M2M telematics and real-time analytics is the key for delivering green solutions. " .. Take an example of electric vehicle (EV) that you drive home and connect to the electric grid, letting it figure out your next commute requirement from the calendar appointments on your hand-held or mobile, and your grid’s active load usage and non-peak hours from the energy provider, all summed up to auto-tune its own recharging schedule to reduce the

Creating Distributed Enterprise CRM System for Service Management

Continual Service Improvement (CSI) in ITSM is a self-invalidating activity that sometimes triggers changes right at the core of operations. It might be the way daily operations are performed by the frontline engineers or the way strategic decisions are made by the business owners, where ever the change be, without a right balance between the need for improvement and the urge to stick to the existing system, the results could be catastrophic. And finding the right balance, it comes only with experience.

Sequence Indexing for Game Development

Sequence indexing is the mathematical art of manipulating computational objects. It tries to answer one simple question: Given a collection of objects, how do we create, access, and identify various groups of objects drawn from that collection? Most games consist of large collections of objects, and mathematically, we represent these collections of objects as a sequence. This gem presents the mathematical formulas for indexing and de-indexing game objects as few well-known sequences, such as range sequences

Data Dependencies and Learning in Artificial Systems

Data-dependencies play an important role in the performance of learning algorithms. In this paper we analyze the concepts of data dependencies in the context of artificial systems. When a problem and its solution are viewed as points in a system configuration, variations in the problem configurations can be used to study the variations in the solution configurations and vice versa. These variations could be used to infer solutions to unknown instances of problems based on the solutions to known instances, thus

On Solving the System

Mathematically a system is said to be solved if its future states can be predicted from the information provided by the present and past state history. In this paper we present a way of solving artificial life systems using the principles of state-machines. We present the view of manipulating the artificial systems considering them as being embedded in external program entities. Further, we discuss the technique of using algorithmic transformations to understand the behavioral complexity of virtual organisms.

Establishing the existence of uncountable number of Accelerated Turing Machines

We examine the converse of Church-Turing thesis and establish the existence of uncountable number of Accelerated Turing machines, which leads to the conclusion that these machines are unaffected by Gödel's incompleteness theorem.

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