
Rathinaraja JeyarajStanford University | SU · School of Medicine
Rathinaraja Jeyaraj
PhD
Deep Learning, AI, Cloud Computing
About
33
Publications
3,909
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
332
Citations
Introduction
I received my PhD in Distributed Systems from the Department of IT, National Institute of Technology Karnataka, India. Unboxing, manipulating and applying machine and deep learning algorithms to solve problems in cloud-IoT, CV, and interdisciplinary research domains, although broad, capture my current research interests most accurately. Refer to my website https://jrathinaraja.co.in/ to know more about my current work. I am always interested in collaborating with researchers and students.
Additional affiliations
September 2020 - September 2020
VIT-AP
Position
- Professor (Assistant)
September 2019 - August 2020
Duratech Solutions
Position
- Researcher
August 2018 - February 2019
Education
July 2015 - May 2020
February 2010 - April 2012
August 2009 - June 2011
Publications
Publications (33)
This research aims to explore the impact of machine learning (ML) on the evolution and efficacy of recommendation systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data...
The food marketplace needs a quick and reliable system for tracking and assessing the freshness of meat products. However, meat experiences a quick process of freshness deterioration, which leads to bacterial growth. As a result, the need for a reliable and quick way of monitoring and evaluating meat deterioration is growing urgent. By Considering...
In cyber-physical systems (CPS), micromachines are typically deployed across a wide range of applications, including smart industry, smart healthcare, and smart cities. Providing on-premises resources for the storage and processing of huge data collected by such CPS applications is crucial. The cloud provides scalable storage and computation resour...
Recent substantial advancements in computational techniques, particularly in artificial intelligence (AI) and machine learning (ML), have raised the demand for smart self-powered devices. But since energy use is a worldwide issue that needs to be resolved immediately, cutting-edge technology should reduce energy consumption without affecting smart...
The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, etc. is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation mo...
Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible w...
Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, u...
Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, u...
Improving the performance of the MapReduce scheduler is a primary objective, especially in a heterogeneous virtual cloud environment. A map task is assigned with an input split(IS) which consists of one or more data blocks. When a map task is assigned to more than one data block, non-local execution is performed. In classical MapReduce scheduling s...
Big data overwhelmed industries and research sectors. Reliable decision making is always a challenging task, which requires cost-effective big data processing tools. Hadoop MapReduce is being used to store and process huge volume of data in a distributed environment. However, due to huge capital investment and lack of expertise to set up an on-prem...
“We aim to make our readers visualize and learn big data and Hadoop MapReduce from scratch.” There is a lot of Big data, and Hadoop MapReduce content (online lectures, websites) available on the Internet and excellent books are at the counters for intermediate level users to master Hadoop MapReduce. Are they helpful for beginners and non-computer s...
This chapter elaborately discusses MapReduce execution cycle, which is very important to implement scalable algorithms in MapReduce. We have given examples to understand the MapReduce execution sequence step-by-step. Finally, MapReduce weaknesses and solutions are mentioned at the end of the chapter.
This chapter covers single node and multi-node implementation step-by-step with basic wordcount MapReduce job. Some Hadoop administrative commands are given to practice with Hadoop tools.
This chapter discusses the reasons that caused big data and why decision making from digital data is essential. We have compared and contrasted the importance of horizontal scalability over vertical scalability for big data processing. History of Hadoop and its features are mentioned along with different big data processing framework.
This chapter shortly describes data science and some big data problems in text analytics, audio analytics, video analytics, graph processing, etc. Finally, we have mentioned different job positions and its requirements in the big data industry.
This chapter is a significant portion in our book that will explain Hadoop v2, single node/multi-node installation on physical/virtual machines, running MapReduce job in Eclipse itself (you need not setup a real Hadoop cluster to frequently test your algorithm), properties used to tune MapReduce cluster and job, art of writing MapReduce jobs, NN hi...
This chapter explains MapReduce version 2, YARN and their features. Moreover, Hadoop cluster and MapReduce job configurations are discussed in detail.
Big data is largely influencing business entities and research sectors to be more data‐driven. Hadoop MapReduce is one of the cost‐effective ways to process large scale datasets and offered as a service over the Internet. Even though cloud service providers promise an infinite amount of resources available on‐demand, it is inevitable that some of t...
“More data, more information.” Big data helps businesses and research communities to gain insights and increase productivity. Many public cloud service providers offer Hadoop MapReduce as a service based on pay-per-use via infrastructure as a service on clusters of virtual machines promising on-demand horizontal scaling. These clusters of virtual m...
We admire the emerging technologies that fascinate us, as it has become part of our daily life. Internet of Things (IoT) plays a major role in simplifying human effort. It leaps forward taking the advantages of latest wireless devices and communication technologies. IoT is a combination of technologies such as ubiquitous and pervasive computing, wi...
Hadoop MapReduce as a service from cloud is widely used by various research, and commercial communities. Hadoop MapReduce is typically offered as a service hosted on virtualized environment in Cloud Data-Center. Cluster of virtual machines for MapReduce is placed across racks in Cloud Data-Center to achieve fault tolerance. But, it negatively intro...
Big data drove businesses and researches more data driven. Hadoop MapReduce is one of the cost-effective ways for processing huge amount of data and also offered as a service from cloud on cluster of Virtual Machines (VM). In Cloud Data Center (CDC), Hadoop VMs are co-located with other general purpose VMs across racks. Such a multi-tenancy leads t...
Accurate classification of diseases from microarray gene expression profile is a challenging task because of its high dimensional low sample data. Most of the gene selection methods employ the criterion function on the entire microarray samples only once which cannot exactly represent the relevance among genes. This paper proposes a hybrid gene sel...
Grid computing is the computational platform that
supports wide area parallel and distributed computing. In
Grid environment, the processing of an application is done by
dividing into small workflows and scheduling them to get the
expected result. These workflows are scheduled based on some
Quality of Services (QoS). The algorithm used before was
f...