DR SUNILVIJAYA KUMAR GADDAM
@lords.ac.in
Professor in Dept. of CSE & DEAN (CSE & Allied Departments)
Lords Institute of Engineering & Technology (Autonomous)
Dr. Sunil VK Gaddam received his B. Tech. from Sri Venkateswara University (SVU), Tirupati, India, in 1993, Post Graduate Diploma in Computer Engineering (PGDCE) from School of Computer Science, Institute of Post Graduate Studies and Research (IPGSR), Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, in 1994, and M. Tech. (Computer Science & Technology) from School of Computer and Systems Sciences (SC & SS), Jawaharlal Nehru University (JNU) - A Central University, New Delhi, India, in 1997. He has completed his Ph.D. in Computer Science from School of Computer and Information Sciences (SOCIS), Indira Gandhi National Open University (IGNOU) - A Central University, New Delhi, India in 2011.
He has over 24 years of experience of teaching courses in the discipline of Computer Science in various Indian Universities. He is currently a Professor of Computer Science & Engineering & DEAN (CSE & Allied Departments) @ Lords Institute of Engineering & Technology, Hyderabad,T.S
EDUCATION
B. Tech (ECE), Post Graduate Diploma in Computer Engineering (PGDCE), M. Tech (Computer Science & Technology), Ph.D. (Computer Science)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science, Computer Science Applications, Computer Networks and Communications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sunil V. K. Gaddam, D. K. Lobial, and Manohar Lal
Springer Science and Business Media LLC
K. Kalpana, G. Kumar and K. Madhavi
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
We are in the information age there by collecting very huge volume of data from diverse sources in structured, unstructured and semi structured form ranging to petabytes to exabytes of data. Data is an asset as valuable knowledge and information is hidden in such massive volumes of data. Data analytics is required to have a deeper insights and identify fine grained patterns so as to make accurate predictions enabling the improvement of decision making. Extracting knowledge from data is done by data analytics, Machine learning forms the core of it. The increase in the dimensionality of data both in terms of number of tuples and also in terms of number of features poses several challenges to the machine learning algorithms . Preprocessing of data is done as a prior step to machine learning, so feature selection is done as a preprocessing step to have the dimensionality reduction of the data and thereby removing the irrelevant features and improving the efficiency and accuracy of a machine learning algorithm. In this paper we are studying various feature selection mechanisms and analyze them whether they can be adopted to sentiment analysis of big data.
K. Samunnisa, G. Kumar and K. Madhavi
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Cloud Computing (CC) is a very big field in the computer industry. It has systems that are connected through communication networks such as Internet. The LBA is an important cloud computing area to prevent overloaded workload and provide the same and valuable service. The various algorithms are used to solve the load balancing complexity. In this paper, we explain various standard load balancing techniques in different algorithms and challenges in assigning tasks to Cloud Computing. The load balancing algorithm can be used to better utilize and better understand user needs
Sunil V. K. Gaddam, D. K. Lobial, and Manohar Lal
Springer Science and Business Media LLC
Sunil V K Gaddam, D K Lobiyal, and Manohar Lal
Informa UK Limited
ABSTRACT Asynchronous transfer mode (ATM) is a cell-switching and multiplexing technology that combines the benefits of circuit switching with those of packet switching. Traffic management in ATM is concerned with ensuring that users get their desired quality of service (QoS). The problem of traffic management is especially difficult during the periods of heavy load particularly if the traffic demands cannot be predicted in advance. The issue of traffic control and bandwidth management in ATM-based networks is complex due to a mixture of different connection traffic types, QoS requirements, and time scales. In this paper, we provide an ant-based integrated technique to control the occurrence of congestion and allocate bandwidth for various traffic services in a prioritized manner. The forward ants collect the QoS information of the nodes regarding the available bandwidth and the buffer size. Then based on the QoS requirements of the various traffic classes, the optimum path is selected based on the observed QoS statistics collected by the ant agents. Therefore, the ant-based integrated technique provides the network to efficiently use the traffic, thus providing an efficient traffic management for the ATM network. By simulation results, we show that the proposed ant-based technique outperforms the existing architecture in terms of throughput and delay.