RAMPATRUNI RAMBABU
@rietrjy.co.in
RAJAMAHENDRI INSTITUTE OF ENINEERING AND TECHNOLOGY
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Artificial Intelligence, Computer Science Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
S Govinda Rao, R RamBabu, B S Anil Kumar, V Srinivas, and P Varaprasada Rao
IEEE
Smart Cities applications, automated traffic control and management is the most trending research area. With the improving needs of developed towns and cities traffic congestion, now a days this the traffic congestion control and its applications has large needed facing problem in the increased population cities. Peeled eye camera photos and videos can be watched efficiently to detect traffic congestions in most of the areas in the grown populated cities. The earlier researchers had observed more on traffic signal controls through photos executed by using different algorithms of machine learning. There is existing research available on traffic signal controls through image processing and various machine learning methods. The image features are extracted and interpreted for the same. Deep learning algorithm, convolutional neural network (CNN) is proposed for effective detection of traffic congestion. There were existing works available in traffic detection using machine learning and deep learning approaches. Machine learning, Nowadays, traffic surveillance systems collect a lot of videos or images and store them for the live monitoring purposes. Deep learning techniques are used sparingly in traffic surveillance and control systems. Various images with various weather conditions are collected and are used as training dataset. Advantages of deep learning have been exploited in many applications, which use computer vision and image analysis. One of such applications is traffic monitoring, in which large amounts of video or images are processed for effective learning. The traffic surveillance can only monitor, which cannot detect the traffic on particular time.
S Govinda Rao, R Rambabu, and P VaraPrasada Rao
IEEE
In the bioinformatics area it expose an amazing development at the crossroads of biology, medicine, information science, and computer science. The pictures neatly explain that nowadays in this field research is as reproductive in the data mining research. However, maximum bioinformatics research handles with the tasks of identification and classification, tree or network induction from data. Clustering techniques are mostly employed in the sector of information technology, medicine as well as bioinformatics.In this paper, the modified hierarchical clustering algorithms are introduced and applied to orthologous IGF-1R protein sequences and it can produce orthologous clusters of sequences and phylogenetic trees are formed Compared to existing hierarchical algorithms these new algorithms are very efficient, it takes less time to execute and clustering accuracy is also better.Another contribution is acceptable attempt has been made on understanding the role of IGF-1R. The outcome enabled research in extended further to delineate the dependency of Physio-chemical properties, on the activity of inhibitors, and to study the multivariate regression analysis on a set of 87 IGF-1R inhibitors are dependent variables and some of independent variables resulted in F-test: 8.812, r value: 0.794 and r2 value of 0.631, respectively. The data set was introduced for the presence of outliers by calculating the leverages and standard residuals and finally 6 compounds were eliminated. A new regression model was attempted 76 compounds training set and 5 compound validation set. A Regression plot is obtained and justifies the predictive ability of the regression model. Finally, the designing or screening compounds libraries for new analogues should enhance the inhibitory activity against IGF-1R.
Govinda Rao S., Varaprasada Rao P., Rambabu R., and Chandra Sekahar Reddy P.
IEEE
This paper, introduced a new methodology to raise the metric of a journal’s impact. This method is depending on finding clusters from SC Imago database and creates datasets utilizing a modified k-means clustering algorithm. Farther, developing of linear regression analysis on these datasets is perplexed by seeing index values are dependent variables and citation parameters as independent variables result in assessing contributing factors to increase bibliometric index of any journal. next step, cluster quality metrics enforced to evaluate the perfectness of fit of the cluster such as homogeneity score, completeness score, V measure, accommodated rand score and silhouette coefficient. The output of modified k-means algorithm on a dataset of 1445 journals resulted in 3 clusters (k=3). Each cluster data clustered depending on the title.The regression analysis states that the publisher who desires to enhance his journal bibliometric indexes should deliberate the advice conferred, in this work, bring large number of paper submissions to their journal especially. Almost four indices which are of main importance in the publisher industry having been used this. The analysis ensure in strong advantage as the testing of output produced including regression parameters clarified with the identification of outliers by the inclusion of relative error calculation. Accordingly, seeing the suggestive features with increase or decrease in TD3, TC3, CD3, CD2 and RD values, the publisher would profit from raising their respective bibliometric index.