REDDI KIRAN KUMAR
@kru.ac.in
Associate Professor, Department of Computer Science
Krishna University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Computer Science Applications, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Gunupudi Sai Chaitanya Kumar, Reddi Kiran Kumar, Kuricheti Parish Venkata Kumar, Nallagatla Raghavendra Sai, and Madamachi Brahmaiah
Elsevier BV
R. Kiran Kumar, G. Ramesh Babu, G. Sai Chaitanya Kumar, and N. Raghavendra Sai
Springer Nature Singapore
P. Bharathi Devi, P. Ravindra, and R. Kiran Kumar
Elsevier
T. Veeranna and Kiran Kumar Reddi
IOS Press
Intrusion Detection is very important in computer networks because the widespread of internet makes the computers more prone to several cyber-attacks. With this inspiration, a new paradigm called Intrusion Detection System (IDS) has emerged and attained a huge research interest. However, the major challenge in IDS is the presence of redundant and duplicate information that causes a serious computational problem in network traffic classifications. To solve this problem, in this paper, we propose a novel IDS model based on statistical processing techniques and machine learning algorithms. The machine learning algorithms incudes Fuzzy C-means and Support Vector Machine while the statistical processing techniques includes correlation and Joint Entropy. The main purpose of FCM is to cluster the train data and SVM is to classify the traffic connections. Next, the main purpose of correlation is to discover and remove the duplicate connections from every cluster while the Joint entropy is applied for the discovery and removal of duplicate features from every connection. For experimental validation, totally three standard datasets namely KDD Cup 99, NSL-KDD and Kyoto2006+ are considered and the performance is measured through Detection Rate, Precision, F-Score, and accuracy. A five-fold cross validation is done on every dataset by changing the traffic and the obtained average performance is compared with existing methods.
R.Kiran Kumar R and K Anji Reddy
The Electrochemical Society
In most parts of India, agriculture has become a risky business and farmers suffer a lot due to unpredictable yield. The risk is mainly due to availability of water resources for cultivation and getting profitable prices in market. Prices alter between very high and very low, so crop planning has become very important for farmers to minimize the losses. Machine learning techniques can help to understand the under laying patterns from mass data and this patterns can be used to help farmers for crop planning, also it would reduce the risk of crop failure and guarantee a maximum profit for farmers to sustain their livelihood. But human knowledge cultivation is not sufficient to cater for the demanding need due to the rapid growth in the world's human population. In order to address this problem, this paper has studied the use of machine learning tools. It experimented with more than 0,3 million data. This dataset identifies key parameters of cultivation collected from the Bangladesh Agriculture Department. This study compared the number of machine learning algorithms to neural networks.
Thotakura Veeranna and Kiran Kumar Reddy
Inderscience Publishers
K. Arunabhaskar and R. Kiran Kumar
Springer Singapore
P. Bharathi Devi, R. Kiran Kumar, and P. Ravindra
Springer Singapore
K. Anji Reddy and R. Kiran Kumar
Springer International Publishing
R. Kiran Kumar and P. Bharathi Devi
Springer Singapore
Ramakrishna Goddu and Kiran Kumar Reddi
Springer Singapore
M. Jayanthi Rao and R. Kiran Kumar
Springer Singapore
Sivala Vishnu Murty, , Dr. R Kiran Kumar, and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Abstract-Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.
In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user
Sivala Vishnu Murty, , Dr. R Kiran Kumar, and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Classification techniques are often used for predicting Liver diseases and assist doctors in early detection of liver diseases. As per studies in the past and our experiments, conventional classification algorithms are found to be less accurate in predicting liver diseases. Therefore, there is a need for sophisticated classifiers in this area. For many medical applications, including Liver Diseases, Deep Neural Networks (DNNs) are used but the accuracies are not satisfactory. Deep Neural Network training is a time taking procedure, particularly if the hidden layers and nodes are more. Most of the times it leads to over fitting and the classifier does not perform well on unseen data samples .We, in this paper, tuned a Multi Layer Feed Forward Deep Neural Network (MLFFDNN) by fitting appropriate number of hidden layer and nodes, dropout function after each hidden layer to avoid over fitting, loss functions, bias, learning rate and activation functions for more accurate liver disease predictions. We used a balanced data set containing 882 samples. The data is collected from north coastal districts of Andhra Pradesh hospitals, India. The training process is carried out for 400 epochs and finally It is .observed that our model exhibited 98% accuracy at epoch 363 which is more than the performance of Neural Network models tuned till now by machine learning researchers and also some regularly used classification algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision Tree, Random Belief Networks and Alternating Decision Trees (ADT) .
R. Kiran Kumar and D. Suneetha
Springer Singapore
D. N. V. S. L. S. Indira, R. Kiran Kumar, G. V. S. N. R. V. Prasad, and R. Usha Rani
Springer Singapore
D. Suneetha and R. Kiran Kumar
Springer Singapore
Bharathi Devi Patnala and R. Kiran Kumar
Springer Singapore
V. Sankara Narayanan, R. Elavarasan, C.N. Gnanaprakasam, N. Sri Madhava Raja, and R. Kiran Kumar
IEEE
Condition of brain can be examined using the brain-signals and brain-images. Signal based evaluation is simple and offers essential information compared with the image based methods. This paper proposes an approach to evaluate the benchmark EEG signals. The implemented approach initially implements an amplitude based assessment to compute the peak-to-peak voltage value of the EEG signal. Later, it implements time-frequency conversation procedure to transfer the signal into image based on the wavelet transform. Further, the S-transform approach is considered to extract the essential signal features for the classifier system. Firefly-Algorithm (FA) based approach is also considered to choose leading signal features considered to train and test the classifier unit. In this work, classifiers, such as Support-Vector-Machine (SVM), Random-Forest (RF) and K-Nearest Neighbor (KNN) are implemented and the result of this work offered an average accuracy of 80.39%. The works confirms that, proposed procedure offers better result on the chosen EEG signals.
T. Santhi Sri, J. Rajendra Prasad, and R. Kiran Kumar
Springer Science and Business Media LLC