REDDI KIRAN KUMAR

@kru.ac.in

Associate Professor, Department of Computer Science
Krishna University



              

https://researchid.co/kirankreddi

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Engineering, Computer Science Applications, Artificial Intelligence

54

Scopus Publications

1981

Scholar Citations

20

Scholar h-index

55

Scholar i10-index

Scopus Publications

  • Deep residual convolutional neural Network: An efficient technique for intrusion detection system
    Gunupudi Sai Chaitanya Kumar, Reddi Kiran Kumar, Kuricheti Parish Venkata Kumar, Nallagatla Raghavendra Sai, and Madamachi Brahmaiah
    Elsevier BV

  • Malicious Social Bots Detection in the Twitter Network Using Learning Automata with URL Features
    R. Kiran Kumar, G. Ramesh Babu, G. Sai Chaitanya Kumar, and N. Raghavendra Sai
    Springer Nature Singapore

  • Hiding information in an image using DNA cryptography
    P. Bharathi Devi, P. Ravindra, and R. Kiran Kumar
    Elsevier

  • Mutual clustered redundancy assisted feature selection for an intrusion detection system
    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.

  • APPLICATIONS OF MACHINE LEARNING TECHNIQUES TO GENERATE CROP PREDICTIONS WITH BETTER PRECISION
    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.

  • Sliding window assisted mutual redundancy-based feature selection for intrusion detection system
    Thotakura Veeranna and Kiran Kumar Reddy
    Inderscience Publishers

  • Robust Blood Vessels Segmentation Based on Memory-Augmented Neural Network
    K. Arunabhaskar and R. Kiran Kumar
    Springer Singapore

  • DNA Playfair Cryptosystem Based on 16 × 16 Key Matrix Using DNA ASCII Table
    P. Bharathi Devi, R. Kiran Kumar, and P. Ravindra
    Springer Singapore

  • An NLP hybrid recommendation system in crop selection for farmers


  • An Implementation on Agriculture Recommendation System Using Text Feature Extraction
    K. Anji Reddy and R. Kiran Kumar
    Springer International Publishing


  • Swarm-Inspired Task Scheduling Strategy in Cloud Computing
    Ramakrishna Goddu and Kiran Kumar Reddi
    Springer Singapore


  • Accurate liver disease prediction with extreme gradient boosting
    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.

  • A research on similarity measure to identify effective similar users in recommender systems
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    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

  • Enhanced classifier accuracy in liver disease diagnosis using a novel multi layer feed forward deep neural network
    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) .


  • Privacy preserving redundant data removal in cloud storage: RDRC


  • A review analysis on recommendation system



  • Detection and classification of trendy topics for recommendation based on Twitter data on different genre
    D. N. V. S. L. S. Indira, R. Kiran Kumar, G. V. S. N. R. V. Prasad, and R. Usha Rani
    Springer Singapore


  • A Novel Level-Based DNA Security Algorithm Using DNA Codons
    Bharathi Devi Patnala and R. Kiran Kumar
    Springer Singapore

  • Heuristic Algorithm based Approach to Classify EEG Signals into Normal and Focal
    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.

  • SEE: Synchronized Efficient Energy Calculation for Topology Maintenance & Power Saving in Ad Hoc Networks
    T. Santhi Sri, J. Rajendra Prasad, and R. Kiran Kumar
    Springer Science and Business Media LLC

RECENT SCHOLAR PUBLICATIONS

  • Interplay among various cavity modes in a microwave plasma system with well-defined cavity geometry
    C Mallick, M Bandyopadhyay, R Kumar
    Physics of Plasmas 32 (1) 2025

  • Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab
    A Sharma, S Srivastava, R Kumar, D Mitra
    Environmental Pollution 359, 124707 2024

  • B-166 Differentiation of Aziridine Functionality from Related Functional Groups in Protonated Analytes by Using Selective Ion-Molecule Reactions
    R Anyaeche, K Alzarieni, R Kumar, J Milton, J Kaur, H Sheng, ...
    Clinical Chemistry 70 (Supplement_1), hvae106. 526 2024

  • Noncompound fusion reactions at low bombarding energy
    RK Sahoo, D Singh, A Mahato, PK Giri, N Sharma, L Chhura, R Mahato, ...
    Physical Review C 110 (4), 044607 2024

  • Multi-Omics Models Can Predict Prostate Specific Membrane Antigen Avidity for Computed Tomography Lesions in Oligo-Metastatic Castration Sensitive Prostate Cancer
    R Kumar, C Zhang, P Sutera, KK English, L Hathout, SK Jabbour, L Ren, ...
    International Journal of Radiation Oncology, Biology, Physics 120 (2), e634 2024

  • Patient costs for drug-resistant TB diagnosis and pre-treatment evaluation in North India
    S Das, R Kumar, A Krishnan, S Kant, A Mohan
    Public Health Action 14 (3), 129-134 2024

  • Incomplete fusion reactions for : Measurement of recoil range distributions
    M Shuaib, MS Asnain, A Siddique, IM Bhat, MK Sharma, A Yadav, ...
    Physical Review C 110 (1), 014621 2024

  • A non-singular terminal sliding mode controls base voltage and frequency control in an islanded microgrid
    OM Meetei, M Prakash, R Kumar
    2024 6th International Conference on Energy, Power and Environment (ICEPE), 1-4 2024

  • Adsorption Potential of Iron-Zirconium Oxide Nanoparticles for 3-Chlorophenol and 3-Nitrophenol: Thermodynamic, Kinetic, and Mechanistic Studies
    R Kumar, SR Ali, R Karmakar, R Sharma, N Haider
    Russian Journal of General Chemistry 94 (6), 1419-1435 2024

  • Experimental study of the effect of projectile and target structure on breakup fusion reactions induced by N projectiles
    M Kumar, A Agarwal, AK Jashwal, K Kumar, A Yadav, S Dutt, S Prajapati, ...
    The European Physical Journal Plus 139 (6), 1-15 2024

  • Assessment of variability, genetic diversity and character association of Chrysanthemum (Dendranthema grandiflora Tzvelve) based on qualitative and quantitative traits
    B KG, K SG, R Kumar, A Taj, S BA
     2024

  • Discovery of a hot post-AGB star in Galactic globular cluster E3
    R Kumar, A Moharana, S Piridi, AC Pradhan, KG Hełminiak, N Ikonnikova, ...
    Astronomy & Astrophysics 685, L6 2024

  • Myths about COVID-19 among Sindh Population: A Survey based Study.
    S Baloch, S Ashraf, AA Khaskheli, SN Hyder, R Kumar, M Ramzan, ...
    Journal of Health and Rehabilitation Research 4 (1), 1547-1551 2024

  • Thrombolytic therapy in ST-elevation myocardial infarction
    C Murray, R Kumar, I Pearson
    Ir Med J 117 (3), P929 2024

  • Deep residual convolutional neural network: an efficient technique for intrusion detection system
    GSC Kumar, RK Kumar, KPV Kumar, NR Sai, M Brahmaiah
    Expert Systems with Applications 238, 121912 2024

  • Achieving extraordinary strength-ductility synergy in Mg-Zn-Zr alloy thin sheet manufactured via load assisted one-step large-strain lowered temperature rolling
    R Kumar, SK Panigrahi
    Journal of Alloys and Compounds 976, 173088 2024

  • Оптимизация термообработки для тонких золь-гель пленок CdS, полученных центрифугированием
    R Aggarwal, R Kumar
    Журнал прикладной спектроскопии 91 (1), 171 2024

  • Level structures of 96Tc and their microscopic description
    AK Rana, S Sihotra, HP Sharma, V Singh, GH Bhat, S Jehangir, ...
    Journal of Physics G: Nuclear and Particle Physics 51 (3), 035104 2024

  • Plume-surface interaction during lunar landing using a two-way coupled DSMC-DEM approach
    A Bajpai, A Bhateja, R Kumar
    Physical Review Fluids 9 (2), 024306 2024

  • Corrigendum to" Numerical simulation of heat transfer in blood flow altered by electroosmosis through tapered micro-vessels"[Microvasc. Res. 118 (2018) 162-172].
    J Prakash, K Ramesh, D Tripathi, R Kumar
    Microvascular Research, 104657-104657 2024

MOST CITED SCHOLAR PUBLICATIONS

  • A survey on conventional encryption algorithms of Cryptography
    R Yegireddi, RK Kumar
    2016 International Conference on ICT in Business Industry & Government 2016
    Citations: 59

  • Different Technique to Transfer Big Data: survey
    KK Reddi, D Indira
    IEEE Transactions on 52 (8), 2348-2355 2013
    Citations: 49

  • Clustering algorithm combined with hill climbing for classification of remote sensing image
    BS Chandana, K Srinivas, RK Kumar
    International Journal of Electrical and Computer Engineering 4 (6), 923 2014
    Citations: 45

  • Deep residual convolutional neural network: an efficient technique for intrusion detection system
    GSC Kumar, RK Kumar, KPV Kumar, NR Sai, M Brahmaiah
    Expert Systems with Applications 238, 121912 2024
    Citations: 43

  • Determination of Optimal Clusters for a Non-hierarchical Clustering Paradigm K-Means Algorithm
    TV Sai Krishna, A Yesu Babu, R Kiran Kumar
    Proceedings of International Conference on Computational Intelligence and 2018
    Citations: 36

  • Multiple feature fuzzy c-means clustering algorithm for segmentation of microarray images
    J Harikiran, PV Lakshmi, RK Kumar
    International Journal of Electrical and Computer Engineering 5 (5) 2015
    Citations: 36

  • Fuzzy c-means with bi-dimensional empirical mode decomposition for segmentation of microarray image
    J Harikiran, D RamaKrishna, ML Phanendra, PV Lakshmi, RK Kumar
    International Journal of Computer Science Issues (IJCSI) 9 (5), 316 2012
    Citations: 33

  • Comparative analysis of google file system and hadoop distributed file system
    R Vijayakumari, R Kirankumar, KG Rao
    International Journal of Advanced Trends in Computer Science and Engineering 2014
    Citations: 30

  • Development and evaluation of blended papaya leather
    R Kumar, RT Patil, G Mondal
    II International Symposium on Papaya 851, 565-570 2008
    Citations: 30

  • An efficient data retrieval approach using blowfish encryption on cloud ciphertext retrieval in cloud computing
    S Mudepalli, VS Rao, RK Kumar
    2017 international conference on intelligent computing and control systems 2017
    Citations: 29

  • Improved cuckoo search with particle swarm optimization for classification of compressed images
    V Enireddy, RK Kumar
    Sadhana 40 (8), 2271-2285 2015
    Citations: 24

  • A novel algorithm for scaling up the accuracy of decision trees
    AM Mahmood, KM Rao, KK Reddi
    International Journal on Computer Science and Engineering 2 (2), 126-131 2010
    Citations: 24

  • Image fusion in hyperspectral image classification using genetic algorithm
    B Saichandana, K Srinivas, RK Kumar
    Indonesian Journal of Electrical Engineering and Computer Science 2 (3), 703-711 2016
    Citations: 23

  • AUTOMATIC GRIDDING METHOD FOR MICROARRAY IMAGES.
    J Harikiran, B Avinash, PV LAKSHMI, R Kirankumar
    Journal of Theoretical & Applied Information Technology 65 (1) 2014
    Citations: 23

  • Fast clustering algorithms for segmentation of microarray images
    J Harikiran, PV Lakshmi, DRK Kumar
    International Journal of Scientific & Engineering Research 5 (10), 569-574 2014
    Citations: 22

  • Huffbit compress—Algorithm to compress DNA sequences using extended binary trees
    PR Rajeswari, A Apparao, RK Kumar
    Journal of Theoretical and Applied Information Technology 13 (2), 101-106 2010
    Citations: 22

  • Occurrence of Beauveria sp. on red palm weevil, Rhynchophorus ferrugineus (Oliv.) of coconut.
    SS Shaiju Simon, RK Kumar, C Gokulapalan
     2003
    Citations: 22

  • Healthcare data breaches: Insights and implications. Healthcare, 8 (2), 133
    AH Seh, M Zarour, M Alenezi, AK Sarkar, A Agrawal, R Kumar, ...
     2020
    Citations: 21

  • Noise removal in microarray images using variational mode decomposition technique
    GSC Kumar, RK Kumar, GA Naidu, J Harikiran
    TELKOMNIKA (Telecommunication Computing Electronics and Control) 15 (4 2017
    Citations: 21

  • Application of BEMD and hierarchical image fusion in hyperspectral image classification
    B Saichandana, K Srinivas, J Harikiran, RK Kumar
    International Journal of Computer Science and Information Security 14 (5), 437 2016
    Citations: 20