REDDI KIRAN KUMAR

Scopus Publications

Intelligent AI-IoT System for Animal Threat Detection and Farm Security
M. Jayanthi Rao, Sivaneasan Bala Krishnan, Reddi Kiran Kumar, Prasun Chakrabarti
Proceedings 2025 2nd International Conference on Electronic Circuits and Signaling Technologies Icecst 2025, 2025
Now a days Agriculture faces major challenges from wild animal, leading to substantial crop losses, economic instability, and human-wildlife conflicts. Conventional protective measures such as fencing, manual fencing, and scare devices are often insufficient, costly, and unsustainable. This work proposes an AI-IoT collaborated system for real-time animal threat detection in agricultural fields. The system combines IoT-enabled sensors (cameras, sound motion, and acoustic sensors) with AI models (CNN/YOLO-based detectors) to identify animal sounds, estimate group size, and assess threat levels accurately. Upon detection, farmers receive instant alerts via mobile phones, SMS, or alarms, and automated non-lethal deterrents such as sound devices, lights, or drones. It can be activated. Historical data further enables predictive analytics for long-term crop protection planning. The proposed system enhances detection accuracy under varying environmental conditions, reduces reliance on manual surveillance, and minimizes human wildlife animals. Overall, it provides a scalable, cost-effective, and intelligent solution for sustainable forming, improving food protection, farmer efficiency, and the improving of smart farming practices.
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, Madamachi Brahmaiah
Expert Systems with Applications, 2024
The fast growth of computer networks over the past few years has made network security in smart cities a significant issue. Network intrusion detection is crucial to maintaining the integrity, confidentiality, and resource accessibility of the various network security rules. Conventional intrusion detection systems frequently use mining association rules to identify intrusion behaviors. They run into issues such as a high false alarm rate (FAR), limited generalization capacity, and slow timeliness because they cannot adequately extract distinctive information about user activities. The primary goal of the current research is to classify attacks using efficient approaches to identify genuine packets. If the number of characteristics in a dataset decreases, the complexity of DL approaches is significantly decreased. In this research work , the Deep Residual Convolutional neural network (DCRNN) is proposed to enhance network security through intrusion detection , which is optimized by the Improved Gazelle Optimization Algorithm (IGOA). Feature selection has eliminated irrelevant features from network data used in obstacle classification processes. Essential features are chosen using the Novel Binary Grasshopper Optimization Algorithm (NBGOA). Experimentation is carried out using the UNSW-NB-15, Cicddos2019 dataset, and CIC-IDS-2017 dataset. According to the experimental findings, the proposed system outperforms existing models regarding classification accuracy and processing time. The results demonstrate that the presented approach efficiently and precisely identifies various assaults.
Malicious Social Bots Detection in the Twitter Network Using Learning Automata with URL Features
R. Kiran Kumar, G. Ramesh Babu, G. Sai Chaitanya Kumar, N. Raghavendra Sai
Lecture Notes in Electrical Engineering, 2024
Design & deployment of a smart chatbot using emerging technologies
S. Venkata Achuta Rao, R. Kiran Kumar, M. Hima Bindu, D. Sai Sanjit, K. Tarun, K. Lakshmi Siva Kanth Reddy
Aip Conference Proceedings, 2023
Mutual Clustered Redundancy and Composite Learning for Intrusion Detection Systems
Thotakura Veeranna, R. Kiran Kumar
International Journal of E Collaboration, 2023
In the area of cyber space security, intrusion detection is a challenging task which aims at the provision of security from various malicious attacks. Hence, this paper proposes a two-stage hybrid intrusion detection system (IDS) mechanism to identify between normal and attack activities. The proposed mechanism is an integrated form of two simple and effective machine learning algorithms; they are support vector machine (SVM) and composite extreme learning machine (CELM). The first stage aims to distinguish the normal activities from abnormal activities and employed SVM. Next, the second stage employs CELM for the detection of different types of attacks . Further, aiming over training data, a clustering followed by duplicate connections removal and duplicate features removal is accomplished through fuzzy C-means clustering, correlation, and mutual information respectively. The proposed method applied eventually on the standard benchmark dataset NSL-KDD and the real modern UNSW-NB15 dataset. The performance analysis validates through accuracy, false alarm rate and computational time.
Hiding information in an image using DNA cryptography
P. Bharathi Devi, P. Ravindra, R. Kiran Kumar
Advances in Computers, 2023
Sliding window assisted mutual redundancy-based feature selection for intrusion detection system
Thotakura Veeranna, Kiran Kumar Reddy
International Journal of Ad Hoc and Ubiquitous Computing, 2022
Mutual clustered redundancy assisted feature selection for an intrusion detection system
T. Veeranna, Kiran Kumar Reddi
Journal of High Speed Networks, 2022
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, K Anji Reddy
Ecs Transactions, 2022
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.
A Hybrid Machine Learning Strategy Assisted Diabetic Retinopathy Detection based on Retinal Images
R. Kiran Kumar, K. Arunabhaskar
Proceedings of the 2021 IEEE International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2021, 2021
Retinopathy is a serious disease occurred over the retinal area of the eye, in which it is mainly raised based on the Diabetic disease. This kind of retinal disease is named as diabetic retinopathy; it may cause the permanent disorder of an eye. This retinopathy disease affects the blood flow ratio of the retinal veins and cause the blindness to the people as well as it is caused by the irregular blood flow over the veins. This kind of diabetic retinopathy disease results from the damage to the retinal back portion, in which it is caused due to the propensity to the retina. An improper maintenance of Blood Sugar level leads to such risk cases and the diabetic retinopathy can easily be identified by some earlier symptoms such as appearance of floaters, decreased visual acuity, redness, yellow, and orange colors and poor color perception. These are all the common symptoms raised on earlier stages of diabetic retinopathy disease, in which it is recoverable but in case of poor consideration regarding such causes leads to permanent blindness. At the low end of the spectrum, the condition can be managed with careful control of one's diabetes. For more difficult cases, surgery or laser resurfacing may be required. In this paper, a digital image processing logic is utilized to process the retinal images and classify the normal and severe states in clear manner with respect to machine learning principles. This paper introduced a new machine learning strategy by means of combining two powerful machine learning algorithms such as Random Forest Classifier and the AdaBoost Classifier, in which it is integrated together to make a hybrid algorithm called Hybrid Retinal Disease Detection Logic (HRDDL). This proposed approach of HRDDL assures the logic of identifying the retinopathy diseases in clear manner with proper classification logics. The digital retinal image dataset downloaded from Kaggle database is utilized to prove the efficiency of the proposed approach and the resulting scenario is cross-validated with traditional Random Forest Classifier to prove the proposed HRDDL classification accuracy. This paper assures the HRDDL accuracy over prediction of diabetic retinopathy on earlier stages as well as the resulting section shows the clear proof for the identification of disease and the accuracy ratio. The proposed approach of HRDDL provides the accuracy range of 92.5% in results as well as this will be cross-validated with the classical Random Forest classifier to prove the efficiency well.
Robust Blood Vessels Segmentation Based on Memory-Augmented Neural Network
K. Arunabhaskar, R. Kiran Kumar
Lecture Notes in Networks and Systems, 2021
DNA Playfair Cryptosystem Based on 16 × 16 Key Matrix Using DNA ASCII Table
P. Bharathi Devi, R. Kiran Kumar, P. Ravindra
Lecture Notes in Networks and Systems, 2021
An NLP hybrid recommendation system in crop selection for farmers
International Journal of Advanced Science and Technology, 2020
Emotional Intelligence of People Towards Political Parties in Andhra Pradesh Based on Twitter Data
R. Kiran Kumar, P. Bharathi Devi
Lecture Notes in Electrical Engineering, 2020
Swarm-Inspired Task Scheduling Strategy in Cloud Computing
Ramakrishna Goddu, Kiran Kumar Reddi
Lecture Notes in Mechanical Engineering, 2020
Follicle Detection in Digital Ultrasound Images Using BEMD and Adaptive Clustering Algorithms
M. Jayanthi Rao, R. Kiran Kumar
Lecture Notes in Mechanical Engineering, 2020
An Implementation on Agriculture Recommendation System Using Text Feature Extraction
K. Anji Reddy, R. Kiran Kumar
Learning and Analytics in Intelligent Systems, 2020
A Novel Approach for Enhancement of Security and Compression for Cloud Data Storage
R. Kiran Kumar, D. Rathna Kishore, D. Suneetha
Lecture Notes in Electrical Engineering, 2020
An efficient data integrity checking based on regenerating code in cloud computing
Srinivas Mudepalli
Journal of Advanced Research in Dynamical and Control Systems, 2020
Accurate liver disease prediction with extreme gradient boosting
Sivala Vishnu Murty, , Dr. R Kiran Kumar, and
International Journal of Engineering and Advanced Technology, 2019
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
International Journal of Recent Technology and Engineering, 2019
Teaching-learning based task scheduling optimization in cloud computing environments
International Journal of Recent Technology and Engineering, 2019
A research on similarity measure to identify effective similar users in recommender systems
Rajeswari Nakka, Gaya Prasad, R. Kiran Kumar, J Breese, D Heckerman, et al.
International Journal of Innovative Technology and Exploring Engineering, 2019
Privacy preserving redundant data removal in cloud storage: RDRC
International Journal of Recent Technology and Engineering, 2019
Severity of defect: An optimised prediction
Reddi Kiran Kumar, S.V. Achuta Rao
International Journal of Advanced Intelligence Paradigms, 2019
Enhancement of security for cloud data using partition-based steganography
D. Suneetha, R. Kiran Kumar
Advances in Intelligent Systems and Computing, 2019
A Novel Level-Based DNA Security Algorithm Using DNA Codons
Bharathi Devi Patnala, R. Kiran Kumar
Springerbriefs in Applied Sciences and Technology, 2019
A novel approach for data security in cloud environment using image segmentation and image steganography
R. Kiran Kumar, D. Suneetha
Advances in Intelligent Systems and Computing, 2019
Construction of breast cancer-based protein–protein interaction network using multiple sources of datasets
M. Mary Sujatha, K. Srinivas, R. Kiran Kumar
Springerbriefs in Applied Sciences and Technology, 2019
A review analysis on recommendation system
Journal of Advanced Research in Dynamical and Control Systems, 2019
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, R. Usha Rani
Smart Innovation Systems and Technologies, 2019
Heuristic Algorithm based Approach to Classify EEG Signals into Normal and Focal
V. Sankara Narayanan, R. Elavarasan, C.N. Gnanaprakasam, N. Sri Madhava Raja, R. Kiran Kumar
2018 IEEE International Conference on System Computation Automation and Networking Icsca 2018, 2018
SEE: Synchronized Efficient Energy Calculation for Topology Maintenance & Power Saving in Ad Hoc Networks
T. Santhi Sri, J. Rajendra Prasad, R. Kiran Kumar
Arabian Journal for Science and Engineering, 2018
DNA cryptography.life blood for new ERA computers
Bharathi Devi Patnala, Kiran Kumar Reddi
2017 International Conference on Energy Communication Data Analytics and Soft Computing Icecds 2017, 2018
Analysis of different methods in data linkage data presentation with anomaly and redundant in data sources
Journal of Engineering and Applied Sciences, 2018
Exhaustive feature set processing by multi objective optimization based on hybrid approach for CBIR
Journal of Advanced Research in Dynamical and Control Systems, 2018
Determination of optimal clusters for a non-hierarchical clustering paradigm k-means algorithm
T. V. Sai Krishna, A. Yesu Babu, R. Kiran Kumar
Lecture Notes on Data Engineering and Communications Technologies, 2018
Software defect prediction using various classifiers
Journal of Advanced Research in Dynamical and Control Systems, 2018
A novel random forest approach using specific under sampling strategy
L. Surya Prasanthi, R. Kiran Kumar, Kudipudi Srinivas
Advances in Intelligent Systems and Computing, 2018
Optimization of neural network for software effort estimation
P Sankara Rao, Kiran Kumar Reddi, R Usha Rani
2017 International Conference on Algorithms Methodology Models and Applications in Emerging Technologies Icammaet 2017, 2017
Noise removal in microarray images using variational mode decomposition technique
G. Sai Chaitanya Kumar, Reddi Kiran Kumar, G. Apparao Naidu, J. Harikiran
Telkomnika Telecommunication Computing Electronics and Control, 2017
An efficient data retrieval approach using blowfish encryption on cloud ciphertext retrieval in cloud computing
Srinivas Mudepalli, V. Srinivasa Rao, R. Kiran Kumar
Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems Iciccs 2017, 2017
A survey on conventional encryption algorithms of Cryptography
Ramesh Yegireddi, R Kiran Kumar
Proceedings of 2016 International Conference on ICT in Business Industry and Government Ictbig 2016, 2017
Detection of burr type III reliable software using SPRT: An order statistic approach
Journal of Theoretical and Applied Information Technology, 2017
Joint fortification model for reliable online rating systems using linear regression
International Journal of Applied Engineering Research, 2017
An improved neural network to estimate effort of medical imaging software development
P. Sankara Rao, Reddi Kiran Kumar
Journal of Medical Imaging and Health Informatics, 2016
Exploring the Similarity/Dissimilarity measures for unsupervised IDS
P. Sita Rama Murty, R. Kiran Kumar, M. Sailaja
Proceedings of 2016 International Conference on Data Mining and Advanced Computing Sapience 2016, 2016
Neural network optimization using shuffledfrog algorithm for software defect prediction
Journal of Theoretical and Applied Information Technology, 2016
Dimensionality reduction and classification of hyperspectral images using genetic algorithm
Indonesian Journal of Electrical Engineering and Computer Science, 2016
Generalized regression neural network for software defect estimation
Iioab Journal, 2016
Image fusion in hyperspectral image classification using genetic algorithm
B. Saichandana, K. Srinivas, R. KiranKumar
Indonesian Journal of Electrical Engineering and Computer Science, 2016
A novel filter based partitioning decision tree model for real-time network security
Journal of Theoretical and Applied Information Technology, 2016
Ayush to kidney (AtoK) data science model for diagnosis and to advice through an expert system
Kiran Kumar Reddi, Usha Rani Rella
Springerbriefs in Applied Sciences and Technology, 2016
In silico pathway analysis of MAPKs and computational investigation of bipyrazole analogues as novel p38alpha MAPK inhibitors
Deepak Nedunuri, Yesu babu Adimulam, Kiran Kumar Reddi
International Journal of Bioinformatics Research and Applications, 2016
Pathway analysis of highly conserved mitogen activated protein kinases (MAPKs)
N. Deepak, Yesu babu Adimulam, R. Kiran Kumar
Springerbriefs in Applied Sciences and Technology, 2016
Computational screening of drugbank database for novel cell cycle inhibitors
Satyanarayana Kotha, Yesu Babu Adimulam, R. Kiran Kumar
Springerbriefs in Applied Sciences and Technology, 2016
Software reliability using SPRT: Burr type III process model
CH. Smitha, R. Satya Prasad, R. Kiran Kumar
International Journal of Electrical and Computer Engineering, 2016
Identification of drug targets from integrated database of diabetes mellitus genes using protein-protein interactions
Duggineni Kalyani, Naresh Babu Muppalaneni, Ch Ambedkar, Kiran Kumar Reddi
Springerbriefs in Applied Sciences and Technology, 2016
Improved cuckoo search with particle swarm optimization for classification of compressed images
VAMSIDHAR ENIREDDY, REDDI KIRAN KUMAR
Sadhana Academy Proceedings in Engineering Sciences, 2015
Multiple feature fuzzy C-means clustering algorithm for segmentation of microarray images
J. Harikiran, P.V. Lakshmi, R. Kiran Kumar
International Journal of Electrical and Computer Engineering, 2015
Compressed medical image retrieval using neural networks
International Journal of Applied Engineering Research, 2015
Software effort estimation through a generalized regression neural network
Parasana Sankara Rao, Reddi Kiran Kumar
Advances in Intelligent Systems and Computing, 2015
Computer-aided virtual screening of Telmisartan analogues as possible CDK cell cycle inhibitors
Satyanarayana Kotha, Yesu Babu Adimulam, Kiran Kumar Reddi
International Journal of Computational Biology and Drug Design, 2015
Vector filtering techniques for impulse noise reduction with application to microarray images
International Journal of Applied Engineering Research, 2015
Improving query processing performance using optimization among CPEL factors
R. Kiran Kumar, K. Suresh
Advances in Intelligent Systems and Computing, 2014
K-means with bi-dimensional empirical mode decomposition for segmentation of microarray image
J. Harikiran, M. Phanendra, N. N. Swamy, P. V. Lakshmi, R. Kiran Kumar
ACM International Conference Proceeding Series, 2012
A novel approach for intrusion detection using swarm intelligence
M. Sailaja, R. Kiran Kumar, P. Sita Rama Murty, P. E. S. N. Krishna Prasad
Advances in Intelligent and Soft Computing, 2012
Huffbit compress - algorithm to compress DNA sequences using extended binary trees
Journal of Theoretical and Applied Information Technology, 2010
A JAVA-based tool to analyze the functional protein sequences of genes causing Alzheimer's disease
Journal of Theoretical and Applied Information Technology, 2010

RECENT SCHOLAR PUBLICATIONS

Impact of sowing date and irrigation regime on wheat performance under heat and drought stress in the Indo-Gangetic plains of Bihar
T Murugesh, A Mishra, R Kumar
Plant Science Today 13, 11981 , 2026
2026
Abstract PS2-08-05: Multimodal integration of real world genomic and clinical data for the prediction of brain metastasis development in breast cancer
A Safonov, D Smith, S Nandakumar, L Boe, E Ferraro, J Shen, K Tsai, ...
Clinical Cancer Research 32 (4_Supplement), PS2-08-05-PS2-08-05 , 2026
2026
Explainable Multimodal Deep Learning Framework for Transparent Yield of Rice Crop Trend Analysis and Strategic guidance in Indian Smallholder Farming
R Yegireddi, SB Krishnan, RK Kumar, P Chakrabarti
2026 6th International Conference on Image Processing and Capsule Networks … , 2026
2026
Study of Positive Parity Band Structures in Doubly Odd 120I Nucleus
AK Rana, S Sihotra, HP Sharma, R Joshi, S Jehangir, GH Bhat, N Nazir, ...
EPJ Web of Conferences 356, 02024 , 2026
2026
P0735Safety and Efficacy of Ustekinumab in Indian Patients with Moderate to Severe Crohn’s Disease: A Multicentre, Interventional, Phase IV study
R Banerjee, V Ahuja, G Choudhuri, A Dalal, M Kalla, R Mehta, V Midha, ...
Journal of Crohn’s and Colitis 20 (Supplement_1), jjaf231. 916 , 2026
2026
Asian and Low-Resource Language Information Processing
KB Nelatoori, AK Sahagal, HB Kommanti, H Sharma, D Padha, Y Singh, ...
ACM Transactions on 25 (1) , 2026
2026
Complemented and completely regular semirings
TR Sharma, R Kumar
Journal of Algebra and Related Topics , 2025
2025
623P Comparison of Intravenous anaesthesia with propofol and lignocaine versus inhalational anaesthesia on VEGF and T cell levels in ovarian cancer surgery: An open-label …
P Sethi, K Mirani, P Bhatia, M Kaur, R Kumar, S Sharma
Annals of Oncology 36, S1986 , 2025
2025
Non-inductive current drive at zero loop voltage using LHCD PAM launcher on ADITYA-U
J Kumar, PK Sharma, KK Ambulkar, PR Parmar, CG Virani, S Sharma, ...
Nuclear Fusion 65 (11), 116029 , 2025
2025
Citations: 1
Intelligent AI-IoT System for Animal Threat Detection and Farm Security
MJ Rao, SB Krishnan, RK Kumar, P Chakrabarti
2025 2nd International Conference on Electronic Circuits and Signaling … , 2025
2025
Doxorubicin-induced palmar-plantar erythrodysesthesia in a patient with metastatic carcinoma ovary
R Kumar, T Batra, A Kakar
Journal of Postgraduate Medicine 71 (4), 208-209 , 2025
2025
SYNTHESIS AND SPECTROSCOPIC CHARACTERIZATION OF TRANSITION METAL COMPLEXES OF Ni (II) AND Cu (II) DERIVED FROM SCHIFF BASE 2-FURFURALTHIOSEMICARBAZONE.
G Kumar, D Kumar, R Kumar, S Equbal, S Kumar
Rasayan Journal of Chemistry 18 (4) , 2025
2025
Potential role of wheat endophytes and weed leaf extracts in the management of Fusarium head blight
JP Singh, MA Khanday, A Chandrapati, R Kumar, D Kaushik, BC Nath, ...
Plant Science Today 12, 9535 , 2025
2025
2399P Comparing the diagnostic efficiency of 68Ga PSMA PET-CT and 18F PSMA PET-CT guided trans-gluteal prostatic biopsy
T Singhal, R Kumar, P Singh, S Gupta, H Singh, S Kumar, BR Mittal
Annals of Oncology 36, S1305-S1306 , 2025
2025
Nature's Remedies and Conservation: Ethnomedicinal Plants in the Tungnath Region, West Himalaya
BS Adhikari, R Kumar, S Verma
Oecologia Montana 34 (1), 17-34 , 2025
2025
Enhanced expression and interaction of GmRDR1 and GmSGS3 proteins in resistant soybean cultivars synergistically regulate antiviral defense against mungbean yellow mosaic India …
DD Chavan, M Sarkar, A Majumdar, F Mondal, YM Babu, SK Lal, ...
Plant Biology , 2025
2025
Impact of Seasonal Dynamics and Agronomic Practices on Soil Health Indicators: Arbuscular Mycorrhizal Fungi, Glomalin-Related Soil Protein, and Ergosterol
S Bhattacharjee, VD Rajput, B Biswal, N Basak, R Kumar
Eurasian Soil Science 58 (6), 80 , 2025
2025
Citations: 2
Time Series Forecasting in Financial Markets using Temporal Convolutional Network with Informer
SB Seshagani, ZA Salami, R Kumar, PG Kulkarni
2025 3rd International Conference on Data Science and Information System … , 2025
2025
Study of Soy Isoflavones (SOYA) Responder Analysis Among Patients Who Have 4G4G/4G5G Serine Protease Inhibitor Family E Member 1 (SERPINE1) Genotypes
J Fowler, JC Cardet, DT Nguyen, RS Mhaskar, AP Baptist, TB Casale, ...
American Journal of Respiratory and Critical Care Medicine 211 (Supplement_1 … , 2025
2025
Effects of Mepolizumab and Systemic Corticosteroids on Airway Gene Expression Patterns Post-exacerbation in Urban Children With Asthma
CL Gaberino, K Dill-McFarland, LB Bacharier, M Gill, J Stokes, AH Liu, ...
American Journal of Respiratory and Critical Care Medicine 211 (Supplement_1 … , 2025
2025

REDDI KIRAN KUMAR

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Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS