Dr Chandra Sekhar Sanaboina

EDUCATION

B.Tech, M.Tech, Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science Applications

Scopus Publications

N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages
Durga Prasad Kavadi, Chandra Sekhar Sanaboina, Rizwan Patan, and Amir Gandomi
ACM
Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection.


Secret image sharing using visual cryptography shares with acknowledgement
Chandra Sekhar Sanaboina, , Srinivasa Rao Odugu, Girish Vanamadi, , and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Visual Cryptography is an encryption technique in which the secret image is encoded and divided into n meaningless images called shares. The shares look like black and white dots embedded randomly in an image. These shares don’t reveal any information about the original image. Every share was printed on transparent paper and decrypted through the superimposition of shares without any computer decryption algorithm. When all n shares were overlapped, the original picture would appear. A (k, n)-threshold visual cryptography is a technique in which n is the maximum number of shares that are to be generated and k is the minimum number of shares that are required to decrypt the original image. If the insufficient number of shares, which are less than the k value is given to the decryption function, the decryption function will generate the output, which doesn’t reveal any clue to the original image. This paper presents how the Entropy, Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) values varies with respect to given same image of different sizes.

RECENT SCHOLAR PUBLICATIONS

A Novel Chaos-Based Cryptographic Scrambling Technique to Secure Medical Images
CS Sanaboina
International Research Journal of Advanced Engineering and Science 10 (1), 90-98 2025



An innovative and cost effective IOT system for character recognition of vehicle license plates
CS Sanaboina
International Journal of Communication and Information Technology 6 (1), 18-23 2024



Risk Assessment in Online Social Networks Through Client Activity Analysis using Machine Learning
SC Sekhar
International Journal of Science and Research 13 (10), 9 2024



Patented Study on BERT-Based Combined Approach for Fake News Detection
N Sandhya, DP Kavadi, CS Sanaboina, KS Rao
Recent Patents on Engineering 2024



A comparative study of different machine learning techniques for forecasting rainfall
CS Sanaboina
International Journal of Computing and Artificial Intelligence 5 (2), 211-219 2024



Win probability prediction for IPL match using various machine learning techniques
MVKK Dr Chandra Sekhar Sanaboina
International Journal of Engineering in Computer Science 5 (2), 13-20 2023



Detecting and Classifying Inappropriate Content in Youtube Videos Using Deep Learning Approach
YEA Dr Chandra Sekhar Sanaboina
International Journal of Science and Research 12 (9), 1447-1451 2023



Enhancing the Security of Smart Grid using Neural Networks
CS Dr Chandra Sekhar Sanaboina
International Journal for Multidisciplinary Research (IJFMR) 5 (4), 1-11 2023



Augmented Reality App for Location based Exploration at JNTUK Kakinada
KS Dr Chandra Sekhar Sanaboina
International Research Journal of Engineering and Technology (IRJET) 10 (8 2023



Prediction of Renal Illness using Machine Learning Models
CS Sanaboina, SRISPC KURELLA
INFOCOMP Journal of Computer Science 22 (1) 2023



SMS Enabled Display Board using Internet of Things
MSP Dr Chandra Sekhar Sanaboina
International Journal of Innovative Science and Research Technology 8 (8 2023



An Automated Power Conservation System (APCS) using Particle Photon and Smartphone
CS Sanaboina, H Bommidi
arXiv preprint arXiv:2305.11889 2023



Performance Evaluation of Advanced Congestion Control Mechanisms for COAP
CS Sanaboina, T Eluri
arXiv preprint arXiv:2305.05310 2023



Identification of the Most Significant Parameter for Optimizing the Performance of RPL Routing Protocol in IoT Using Taguchi Design of Experiments
CS Sanaboina, P Sanaboina
arXiv preprint arXiv:2310.19172 2023



Impact of mobility on power consumption in RPL
CS Sanaboina, P Sanaboina
arXiv preprint arXiv:2305.05308 2023



Augmented Reality App for Location based Exploration at JNTUK Kakinada
SC Sekhar, K Sravan
2023



An Effective Approach to Scramble Multiple Diagnostic Imageries Using Chaos-Based Cryptography
MTY Dr Chandra Sekhar Sanaboina
International Journal of Next-Generation Computing 13 (4), 851-861 2022



N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages
AG Durga Prasad Kavadi , Chandra Sekhar Sanaboina , Rizwan Patan
ISMSI '22: 2022 6th International Conference on Intelligent Systems 2022



Mitigation and Identification of Camouflage Attack Over Computer Vision Applications
SBG Chandra Sekhar Sanaboina
International Journal of Innovative Research in Computer Science Technology 2022



Credit Card Fraud Detection Using Lightgbm And Catboost
RRK Chandra Sekhar Sanaboina, Sampath Inumukkala
International Journal of Grid and Distributed Computing13 13 (2) 2020

MOST CITED SCHOLAR PUBLICATIONS

Secret Image Sharing using Visual Cryptography Shares with Acknowledgement
GV Chandra Sekhar Sanaboina, Srinivasa Rao Odugu
International Journal of Innovative Technology and Exploring Engineering 2019
Citations: 3



Performance Evaluation of Advanced Congestion Control Mechanisms for COAP
CS Sanaboina, T Eluri
arXiv preprint arXiv:2305.05310 2023
Citations: 2



Impact of mobility on power consumption in RPL
CS Sanaboina, P Sanaboina
arXiv preprint arXiv:2305.05308 2023
Citations: 2



A Novel Fuzzy Based Method to Improve the Network Lifetime in Internet of Things
CS Sanaboina, P Sanaboina
International Journal of Recent Technology and Engineering (IJRTE) 8 (4) 2019
Citations: 1