Dr Chandra Sekhar Sanaboina

@jntucek.ac.in

Assistant Professor
University College of Engineering Kakinada, JNTUK

Dr Chandra Sekhar Sanaboina


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https://researchid.co/drcs8283

EDUCATION

B.Tech, M.Tech, Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Science Applications
5

Scopus Publications

15

Scholar Citations

2

Scholar h-index

Scopus Publications

  • Towards energy-efficient IoT routing: A hybrid optimization and DL approach
    Anjana Devi Nandam, Chandra Sekhar Sanaboina
    Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2026
    While the energy levels of nodes in these networks vary, there is still a need for further advancements to fully optimize the performance of heterogeneous Wireless Sensor Networks (WSNs). Efficient data routing plays a vital role in improving the IoT-enabled WSNs overall performance. Existing routing protocols face difficulties in addressing issues like frequent node movement, optimizing energy efficiency, scalability, and adapting to changing network conditions. These challenges need to be addressed to ensure effective routing in IoT-based heterogeneous WSNs. Thus, a new approach for optimal routing in IoT-based heterogeneous WSNs that utilizes the STGOA is proposed. In the Network Modeling stage, various components of the IoT-enabled heterogeneous WSN, including sensor nodes, servers, the internet, BS, and the network topology, are defined. The interactions between these elements are established to define the communication structure and flow within the network. The Optimal Routing stage begins with the clustering of sensor nodes via the Fuzzy C Means (FCM) method. After clustering, the STGOA approach is employed to choose the optimal paths while considering critical constraints like energy consumption, trust, distance, security, and delay. The proposed STGOA method achieved maximum energy rate of 0.368 J at node 100 and 0.375 J at node 200 as compared to the other methods like GWO, SHO, GOA, STO, BOA, NBO, JFO, EEMCM and GMPSO.
  • An Empirical Literature Review on Multi Class Data Stream Learning with Class Imbalance Problem in Real World Data Sources
    Rajesh Reddy Muley, Bhanu Prakash Battula, Chandra Sekhar Sanaboina
    Esic 2025 5th International Conference on Emerging Systems and Intelligent Computing Proceedings, 2025
    Data mining and knowledge discovery is one of the cutting-edge fields in science and technology for identifying pressing issues in real world. The amount of data collected has increased due to the quick development of technology and the low cost of internet access. One of the difficult problems is analyzing the massive volume of data that comes in from various data streams. Machine learning research has primarily concentrated on binary classification data problems with high unbalanced data from the earliest literatures published on till recently. When it comes to processing data streams effectively, research on highly unbalanced multi-class data is still mostly underdeveloped. This study focuses on reviews of the models or methods for dealing with extremely unbalanced multi-class data, as well as their advantages and disadvantages and related fields. The objectives of this article are to: comprehend the trend of highly imbalanced multi-class data through examination of related literatures; to analyze the traditional and contemporary approaches to handling highly imbalanced multi-class data; and develop a framework for highly imbalanced multi-class data. After doing the chosen highly imbalanced multi-class dataset analysis and adapting it to the most recent machine learning algorithms or approaches, there will be talks on unresolved issues and the way highly imbalanced multi-class data should go in the future.
  • Patented Study on BERT-Based Combined Approach for Fake News Detection
    N. Sandhya, Durga Prasad Kavadi, Chandra Sekhar Sanaboina, Kongara Srinivasa Rao
    Recent Patents on Engineering, 2025
    Advanced technologies on the internet create an environment for information exchange among communities. However, some individuals exploit these environments to spread false news. False News, or Fake News (FN), refers to misleading information deliberately crafted to harm the reputation of individuals, products, or services. Identifying FN is a challenging issue for the research community. Many researchers have proposed approaches for FN detection using Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this patent article, we propose a combined approach for FN detection, leveraging both ML and NLP techniques. We first extract all terms from the dataset after applying appropriate preprocessing techniques. A Feature Selection Algorithm (FSA) is then employed to identify the most important features based on their scores. These selected features are used to represent the dataset documents as vectors. The term weight measure determines the significance of each term in the vector representation. These document vectors are combined with vector representations obtained through an NLP technique. Specifically, we use the Bidirectional Encoder Representations from Transformers (BERT) model to represent the document vectors. The BERT small case model is employed to generate features, which are then used to create the document vectors. The combined vector, comprising ML-based document vector representations and NLP-based vector representations, is fed into various ML algorithms. These algorithms are used to build a model for classification. Our combined approach for FN detection achieved the highest accuracy of 96.72% using the Random Forest algorithm, with document vectors that included content-based features of size 4000 concatenated with outputs from the 9th to 12th BERT encoder layers.
  • N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages
    Durga Prasad Kavadi, Chandra Sekhar Sanaboina, Rizwan Patan, Amir Gandomi
    ACM International Conference Proceeding Series, 2022
    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
    International Journal of Innovative Technology and Exploring Engineering, 2019
    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

  • EduBot: A rag based bot using cloud-based models and hybrid embedding strategies
    CSS Vanka Vinay
    International Journal of Computing and Artificial Intelligence 7 (5), 43-57 , 2026
    2026
  • Calibrated Multi-Evidence Fusion Framework for Clinical Decision Support Systems
    CSS Addakula Chaithanya
    International Journal of Innovative Research in Engineering 7 (31), 59-68 , 2026
    2026
  • Energy and Trust Aware Optimal Routing in Heterogeneous IoT Network
    AD Nandam, CS Sanaboina
    2026 International Conference on Smart Electronic Devices and Intelligent … , 2026
    2026
  • Towards energy-efficient IoT routing: A hybrid optimization and DL approach
    AD Nandam, CS Sanaboina
    Journal of Power and Energy, 1-17 , 2026
    2026
    Citations: 1
  • A Comparative Study of Beam and Greedy Decoding Strategies for Image Captioning using Hybrid VIT-LSTM and Lightning Search Algorithm
    GSR Chandra Sekhar Sanaboina
    International Journal of Computer Applications 187 (31), 10-19 , 2025
    2025
  • Optimizing crop prediction, yield prediction and fertilizer recommendation with machine learning, feature selection, and sampling techniques for sustainable agriculture
    CS Sanaboina
    International Journal of Agriculture and Food Science 7 (8), 1245-1257 , 2025
    2025
  • Hybrid CNN-LBP Model for Realtime Emotion Classification
    CS Sanaboina
    IOSR Journal of Computer Engineering 27 (3), 6-19 , 2025
    2025
  • A Pipeline-Based Approach for Enhancing Political Threat Detection Using Machine Learning
    CS Sanaboina
    International Journal of Innovative Science and Research Technology 10 (7 … , 2025
    2025
  • Integrating Local Texture Capturing Mechanisms With Convolutional Neural Networks For Enhanced Multi‑Class Classification of Plant Leaf Diseases
    CS Sanaboina
    Intelligent Agriculture 1 (1), 53-71 , 2025
    2025
  • BRAIN TUMOR SEGMENTATION IN 3D MRI IMAGES USING W-NET ARCHITECTURE
    CS Sanaboina
    Journal on Future Engineering & Technology 20 (2), 33-43 , 2025
    2025
  • An Empirical Literature Review on Multi Class Data Stream Learning with Class Imbalance Problem in Real World Data Sources
    RR Muley, BP Battula, CS Sanaboina
    2025 International Conference on Emerging Systems and Intelligent Computing … , 2025
    2025
  • 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
    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
    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
    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
    2024
    Citations: 1
  • 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
    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
    2023
    Citations: 2
  • 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
    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
    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
    2023

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
    2019
    Citations: 3
  • 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
    2023
    Citations: 2
  • Performance Evaluation of Advanced Congestion Control Mechanisms for COAP
    CS Sanaboina, T Eluri
    arXiv preprint arXiv:2305.05310 , 2023
    2023
    Citations: 2
  • Impact of mobility on power consumption in RPL
    CS Sanaboina, P Sanaboina
    arXiv preprint arXiv:2305.05308 , 2023
    2023
    Citations: 2
  • 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
    2022
    Citations: 2
  • Towards energy-efficient IoT routing: A hybrid optimization and DL approach
    AD Nandam, CS Sanaboina
    Journal of Power and Energy, 1-17 , 2026
    2026
    Citations: 1
  • Patented Study on BERT-Based Combined Approach for Fake News Detection
    N Sandhya, DP Kavadi, CS Sanaboina, KS Rao
    Recent Patents on Engineering , 2024
    2024
    Citations: 1
  • Prediction of Renal Illness using Machine Learning Models
    CS Sanaboina, SSPC Kurella
    INFOCOMP Journal of Computer Science 22 (1) , 2023
    2023
    Citations: 1
  • 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
    2019
    Citations: 1
  • EduBot: A rag based bot using cloud-based models and hybrid embedding strategies
    CSS Vanka Vinay
    International Journal of Computing and Artificial Intelligence 7 (5), 43-57 , 2026
    2026
  • Calibrated Multi-Evidence Fusion Framework for Clinical Decision Support Systems
    CSS Addakula Chaithanya
    International Journal of Innovative Research in Engineering 7 (31), 59-68 , 2026
    2026
  • Energy and Trust Aware Optimal Routing in Heterogeneous IoT Network
    AD Nandam, CS Sanaboina
    2026 International Conference on Smart Electronic Devices and Intelligent … , 2026
    2026
  • A Comparative Study of Beam and Greedy Decoding Strategies for Image Captioning using Hybrid VIT-LSTM and Lightning Search Algorithm
    GSR Chandra Sekhar Sanaboina
    International Journal of Computer Applications 187 (31), 10-19 , 2025
    2025
  • Optimizing crop prediction, yield prediction and fertilizer recommendation with machine learning, feature selection, and sampling techniques for sustainable agriculture
    CS Sanaboina
    International Journal of Agriculture and Food Science 7 (8), 1245-1257 , 2025
    2025
  • Hybrid CNN-LBP Model for Realtime Emotion Classification
    CS Sanaboina
    IOSR Journal of Computer Engineering 27 (3), 6-19 , 2025
    2025
  • A Pipeline-Based Approach for Enhancing Political Threat Detection Using Machine Learning
    CS Sanaboina
    International Journal of Innovative Science and Research Technology 10 (7 … , 2025
    2025
  • Integrating Local Texture Capturing Mechanisms With Convolutional Neural Networks For Enhanced Multi‑Class Classification of Plant Leaf Diseases
    CS Sanaboina
    Intelligent Agriculture 1 (1), 53-71 , 2025
    2025
  • BRAIN TUMOR SEGMENTATION IN 3D MRI IMAGES USING W-NET ARCHITECTURE
    CS Sanaboina
    Journal on Future Engineering & Technology 20 (2), 33-43 , 2025
    2025
  • An Empirical Literature Review on Multi Class Data Stream Learning with Class Imbalance Problem in Real World Data Sources
    RR Muley, BP Battula, CS Sanaboina
    2025 International Conference on Emerging Systems and Intelligent Computing … , 2025
    2025
  • 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
    2025