Dr Suneetha Eluri

@jntucek.ac.in

Assistant Professor Computer Science and Engineering
Jawaharlal Nehru Technological University Kakinada

Dr Suneetha Eluri


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https://researchid.co/suneethaeluri

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering
9

Scopus Publications

Scopus Publications

  • Exploring Hybrid Approaches in NLP: Enhancing Text Summarization and Classification Techniques
    Gadi Mounica, Suneetha Eluri
    Lecture Notes in Electrical Engineering, 2026
  • Unstructured data clustering based on layer improved transformer features connected with deep neural network-based adaptive clustering
    Bankapalli Jyothi, L. Sumalatha, Suneetha Eluri
    Cluster Computing, 2025
  • A Real-Time Anomaly Detection Framework for Ethereum Using Rule-Based Logic, GraphSAGE and Consensus-Driven Event Processing
    Balireddi Durga Anuja, Suneetha Eluri
    Conference Proceedings 2025 IEEE Silchar Subsection Conference IEEE Silcon 2025, 2025
    Ethereum is one of the key platforms in public blockchain networks. Ethereum used for various critical applications like decentralized finance (DeFi), smart contract based and supply chain operations etc., Because of its growing popularity along with its open and anonymous nature, network can contribute fraudulent or abnormal transactions. The existing anomaly detection methods suffer from real time detection as they use rule based or statistical methods. These methods are failed to identify dynamic or structural transactional anomalies and has no consensus verification. To address these challenges, in this study we designed an anomaly detection model that features rule based logic, Graph Neural Networks with GraphSAGE and Consensus-driven Complex Event Processing (CEP). We developed this model by combining three key procedures primarily the transactions are fetched from live Ethereum network and presented to rule based filtering. Graph construction and feature extraction will be done at second stage in the model to understand structural pattern and detects dynamically evolving abnormal patterns. In final phase consensus verification introduced as novel contribution in this model to reduce false positives. The framework tested for live Ethereum data that shows improvements detection accuracy of GNN model with low latency time. On the whole, the proposed system model supports practical security monitoring in Ethereum networks. This framework efficiently helps in detecting fraud or abnormal transactions that degrades the performance, reliability or accuracy of the decentralized network.
  • Secure Blockchain System for Digital Content Trading
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Restoration of dehaze and defog image using novel cross entropy-based deep learning neural network
    Ch. Sabitha, Suneetha Eluri
    Multimedia Tools and Applications, 2024
  • Hybrid unstructured text features for meta-heuristic assisted deep CNN-based hierarchical clustering
    Bankapalli Jyothi, L. Sumalatha, Suneetha Eluri
    Intelligent Decision Technologies, 2023
    The text clustering model becomes an essential process to sort the unstructured text data in an appropriate format. But, it does not give the pave for extracting the information to facilitate the document representation. In today’s date, it becomes crucial to retrieve the relevant text data. Mostly, the data comprises an unstructured text format that it is difficult to categorize the data. The major intention of this work is to implement a new text clustering model of unstructured data using classifier approaches. At first, the unstructured data is taken from standard benchmark datasets focusing on both English and Telugu languages. The collected text data is then given to the pre-processing stage. The pre-processed data is fed into the model of the feature extraction stage 1, in which the GloVe embedding technique is used for extracting text features. Similarly, in the feature extraction stage 2, the pre-processed data is used to extract the deep text features using Text Convolutional Neural Network (Text CNN). Then, the text features from Stage 1 and deep features from Stage 2 are all together and employed for optimal feature selection using the Hybrid Sea Lion Grasshopper Optimization (HSLnGO), where the traditional SLnO is superimposed with GOA. Finally, the text clustering is processed with the help of Deep CNN-assisted hierarchical clustering, where the parameter optimization is done to improve the clustering performance using HSLnGO. Thus, the simulation findings illustrate that the framework yields impressive performance of text classification in contrast with other techniques while implementing the unstructured text data using different quantitative measures.
  • A novel Leaky Rectified Triangle Linear Unit based Deep Convolutional Neural Network for facial emotion recognition
    Anjani Suputri Devi D, Suneetha Eluri
    Multimedia Tools and Applications, 2023
  • Intelligent deep learning-based hierarchical clustering for unstructured text data
    Bankapalli Jyothi, Sumalatha Lingamgunta, Suneetha Eluri
    Concurrency and Computation Practice and Experience, 2022
    SummaryDocument clustering is a technique used to split the collection of textual content into clusters or groups. In modern days, generally, the spectral clustering is utilized in machine learning domain. By using a selection of text mining algorithms, the diverse features of unstructured content is captured for ensuing in rich descriptions. The main aim of this article is to enhance a novel unstructured text data clustering by a developed natural language processing technique. The proposed model will undergo three stages, namely, preprocessing, features extraction, and clustering. Initially, the unstructured data is preprocessed by the techniques such as punctuation and stop word removal, stemming, and tokenization. Then, the features are extracted by the word2vector using continuous Bag of Words model and term frequency‐inverse document frequency. Then, unstructured features are performed by the hierarchical clustering using the optimizing the cut‐off distance by the improved sensing area‐based electric fish optimization (FISA‐EFO). Tuned deep neural network is used for improving the clustering model, which is proposed by same algorithm. Thus, the results reveal that the model provides better clustering accuracy than other clustering techniques while handling the unstructured text data.
  • Statistical method for named entity recognition in Telugu, an Indian Language
    , Suneetha Eluri, Sumalatha Lingamgunta, and
    International Journal of Recent Technology and Engineering, 2019
    One of the important tasks of Natural Language Processing (NLP) is Named Entity Recognition (NER). The primary operation of NER is to identify proper nouns i.e. to locate all the named entities in the text and tag them as certain named entity categories such as Entity, Time expression and Numeric expression. In the previous works, NER for Telugu language is addressed with Conditional Random Fields (CRF) and Maximum Entropy models however they failed to handle ambiguous named entity tags for the same named entity. This paper presents a hybrid statistical system for Named Entity Recognition in Telugu language in which named entities are identified by both dictionary-based approach and statistical Hidden Markov Model (HMM). The proposed method uses Lexicon-lookup dictionary and contexts based on semantic features for predicting named entity tags. Further HMM is used to resolve the named entity ambiguities in predicted named entity tags. The present work reports an average accuracy of 86.3% for finding the named entities.