suryanarayanaraju saripalle

@srkrec.edu.in

Assistant Professor, Information Technology
SRKR Engineering College

suryanarayanaraju saripalle


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https://researchid.co/snrajusaripalle

EDUCATION

M.Tech(

RESEARCH, TEACHING, or OTHER INTERESTS

Management Information Systems
8

Scopus Publications

2

Scholar Citations

1

Scholar h-index

Scopus Publications

  • Designing AI-Driven Load Distribution Framework for Federated Networks on Electronic Health Records Using Adaptive Temporal Convolution Network With Attention Mechanism
    S. Suryanarayanaraju, M. Chandra Naik, R. N. V. Jagan Mohan
    Computational Intelligence, 2026
    The vast amount of electronic health records (EHRs) is a norm to specify the health data in a digital world. As the amount of data starts to grow, handling the tasks on the basis of attributes such as historical performance, computational capacity, and workload results in a serious problem. The high processing loads can consume more resources and result in inefficiencies or delays in using and evaluating sensitive health data. With the emergence of the internet of things (IoT), artificial intelligence (AI), machine learning, and deep learning strategies, data‐driven applications become a promising solution for designing robust diagnostic approaches utilizing healthcare data. These mechanisms obtained much attention from industries and the educational sectors and resulted in better advancements in healthcare models. The AI‐driven approaches associated with healthcare applications still encounter some problems including security, privacy, and the quality‐of‐service (QoS). Machine learning with privacy‐preserving approaches safeguards the data; yet still, it is complex to formulate the infrastructural facilities. Certain problems are efficiently resolved by federated learning (FL) since it enables personal communication between distinct technologies and lightens the system's computational overload. Moreover, it efficiently manages the EHR data. Therefore, this work designs an efficient AI‐driven load distribution in the federated network by considering the limitations of the existing mechanisms. In the developed framework, at first, the essential EHRs are collected from the benchmark resources and provided to the FL network to resolve the overloading issues. Further, the EHR data is used to detect the disease using an Adaptive Temporal Convolution Network with Attention Mechanism (ATCN‐AM) in the FL network. Moreover, the parameters in the ATCN‐AM model are tuned using Improved Dung Beetle Optimizer (IDBO). Finally, different experimental validations are performed in the developed framework over the conventional mechanisms.
  • AI System for Detecting Lung Diseases & Patient Management: Acoustic Diagnostics
    S. Suryanarayanaraju, V. Charan Prem, V. Sai Krishnam Raju, V. Lakshmi Narayana Raju, Y. Rohith Siva Sai
    Lecture Notes in Networks and Systems, 2026
  • Comprehensive Exploration of Generative Pre-trained Transformer
    Chandra Sekhar Kolli, Sreenivasu Seelamanthula, V. Venkata Krishna Reddy, S. Suryanarayanaraju, E. Elamathi, Ravi Kumar Balleda
    Lecture Notes in Networks and Systems, 2025
  • An Interpretable Stacking Ensemble Model AB-CBLC for High-Accuracy Lung Cancer Detection Using Clinical and Behavioral Data
    Vijai Bhaskar P, Veeramani R, Babu Rao K, Edagotti Lakshmi Devi, Suryanarayanaraju Saripalle, Raghu Dhumpati
    Proceedings of 8th International Conference on Computing Methodologies and Communication Iccmc 2025, 2025
    Lung cancer diagnosis at an early stage proves essential in enhancing patient survival rates together with treatment results. The authors developed ACBoost Lung Cancer Classifier (AB-CBLC) as a new ensemble framework that combines AdaBoost and CatBoost algorithms in stacking ensemble structure to boost diagnostic predications and clinical utility in a systematic fashion. The model development utilizes data from the Kaggle database which contains diverse features from clinical tests and behavioral information as well as environmental aspects from lung cancer patients. Advanced imputation methods together with normalization and the Interquartile Range (IQR) method and Synthetic Minority Over-sampling Technique (SMOTE) for class balancing provide a strong preprocessing framework. This framework ensures optimal data representativeness and quality for the classification analysis. ANOVA-based feature selection achieves efficient model optimization by discovering important statistically relevant predictors which minimizes dataset dimensions. Superior performance can be observed with the AB-CBLC model because its accuracy reaches 99.02% and precision stands at 98.74% with recall at 99.21% while AUC-ROC measures 99.60% better than standard classification models. By integrating SHAP (SHapley Additive exPlanations) techniques the model becomes more transparent for clinicians to decipher because it enables feature contribution quantification during prediction which supports both clinical trust and ethical AI practices. Clinical real-world systems receive fundamental assistance through the AB-CBLC framework since it shows robust capabilities as an early lung cancer diagnosis tool which combines interpretability with accuracy and performance excellence.
  • Efficient deep learning models for Telugu handwritten text recognition
    Buddaraju Revathi, B. N. V. Narasimha Raju, Boddu L. V. Siva Rama Krishna, Ajay Dilip Kumar Marapatla, S. Suryanarayanaraju Saripalle
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    <p>Optical character recognition (OCR) technology is indispensable for converting and analyzing text from various sources into a format that is editable and searchable. Telugu handwriting presents notable challenges due to the resemblance of characters, the extensive character set, and the need to segment overlapping characters. To segment the overlapping characters, we assess the width of small characters within a word and segment the overlapping characters accordingly. This method is well suited for the segmentation of overlapping compound characters. To address the recognition of similar characters with less training periods we have used ResNet 18 and SqueezeNet models which have achieved character recognition rates of 95% and 94% respectively.</p>
  • AI Driven Load Distribution for Federated Network on Electronic Health Records
    S. Suryanarayanaraju, M. Chandra Naik, R. N. V. Jagan Mohan
    Algorithms in Advanced Artificial Intelligence, 2024
    The enormous of Electronic Health Records is a norm to represent health data in the digital world. As the volume records continues to grow, managing tasks based on factors like workload, computational capacity, and historical performance will indeed become a critical challenge. High processing loads can strain resources and lead to delays or inefficiencies in accessing and analyzing crucial health data. This research proposes to address this issue using Artificial Intelligence (AI) for load distribution in federated networks which enhances system efficiency and responsiveness.
  • A Short Survey of AI-driven Load Distribution for Electronic Health Record Management with Recent Deployed Techniques, Research Gaps and Challenges
    S. Suryanarayanaraju, M.Chandra Naik, R.N. V Jagan Mohan
    Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
    With the arrival of Artificial Intelligence (AI), the Internet of Things (IoT), machine learning, and deep learning methodologies, data-driven medical applications have appeared as a promising solution for implementing scalable and robust prognostic and diagnostic approaches with the utilization of medical data. These technologies attained a lot of interest from academia and industry and led to insightful advancements in healthcare systems. The AI-driven techniques related to medical applications still face several issues including privacy, security, and Quality-of-Service (QoS) standards. The complex machine learning models are effectively trained via Federated Learning (FL) in a distributed manner, this advanced technique has become an attractive research area, especially in processing healthcare data at the network edge in a decentralized way to address security concerns and preserve privacy of the information. The availability of FL in the medical domain has several benefits, but it has not been explored to its extent. Existing review works related to FL mainly concentrated on the importance of FL in diverse applications, but no brief or comprehensive survey exists on the distribution of Electronic Health Records (EHR). Thus, this review work focuses on various techniques of AI-driven load distribution for federated networks on EHR. For this purpose, a detailed literature survey is conducted on AI-driven load distribution framework for federated networks on EHR from the previous decades. The AI techniques employed for disease detection are analyzed and categorized. The dataset details, performance metrics, and simulation tools are gathered and listed for a better understanding of the process. Subsequently, the research gaps on the conventional AI-driven load distribution framework for federated networks on EHR are provided to facilitate future work.
  • Quantifying inference learning model to explore twitter user emotions
    , G.SRINIVASA RAJU, M.AJAY DILIP KUMAR, , S.SURYANARAYANA RAJU, and
    International Journal of Innovative Technology and Exploring Engineering, 2019
    Increasing social media used by different peoples express their opinions and feelings in the form sentences and text messages. So that extracting the information from message i.e which consists different issues in text and identifying anxiety depression of individuals and measuring well-being or mood of a community. This is because of its significance in a wide scope of fields, for example, business and governmental issues. Individuals express assessments about explicit themes or elements with various qualities and powers, where these estimations are firmly identified with their own sentiments and feelings. Various techniques and lexical assets have been proposed to break down feeling from normal language writings, tending to various assessment measurements. In this article, we propose a novel inference methodology for quantifying and inferring the Twitters users’ conclusion grouping utilizing distinctive notion measurements as meta-level highlights. We consolidate angles, for example, assessment quality, feeling and extremity markers, created by existing estimation investigation strategies and assets. Our exploration demonstrates that the mix of assumption measurements gives critical improvement in Twitter feeling characterization errands, for example, extremity and subjectivity.

RECENT SCHOLAR PUBLICATIONS

  • Designing AI‐Driven Load Distribution Framework for Federated Networks on Electronic Health Records Using Adaptive Temporal Convolution Network With Attention Mechanism
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    Computational Intelligence 42 (2), e70216 , 2026
    2026
  • AI System for Detecting Lung Diseases & Patient Management: Acoustic Diagnostics
    S Suryanarayanaraju, VC Prem, VSK Raju, VLN Raju, YRS Sai
    Smart Computing Paradigms: Human-Centric Systems for Sustainable Development … , 2026
    2026
  • An Effective Optimal Load Scheduling System on Electronic Health Record Utilizing Heuristic Algorithm with Multi-Objective Constraints for Empowering Healthcare System
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    2025 International Conference on Electronics and Computing, Communication … , 2025
    2025
  • An Interpretable Stacking Ensemble Model AB-CBLC for High-Accuracy Lung Cancer Detection Using Clinical and Behavioral Data
    R Veeramani, EL Devi, S Saripalle, R Dhumpati
    2025 8th International Conference on Computing Methodologies and … , 2025
    2025
  • Comprehensive Exploration of Generative Pre-trained Transformer
    CS Kolli, S Seelamanthula, VVK Reddy, S Suryanarayanaraju, E Elamathi, ...
    International Conference on Data Analytics & Management, 487-507 , 2025
    2025
    Citations: 1
  • AI System for Detecting Lung Diseases & Patient Management: Acoustic Diagnostics
    S Suryanarayanaraju, V Charan Prem, V Sai Krishnam Raju, ...
    International Conference on Smart Computing and Informatics, 63-75 , 2025
    2025
  • AI-Powered Electronic Health Record Analysis System
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    Algorithms in Advanced Artificial Intelligence, 539-544 , 2025
    2025
  • Efficient deep learning models for Telugu handwritten text recognition
    B Revathi, BNVN Raju, BLVSR Krishna, ADK Marapatla, SS Saripalle
    Indonesian Journal Electrical Engineering and Computer Science 36 (3), 1564~1572 , 2024
    2024
  • A Short Survey of AI-driven Load Distribution for Electronic Health Record Management with Recent Deployed Techniques, Research Gaps and Challenges
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    2024 5th International Conference on Smart Electronics and Communication … , 2024
    2024
    Citations: 1
  • AI Driven Load Distribution for Federated Network on Electronic Health Records
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    Algorithms in Advanced Artificial Intelligence, 210-216 , 2024
    2024
  • DESIGN A DETECTION MODEL OF DDOS AT TACKS IN SDN ENVIRONMENT USING DECISION TREE AND SUPPORT VECTOR MACHINE
    DVNB Kishore Varma Mantena, B.V.N.Praveena, S.Suryanarayana Raju
    NEUROQUANTOLOGY 20 (11), 4792-4799 , 2022
    2022
  • An Enhanced Technique to discover web data extraction and Data mining in Multi Cloud Server
    ADKM Dadi Madhu SivaRama Krishna, S.Suryanarayana Raju
    International Journal for Modern Trends in Science and Technology 7 (10), 40-47 , 2021
    2021
  • Quantifying Inference Learning Model to Explore Twitter User Emotions
    G.Srinivasa Raju, M.AjayDilipKumar, S.Suryanarayana Raju
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
  • AN ECONOMICAL SEPARATION PROPOSAL WITHOUT SIGNIFICANCE FLOODING
    K.V.V.Satyanarayana Murthy,V.V.Sivarama Raju, S.Suryanarayana Raju
    INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY, 722-726 , 2016
    2016
  • WELL REFINED SCHEME BY VISUAL INFORMATION
    S.SOWJANYA,K.R.S.RAMARAJU, S.SURYANARAYANARAJU
    International Journal of Innovative Technology and Research 4 (6), 5076-5079 , 2016
    2016
  • Big data Analytics and Scheduling: A Survey
    S.Suryanarayana Raju,V.V.Sivarama Raju
    6th International Advanced Computing Conference , 2016
    2016
  • Information Retrieval Based on CPHC in Location Based Search
    Y.Vijaya Babu, S. S. N. Raju, V. V. S. Ramaraj
    International Journal of Applied Sciences, Engineering and Management 4 (03 … , 2015
    2015

MOST CITED SCHOLAR PUBLICATIONS

  • Comprehensive Exploration of Generative Pre-trained Transformer
    CS Kolli, S Seelamanthula, VVK Reddy, S Suryanarayanaraju, E Elamathi, ...
    International Conference on Data Analytics & Management, 487-507 , 2025
    2025
    Citations: 1
  • A Short Survey of AI-driven Load Distribution for Electronic Health Record Management with Recent Deployed Techniques, Research Gaps and Challenges
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    2024 5th International Conference on Smart Electronics and Communication … , 2024
    2024
    Citations: 1
  • Designing AI‐Driven Load Distribution Framework for Federated Networks on Electronic Health Records Using Adaptive Temporal Convolution Network With Attention Mechanism
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    Computational Intelligence 42 (2), e70216 , 2026
    2026
  • AI System for Detecting Lung Diseases & Patient Management: Acoustic Diagnostics
    S Suryanarayanaraju, VC Prem, VSK Raju, VLN Raju, YRS Sai
    Smart Computing Paradigms: Human-Centric Systems for Sustainable Development … , 2026
    2026
  • An Effective Optimal Load Scheduling System on Electronic Health Record Utilizing Heuristic Algorithm with Multi-Objective Constraints for Empowering Healthcare System
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    2025 International Conference on Electronics and Computing, Communication … , 2025
    2025
  • An Interpretable Stacking Ensemble Model AB-CBLC for High-Accuracy Lung Cancer Detection Using Clinical and Behavioral Data
    R Veeramani, EL Devi, S Saripalle, R Dhumpati
    2025 8th International Conference on Computing Methodologies and … , 2025
    2025
  • AI System for Detecting Lung Diseases & Patient Management: Acoustic Diagnostics
    S Suryanarayanaraju, V Charan Prem, V Sai Krishnam Raju, ...
    International Conference on Smart Computing and Informatics, 63-75 , 2025
    2025
  • AI-Powered Electronic Health Record Analysis System
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    Algorithms in Advanced Artificial Intelligence, 539-544 , 2025
    2025
  • Efficient deep learning models for Telugu handwritten text recognition
    B Revathi, BNVN Raju, BLVSR Krishna, ADK Marapatla, SS Saripalle
    Indonesian Journal Electrical Engineering and Computer Science 36 (3), 1564~1572 , 2024
    2024
  • AI Driven Load Distribution for Federated Network on Electronic Health Records
    S Suryanarayanaraju, MC Naik, RNVJ Mohan
    Algorithms in Advanced Artificial Intelligence, 210-216 , 2024
    2024
  • DESIGN A DETECTION MODEL OF DDOS AT TACKS IN SDN ENVIRONMENT USING DECISION TREE AND SUPPORT VECTOR MACHINE
    DVNB Kishore Varma Mantena, B.V.N.Praveena, S.Suryanarayana Raju
    NEUROQUANTOLOGY 20 (11), 4792-4799 , 2022
    2022
  • An Enhanced Technique to discover web data extraction and Data mining in Multi Cloud Server
    ADKM Dadi Madhu SivaRama Krishna, S.Suryanarayana Raju
    International Journal for Modern Trends in Science and Technology 7 (10), 40-47 , 2021
    2021
  • Quantifying Inference Learning Model to Explore Twitter User Emotions
    G.Srinivasa Raju, M.AjayDilipKumar, S.Suryanarayana Raju
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
  • AN ECONOMICAL SEPARATION PROPOSAL WITHOUT SIGNIFICANCE FLOODING
    K.V.V.Satyanarayana Murthy,V.V.Sivarama Raju, S.Suryanarayana Raju
    INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY, 722-726 , 2016
    2016
  • WELL REFINED SCHEME BY VISUAL INFORMATION
    S.SOWJANYA,K.R.S.RAMARAJU, S.SURYANARAYANARAJU
    International Journal of Innovative Technology and Research 4 (6), 5076-5079 , 2016
    2016
  • Big data Analytics and Scheduling: A Survey
    S.Suryanarayana Raju,V.V.Sivarama Raju
    6th International Advanced Computing Conference , 2016
    2016
  • Information Retrieval Based on CPHC in Location Based Search
    Y.Vijaya Babu, S. S. N. Raju, V. V. S. Ramaraj
    International Journal of Applied Sciences, Engineering and Management 4 (03 … , 2015
    2015