K.N.V. SATYANARAYANA

@srkrec.edu.in

ASSISTANT PROFESSOR and Electronics and Communication Engineering College
SRKR Engineering College Bhimavaram.

K.N.V. SATYANARAYANA


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https://researchid.co/satya14
K.N.V. Satyanarayana presently working as an assistant professor in Department of Electronics and Communication Engineering, S.R.K.R. Engineering College, Bhimavaram, A.P, India. He is currently pursuing PhD from Annamalai University. His current research interests include Image processing, Signal processing, Machine learning and Internet of things.

EDUCATION

Mtech.

RESEARCH INTERESTS

image Processing ,signal processing ,IoT ,Machine learning,
16

Scopus Publications

134

Scholar Citations

8

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Deep learning based social media aware semantic computing model to predict user reviews
    Sonia Daniel, Rakesh Arya, Arun R. Nair, Swaroop Reddy Gayam, K. N. V. Satyanarayana, Jitendra Kumar Chaudhary
    Aip Conference Proceedings, 2025
  • Real-time plants recognition and medicinal insights using deep learning
    K. N. V. Satyanarayana, T. S. S. Harsha, V. Adarsh, I. Mahesh Babu, S. K. Mohammad Hujaifa, C. Satheesh
    Machine Learning Predictive Analytics and Optimization in Complex Systems, 2025
    In recent times, Deep learning has been utilized in many areas, particularly for medicinal plant identification and evaluation of their therapeutic values. In this research, we use transfer learning capabilities of three state-of-the-art architectures: ResNet-50, VGG-16, and Xception. They are trained for the high-accuracy classification of plants and assessment of their medicinal properties. Our experiments demonstrate that the performance is significantly high, with ResNet-50 having 91.41%, VGG-16 84.27%, and an impressive 91.68% from Xception models, respectively. The findings from these trials highlight how effective transfer learning can be over limited data availability and complex feature extraction that is typically associated with medicinal plant identification tasks. Moreover, we perform a comprehensive analysis to connect identified plants to their respective medical values, which gives useful information about their treatment uses as the therapeutic application (s).
  • Smart traffic management for emergency vehicles using YOLOv8 algorithm
    K. N. V. Satyanarayana, Rongali Gnana Prasanna, Ramineedi Rama Krishna Sai Satwik, Ponala Rupchand, Pigilam Srihaas, S. Bhuvanapriya
    Multidisciplinary Approaches to AI Data and Innovation for A Smarter World, 2025
    Public safety and emergency response depend on rapid and accurate identification of emergency vehicles, particularly ambulances, in increasingly complicated urban traffic. This study uses deep learning to identify ambulances in congested traffic using cutting-edge object detection models like YOLOv5 and YOLOv8 [YOLO (You Only Look Once) is a family of CNN-based object detection models]. This project focusses on recognising ambulances in complex urban circumstances to provide holistic understanding and effective emergency management techniques in urban landscapes. The authors used YOLOv8, a cutting-edge object identification model with high real-time performance, for robust detection. The model, rigorously trained with a precision rate of 0.762, recall rate of 0.631, and mAP50 of 0.69, accurately identifies ambulances under difficult settings. At the IoU threshold of 0.50, the mAP50 of 0.69 indicates strong average precision.
  • Neuroadaptive AI Framework for Personalized English Language Instruction using BrainwaveInformed Contextual Learning Models
    Cpc Priyadharshini, K.N.V. Satyanarayana, Dr P. Bindhu, T. Jayasudha, G.S Bansode, B Sreela
    International Conference on Nexgen Networks and Cybernetics Ic2nc 2025 Proceedings, 2025
    Personalized English language training is facilitated by adjusting the difficulty and pace of lessons to individuals’ needs, but current systems primarily depend on performance measures, ignoring actual time-based cognitive states. This leaves learners facing matched yet less engaging content too frequently. An innovative hybrid architecture combines EEG-based cognitive state estimation with transformer-based contextual language modeling to allow neuroadaptive learning. The method uses dual Transformer Encoders for language and EEG inputs, combined using bidirectional cross-attention, with BiLSTM layers to capture temporal dynamics. Adaptive feedback dynamically scales difficulty, modality, and speed depending on ongoing brainwave-informed engagement predictions. Testing on EEG signals of 124 readers in reading comprehension tasks resulted in 89.3% accuracy, outperforming SVM, Random Forest, and LSTM baselines. Created with Python, PyTorch, and NVIDIA RTX 3080 GPU, the framework is highly scalable and performs well. Results affirm its ability for real-time, personalized, and cognitively adapted language learning, extending the horizon of neuroadaptive education platforms.
  • Real-time signal analysis for remote patient monitoring
    Amruta Mahalle, S. Hema Priyadarshini, Hari Krishna Moorthy, Shraddha V. Pandit, K. N. V. Satyanarayana, Kommabatla Mahender, Aakifa Shahul
    Role of Internet of Everything Ioe VLSI Architecture and AI in Real Time Systems, 2024
    When it comes to the development of improvement systems for remote healthcare coverage, signal processing is a crucial component. In this work, we will analyze recent approaches and algorithms that are aimed at recycling real-time physiological signals gathered from cases that have ever been recorded. The goal of this work is to examine these methods and algorithms. This study places a strong emphasis on the integration of these technologies to enable continuous and reliable monitoring of patient health indicators. These indices include the variability of the patient's heart rate, trends in blood pressure, and patterns of respiration. The enhancement of signal processing abilities is one of the ways this inquiry contributes to the production of reliable telemedicine findings. These findings have the potential to successfully support healthcare staff in delivering timely treatments and resolving patient difficulties in settings that are located in remote locations.
  • Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model
    Kalyanapu Srinivas, Vijayalakshmi Ch, Subba Reddy Borra, K. Srujan Raju, Ganga Rama Koteswara Rao, K. N. V Satyanarayana, Pala Mahesh Kumar
    Informatica Slovenia, 2024
    The irregularity in the heartbeats caused cardiac arrhythmia, which resulted in serious health problems. This cardiac arrhythmia is monitored by electrocardiogram (ECG) signals. As a result, an accurate and timely analysis of ECG data can prevent serious health problems. However, the conventional manual prediction systems and artificial intelligence (AI) methods failed to detect the cardiac arrhythmia because they failed to extract the deep salient features from the ECG dataset. So, this research work implements a model named as CardiacNet, which is used to identify and classify cardiac arrhythmias from a MIT-BIH-based dataset. Initially, the pre-processing operation is performed to remove the non-linearities from the dataset. Then, unsupervised machine learning algorithm-based principal component analysis (UML-PCA) is used to extract the features of the pre-processed dataset. Further, the optimal feature selection operation is carried out using improved Harris Hawk’s optimization (IHHO), which is a naturally inspired model. Moreover, a customised convolutional neural network (CCNN) model performs the classification of various cardiac arrhythmia diseases using IHHO features. The simulation results show that the proposed CardiacNet resulted in accuracy of 97.57%, sensitivity of 98.29%, specificity of 97.97%, F-measure of 97.40%, precision of 98.66%, Matthew’s correlation coefficient (MCC) of 98.17%, dice of 98.96%, and Jaccard of 97.12%. The performance comparisons show that the proposed CardiacNet resulted in improved metrics over all existing methods.
  • Optimization of Natural Language Processing Models for Multilingual Legal Document Analysis
    A. Radhika, Narinder Kumar Bhasin, Sabareesh R, Yallapragada Ravi Raju, K.N.V. Satyanarayana, I Infant Raj
    2024 IEEE International Conference on Intelligent Techniques in Control Optimization and Signal Processing Incos 2024 Proceedings, 2024
    Multilingual legal document analysis poses unique challenges in the field of Natural Language Processing (NLP) due to the intricacies of legal language and the diverse linguistic landscape of legal texts across jurisdictions. This paper presents an optimization framework designed to enhance the performance of NLP models specifically tailored for multilingual legal document analysis. The proposed framework incorporates advanced techniques in pre-processing, feature engineering, and model architecture to address the complexities inherent in legal language. Leveraging multilingual embeddings and domain-specific knowledge, the model demonstrates improved accuracy in tasks such as named entity recognition, sentiment analysis, and document categorization across a range of languages. Additionally, the optimization framework emphasizes the importance of domain adaptation, acknowledging the nuances and variations in legal terminology across different legal systems. Through a combination of transfer learning and fine-tuning strategies, the model adapts to specific legal domains, ensuring robust performance in diverse legal contexts. Experimental results on a comprehensive dataset of multilingual legal documents validate the effectiveness of the proposed optimization framework. Comparative analyses with baseline models showcase significant improvements in precision, recall, and overall model performance. The findings underscore the potential of the optimized NLP model for applications in legal information retrieval, contract analysis, and legal knowledge management in a multilingual context. This research contributes to the growing body of knowledge in NLP and legal informatics, offering a valuable resource for researchers, practitioners, and developers working on multilingual legal document analysis. The optimized model presented in this paper has the potential to enhance the efficiency and accuracy of automated systems in handling legal texts across diverse linguistic environments.
  • A Mixed-Methods Investigation of Trait Emotional Intelligence and Emotional Expression in Language Classes
    J Naga Madhuri, Sreela B, Giftsy Dorcas E, S. Sudeshna, K.N.V. Satyanarayana, B Kiran Bala
    2nd IEEE International Conference on Innovations in High Speed Communication and Signal Processing Ihcsp 2024, 2024
    This mixed-methods investigation explores the relationship between trait emotional intelligence (TEI) and emotional expression in language classes, aiming to provide a comprehensive understanding of emotional dynamics within language learning environments. Quantitative data was collected through a survey-based approach, incorporating established scales like the Trait Emotional Intelligence Questionnaire (TEIQue) to assess TEI levels, alongside tailored questions to gauge emotional expression. Participant demographics were gathered to account for potential confounding variables, and structured classroom observations documented instances of emotional expression among students and teachers. Qualitative data was obtained through semi-structured interviews and focus group discussions, delving into themes such as the influence of emotional intelligence on language learning and strategies to promote emotional expression. Additionally, language class activities were designed to foster emotional expression and awareness among students, providing opportunities for reflection and engagement. Data analysis involved both quantitative techniques, including descriptive and inferential statistics, and qualitative approaches, such as thematic analysis. Integration of data through triangulation and mixed methods analysis facilitated a comprehensive understanding of how TEI influences emotional expression in language classes. The findings offer insights into the interplay between emotional intelligence and classroom dynamics, informing educational practices and interventions aimed at enhancing emotional and academic outcomes in language learning settings. Ethical considerations were prioritized throughout the research process to safeguard participants' rights and privacy
  • ASERNet: Automatic speech emotion recognition system using MFCC-based LPC approach with deep learning CNN
    Kalyanapu Jagadeeshwar, T. Sreenivasarao, Padmaja Pulicherla, K. N. V. Satyanarayana, K. Mohana Lakshmi, Pala Mahesh Kumar
    International Journal of Modeling Simulation and Scientific Computing, 2023
    Automatic speech emotion recognition (ASER) from source speech signals is quite a challenging task since the recognition accuracy is highly dependent on extracted features of speech that are utilized for the classification of speech emotion. In addition, pre-processing and classification phases also play a key role in improving the accuracy of ASER system. Therefore, this paper proposes a deep learning convolutional neural network (DLCNN)-based ASER model, hereafter denoted with ASERNet. In addition, the speech denoising is employed with spectral subtraction (SS) and the extraction of deep features is done using integration of linear predictive coding (LPC) with Mel-frequency Cepstrum coefficients (MFCCs). Finally, DLCNN is employed to classify the emotion of speech from extracted deep features using LPC-MFCC. The simulation results demonstrate the superior performance of the proposed ASERNet model in terms of quality metrics such as accuracy, precision, recall, and F1-score, respectively, compared to state-of-the-art ASER approaches.
  • Electroencephalograph based Human Emotion Recognition Using Artificial Neural Network and Principal Component Analysis
    Satyanarayana Naga V. Kanuboyina, Shankar T, Rama Raju Venkata Penmetsa
    IETE Journal of Research, 2023
    In recent decades, automatic human emotion detection plays a crucial role in human and machine interaction. Electroencephalograph (EEG) based human emotion detection is a challenging process due to the diversity, and complexity of human emotions. For recognizing diverse emotions, a novel model is presented in this paper. Initially, an average mean reference technique is used to eliminate the environmental artifacts, instrumentation artifacts, and biological artifacts from the EEG signals, which are collected from DEAP dataset. Next, feature extraction is carried out using Fast Fourier transform (FFT) with Power Spectral Density (PSD) to extract feature vectors from the denoised EEG signals. Further, feature dimensionality reduction is performed utilizing Principal Component Analysis (PCA) to diminish the dimensions of the extracted features. A total of 230 EEG feature vectors are given as the input to Artificial Neural Network (ANN) for classifying valence and arousal emotion states. The proposed PCA-ANN model performance is validated in terms of average classification accuracy and f-score. The experimental outcome demonstrates that the proposed PCA-ANN model achieved an improved accuracy in emotion classification, which is effective compared to the existing models such as ensemble learning algorithm, a convolutional neural network with the statistical method, and sparse autoencoder with logistic regression. The proposed PCA-ANN model achieved 87.14% and 86.31% of accuracy in valence and arousal states, and obtained 90.45% and 92.03% of f-score value in valence and arousal emotion states.
  • Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers
    R. Saranya, M. Vijayaragavan, S. Ismail Kalilulah, K.N.V. Satyanarayana, M. Suresh, S Srimathi
    2nd International Conference on Automation Computing and Renewable Systems Icacrs 2023 Proceedings, 2023
  • An Approach to EEG based Emotion Identification by SVM classifier
    K. N. V Satyanarayana, T. Shankar, G. Poojita, G Vinay, H. N. S. V. l Suvarna Amaranadh, A. Gourisankar Babu
    Proceedings 6th International Conference on Computing Methodologies and Communication Iccmc 2022, 2022
  • Human Emotion Classification using KNN Classifier and Recurrent Neural Networks with Seed Dataset
    K. N. V Satyanarayana, V. Tejasri, Y. S. Naga. Srujitha, K. Nitya Sai Mounisha, Sai Tejasri Yerramsetti, Gayathri Devi Darapu
    Proceedings 6th International Conference on Computing Methodologies and Communication Iccmc 2022, 2022
  • A survey on assessment of sentiments and emotions using EEG signals
    International Journal of Advanced Science and Technology, 2020
  • Human detection and depth calculation using stereographic vision with pixel disparity and HOG classifier
    K. N. V Satyanarayana, P. V Ramaraju, K. N. V Suresh Varma, Y Ramalaxmanna
    Journal of Computational and Theoretical Nanoscience, 2019
  • IOT based vehicle speed control automatically in restricted areas using RFID
    K N. V. Satyanarayana, G Yaswanthini, P L. Kartheeka, N Rajkumar
    International Journal of Engineering and Technology Uae, 2018

RECENT SCHOLAR PUBLICATIONS

  • Neuroadaptive AI Framework for Personalized English Language Instruction using Brainwave Informed Contextual Learning Models
    BS Cpc Priyadharshini, K.N.V. Satyanarayana, Dr P. Bindhu, T. Jayasudha, G.S ...
    2025 International Conference on NexGen Networks and Cybernetics (IC2NC) , 2026
    2026
  • Real-Time Exercise Monitoring and Posture Correction System Using Deep Learning Algorithms
    MF K. N. V. Satyanarayana, Yarlanki Kushanth, Lingam Sai Geethika Thanmayie ...
    AVE Trends in Intelligent Computer Letters 2 (2), 42-52 , 2026
    2026
  • Deep learning based social media aware semantic computing model to predict user reviews
    S Daniel, R Arya, AR Nair, SR Gayam, KNV Satyanarayana, ...
    INTERNATIONAL CONFERENCE RECENT ADVANCEMENTS IN COMMUNICATION, COMPUTING AND … , 2025
    2025
  • Real-Time Plants Recognition and Medicinal Insights Using Deep Learning
    CS K.N.V. Satyanarayana, T.S.S. Harsha, V. Adarsh, I. Mahesh Babu, S.K ...
    Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare … , 2025
    2025
  • Identification of Parkinson’s disease progression with EEG signals using hybrid optimization approach
    RM B. Arivu Selvam,C.P. Shirley,K.N.V. Satyanarayana,M. Rajendiran,T.R ...
    Hybrid and Advanced Technologies, 272-277 , 2025
    2025
    Citations: 1
  • Smart Traffic Management for Emergency Vehicles Using YOLOv8 Algorithm
    SB K. N. V. Satyanarayana, Rongali Gnana Prasanna, Ramineedi Rama Krishna ...
    Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World … , 2025
    2025
  • High-throughput screening for novel medical materials: machine learning-enabled approaches
    KNVS Spoorthi P. Shetty, N. Pragadish, Ashish Verma
    Machine Learning for Medical Applications 14, 445-488 , 2025
    2025
  • Real-Time Signal Analysis for Remote Patient Monitoring
    A Mahalle, SH Priyadarshini, HK Moorthy, SV Pandit, KNV Satyanarayana, ...
    Role of Internet of Everything (IOE), VLSI Architecture, and AI in Real-Time … , 2025
    2025
  • Biomedical Signal Processing
    DSNVK Dr. P. Prasant, Prof. Pradeep Devendra Gaikwad, Dr. S. Praveena
    ISBN13: 8197282129 | ISBN10: 9788197282126 RADemics Research Institute , 2025
    2025
  • A Mixed-Methods Investigation of Trait Emotional Intelligence and Emotional Expression in Language Classes
    JN Madhuri, B Sreela, S Sudeshna, KNV Satyanarayana, BK Bala
    2024 IEEE 2nd International Conference on Innovations in High Speed … , 2024
    2024
  • A Mixed-Methods Investigation of Trait Emotional Intelligence and Emotional Expression in Language Classes
    J Naga Madhuri, Sreela B, Giftsy Dorcas E, S. Sudeshna, K.N.V. Satyanarayana ...
    2024 IEEE 2nd International Conference on Innovations in High Speed … , 2024
    2024
  • Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model
    PMK Kalyanapu Srinivas1 , Vijayalakshmi Ch2 , Subba Reddy Borra3 , K. Srujan ...
    an international journal of computing and informatics 48 (2 https://www … , 2024
    2024
    Citations: 5
  • Optimization of Natural Language Processing Models for Multilingual Legal Document Analysis
    A. Radhika,Narinder Kumar Bhasin,Sabareesh R,Yallapragada Ravi Raju,K.N.V ...
    IEEE CONFERENCE 10.1109/INCOS59338.2024.10527598 , 2024
    2024
    Citations: 11
  • Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers
    R Saranya, M Vijayaragavan, SI Kalilulah, KNV Satyanarayana, M Suresh, ...
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023
    Citations: 1
  • DEEP LEARNING EMBEDDED DEVICE TO EVALUATE MENTAL STATE OF A PERSON
    CS K. N. V. Satyanarayana,Partha Sarkar, Bhagyashree Deshpande,Tri Duc Ta ...
    Canadian Copyright Database , 2023
    2023
  • HUMRAN COMPUTER INTERCATION
    KNVS Dr.S.Roselin mary ,s.karthik, A.sundarmurthy
    SCIENTIFIC INTERNATIONAL PUBLISHING HOUSE : ISBN : 978-93-5757-500-3 1, 235 , 2023
    2023
  • ENHANCED FOG REMOVAL AND OBJECT DETECTION FOR VEHICLE NAVIGATION
    BC K.N.V. SURESH VARMA,K N V Satyanarayana, Chaitanya Krishna,.Meghana
    Indian Institution of Industrial Engineering Journal 52 (9), 363-375 , 2023
    2023
  • ASERNet: Automatic speech emotion recognition system using MFCC-based LPC approach with deep learning CNN
    K Jagadeeshwar, T Sreenivasarao, P Pulicherla, KNV Satyanarayana, ...
    International Journal of Modeling, Simulation, and Scientific Computing 14 … , 2023
    2023
    Citations: 21
  • MACHINE LEARNING BASED STRESS DETECTION DEVICE
    APSVDK Ch. Nanda Krishna, Prof Venkat Namdev Ghodke,Dr. K. S. Jeen Marseline ...
    2023
  • SMART CRADDLE USING IOT
    NR N.Venkata SaiKarthikReddy,P.Bhairava Swamy,M.Sudheer Babu
    Indian Institution of Industrial Engineering Journal 52 (4), 253-262 , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • ASERNet: Automatic speech emotion recognition system using MFCC-based LPC approach with deep learning CNN
    K Jagadeeshwar, T Sreenivasarao, P Pulicherla, KNV Satyanarayana, ...
    International Journal of Modeling, Simulation, and Scientific Computing 14 … , 2023
    2023.0
    Citations: 21
  • Mobile app & iot based smart weather station
    KNV Satyanarayana, SRN Reddy, KNVS Varma, PK Raju
    International Journal of Electronics, Communication and Instrumentation … , 2017
    2017.0
    Citations: 19
  • IoT based smart weather station using Raspberry-PI3
    KNV Satyanarayana, SRN Reddy, PS Teja, MD BasitHabibuddin
    Journal of Chemical and Pharmaceutical Sciences ISSN 974, 2115 , 2016
    2016.0
    Citations: 19
  • IoT based vehicle speed control automatically in restricted areas using RFID
    KNV Satyanarayana, G Yaswanthini, PL Kartheeka, N Rajkumar, ...
    Int J Eng Technol 7 (3.31), 72-74 , 2018
    2018.0
    Citations: 14
  • Optimization of Natural Language Processing Models for Multilingual Legal Document Analysis
    A. Radhika,Narinder Kumar Bhasin,Sabareesh R,Yallapragada Ravi Raju,K.N.V ...
    IEEE CONFERENCE 10.1109/INCOS59338.2024.10527598 , 2024
    2024.0
    Citations: 11
  • Electroencephalograph based Human Emotion Recognition Using Artificial Neural Network and Principal Component Analysis
    DPVR K.N.V.Satyanarayana, T.shankar
    IETE Journal of Research 69 (3), 1200-1209 , 2021
    2021.0
    Citations: 10
  • An Approach to EEG based Emotion Identification by SVM classifier
    KNV Satyanarayana, T Shankar, G Poojita, G Vinay, HNSVS Amaranadh, ...
    International Conference on Computing Methodologies and Communication (ICCMC … , 2022
    2022.0
    Citations: 8
  • An Approach for finding emotions using Seed Dataset with KNN Classifier
    KNV Satyanarayana, T Shankar, PVR Raju
    Turkish Journal of Computer and Mathematics Education 12 (10), 2838-2846 , 2021
    2021.0
    Citations: 8
  • Human Emotion Classification using KNN Classifier and Recurrent Neural Networks with Seed Dataset,
    K. N. V. Satyanarayana, V. Tejasri, Y. S. N. Srujitha, K. N. S. Mounisha, S ...
    International Conference on Computing Methodologies and Communication (ICCMC … , 2022
    2022.0
    Citations: 6
  • Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model
    PMK Kalyanapu Srinivas1 , Vijayalakshmi Ch2 , Subba Reddy Borra3 , K. Srujan ...
    an international journal of computing and informatics 48 (2 https://www … , 2024
    2024.0
    Citations: 5
  • Electroencephalography based human emotion state classification using principal component analysis and artificial neural network
    DPVRR K.N.V. Satyanarayana , Dr.T. Shankar
    An International Journal of Data Science and Engineering 18 (3-4), 263-278 , 2022
    2022.0
    Citations: 4
  • AUTHOR: KNV SATYANARAYANA, SRN REDDY, KNV SURESH VARMA & P
    APP Mobile, ITBSW Station
    Kanaka Raju , 0
    Citations: 4
  • Based on machine learningAutonomous car using raspberry-pi.
    GRB K.N.V.Satyanarayana, B.Tapasvi, P.KanakaRaju
    Int. Journal of Engineering Research and Application 7 (12), 76-82 , 2017
    2017.0
    Citations: 3
  • Identification of Parkinson’s disease progression with EEG signals using hybrid optimization approach
    RM B. Arivu Selvam,C.P. Shirley,K.N.V. Satyanarayana,M. Rajendiran,T.R ...
    Hybrid and Advanced Technologies, 272-277 , 2025
    2025.0
    Citations: 1
  • Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers
    R Saranya, M Vijayaragavan, SI Kalilulah, KNV Satyanarayana, M Suresh, ...
    2023 2nd International Conference on Automation, Computing and Renewable … , 2023
    2023.0
    Citations: 1
  • Neuroadaptive AI Framework for Personalized English Language Instruction using Brainwave Informed Contextual Learning Models
    BS Cpc Priyadharshini, K.N.V. Satyanarayana, Dr P. Bindhu, T. Jayasudha, G.S ...
    2025 International Conference on NexGen Networks and Cybernetics (IC2NC) , 2026
    2026.0
  • Real-Time Exercise Monitoring and Posture Correction System Using Deep Learning Algorithms
    MF K. N. V. Satyanarayana, Yarlanki Kushanth, Lingam Sai Geethika Thanmayie ...
    AVE Trends in Intelligent Computer Letters 2 (2), 42-52 , 2026
    2026.0
  • Deep learning based social media aware semantic computing model to predict user reviews
    S Daniel, R Arya, AR Nair, SR Gayam, KNV Satyanarayana, ...
    INTERNATIONAL CONFERENCE RECENT ADVANCEMENTS IN COMMUNICATION, COMPUTING AND … , 2025
    2025.0
  • Real-Time Plants Recognition and Medicinal Insights Using Deep Learning
    CS K.N.V. Satyanarayana, T.S.S. Harsha, V. Adarsh, I. Mahesh Babu, S.K ...
    Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare … , 2025
    2025.0
  • Smart Traffic Management for Emergency Vehicles Using YOLOv8 Algorithm
    SB K. N. V. Satyanarayana, Rongali Gnana Prasanna, Ramineedi Rama Krishna ...
    Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World … , 2025
    2025.0