Dr.Nemani Subash

@vnrvjiet.ac.in

Assistant Professor, ECE
VNR Vignana Jyothi Institute of Engineering and Technology

Dr.Nemani Subash


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https://researchid.co/nsubash

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Signal Processing, Electrical and Electronic Engineering, Inorganic Chemistry
6

Scopus Publications

3

Scholar Citations

1

Scholar h-index

Scopus Publications

  • DUALATT-VITRESNET WITH FENNEC FOX OPTIMIZED HYPER PARAMETERS FOR AUTOMATED OSTEOPOROSIS CLASSIFICATION USING MEDICAL IMAGING
    V. Venkatachalam, N. Subash, P. Archana Devi, S Saranya, Tharwin Kumar, Priyadharshin Chokkan
    Journal of Engineering and Technology for Industrial Applications, 2026
  • Sentiment Analysis using Light Weight - Gradient BoostingMachine based Feature Selection
    Bikku Ramavath, Nemani Subash, Srikanth Kadainti
    Journal of Computer Science, 2025
    : Sentiment analysis is a significant task in Natural Language Processing (NLP) that differentiates the emotions and opinions expressed in text or reviews. The sentiment analysis is challenging due to the complex language patterns and inappropriate or redundant features used for classification. In this research, the Light Weight - Gradient Boosting Machine (LWGBM) based feature selection is proposed to choose relevant features for classification to eliminate inappropriate or redundant features and learn the complex language patterns. Then, the classification is performed by using H2O Automatic Machine Learning (H2O AutoML) algorithm which classifies the sentiments as positive, neutral and negative with high accuracy. The performance of the proposed method is analyzed with different metrics: accuracy, precision, recall and f1-score. The proposed LWGBM and H2O ML method attains an accuracy of 95.39% on the Internet Movie Data Base (IMDB) dataset, and 92.41% accuracy on SemEval - 2016 dataset, which is more effective than the conventional methods namely, Extra-Long Neural Network (XLNet) and Arabic Bidirectional Encoder Representation Transformer (AraBERT).
  • Sentiment Analysis using Multi Head Self-Attention Mechanism Based Bidirectional Gated Recurrent Unit
    Bikku Ramavath, Srikanth Kadainti, Nemani Subash
    International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024, 2024
    Sentiment Analysis plays a vital role in Natural Language Processing (NLP) which aims to discern opinions and emotions expressed in text. However, the data sparsity and disambiguation of natural languages make it challenging for the existing approaches to provide accurate extraction and classification when subjected to text data. Hence, this research proposes Multihead Self-Attention Mechanism based Bidirectional Gated Recurrent Unit (MSA-BiGRU) approach for the classification of sentiment data into multi-classes. The MSA allows the BiGRU to consider various parts of the sequence, capturing long-term dependencies and relationships within the text. Initially, three standard datasets namely, Internet Movie Database (IMDB), Sentiment140 and World Cup Soccer are utilized to estimate the effectiveness of the MSA-BiGRU method. The Word-2-Vector (Word2Vec) is utilized for the feature extraction process and Analysis of Variance (ANOVA) is utilized for the selection of features. The performance metrices: accuracy, precision, recall and F1-score are utilized to validate the model’s effectiveness. The experimental results show that the MSA-BiGRU method attains a better accuracy of 98.91% on sentiment140 dataset as compared to Automated Sentiment Analysis in social media based on Harris Hawks Optimization with Deep Learning (ASASM-HHODL) and Gated Attention Mechanism and Recurrent Neural Network (GARN).
  • Wheat Leaf Disease Detection Using CapsNet in Agri-Tech
    Nemani Subash, Samala Sushanth, Tumu Sai Shivani, Puppala Sooraj
    Proceedings of the 2024 2nd International Conference on Cyber Physical Systems Power Electronics and Electric Vehicles Icpeev 2024, 2024
    Various diseases that affect the growth of wheat are important threats to food security in the world. This project aims at developing a system of wheat diseases diagnosis based on the integration of CapsNet into IoT. This dataset includes the images of different types of wheat diseases to include STB, stem rust, Septoria, tannin spot and powdery mildew, that affects Tunisia. Pre-processing and data augmentation bring in conformity to methods of training and improving the non uniformity and a diversified type of the dataset. CapsNet also has the feature called affiliative invariance which helps to solve the problem of varying camera angles and increases the model's accuracy. Even deeper capabilities can be achieved in transfer learning within hyperparameter tuning of the pre-trained CapsNets models. Applications of IoT empowers a continuous monitoring of the disease that affects crops, extensive and efficient spraying of biopesticides in agriculture. Wheat grains that are transformed into sensors are able to track changes in their environment; on the other hand, cameras of specific functions capture image data. Prompts and advisories from time to time assist farmers in preventing such challenges; hence, enhancing the productions and qualities. This work is helpful to agricultural in general and more specifically it is a supplement to the process of crop health monitoring and early signs of diseases detection, thus being a boost to global food security.
  • A Survey Towards Implementing Smart Campus
    Anakhi Hazarika, K. D. K. Ajay, Nemani Subash, G. Srinivasa Yeshwanth, Lanka Raju, P. Kushal Swarup, S. K. Shireen Kasuar, A. T. Antony
    Smart Innovation Systems and Technologies, 2023
  • IoT based Waste Management System
    P Ramesh, J Martin Sahayaraj, N Subash, S R Mugunthan, S. Jaya Pratha
    Proceedings of the International Conference on Electronics and Renewable Systems Icears 2022, 2022
    Every year, 62 million tons of garbage is generated in India, and 45 million tons of that waste is disposed of in an unhygienic manner, resulting in serious health consequences and environmental distress. In smart cities, an efficient waste management system is crucial. To address the current issues, this paper proposes an Internet of Things (IoT) based waste management system which aims to segregate the waste materials of different streams such as wet, dry, plastic, paper, metal, and glass. It increases the potential for recovery and consequent recycling processes. This system is implemented using Arduino microcontroller, which controls the entire process with ease and simplicity. This segregator system consists of various stages including of infrared sensor, inductive proximity sensor, rain drop sensor, photoelectric sensor, and the segregation bins. Each waste is detected by the relevant sensor and drops into the bins allocated to it for further processing. The status of the segregated data is made available in the cloud for monitoring and controlling purposes. These enabled policies empower cities to manage waste collection effectively.

RECENT SCHOLAR PUBLICATIONS

  • Artificial Intelligence and Machine Learning based Robotic ARM using Raspberry Pi
    SBS Avvaru, N Subash
    Design Engineering, 8919-8929 , 2021
    2021
  • VLSI DESIGN FOR TURBO DECODER MODULE FOR IN VEHICAL SYSTEM
    VA N. SUBHASH
    Juni Khyat 11 (Issue-08 No.01), 179-186 , 2021
    2021
  • A 4K RESOLUTION-ACCOMPLISHED FPGA IMPLEMENTATION OF SOLITARY IMAGE PARTICLE MAPS
    N. SUBASH*, P. SRIKANTH**
    Dogo Rangsang Research Journal 8 (Issue-14 No. 02), 219-232 , 2021
    2021
  • 13/9 Lifting Wavelet Transform based Reversible Data Hiding
    SV subash n,Jayachandra Prasad T
    International Journal of Computer Applications 179 (No.53), 20-26 , 2018
    2018
  • Reversible Data hiding using Secure Force Algorithm
    SV subash n,Jayachandra Prasad T
    Journal of Advanced Research in Dynamical and Control Systems, 238-244 , 2018
    2018
  • Distortionless Data Hiding for Encrypted Images: A DCT Approach.
    S Nemani, JP Talari, S Vangala
    Journal of Engineering Science & Technology Review 10 (5) , 2017
    2017
  • Estimation of performance metrics for reversible data hiding before encryption
    S Nemani, JP Talari, S Vangala
    2017 International Conference on Intelligent Computing and Control Systems … , 2017
    2017
    Citations: 3
  • Experimental evaluation setup to Measure Inductor Current in a Buck Converter
    DC NEMANI SUBASH
    BLEKINGE INSTITUTE OF TECHNOLOGY , 2011
    2011

MOST CITED SCHOLAR PUBLICATIONS

  • Estimation of performance metrics for reversible data hiding before encryption
    S Nemani, JP Talari, S Vangala
    2017 International Conference on Intelligent Computing and Control Systems … , 2017
    2017
    Citations: 3
  • Artificial Intelligence and Machine Learning based Robotic ARM using Raspberry Pi
    SBS Avvaru, N Subash
    Design Engineering, 8919-8929 , 2021
    2021
  • VLSI DESIGN FOR TURBO DECODER MODULE FOR IN VEHICAL SYSTEM
    VA N. SUBHASH
    Juni Khyat 11 (Issue-08 No.01), 179-186 , 2021
    2021
  • A 4K RESOLUTION-ACCOMPLISHED FPGA IMPLEMENTATION OF SOLITARY IMAGE PARTICLE MAPS
    N. SUBASH*, P. SRIKANTH**
    Dogo Rangsang Research Journal 8 (Issue-14 No. 02), 219-232 , 2021
    2021
  • 13/9 Lifting Wavelet Transform based Reversible Data Hiding
    SV subash n,Jayachandra Prasad T
    International Journal of Computer Applications 179 (No.53), 20-26 , 2018
    2018
  • Reversible Data hiding using Secure Force Algorithm
    SV subash n,Jayachandra Prasad T
    Journal of Advanced Research in Dynamical and Control Systems, 238-244 , 2018
    2018
  • Distortionless Data Hiding for Encrypted Images: A DCT Approach.
    S Nemani, JP Talari, S Vangala
    Journal of Engineering Science & Technology Review 10 (5) , 2017
    2017
  • Experimental evaluation setup to Measure Inductor Current in a Buck Converter
    DC NEMANI SUBASH
    BLEKINGE INSTITUTE OF TECHNOLOGY , 2011
    2011