Ravuri Daniel

@pvpsiddhartha.ac.in

Associate Professor
PVP siddhartha institute of technology

https://researchid.co/danravuri

RESEARCH INTERESTS

IoT data science and wsn

Scopus Publications

186

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Role of tuning techniques in advancing the performance of negative capacitance field effecting based full adder
Ravuri Daniel, Bode Prasad, Abhay Chaturvedi, Chinthaguntla Balaswamy, Dorababu Sudarsa, Nallathambi Vinodhkumar, Ramakrishna Reddy Eamani, Ambarapu Sudhakar, and Bodapati Venkata Rajanna
Institute of Advanced Engineering and Science
The increasing demand for faster, robust, and efficient device development of enabling technology to mass production of industrial research in circuit design deals with challenges like size, efficiency, power, and scalability. This paper, presents a design and analysis of low power high speed full adder using negative capacitance field effecting transistors. A comprehensive study is performed with adiabatic logic and reversable logic. The performance of full adder is studied with metal oxide field effect transistor (MOSFET) and negative capacitance field effecting (NCFET). The NCFET based full adder offers a low power and high speed compared with conventional MOSFET. The complete design and analysis are performed using cadence virtuoso. The adiabatic logic offering low delay of 0.023 ns and reversable logic is offering low power of 7.19 mw.

Handwritten digit recognition using quantum convolution neural network
Ravuri Daniel, Bode Prasad, Pasam Prudhvi Kiran, Dorababu Sudarsa, Ambarapu Sudhakar, and Bodapati Venkata Rajanna
Institute of Advanced Engineering and Science
The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.

A Novel Electrical Load Forecasting Model Using a Deep Learning Approach
Neelapala Anil Kumar, Ravuri Daniel, and Prudhvi Kiran Pasam
Apple Academic Press

Artificial intelligence-based energy efficiency models in green communications towards 6G
Neelapala Anil Kumar and Ravuri Daniel
CRC Press

Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors
Sudhakar Ambarapu, Ravuri Daniel, Sreekanth Puli, Satyanarayana Mummana, Nitalaksheswara Rao Kolukula, and Bodapati Venkata Rajanna
Institute of Advanced Engineering and Science
In this study, multiple intelligent control systems for direct torque-controlled Synchronous motors are implemented and compared. Using a lookup table to pick a vector through the inverter voltage space, the direct torque control (DTC) system can be obtained. To replicate the state selector in relation to the look-up table, intelligent controllers are deployed. Intelligent logic controllers like fuzzy and neural are used to regulate the performance of permanent magnet synchronous motors (PMSM). In steady-state applications, neural and fuzzy controllers reduce the torque ripple and stator current harmonic distortion. These outcomes are compared with those obtained when the synchronous motor was put under the basic direct torque control method using a proportional integral (PI) controller. The accuracy and effectiveness of the suggested control topologies have been verified using computer simulation software like MATLAB/Simulink.

Optimizing Routing in Nature-Inspired Algorithms to Improve Performance of Mobile Ad-Hoc Network

Ensemble Learning with Tournament Selected Glowworm Swarm Optimization Algorithm for Cyberbullying Detection on Social Media
Ravuri Daniel, T. Satyanarayana Murthy, Ch. D. V. P. Kumari, E. Laxmi Lydia, Mohamad Khairi Ishak, Myriam Hadjouni, and Samih M. Mostafa
Institute of Electrical and Electronics Engineers (IEEE)
Online social network (OSN) plays a crucial role to facilitate social connections; but, this social networking media increases antisocial behaviors, like trolling, cyberbullying, and hate speech. Cyberbullying has often resulted in serious physical and mental distress, especially for children and women, and even sometimes forces them to commit suicide. Conventional techniques for detecting cyberbullying, such as relying on users to report the instance of bullying, are not always effective. Deep learning (DL) and Machine learning (ML) techniques are trained to automatically recognize and flag potential cyberbullying content, along with identifying behavior patterns that are indicative of cyberbullying. Therefore, this study concentrates on the design and development of ensemble deep learning with tournament-selected glowworm swarm optimization (EDL-TSGSO) algorithm for cyberbullying detection and classification on Twitter data. The goal of the study is to examine social media data through the use of natural language processing (NLP) and ensemble learning process. This EDL-TSGSO technique preprocesses the raw tweets and then employs the Glove word embedding technique. In addition, the presented EDL-TSGSO technique utilizes ensemble long short-term memory with Adaboost (ELSTM-AB) model for effective cyberbullying detection and classification. The ensemble ELSTM-AB classifier integrates the prediction of LSTM and Adaboost models to enhance the overall classification performance. To further develop the cyberbullying detection performance of the EDL-TSGSO algorithm, the TSGSO algorithm is applied as a hyperparameter optimizer. The experimental validation of the EDL-TSGSO algorithm on the Twitter dataset demonstrates its promising performance over other state of art approaches in terms of different measures.

Transformer Architecture-based Multivariate Time Series Prediction
Ganga Rama Koteswara Rao, Jeevana Jyothi Pujari, Ravuri Daniel, Sunkari Venkata Rama Krishna, and Chindu Hema
IEEE
Transformer design, which recently emerged as the most promising solution for Natural Language Processing (NLP) tasks, is now state-of-the-art for these tasks. However, since sentences are composed of words and words are stored as vectors in NLP tasks, every problem involving multidimensional vector sequences should theoretically be able to be solved using a transformer model. In this article, the author will clearly explain the issues that motivated the research on transformers as well as the specifics of their architecture. He will also conduct a data analysis on a specific dataset and demonstrate a model that, by applying transformers to a series of multidimensional tabular data, can predict which should be used next.

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops
Saud Yonbawi, Sultan Alahmari, T. Satyanarayana murthy, Ravuri Daniel, E. Laxmi Lydia, Mohamad Khairi Ishak, Hend Khalid Alkahtani, Ayman Aljarbouh, and Samih M. Mostafa
Computers, Materials and Continua (Tech Science Press)

Class Imbalance of Bio-Medical Data by Using PCA-Near Miss for Classification
Praveen Tumuluru, Ravuri Daniel, Gundabathula Mahesh, Kalavala Deekshitha Lakshmi, Pakalapati Mahidhar, and Muttum Vinay Kumar
IEEE
Class imbalance is a significant problem in many real-time domains dealing with healthcare data, which leads to poor classification performance and may have severe consequences. This work addresses the class imbalance problem in healthcare data, which often leads to poor classification performance and potentially serious consequences. This study proposes a hybrid PNM model that integrates the Near-Miss sampling technique and Principal Component Analysis to overcome this challenge. This study aims to investigate the effectiveness of the PNM model in improving classifier performance and compare it with several baseline classifiers. Further, the experiments are conducted on a real-world healthcare dataset containing various healthcare records. The results showed that the PNM model outperformed the baseline classifiers regarding the precision, recall, F1 score, and area under the curve (AUC). Specifically, the PNM model achieved a precision of - 0.85, recall of - 0.82, F1 score - 0.83, and AUC - 0.91, while the best-performing baseline classifier achieved a precision of 0.72, recall of 0.64, F1 score of 0.68, and AUC of 0.83. Our study demonstrates that the PNM model offers a promising approach to addressing the class imbalance in healthcare data and improving classifier performance. Integrating the Near-Miss sampling technique and Principal Component Analysis enables the model to achieve a better balance among the majority and minority classes, resulting in more accurate classification. The PNM model has the potential to be applied to various healthcare domains, such as disease diagnosis, patient risk stratification, and treatment prediction.

Quantum Dwarf Mongoose Optimization with Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems
Laila Almutairi, Ravuri Daniel, Shaik Khasimbee, E. Laxmi Lydia, Srijana Acharya, and Hyun-Il Kim
Institute of Electrical and Electronics Engineers (IEEE)

Classification of Precious and Non-precious Tweets Using Deep Learning
T. Sathyanarayana Murthy, N. Mohan Krishna Varma, Daniel Ravuri, D. Kishore Babu, and Shaik Nazeer
Springer Nature Singapore

Cloud-based smart environment using internet of things (IoT)
E. Laxmi Lydia, Jose Moses Gummadi, Sharmili Nukapeyi, Sumalatha Lingamgunta, A. Krishna Mohan, and Ravuri Daniel
Springer Singapore

Collective Examinations of Documents on COVID-19 Peril Factors Through NLP
E. Laxmi Lydia, Jose Moses Gummadi, Chinmaya Ranjan Pattanaik, B. Prasad, CH. Usha Kumari, and Ravuri Daniel
Springer Singapore

Interdependence in Artificial Intelligence to Empower Worldwide COVID-19 Sensitivity
E. Laxmi Lydia, Jose Moses Gummadi, Chinmaya Ranjan Pattanaik, A. Krishna Mohan, G. Jaya Suma, and Ravuri Daniel
Springer Singapore

Global Integration and Distribution of Data Through Machine Learning for COVID-19
E. Laxmi Lydia, Jose Moses Gummadi, Chinmaya Ranjan Pattanaik, G. Jaya Suma, A. Krishna Mohan, and Ravuri Daniel
Springer Singapore

MCRO-ECP: Mutation chemical reaction optimization based energy efficient clustering protocol for wireless sensor networks
R. Daniel and K. N. Rao
Korean Society for Internet Information (KSII)

Evolutionary based clustering protocol for wireless sensor networks
Melaku Tamene, Kuda Nageswara, and Ravuri Daniel
Springer International Publishing
Many cluster based routing protocols have been developed in order to enhance the network lifetime, but the potency of clustering in energy management highly relies on the optimality of clusters. Optimal cluster formation is the chief source of challenges in clustering protocols. In this paper, new approach has been introduced to formulate the optimization problem in the partition of networks into optimal organization of clusters. The optimization problem consists of finding optimal configuration of clusters such that the distance of cluster heads from the pre-computed cluster centers, communication cost of nodes to transport data and the expected energy dissipation of the network per the residual energy of cluster heads are minimized. The solution to the devised nonlinear clustering problem is found using the genetic algorithm. The genetic algorithm toolbox is developed in C++ and integrated with OMNeT++ simulation platform to implement the protocol. The experimental results verify that the proposed protocol extends the network lifetime compared to the prominent LEACH, LEACH-C and CHEF protocols.

EEC-FM: Energy efficient clustering based on firefly and midpoint algorithms in wireless sensor network
R. Daniel and K. N. Rao
Korean Society for Internet Information (KSII)

Event driven dynamic traffic routing protocol (EDDTRP) based on gradient search approach in multi-sink wireless sensor networks

An optimal power conservation cluster based routing algorithm using Fuzzy Verdict Mechanism for Wireless Sensor Networks
Ravuri Daniel and Kuda Nageswara Rao
IEEE
Wireless Sensor Networks have found thousands of applications duo to new era of technologies like Internet of Things and Cloud to simplify the management of complex problems. Energy conservation in wireless sensor nodes is important concern to most of its applications. Duo to inherent issue of power limitation of sensor nodes, enhancing the specified network life time is the prime requirement. Therefore, an efficient routing protocol is needed to construct any real time applications of wireless sensor networks. The proposed, Fuzzy Verdict Mechanism Cluster Protocol (FVMCP) is used to construct knowledge based system effectively for the eligibility of the sensor node to be elected as a Cluster Header(CH). The eligibility of CH nodes based on their respective sensor nodes has important characteristics like duty cycle, energy level, geographical position, and number of hops. The simulation of FVMCP algorithm carried out in Mat Lab which shows enhancement of the performance accuracy up to 17 to 21 % in compassion with hard clustering methods.

RECENT SCHOLAR PUBLICATIONS

Artificial intelligence-based energy efficiency models in green communications towards 6G
NA Kumar, R Daniel
Towards Wireless Heterogeneity in 6G Networks, 158-179 2024

Role of tuning techniques in advancing the performance of negative capacitance field effecting based full adder
R Daniel, B Prasad, A Chaturvedi, C Balaswamy, D Sudarsa, ...
Int J Reconfigurable & Embedded Syst 13 (1), 59-68 2024

Handwritten digit recognition using quantum convolution neural network
R Daniel, B Prasad, PK Pasam, D Sudarsa, A Sudhakar, BV Rajanna
Int J Artif Intell 13 (1), 533-541 2024

A novel electrical load forecasting model using a deep learning approach
NA Kumar, R Daniel, PK Pasam
The Internet of Energy, 67-89 2024

Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors
A Sudhakar, R Daniel, S Puli, S Mummana, NR Kolukula, BV Rajanna
Indonesian Journal of Electrical Engineering and Computer Science 32 (2 2023

Ensemble learning with tournament selected glowworm swarm optimization algorithm for cyberbullying detection on social media
R Daniel, TS Murthy, CDVP Kumari, EL Lydia, MK Ishak, M Hadjouni, ...
IEEE Access 11, 123392-123400 2023

Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops.
S Yonbawi, S Alahmari, R Daniel, EL Lydia, MK Ishak, HK Alkahtani, ...
Computer Systems Science & Engineering 46 (3) 2023

Transformer Architecture-based Multivariate Time Series Prediction
GRK Rao, JJ Pujari, R Daniel, SVR Krishna, C Hema
2023 Second International Conference on Augmented Intelligence and 2023

Class imbalance of bio-medical data by using pca-near miss for classification
P Tumuluru, R Daniel, G Mahesh, KD Lakshmi, P Mahidhar, MV Kumar
2023 5th International Conference on Inventive Research in Computing 2023

Quantum dwarf mongoose optimization with ensemble deep learning based intrusion detection in cyber-physical systems
L Almutairi, R Daniel, S Khasimbee, EL Lydia, S Acharya, HI Kim
IEEE Access 11, 66828-66837 2023

Optimizing routing in nature-inspired algorithms to improve performance of mobile ad-hoc network
R Daniel, DD Rao, J Emerson Raja, DC Rao, A Deshpande
International Journal of Intelligent Systems And Applications In Engineering 2023

Convolutional Neural Network based COVID-19 Disease Detection Framework using X-Rays
KR Muvva, R Daniel, LP Routhu, S Kolasani
Journal of Emerging Technologies and Innovative Research (JETIR) 9 (9), 357-363 2022

Classification of Precious and Non-precious Tweets Using Deep Learning
T Sathyanarayana Murthy, N Mohan Krishna Varma, D Ravuri, ...
Advances in Distributed Computing and Machine Learning: Proceedings of 2022

CONTOUR BASED X-RAY IMAGE CLASSIFICATION SYSTEM FOR DETECTION OF COVID-19.
NA Kumar, R DANIEL, PK Pasam
I-Manager's Journal of Pattern Recognition 9 (1) 2022

Quick and Accurate Forecasting of COVID-19 Cases using Prophet Model.
GH Bindu, PK Pasam, B Prasad, R Daniel
Annals of the Romanian Society for Cell Biology 25 (4) 2021

IoT Based Multipurpose Field Surveillance Robot
DR Srinivasulu Vardhineni
International Journal of Scientific Research in Engineering and Management 2021

Interdependence in Artificial Intelligence to Empower Worldwide COVID-19 Sensitivity
E Laxmi Lydia, J Moses Gummadi, C Ranjan Pattanaik, A Krishna Mohan, ...
International Conference on Communication, Computing and Electronics Systems 2021

Collective Examinations of Documents on COVID-19 Peril Factors Through NLP
E Laxmi Lydia, J Moses Gummadi, C Ranjan Pattanaik, B Prasad, ...
International Conference on Communication, Computing and Electronics Systems 2021

Global Integration and Distribution of Data Through Machine Learning for COVID-19
E Laxmi Lydia, JM Gummadi, CR Pattanaik, G Jaya Suma, ...
International Conference on Communication, Computing and Electronics Systems 2021

Cloud-Based Smart Environment Using Internet of Things (IoT)
E Laxmi Lydia, JM Gummadi, S Nukapeyi, S Lingamgunta, ...
Computer Networks, Big Data and IoT: Proceedings of ICCBI 2020, 217-225 2021

MOST CITED SCHOLAR PUBLICATIONS

& Mostafa, SM (2023). Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops
S Yonbawi, S Alahmari, R Daniel, EL Lydia, MK Ishak, HK Alkahtani
Computer Systems Science & Engineering 46 (3)
Citations: 34

Active contours techniques for automatic detection of glaucoma
NA Kumar, MS Anuradha, PS Vepa, R Daniel
International Journal of Recent Technology and Engineering 1 (4), 2277-3878 2012
Citations: 7

Handwritten digit recognition using quantum convolution neural network
R Daniel, B Prasad, PK Pasam, D Sudarsa, A Sudhakar, BV Rajanna
Int J Artif Intell 13 (1), 533-541 2024
Citations: 3

EEC-FM: Energy efficient clustering based on firefly and midpoint algorithms in wireless sensor network
R Daniel, KN Rao
KSII Transactions on Internet and Information Systems (TIIS) 12 (8), 3683-3703 2018
Citations: 3

MCRO-ECP: mutation chemical reaction optimization based energy efficient clustering protocol for wireless sensor networks
R Daniel, KN Rao
KSII Transactions on Internet and Information Systems (TIIS) 13 (7), 3494-3510 2019
Citations: 2

A Cost Effective Automatic Online Bus Information System using RFID and ZigBee
KVP P.Prudhvi Kiran, R.Daniel
International Journal of Computer Science and Information Technologies 5 (3 2014
Citations: 2

A novel electrical load forecasting model using a deep learning approach
NA Kumar, R Daniel, PK Pasam
The Internet of Energy, 67-89 2024
Citations: 1

Transformer Architecture-based Multivariate Time Series Prediction
GRK Rao, JJ Pujari, R Daniel, SVR Krishna, C Hema
2023 Second International Conference on Augmented Intelligence and 2023
Citations: 1

Automatic Detection of Adenocarcinoma using Active Contours
M NeelapalaAnilKumar, P Srinivas, R Daniel
communicated toInternationalJournalofAdvancedComputerResearch 2013
Citations: 1