Post-corrosion mechanical performance of fine grained AZ31 Mg alloy sheets produced by groove pressing Katepalli Srivalli Rani, Ravuri Daniel, Kanumuri Singaiah, G V N B Prabhakar, Ratna Sunil B Engineering Research Express, 2025 Fine grained AZ31 magnesium alloy sheets were produced by groove pressing at 250 °C processing temperature targeted for biomedical implant applications. Microstructural studies revealed a grain refinement up to 12.5 ± 6.3 μm from 64 ± 14.5 μm. Higher hardness (95.1 ± 2.1 HV0.1) was measured for groove pressed AZ31 compared with AZ31 (58.7 ± 4.9 HV0.1). From the x-ray diffraction studies, basal dominated texture appeared in groove pressed samples. Corrosion performance assessed by conducting electrochemical studies using simulated body fluid (SBF) showed lower corrosion current density demonstrates increased corrosion resistance after groove pressing due to decreased grain size and texture. The post corrosion mechanical performance was assessed by conducting tensile tests after subjecting the samples to corrosion in SBF for 72 h. Grain refined AZ31 alloy exhibited improved strength with marginal decreased ductility before corrosion. Post corrosion experiments revealed better mechanical performance for the grain refined alloy as reflected in higher strength and % of elongation than the base alloy. The findings demonstrate the potential of grain refinement by groove pressing on enhancing resistance towards corrosion initiated mechanical failure of AZ31 in the presence of corrosion defects.
Secure Attendance System Leveraging Machine Learning and Blockchain R Daniel, Akhila Nidumolu, Kumara Sai Manikonda, Manoj Kumar Movva, Sivani Majji Proceedings of International Conference on Visual Analytics and Data Visualization Icvadv 2025, 2025 The system enables schools to reach maximum operational success together with reliable accountability in attendance handling. The tracking solution unites decentralized blockchain with facial recognition technology to provide a safe contemporary monitoring platform. The system provides precise identification along with security risk elimination which surpasses traditional insufficient performance methods. The blockchain system protects data integrity by stopping unauthorized modifications so that only authorized personnel maintain exclusive access to confidential attendance records. The system resolves privacy matters by using an organized data management system that combines sensitive information protection with adaptable scalability options. The system maintains high reliability according to performance evaluations while biometric authentication with blockchain integration establishes it as a leading solution for modern automated attendance tracking.
EFFICIENT BIG DATA STORAGE SOLUTIONS FOR DISTRIBUTED CLOUD COMPUTING SYSTEMS Journal of Theoretical and Applied Information Technology, 2025
Handwritten digit recognition using quantum convolution neural network Ravuri Daniel, Bode Prasad, Pasam Prudhvi Kiran, Dorababu Sudarsa, Ambarapu Sudhakar, Bodapati Venkata Rajanna Iaes International Journal of Artificial Intelligence, 2024 <span lang="EN-US">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%.</span>
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, Bodapati Venkata Rajanna International Journal of Reconfigurable and Embedded Systems, 2024 <p>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.</p>
Artificial intelligence-based energy efficiency models in green communications towards 6G Neelapala Anil Kumar, Ravuri Daniel Towards Wireless Heterogeneity in 6g Networks, 2024 The information sector has always aspired to use green communications to minimize energy consumption and use fewer fossil fuels. There seems to be a certain amount of network framework and several associated terminals will advance to enlarge exponentially in the modern 5G and forthcoming 6G regimes, resulting in increased energy costs. The focused advancement of green communications is becoming more and more crucial and essential. However, it is undeniable that the commitment to quality of service, encryption, adaptability, and cognition in 6G will become more demanding and diversified, which will provide challenges for energy-efficient advancement. The mechanism for compelling energy harvesting, which will be extensively used all along 6G, nevertheless makes the network maintenance and power regulation more complicated. To conquer these challenges and diminish human efforts. Artificial intelligence (AI) approaches are the most recognized for present-day applications. Research has been conducted comprehensively in academia and industry to mitigate energy claims, and advance energy efficiency to regulate energy accumulation in different networking schemes. The critical factors for green communications are well addressed in this study, together with the associated research review on AI-positioned green communications. Emphasis is given to various methods and approaches employed in the green era to establish plans and enable greater efficiency. To curtail algorithm complications with high accuracy in forthcoming 6G, the analysis of machine learning techniques, including cutting-edge technologies like deep learning, conventional AI techniques, and analytical models were proposed future directions of research in AI models towards a green 6G.
A Novel Electrical Load Forecasting Model Using a Deep Learning Approach Neelapala Anil Kumar, Ravuri Daniel, Prudhvi Kiran Pasam Internet of Energy A Pragmatic Approach Towards Sustainable Development, 2024 The estimate of electricity appeal in modernistic years is becoming progressively relevant thanks to market-free trade and, thus, the initiation of sustainable assets. To satisfy the demands, leading intelligent models are built to form sure explicit power forecasts for multi-time prospects. The load forecasting of electric Power is a crucial process in devising the electric industry and operating electric power systems. Short-term forecasts are adopted to program the power generation and transmission of electricity. Medium-term forecasts are meant to line up the fuel purchases. This necessitates the implementation of the productive determination of algorithms could be a fundamental feature of smart grids and an efficient tool for determining ambiguity for better cost and energy ability decisions like slate the origination, authenticity, power escalation of the system, and monetary smart grid activities. This work introduces a model for the evaluation of the utilization of electricity, which can accurately forecast 68subsequently estimated from minimum to maximum duration with significant improvement in the accuracy of forecasting through advanced deep learning techniques. The analyzes or findings also can provide interesting results for energy consumption with parameters like forecasting efficiency and error with duration of data monitoring algorithms namely (LSTM)–long short-term memory (RNN) – recurrent neural networks and multi-layer perceptron algorithms (MLP). These algorithms furnish the most interesting results with respective to the duration of data. Mainly, MLP and RNN proved to produce favorable results for 24-hour data. Similarly, LSTM has proved better for 15-day data and monthly data with consistency in terms of errors, squared, and mean square. To anticipate data ranging from day to month, the minimal Forecasting error was attained by adopting MLP with R2(0.91). On hour-based data, R2of LSTM holds effective for half-monthly and monthly data with (0.88 and 0.93), RMSE (89.54 and 84.98), MAPE (3.51 and 2.47). RNN has been proven to attain the moderate outputs comparatively. MLP for half-monthly and monthly in terms of R2(0.81 and 0.92), RMSE (90.72 and 85.78) and MAPE (4.25 and 4.01). The result of LSTM acknowledges the enhanced attainment and substantial achievements of electrical load forecasting.
Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors Sudhakar Ambarapu, Ravuri Daniel, Sreekanth Puli, Satyanarayana Mummana, Nitalaksheswara Rao Kolukula, Bodapati Venkata Rajanna Indonesian Journal of Electrical Engineering and Computer Science, 2023 <p><span>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.</span></p>
Optimizing Routing in Nature-Inspired Algorithms to Improve Performance of Mobile Ad-Hoc Network International Journal of Intelligent Systems and Applications in Engineering, 2023
Transformer Architecture-based Multivariate Time Series Prediction Ganga Rama Koteswara Rao, Jeevana Jyothi Pujari, Ravuri Daniel, Sunkari Venkata Rama Krishna, Chindu Hema Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2023, 2023
Class Imbalance of Bio-Medical Data by Using PCA-Near Miss for Classification Praveen Tumuluru, Ravuri Daniel, Gundabathula Mahesh, Kalavala Deekshitha Lakshmi, Pakalapati Mahidhar, Muttum Vinay Kumar Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023
Cloud-based smart environment using internet of things (IoT) E. Laxmi Lydia, Jose Moses Gummadi, Sharmili Nukapeyi, Sumalatha Lingamgunta, A. Krishna Mohan, Ravuri Daniel Lecture Notes on Data Engineering and Communications Technologies, 2021
Event driven dynamic traffic routing protocol (EDDTRP) based on gradient search approach in multi-sink wireless sensor networks International Journal of Applied Engineering Research, 2015
Spatial patterns of neighborhood change in l0 small metropolitan areas in the Midwest during deindustrialization, 1970–2019 ED Ravuri Sociological Spectrum, 1-19 , 2026 2026
Secure Transmission of Medical Images in IoMT for Smart Cities Using Data Hiding Scheme KJ Devi, R Daniel, B Prasad, B Ratna Sunil Machine Learning and Deep Learning Modeling and Algorithms with Applications … , 2025 2025
Automated Detection of Diabetic Retinopathy Using ResNet-50 Deep Learning Model R Daniel, KJ Devi, B Prasad, B Ratna Sunil, G Naga Deekshitha Machine Learning and Deep Learning Modeling and Algorithms with Applications … , 2025 2025
Post-corrosion mechanical performance of fine grained AZ31 Mg alloy sheets produced by groove pressing K Srivalli Rani, R Daniel, K Singaiah, G Prabhakar, RS B Engineering Research Express 7 (3), 035583 , 2025 2025 Citations: 2
EFFICIENT BIG DATA STORAGE SOLUTIONS FOR DISTRIBUTED CLOUD COMPUTING SYSTEMS R DANIEL, YS BODE PRASAD, K CHINNAIAH, D SUDARSA, ... Journal of Theoretical and Applied Information Technology 103 (15) , 2025 2025
Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout A Ravuri, ND Lawrence arXiv preprint arXiv:2506.14793 , 2025 2025
Secure Attendance System Leveraging Machine Learning and Blockchain R Daniel, A Nidumolu, KS Manikonda, MK Movva, S Majji 2025 International Conference on Visual Analytics and Data Visualization … , 2025 2025 Citations: 2
An AI-powered adaptive learning framework for personalized education HA Sendeku, R Daniel, GV Gopal Inf. Dyn. Appl 4 (4), 212-223 , 2025 2025
Weakly supervised latent variable inference of proximity bias in crispr gene knockouts from single-cell images A Ravuri, K Ulicna, J Osea, K Donhauser, J Hartford Learning Meaningful Representations of Life (LMRL) Workshop at ICLR 2025 , 2025 2025 Citations: 1
Integrity verification of medical images in internet of medical things for smart cities using data hiding scheme KJ Devi, R Daniel, B Prasad, MK Ishak, D Sudarsa, PP Kiran International Journal of Electrical and Computer Engineering (IJECE) 15 (6 … , 2025 2025
Artificial intelligence-based energy efficiency models in green communications towards 6G NA Kumar, R Daniel Towards Wireless Heterogeneity in 6G Networks, 158-179 , 2024 2024 Citations: 1
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 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 2024 Citations: 5
A novel electrical load forecasting model using a deep learning approach NA Kumar, R Daniel, PK Pasam The Internet of Energy, 67-89 , 2024 2024 Citations: 2
Application of Intelligent Controllers in Speed Control of Synchronous Motor A Sudhakar, R Daniel, S Puli, S Mummana, NR Kolukula, BV Rajanna Theory and Applications of Engineering Research Vol. 4, 22-57 , 2024 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 2023 Citations: 3
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 2023 Citations: 27
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 2023 Citations: 12
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 2023 Citations: 5
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 2023 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
Optimizing routing in nature-inspired algorithms to improve performance of mobile ad-hoc network A Deshpande, M Arshey, R Daniel, DD Rao, E Raja, DC Rao International Journal of Intelligent Systems and Applications in Engineering … , 2023 2023.0 Citations: 77
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 2023.0 Citations: 39
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 2023.0 Citations: 27
& 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) , 0 Citations: 13
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 2023.0 Citations: 12
Dimensionality reduction as probabilistic inference A Ravuri, F Vargas, V Lalchand, ND Lawrence arXiv preprint arXiv:2304.07658 , 2023 2023.0 Citations: 10
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 2023.0 Citations: 7
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 2022.0 Citations: 7
Hybrid DNN-latent structured SVM acoustic models for continuous speech recognition S Ravuri 2015 IEEE workshop on automatic speech recognition and understanding (ASRU … , 2015 2015.0 Citations: 7
Notice of Removal: An optimal power conservation cluster based routing algorithm using Fuzzy Verdict Mechanism for Wireless Sensor Networks R Daniel, KN Rao 2015 International Conference on Electrical, Electronics, Signals … , 2015 2015.0 Citations: 7
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 2012.0 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 2024.0 Citations: 5
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 2023.0 Citations: 5
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 2023.0 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 2018.0 Citations: 3
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 2014.0 Citations: 3
Post-corrosion mechanical performance of fine grained AZ31 Mg alloy sheets produced by groove pressing K Srivalli Rani, R Daniel, K Singaiah, G Prabhakar, RS B Engineering Research Express 7 (3), 035583 , 2025 2025.0 Citations: 2
Secure Attendance System Leveraging Machine Learning and Blockchain R Daniel, A Nidumolu, KS Manikonda, MK Movva, S Majji 2025 International Conference on Visual Analytics and Data Visualization … , 2025 2025.0 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 2024.0 Citations: 2
MCRO-ECP: Mutation Chemical Reaction Optimization based Energy Efficient Clustering Protocol for Wireless Sensor Networks. R Daniel, KN Rao KSII Transactions on Internet & Information Systems 13 (7) , 2019 2019.0 Citations: 2
