Knowledge Engineering, Data Mining, Software Engineering
21
Scopus Publications
Scopus Publications
1D-CNN: ONE DIMENSIONAL CONVOLUTION NEURAL NETWORK-BASED ELECTROENCEPHALOGRAM (EEG) SIGNALS CLASSIFICATION WITH EFFICIENT ARTIFACT REMOVAL FOR REAL-TIME MEDICAL APPLICATIONS Padmini Chattu, C.V.P.R. Prasad Transactions of the Royal Institution of Naval Architects Part A International Journal of Maritime Engineering, 2025 Mental task detection and classification employing solo/restricted channel(s) electroencephalogram (EEG) signals in actual time perform a significant part in the pattern of mobile brain-computer interface (BCI) and neurofeedback (NFB) schemes. Nevertheless, the actual time registered EEG signals remain frequently adulterated with noises like ocular artifacts (OAs) and muscle artifacts (MAs) that decline the handmade features extracted out of EEG signal leading to insufficient detection and classification of mental tasks. Hence, we analyse the employment of the latest deep learning approaches that in no way need whatsoever physical feature extraction or artifact repression phase. This study proffers a one-dimensional convolutional neural network (1D-CNN) framework for mental job detection and classification. The proffered framework’s strength can be analysed employing artifact-free and artifact-adulterated EEG signals obtained out of publicly accessible datasets especially the Emotiv EPOC headset. It is observed that the proffered 1D-CNN attains 0.992 of accuracy, 0.993 of precision, 0.9905 of recall, 0.0065 of FPR, and 0.992 of F-measure. Correlative execution assessment exhibit that the proffered framework surpasses prevailing techniques not merely concerning classification precision yet as well as in strength opposing artifacts.
Integrating Enhanced Learning to Rank into a Hybrid Deep Learning System for Optimized Recommendations Naveen Kumar Navuri, Cvpr Prasad 2025 IEEE 1st Secure and Trustworthy Cyberinfrastructure for Iot and Microelectronics Satc 2025 Conference Proceedings, 2025 We present a state-of-the-art LTR method that is integrated into our recommender system to boost its performance. We’re starting with a recommendation list generated by a custom-built deep-learning framework. For a better ranking of these items, we use a listwise LTR method specifically designed for this purpose. This LTR model is trained for optimizing the key performance metrics like Normalized Discounted Cumulative Gain (NDCG), and Mean Average Precision (MAP) which are important for top-N-based evaluation. Our experimental results mainly conducted on the Movie Lens dataset show that the proposed system significantly outperforms conventional models in ranking performance.
Feature Extraction of EEG Signal Using Convolutional Neural Networks by Removing Artifacts Padmini Chattu, C.V.P.R. Prasad Informatica Slovenia, 2025 Clinical depression is a neurological disease identifiable by the analysis of the electroencephalography signals (EEG). The electroencephalographic signals (EEG) are often polluted by many artifacts. Deep study models have been employed in recent years to denoise electroencephalography. The main difficulty in medical analysis is the extraction of true brain signals from the polluted EEG data. Noise reduction from recorded EEG data is very important for better brain disorder investigation. This paper proposed an effective EEG signal estimation model for the process of EEG signals. The proposed model uss the Morelette wavelet transformation model for the pre-processing of the EEG signal. With the pre-processed EEG signal model feature extraction is performed with the Convolutional Neural Network (CNN) for the EEG signal. With the pre-processed EEG signal model training and testing are estimated for the classification of the EEG signal. The EEG signal categorization was carried out utilizing characteristics derived from EEG data. Many characteristics have proven sufficiently distinctive for usage in all applications linked to the brain. The EEG may be categorized using a range of functions such as autoregression, energy spectrum density, energy entropy and linear complexity. However, various characteristics indicate varying strength of discrimination for different individuals or trials. Two characteristics are utilized in this study to enhance the performance of EEG signals. Techniques based on the neural network are used for the extraction of EEG signal. Classification methods include the Random Forest Classification. The model was tested using a random splitting method and 93.4 percent of the EEG signals were received accordingly.
Enhancing Ophthalmological Diagnoses: An Adaptive Ensemble Learning Approach Using Fundus and OCT Imaging Narasimha Swamy LAVUDIYA, C.V.P.R Prasad International Journal of Computational and Experimental Science and Engineering, 2024 This study presents an innovative Ensemble Disease Learning Algorithm (EDL) for the detection and classification of retinal diseases using fundus images. We enhance our method by incorporating deep learning techniques and multi-modal imaging data, including optical coherence tomography (OCT) images alongside fundus photographs, to provide a more comprehensive understanding of retinal pathology. The advanced EDL integrates Convolutional Neural Networks (CNNs) and attention mechanisms with Capsule Networks (CapsNet) and Support Vector Machine (SVM) classifiers for more nuanced feature extraction and classification. We introduce a novel ensemble adaptive weighting approach that dynamically adjusts classifier weights based on performance across disease types and severity levels, significantly improving the algorithm's handling of complex and rare cases. To enhance model interpretability, we implement an explainable AI component that provides visual heatmaps of the most significant regions for each diagnosis to clinicians. We evaluate the enhanced EDL on a large, diverse dataset encompassing multiple retinal diseases, including diabetic retinopathy, age-related macular degeneration, and glaucoma, across various ethnicities and age groups. Our results demonstrate superior accuracy, sensitivity, and specificity compared to our previous model and other state-of-the-art approaches. A prospective clinical validation study assesses the algorithm's real-world performance. This research advances automated retinal disease diagnosis by making it more robust, accurate, and clinically relevant, potentially improving patient outcomes and global eye care through early disease detection and treatment planning.
Ensemble deep learning approach with hybrid optimisation for enhanced underwater acoustic OFDM communication systems S. Pradeep, Subba Reddy Borra, C.V.P.R. Prasad, N. Sreekanth, Sudhakar Kallur International Journal of Ad Hoc and Ubiquitous Computing, 2024 Underwater acoustic (UWA) communication involves transmitting information through sound waves in aquatic environments, which presents challenges due to signal attenuation, multi-path propagation, and background noise. This research presents a novel approach using ensemble deep learning (EDL) combined with hybrid optimisation for UWA-orthogonal-frequency-division-multiplexing (OFDM) systems. Contrary to the traditional receiver dependence on channel estimation and equalisation for symbol detection, the proposed EDL model directly retrieves transmitted symbols after sufficient training. It employs convolutional neural networks (CNNs), bi-directional long-short-term memory (bi-LSTM) networks, and recurrent neural networks (RNNs) to capture spatial, temporal, and sequence-based dependencies in the signal. To optimise the training process, a hybrid strategy, OppTalO, which integrates driving training-based optimisation (DTBO) and osprey optimisation algorithm (OOA), is utilised. The effectiveness of this EDL approach with hybrid optimisation is assessed across various system parameters: cyclic prefix length and pilot symbol count; and found to have less error rate than existing methods.
An Advanced Keyword Searching Model with Data Security in Cloud Computing Afsha Jabeen, C.V.P.R. Prasad 2023 4th International Conference on Electronics and Sustainable Communication Systems Icesc 2023 Proceedings, 2023 The growing popularity of cloud computing has necessitated the development of more efficient and secure methods of searching and retrieving data from cloud storage. In this paper, we propose an advanced keyword-searching model that ensures data security in cloud computing environments. To ensure data confidentiality and safety, our model employs advanced encryption techniques such as symmetric-key, homomorphic, and attribute-based encryption. Also, this study introduces an optimized indexing technique using a binary search algorithm to reduce search time and improve search efficiency. Furthermore, our model employs a secure multi-party computation approach to enable fast computation between multiple parties while keeping private information private. Using a benchmark dataset, this study demonstrates that the proposed model achieves high accuracy and efficiency while maintaining data security. The proposed model can be used in various applications, such as healthcare, finance, and ecommerce, where sensitive data must be securely stored and retrieved. The proposed model provides an efficient and secure solution for keyword searching in cloud computing environments.
Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation S. Pradeep, Yogesh Kumar Sharma, Chaman Verma, Surjeet Dalal, Cvpr Prasad Applied System Innovation, 2022 Wireless sensor networks (WSNs) are a comparatively new revolutionary technology that has the potential to revolutionize how we live together with the present system. To enhance data archiving, WSNs are frequently used in scientific studies. Many applications have proved the value of wired sensors; however, they are prone to wire cutting or damage. While preventing wire tangles and damage, wireless sensor networks provide autonomous monitoring. The WS network suffers from a number of fundamental restrictions, including insufficient processing power, storage space, available bandwidth, and information exchange. Consequently, energy-efficient strategies are necessary for maximizing the performance and lifespan of WSNs. As a result, the special cluster head relay node and energy balancing techniques will be applied to deal with WSN energy consumptions. This extends the life of the network. In wireless sensor networks, clustering is a smart approach to reduce energy consumption. Energy scarcity and consumption are serious issues that must be addressed with effective and dependable solutions. The proposed MGSA considers the distance between each node and its corresponding CHs, as well as the residual energy and delay, as important factors in the relay node selection. The proposed approach outperforms the current methods, such as low-energy adaptive clustering hierarchy, LEACH (in terms of data delivery rate), energy efficiency, and network longevity. The next level, which will boost the efficiency of wireless sensor networks, with two fitness functions, is proposed. The cluster head (CH) is in charge of collecting and transmitting data from all other cluster nodes. The flow of the consistency of the cluster head selection process will beat the improved data delivery rate, energy efficiency, recommended fuzzy clustering performance experiments, and assessments. As a result, energy-efficient operations are necessary to maximize the WSN performance and lifespan.
A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment C.V.P.R. Prasad, Veera Ankalu. Vuyyuru, T. Sushma, Sri Lakshmi Uppalapati, G. Apparao International Journal on Recent and Innovation Trends in Computing and Communication, 2022 A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack.
Removing artifacts in EEG data based on wavelets and neural networks Journal of Theoretical and Applied Information Technology, 2021
A research on frequent sub graph mining from distributed database Journal of Advanced Research in Dynamical and Control Systems, 2019
Business intelligence and data mining techniques: A survey Journal of Advanced Research in Dynamical and Control Systems, 2018
An efficient approach for knowledge discovery in decision trees using attribute transform and outlier detection International Journal of Applied Engineering Research, 2015
A novel prototype decision tree method using sampling strategy Bhanu Prakash Battula, Debnath Bhattacharyya, C. V. P. R. Prasad, Tai-hoon Kim Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
