Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm) R. R., Jagannadha Naidu. K., V. Jeya Ramya, Regan. D. Journal of Cybersecurity and Information Management, 2024 Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.
Enhancing Dehazing Performance of Single Optical Satellite Images using Gamma Correction and Improved DCP Regan D, S. Vishnu Priyan Proceedings of the 2023 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2023, 2023 Dehazing is an essential preprocessing step for improving the quality of satellite imagery. Hazy conditions can significantly impact the visibility and clarity of satellite images, making it difficult to extract useful information from them. In recent years, several dehazing techniques have been proposed for satellite imagery, including contrast stretching, dark channel prior, and adaptive gamma correction. In this research work, we propose a novel haze removal approach that combines gamma correction with modified dark channel prior-based haze removal approach in the YCbCr color space. Our approach enhances the luminance component of the image by applying gamma correction to the Y channel, and reduces computational complexity by subsampling and combining the chrominance channels. The performance of the proposed system is assessed using peak signal to noise ratio (PSNR) and structural similarity index (SSIM) and compared with the state-of-the-art methods. Experimental results demonstrate that the proposed our approach outperforms other methods in terms of dehazing and contrast improvement, suggesting its potential for various applications that require high-quality satellite image processing.
Lightweight GWO-LSTM-Based Land Cover Classification using Remote Sensing Images Regan D, Gobinath Ravindran, P. Senthil, M. Jamuna Rani, Subhra Chakraborty 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023 The classification of land use and land cover (LULC) using remote sensing data is essential for many environmental models and land-use inventories. A lightweight deep learning classifier is implemented in this research to improve the performance of LULC classification, assisting in the prediction of declining environmental quality, haphazard elements, wildlife habitat, and so on. LULC classification is evaluated using Eurosat dataset and it uses algorithms like the Haralick texture features, histogram of the oriented gradient, and local Gabor binary pattern histogram sequence for feature extraction. The Grey Wolf Optimization (GWO) method is applied to select the best features, offering fast convergence tares and ease of implementation. A Long Short-Term Memory (LSTM) network is then used to categorize the LULC. The research outcomes show that the GWO method with an LSTM classifier efficiently differentiates the classification of LULC in terms of 99.80% accuracy, 98.99% precision, and 99.53% recall when compared to the Deep SHAP, HFEL-CCGSA, Human group-based PSO with LSTM and IMO-mLSTM.
SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique Ponugoti Kalpana, Yerrolla Chanti, Ravi G, Regan D, Piyush Kumar Pareek 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques Easct 2023, 2023 Various corn leaf diseases reduce the quantity and quality of corn crop production, so early detection and classification are important for preventing crop yield. However, the detection and classification of corn leaf disease are more difficult due to the regions of the leaf blur and noise effect. To solve the above-mentioned problems, a feature-based transfer learning approach called the Convolutional Deep Learning (CDL) model is proposed. First, the images of corn leaves taken from Plant Village are preprocessed with Green Channel Conversion (GCC) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to enhance low-contrast images. Then the important features are extracted through ResNet152, which contains two fully connected layers with a sigmoid function. Next, squeeze and excitation are combined with ResNet152 to gain better performance in detection and classification. The proposed SE-ResNet152 model outperforms with a Precision 98.71%, Recall 98.14%, F1-score 96.87% and accuracy 99.02% respectively compared to existing model like U-Net and ResNet50 models.
Selection of Trust Nodes for Efficient Data Transmission in MANET K. Gunasekaran, D. Regan, Basavaraj G Kudamble, M. A. Manivasagam 2nd IEEE International Conference on Advanced Technologies in Intelligent Control Environment Computing and Communication Engineering Icatiece 2022, 2022 Reliable Routing through Trust Node Selection scheme is proposed to implement improved security measures and efficient routing in MANET. The friend list is created for each and every node and this task is performed for identifying the node ratings through the challenging process for its neighbor nodes. Challenge is a process carried out for determining the ratings obtained for nodes to prove their integrity and honesty. The node challenge process is carried between the nodes through the count of control messages that have been processed. From the consequences of node ratings if the nodes achieve a certain value then the nodes come under the friend node list else the node falls under the unfriend node list and isolates from the routing process. Finally, the data transmission is done through reliable and trusted routes by utilizing a key management model for encrypting the data. Recreation investigation is supported obtainable intended for demonstrating the effectiveness of the future outline.
Mixed pixel wise characterization based on hdp-hmm and hyperspectral image shape detection using hybrid canny edge detection and wpdf Arpn Journal of Engineering and Applied Sciences, 2015
Adaptive artificial bee colony based parameter selection for subpixel mapping multiagent system in remote-sensing imagery D. Regan, S.K. Srivatsa Iciiecs 2015 2015 IEEE International Conference on Innovations in Information Embedded and Communication Systems, 2015 Remote sensing has become an important source of land use/cover information at a range of spatial and temporal scales. The existence of mixed pixels is a major problem in remote-sensing image classification. Although the soft classification and spectral unmixing techniques can obtain an abundance of different classes in a pixel to solve the mixed pixel problem, the subpixel spatial attribution of the pixel will still be unknown. The subpixel mapping technique can effectively solve this problem by providing a fine-resolution map of class labels from coarser spectrally unmixed fraction images. However, most traditional subpixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear subpixel, leading to incomplete and inaccurate results. To improve the subpixel mapping accuracy, this paper proposes an adaptive subpixel mapping framework based on a multiagent system for remote sensing imagery. In the proposed multiagent subpixel mapping framework, three kinds of agents, namely, feature detection agents, subpixel mapping agents and decision agents, are designed to solve the subpixel mapping problem. This confirms that MASSM is appropriate for the subpixel mapping of remote-sensing images. But the major problem is that the selection of the parameters becomes assumption in order to overcome these problems proposed work focus on adaptive selection of parameters based on the optimization methods, it automatically selects the parameters value in the classification, and it improves the classification results in the remote-sensing imagery. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed artificial bee colony based optimization subpixel mapping algorithm in comparison with the hard classification method and other subpixel mapping algorithms: subpixel mapping based on a back-propagation neural network and the spatial attraction model. The experimental results indicate that the proposed algorithm outperforms the other two subpixel mapping algorithms in reconstructing the different structures in mixed pixels.
Dimensionality reduction of hyperspectral image using RBF-PCA and mixed pixel wise HSMM characterization using SVM-FSK forclassification Global Journal of Pure and Applied Mathematics, 2015
A novel sensing noise and Gaussian noise removal methods via sparse representation using SVD and compressive sensing methods Arpn Journal of Engineering and Applied Sciences, 2015
A novel framework for hyper spectral image classification with the development of multiple kernel machines International Journal of Applied Engineering Research, 2015
Mixed pixel wise characterization based on HMM and hyper spectral image gradient enhancement for classification using SVM-FSK International Review on Computers and Software, 2014
RECENT SCHOLAR PUBLICATIONS
DenseNet-121-Based Deep Learning Framework for Accurate Detection and Classification of Lung Fibrosis B Girirajan, S Prabu, G Visalaxi, M Sasikala, D Regan 2025 5th International Conference on Mobile Networks and Wireless … , 2025 2025.0
An Improved Dehazing Algorithm for Remote Sensing Images Using Multi-Scale Atmospheric Light Estimation and Adaptive Side Window Filtering R D, SS Nikitha, N Pavana, C Praveen, SR Siddartha, P Sivareddy 2025 International Conference on Emerging Technologies in Engineering … , 2025 2025.0
Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm) RL Babu, J Naidu, VJ Ramya, D Regan Journal of Cybersecurity and Information Management 14 (1), 218-18-226 , 2024 2024.0
Enhancing Dehazing Performance of Single Optical Satellite Images using Gamma Correction and Improved DCP SVP Regan D 2023 International Conference on Innovative Computing, Intelligent … , 2023 2023.0 Citations: 1
Lightweight GWO-LSTM-Based Land Cover Classification using Remote Sensing Images SC Regan. D, Gobinath Ravindran, P. Senthil, M. Jamuna Rani 2023 International Conference on Evolutionary Algorithms and Soft Computing … , 2023 2023.0 Citations: 1
Selection of Trust Nodes for Efficient Data Transmission in MANET K Gunasekaran, D Regan, BG Kudamble, MA Manivasagam 2022 Second International Conference on Advanced Technologies in Intelligent … , 2022 2022.0 Citations: 2
An Unsharp Masking Algorithm Embedded With Bilateral Filter System for Enhancement of Aerial Photographs CP D. Regan International Journal of Recent Technology and Engineering (IJRTE) 8 (4 … , 2019 2019.0 Citations: 2
A Study on Performance of Bilateral and Trilateral Filters in the Mixed Noise Environment DRC Padmavathi Journal of Computational and Theoretical Nanoscience 15 (6/7), 2089-91 , 2018 2018.0 Citations: 1
Textural Image Segmentation Framework based on Edge Preserving Bilateral Filter CPDD Regan Journal of Advanced Research in Dynamical and Control Systems 10 (12), 719-23 , 2018 2018.0
A Study on Local Variance Threshold Based Boundary Detector with Median Filter D Regan, MV Raghavendra, R Purushotham Naik International Journal of Innovative Research in Electronics and … , 2018 2018.0
Design of enhanced half ripple carry adder for VLSI implementation of two-dimensional discrete wavelet transform K Gunasekaran, D Regan Int J MC Square Sci Res 8 (1), 50-59 , 2016 2016.0 Citations: 3
PERFORMANCE EVALUATION OF MIXED PIXEL-WISE CHARACTERIZATION METHODS ADOPTED IN HYPERSPECTRAL IMAGE CLASSIFICATIONS D REGAN 2016.0
Adaptive artificial bee colony based parameter selection for subpixel mapping multiagent system in remote-sensing imagery D Regan, SK Srivatsa 2015 International Conference on Innovations in Information, Embedded and … , 2015 2015.0 Citations: 2
A Novel Sensing Noise and Gaussian Noise Removal Methods via Sparse representation using SVD and Compressive Sensing Methods D Regan, SK Srivatsa ARPN Journal of Engineering and Applied Sciences 10 (9), 4047-4054 , 2015 2015.0 Citations: 1
A novel framework for Hyperspectral image classification with the development of multiple kernel machines D Regan, SK Srivatsa IEEE Sponsored 2nd International Conference on Innovations in information … , 2015 2015.0
Dimensionality Reduction of Hyperspectral Image Gradient Enhancement Images Using RBF-PCA for Mixed Pixel Wise HSMM Characterization for SVM-FSK Classification D Regan, SK Srivatsa Global Journal of Pure and Applied Mathematics 11 (3), 1603-1626 , 2015 2015.0
Mixed pixel wise characterization based on HDP-HMM and Hyperspectral image shape detection using hybrid canny edge detection and WDPF D Regan., SK Srivatsa. ARPN Journal of Engineering and Applied Sciences 10 (5), 6965-6979 , 2015 2015.0
Masked Image Registration Using Wavelet Transforms HH Rosi, D Regan International Journal of innovative Research & Development 2 (5), 1482 - 1491 , 2013 2013.0
Image De-noising Performance of Rank filters D Regan, B Senthilkumar, S Vishnupriyan, K Gunasekaran International Journal of Advanced Research in Computer Science 2 (4), 408-411 , 2011 2011.0
Mixed Pixel Wise Characterization Based on HMM and Hyper spectral Image Gradient Enhancement for Classification Using SVM-FSK,(2014) D Regan, S Srivatsa International Review on Computers and Software (IRECOS) 9 (6), 1017-1026 , 0 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Mixed Pixel Wise Characterization Based on HMM and Hyper spectral Image Gradient Enhancement for Classification Using SVM-FSK,(2014) D Regan, S Srivatsa International Review on Computers and Software (IRECOS) 9 (6), 1017-1026 , 0 Citations: 4
Design of enhanced half ripple carry adder for VLSI implementation of two-dimensional discrete wavelet transform K Gunasekaran, D Regan Int J MC Square Sci Res 8 (1), 50-59 , 2016 2016.0 Citations: 3
Selection of Trust Nodes for Efficient Data Transmission in MANET K Gunasekaran, D Regan, BG Kudamble, MA Manivasagam 2022 Second International Conference on Advanced Technologies in Intelligent … , 2022 2022.0 Citations: 2
An Unsharp Masking Algorithm Embedded With Bilateral Filter System for Enhancement of Aerial Photographs CP D. Regan International Journal of Recent Technology and Engineering (IJRTE) 8 (4 … , 2019 2019.0 Citations: 2
Adaptive artificial bee colony based parameter selection for subpixel mapping multiagent system in remote-sensing imagery D Regan, SK Srivatsa 2015 International Conference on Innovations in Information, Embedded and … , 2015 2015.0 Citations: 2
Enhancing Dehazing Performance of Single Optical Satellite Images using Gamma Correction and Improved DCP SVP Regan D 2023 International Conference on Innovative Computing, Intelligent … , 2023 2023.0 Citations: 1
Lightweight GWO-LSTM-Based Land Cover Classification using Remote Sensing Images SC Regan. D, Gobinath Ravindran, P. Senthil, M. Jamuna Rani 2023 International Conference on Evolutionary Algorithms and Soft Computing … , 2023 2023.0 Citations: 1
A Study on Performance of Bilateral and Trilateral Filters in the Mixed Noise Environment DRC Padmavathi Journal of Computational and Theoretical Nanoscience 15 (6/7), 2089-91 , 2018 2018.0 Citations: 1
A Novel Sensing Noise and Gaussian Noise Removal Methods via Sparse representation using SVD and Compressive Sensing Methods D Regan, SK Srivatsa ARPN Journal of Engineering and Applied Sciences 10 (9), 4047-4054 , 2015 2015.0 Citations: 1
DenseNet-121-Based Deep Learning Framework for Accurate Detection and Classification of Lung Fibrosis B Girirajan, S Prabu, G Visalaxi, M Sasikala, D Regan 2025 5th International Conference on Mobile Networks and Wireless … , 2025 2025.0
An Improved Dehazing Algorithm for Remote Sensing Images Using Multi-Scale Atmospheric Light Estimation and Adaptive Side Window Filtering R D, SS Nikitha, N Pavana, C Praveen, SR Siddartha, P Sivareddy 2025 International Conference on Emerging Technologies in Engineering … , 2025 2025.0
Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm) RL Babu, J Naidu, VJ Ramya, D Regan Journal of Cybersecurity and Information Management 14 (1), 218-18-226 , 2024 2024.0
Textural Image Segmentation Framework based on Edge Preserving Bilateral Filter CPDD Regan Journal of Advanced Research in Dynamical and Control Systems 10 (12), 719-23 , 2018 2018.0
A Study on Local Variance Threshold Based Boundary Detector with Median Filter D Regan, MV Raghavendra, R Purushotham Naik International Journal of Innovative Research in Electronics and … , 2018 2018.0
PERFORMANCE EVALUATION OF MIXED PIXEL-WISE CHARACTERIZATION METHODS ADOPTED IN HYPERSPECTRAL IMAGE CLASSIFICATIONS D REGAN 2016.0
A novel framework for Hyperspectral image classification with the development of multiple kernel machines D Regan, SK Srivatsa IEEE Sponsored 2nd International Conference on Innovations in information … , 2015 2015.0
Dimensionality Reduction of Hyperspectral Image Gradient Enhancement Images Using RBF-PCA for Mixed Pixel Wise HSMM Characterization for SVM-FSK Classification D Regan, SK Srivatsa Global Journal of Pure and Applied Mathematics 11 (3), 1603-1626 , 2015 2015.0
Mixed pixel wise characterization based on HDP-HMM and Hyperspectral image shape detection using hybrid canny edge detection and WDPF D Regan., SK Srivatsa. ARPN Journal of Engineering and Applied Sciences 10 (5), 6965-6979 , 2015 2015.0
Masked Image Registration Using Wavelet Transforms HH Rosi, D Regan International Journal of innovative Research & Development 2 (5), 1482 - 1491 , 2013 2013.0
Image De-noising Performance of Rank filters D Regan, B Senthilkumar, S Vishnupriyan, K Gunasekaran International Journal of Advanced Research in Computer Science 2 (4), 408-411 , 2011 2011.0
