Dr. SHANTY CHACKO
@karunya.edu
Assistant Professor, EEE
KARUNYA INSTITUTE OF TECHNOLOGY AND SCIENCES
EDUCATION
M.E (Applied Electronics), PhD (Electronics and Communication)
RESEARCH INTERESTS
Digital Image Processing
Scopus Publications
Scopus Publications
H. James Deva Koresh and Shanty Chacko
Springer Science and Business Media LLC
Anusha Chacko and Shanty Chacko
Springer International Publishing
Reshma Jose, Shanty Chacko, J. Jayakumar, and T. Jarin
World Scientific Pub Co Pte Ltd
Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.
Anusha Chacko and Shanty Chacko
Wiley
Image watermarking is an effective way to secure the ownership of digital photographs. This paper proposes a new methodology for integrating a watermark on the basis of various integrative strengths. The image is separated as 8 × 8 pixels blocks that do not overlap. The pixel size for each image block has been determined. For the embedding areas, picture blocks with the highest value have been chosen. Therefore, discrete cosine transformation (DCT) is transformed. The DCT coefficients are chosen in the midfrequency and the average selected DCT blocks are determined using a series of rules to produce various integration strengths. The watermarking bits were merged with the proposed deep learning convolution neural network (DLCNN) through a series of integration standards. The binary watermark has been scrambled by an Arnold transform until it is incorporated for additional stability. During the image carrier, a pattern recognition model depending on DLCNN is utilized to identify and extract the watermark and to recognize the watermark using the Harris hawks optimization (HHO) algorithm. The findings of the tests demonstrated that the system suggested is most imperceptible than the other current systems. The proposed method attains the efficiency watermarked picture with 46 dB peak signal‐to‐noise ratio value. This paper focuses on robust medical image watermarking exploiting DCT by using the HHO algorithm. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the consolidation of DCT and image watermarking secret key. The performance of robust medical image watermarking exploiting DCT with the HHO algorithm is compared with other conventional compression methods. HHO is found better and used to control watermarked image degradation in medical images watermarking. The proposed system also created a high resistance to remove watermarks during many attacks.
Chitra S, Jayakumar J, Venkateshkumar P, Shanty Chacko, and Sivabalan
FOREX Publication
In many residential buildings the electrical wires of individual houses are laid in the same conduit pipe and some mistakes could be made in identifying similar coloured wires when they are laid in same conduit pipe. Most of the faults are caused by the neutral interconnection in the wiring system. Usually neutral wires are connected to neutral bus within the panel board or switchboard, and are "bonded" to earth ground. In our secondary distribution, tree system of supply is mostly utilized. The voltage of each phase to neutral will be maintained at rated value even during the unbalanced load conditions. If neutral wire connection is poor the voltage at each phase will be different from one another, such an isolated neutral point is called floating neutral and the voltage of the point is always changing. This is the reason for over voltage causing damage to appliance’s which should be protected. In this paper, a smart system that identifies power leakage and provides over voltage protection to the residential building is proposed.
Dr. K. Sasikala, Dr. J. Jayakumar, Dr. A. Senthil Kumar, Dr. Shanty Chacko, and Dr. Hephzibah Jose Queen
FOREX Publication
The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained. Correlation between Input and output variable using spearman’s correlation analysis using Heat maps. Followed by the Multiple Linear Regression based Training and testing of the Modified IEEE 14 and IEEE30 Bus systems for base load case, 10% and 20% load increment with the 5-fold cross validation is also presented. Comparative analysis is performed to find the best fit ML Model for our research.
Reshma Jose and Shanty Chacko
Inderscience Publishers
Reshma Jose, Shanty Chacko, and Jarin T.
AIP Publishing
Liver cancer is the leading cause of cancer-related death worldwide. Since the radiologist's ability to diagnose liver cancer at an early stage is zero, the prognosis is poor. According to numerous investigations performed so far, the nodule segmentation algorithms are clearly ineffective. As a result, for specific pulmonary nodule segmentation, this study made use of the advanced optimization tool and centralized super pixels segmentation based iterative clustering (SSBIC). To remove noise from the images, start by using ADF and unsharp masking enhancement techniques. In order to predict abnormal liver tissue, an enhanced nodule image sequence is subjected to the Super pixel Segmentation Based Iterative Clustering (SSBIC) algorithm. Finally, to photograph liver nodules, a deep learning-based Advanced GWO with CNN (AGWO-ONN) and an Advanced GWO with ONN (AGWO-ONN) are used (AGWO-CNN).For nodule slice order, the average segmentation time is 1.06s. The classification accuracy of the Advanced GWO with ONN (AGWO-ONN) method is 97 percent, while the classification accuracy of the Advanced GWO with CNN (AGWO-CNN) method is 97.6 percent.
Tintu Mary John and Shanty Chacko
Institution of Engineering and Technology (IET)
Numerous applications based on very large scale intergration (VLSI) architecture suffer from large size components that lead to an error in the design of the filter during the stages of floating point arithmetic. Hence, it is necessary to change the architectural model that increases the design complexity and the time delayeffect. The issue encountered in the VLSI architectures for finiteimpulse response (FIR) filteris the increased number of components, especially delay elements. For the VLSI architecture reconfigured with reduced register usage, this article provides the floating point processing element (FPPE) implementation with Cross ‐ Folded Shifting. The proposed FIR filter system reduces the number of components in the circuit which increases the complexity and high delay rate in the logical operation. The system has a comparatively reduced delay rate and power consumption. Hence, an efficient fast architecture based on the FPPE method is developed in this paper.
H. James Deva Koresh, Shanty Chacko, and M. Periyanayagi
Elsevier BV
Vinoth Kumar K, Josh F T, Vinodha K, Ramya K C, Shanty Chacko, and Gunapriya B
IEEE
Anusha Chacko and Shanty Chacko
Springer International Publishing
Jayaraj Jayakumar, Balakrishnan Nagaraj, Shanty Chacko, and P. Ajay
Hindawi Limited
Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high-performance and high-precision subsystems. The existing systems confine themselves with limited features and have driving range anxiety, charging and discharging time issues, and inter- and intravehicle communication problems. The above issues are the critical barriers to the penetration of EVs with a smart grid. This paper proposes the concepts which consist of connected vehicles that exploit vehicular ad hoc network (VANET) communication, embedded system integrated with sensors which acquire the static and dynamic parameter of the electrical vehicle, and cloud integration and dig data analytics tools. Vehicle control information is generated based on machine learning-based control systems. This paper also focuses on improving the overall performance (discharge time and cycle life) of a lithium ion battery, increasing the range of the electric vehicle, enhancing the safety of the battery that acquires the static and dynamic parameter and driving pattern of the electrical vehicle, establishing vehicular ad hoc network (VANET) communication, and handling and analyzing the acquired data with the help of various artificial big data analytics techniques.
Reshma Jose and Shanty Chacko
Inderscience Publishers
H. James Deva Koresh and Shanty Chacko
Springer International Publishing
Reshma Jose and Shanty Chacko
IEEE
Deep learning is a recent field of machine learning that has garnered a great deal of attention in recent years. It has been commonly used for a variety of applications and has proved to be a strong machine learning method for many complex problems. Liver cancer appears to be a leading cause of female death, and a lot of money has been spent in the form of preventative screening programs. The use of automatic image processing techniques resulting from deep learning in that same sense represents a promising way of helping to detect liver cancer. In this paper, ADF-USM (Anisotropic diffusion filtering with Unsharp masking) completed the liver malignant growth CT (Computer Tomography) image preprocessing, the shapes and curves were effectively differentiated and upgraded by histogram equalization. It is seen by analyzing the results of the division method that the SSBIC (Superpixel Segmentation Based Iterative Clustering) algorithm produces favorable results than other current strategies. Finally, the process for classification relies mostly on execution of AGWO-CNN (Adaptive Grey Wolf Optimization with Convolution Neural Network classifier) to verify whether the images are benign or malignant.Experimental results on CT images show that the AGWOCNN model achieved high processing efficiency with an accuracy of 97.6 percent in the classification of liver cancer compared to other classification models.
H. James Deva Koresh and Shanty Chacko
Springer Science and Business Media LLC
Tintu Mary John and Shanty Chacko
Emerald
Purpose This paper aims to concentrate on an efficient finite impulse response (FIR) filter architecture in combination with the differential evolution ant colony algorithm (DE-ACO). For the design of FIR filter, the evolutionary algorithm (EA) is found to be very efficient because of its non-conventional, nonlinear, multi-modal and non-differentiable nature. While focusing with frequency domain specifications, most of the EA techniques described with the existing systems diverge from the power related matters. Design/methodology/approach The FIR filters are extensively used for many low power, low complexities, less area and high speed digital signal processing applications. In the existing systems, various FIR filters have been proposed to focus on the above criterion. Findings In the proposed method, a novel DE-ACO is used to design the FIR filter. It focuses on satisfying the economic power utilization and also the specifications in the frequency domain. Originality/value The proposed DE-ACO gives outstanding performance with a strong ability to find optimal solution, and it has got quick convergence speed. The proposed method also uses the Software integrated synthesis environment (ISE) project navigator (p.28xd) for the simulation of FIR filter based on DE-ACO techniques.
James Deva Koresh Hezekiah and Shanty Chacko
Bentham Science Publishers Ltd.
Background: Measuring cornea thickness is an essential parameter for patients undergoing refractive Laser-Assisted in SItu Keratomileusis (LASIK) surgeries. Discussion: This paper describes about the various available imaging and non-imaging methods for identifying cornea thickness and explores the most optimal method for measuring it. Along with the thickness measurement, layer segmentation in the cornea is also an essential parameter for diagnosing and treating eye-related disease and problems. The evaluation supports surgical planning and estimation of the corneal health. After surgery, the thickness estimation and layer segmentation are also necessary for identifying the layer surface disorders. Conclusion: Hence the paper reviews the available image processing techniques for processing the corneal image for thickness measurement and layer segmentation.
Shanty Chacko, , F. T. Josh, J. Jayakumar, , and
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Digital images that are compressed by JPEG compression schemes are affected by compression artifacts. The blocking artifacts and the ringing artifacts are the commonly seen compression artifacts. These artifacts need to be removed when we decompress the image for various applications. The objective of this work is to remove these artifacts without degrading the image. These compression artifacts can be reduced by low pass filtering of compressed mages. Low pass filtering of images cause loss of information whereby the images get blurred or the clarity of images get reduced. In this work, compressed image is first enhanced using linear enhancement technique and the resulting enhanced image is filtered using Gaussian type filter. The enhanced artifacts are removed more effectively when filtering is done in the enhanced image domain. After the removal of compression artifacts the linear enhancement is removed by the inverse of the transform which is used for linearly enhancing the image. After the removal of artifacts images will be of good quality which can be used in application areas like analysis medical image, analysis of satellite image, remote sensing and product inspection in industry.
H. James Deva Koresh and Shanty Chacko
Springer International Publishing
Shanty Chacko and J. Jayakumar
Inderscience Publishers
When a digital image is compressed at high compression ratio, visual quality of the image is affected because of the formation of compression artefacts. Blocking artefact and ringing artefact are the two major compression artefacts. In this paper, a new filtering technique is proposed to remove the blocking and the ringing artefacts from the compressed digital images. In the proposed method, a Gaussian type filter is used with variable filtering strength based on the information content in the image. For the filtering, window size of 1 × N is used where N is a variable depending on the activities in the region. The filter is applied horizontally, vertically, along 45L and along 135L. The quality of the artefacts removed images are compared with other similar techniques. The results show that the proposed method outperforms other methods.