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202 results on '"Kalaiarasi, G."'

2. Pulse Coupled Neural Network based Near-Duplicate Detection of Images (PCNN - NDD)

Author

THYAGHARAJAN, K. K. and KALAIARASI, G.

Subjects
computer vision, copyright protection, feature extraction, image processing, neural networks, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer engineering. Computer hardware, TK7885-7895
Abstract

Near Duplicate images are variants of original image with some transformations / manipulations / forgeries in it. The illegal copies of images are identified to protect copyright enforcement and reduce redundancy. The existing works in ND detection are less accurate in the identification of similar images as near duplicates. Pulse Coupled Neural Network (PCNN) is found to be a suitable processor for all the image processing techniques including feature extraction. In this paper, PCNN is applied in the detection of near duplicate (ND) images. The proposed work Pulse Coupled Neural Network based Near Duplicate Detection of Images (PCNN-NDD) is a two-step process – (1) feature extraction using PCNN and (2) fast image similarity measurement using correlation coefficient. Our system is capable of improving the accuracy effectively. The advantage of the proposed work lies in the proper setting of PCNN parameters to identify the similar images. The experimental results show that our PCNN-NDD system enhances the detection results and improves the accuracy when compared to other traditional systems.

Published
2018
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7. Improving Software Quality: Realizing the Power of Software Design Patterns

Author

Krishnan, Reshmy, Al Salmani, Khalid Rashid, Kalaiarasi, G., Sivabalan, S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bhateja, Vikrant, editor, Lin, Hong, editor, Simic, Milan, editor, Tang, Jinshan, editor, and Sivakumar Reddy, Vustikayala, editor

Published
2024
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9. Prediction and Analysis of Water Quality Using Machine Learning Techniques

Author

Krishnan, Reshmy, Stephen Sagayaraj, A., Elango, S., Kaviya Nachiyar, R., Indhuja, T., Kanishma, J., Mohamed Uvaise, A., Kalaiarasi, G., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Márquez, Fausto Pedro, editor, Jamil, Akhtar, editor, Hameed, Alaa Ali, editor, and Segovia Ramírez, Isaac, editor

Published
2024
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10. Super Store Sales Management System

Author

Valliappan, L., Tamil Selvan, J., Selvi, M., Kalaiarasi, G., Yogitha, R., Fournier-Viger, Philippe, Series Editor, Visvam Devadoss, Ambeth Kumar, editor, Subramanian, Malathi, editor, Emilia Balas, Valentina, editor, Turjman, Fadi Al, editor, and Malaichamy, Ramakrishnan, editor

Published
2024
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18. A Review on Near Duplicate Detection of Images using Computer Vision Techniques

Author

Thyagharajan, K. K. and Kalaiarasi, G.

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.7, I.4.9, I.4.10, I.5.2, I.5.3, I.5.4, I.2.4, I.2.6, I.2.10, H.3.1, H.3.3
Abstract

Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision-based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field., Comment: 37 Pages, 7 figures, "For online first version, see https://link.springer.com/article/10.1007/s11831-020-09400-w"

Published
2020
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19. Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification

Author

Kalaiarasi, G. and Maheswari, S.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Hyperspectral image (HSI) classification plays a significant in the field of remote sensing due to its ability to provide spatial and spectral information. Due to the rapid development and increasing of hyperspectral remote sensing technology, many methods have been developed for HSI classification but still a lack of achieving the better performance. A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image with higher accuracy and minimum time consumption. The FFSIFT-MLPC technique performs three major processes, namely preprocessing, feature extraction and classification using multiple layers. Initially, the hyperspectral image is divided into number of spectral bands. These bands are given as input in the input layer of perceptron. Then the Frost filter is used in FFSIFT-MLPC technique for preprocessing the input bands which helps to remove the noise from hyper-spectral image at the first hidden layer. After preprocessing task, texture, color and object features of hyper-spectral image are extracted at second hidden layer using Gaussian distributive scale-invariant feature transform. At the third hidden layer, Euclidean distance is measured between the extracted features and testing features. Finally, feature matching is carried out at the output layer for hyper-spectral image classification. The classified outputs are resulted in terms of spectral bands (i.e., different colors). Experimental analysis is performed with PSNR, classification accuracy, false positive rate and classification time with number of spectral bands. The results evident that presented FFSIFT-MLPC technique improves the hyperspectral image classification accuracy, PSNR and minimizes false positive rate as well as classification time than the state-of-the-art methods.

Published
2020

22. IoT-Enabled Smart Helmet for Site Workers

Author

Mohanapriya, D., Kabilesh, S. K., Nandhini, J., Stephen Sagayaraj, A., Kalaiarasi, G., Saritha, B., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Raj, Jennifer S., editor, Shi, Yong, editor, Pelusi, Danilo, editor, and Balas, Valentina Emilia, editor

Published
2022
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23. Detection of Heart Disease Using Data Mining

Author

Kalaiarasi, G., Maheswari, M., Selvi, M., Yogitha, R., Devadas, Prathima, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Dehuri, Satchidananda, editor, Prasad Mishra, Bhabani Shankar, editor, Mallick, Pradeep Kumar, editor, and Cho, Sung-Bae, editor

Published
2022
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24. Analysis on Online Teaching Learning Methodology and Its Impact on Academics Amidst Pandemic

Author

Yogitha, R., Aishwarya, R., Kalaiarasi, G., Lakshmanan, L., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mohanty, Mihir Narayan, editor, and Das, Swagatam, editor

Published
2022
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25. New Organoruthenium(II) Complexes Containing Andrographolide‐Appended Hydrazide Derivatives: Synthesis, Spectral Characterization, Anticancer Evaluation.

Author

Dhivakar, P., Kalaiarasi, G., Parveen, S., Alyami, Nouf M., Alkhattaf, Fatimah S., and Prasad, Priya

Subjects
*STAINS & staining (Microscopy), *HELA cells, *UMBILICAL veins, *LIGANDS (Chemistry), *CANCER cells, *RUTHENIUM catalysts, *CARBOXYLIC acids, *RUTHENIUM compounds
Abstract

A new set of andrographolide‐appended hydrazide derivatives (AFC, AGC, and ATC) were prepared from the reaction of andrographolide with furan‐2‐carboxylic acid hydrazide (AFC), pyridine‐3‐carboxylic acid hydrazide (AGC), and thiophene 2‐carboxylic acid hydrazide (ATC), and their corresponding ruthenium(II) complexes (Ru‐AFC, Ru‐AGC, and Ru‐ATC) were synthesized by the direct reaction of [RuCl2(η6‐p‐cymene)]2 with ligands in dichloromethane under stirring condition. The compounds were analyzed by elemental analysis, IR, UV‐Vis, 1H NMR, and ESI‐MS spectrometric techniques, and the obtained spectral data revealed that the ligands coordinated to Ru(II) ion through their enone oxygen and amine nitrogen. Antioxidant activities of the ligands and complexes were calculated against DPPH•, ABTS•+, O2−, and NO• free radicals and their results were compared with standard antioxidants. The cytotoxic activity of the synthesized ligands and complexes toward A549 and HeLa cell lines was evaluated using MTT method (MTT = 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide). Among the compounds, the complex Ru‐AGC showed better cytotoxic effect against A549 and HeLa cancer cells with IC50 values of 4.75 ± 0.17 and 6.32 ± 0.26 μM, respectively. Further, the nontoxic nature of the compounds was confirmed by using human umbilical vein endothelial (HUVEC) cells. The apoptosis was inspected with AO/EB and DAPI staining methods; further, the percentages of the apoptotic and necrotic cells were determined by flow cytometry. Overall, the biological activities of our ruthenium(II)‐arene complexes were found to be more potent than their parent ligands due to chelation, and among the complexes, Ru‐AGC showed better activity due to the presence of pyridine ring in the N‐terminal position of the ligand. The obtained results highlighted the strong possibility to develop highly active ruthenium complexes as anticancer agents. [ABSTRACT FROM AUTHOR]

Published
2025
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28. Implementation and Performance Analysis of Various Models of PCNN for Medical Image Segmentation

Author

Vignesh, T., Thyagharajan, K. K., Balaji, L., Kalaiarasi, G., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Peng, Sheng-Lung, editor, Hsieh, Sun-Yuan, editor, Gopalakrishnan, Suseendran, editor, and Duraisamy, Balaganesh, editor

Published
2021
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29. Identifying and Detection of Advertisement Click Fraud Based on Machine Learning

Author

Raj, Jaladi Guna Vardhan Amrutha, Allupati, Jagannath Patro, Kalaiarasi, G., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Mallick, Pradeep Kumar, editor, Bhoi, Akash Kumar, editor, Chae, Gyoo-Soo, editor, and Kalita, Kanak, editor

Published
2021
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30. Survey of Cervical Cancer Prediction Using Machine Learning

Author

Benita, Natasha Shereen, Vaishnavi, S., Kalaiarasi, G., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Mallick, Pradeep Kumar, editor, Bhoi, Akash Kumar, editor, Marques, Gonçalo, editor, and Hugo C. de Albuquerque, Victor, editor

Published
2021
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43. Comparative analysis of deep learning and machine learning algorithm for concrete crack detection.

Author

Padmavathy, R., Kalaiarasi, G., Venkatasubramanian, R., Devi, M., and Grace, L. K. Joshila

Subjects
*CONVOLUTIONAL neural networks, *MACHINE learning, *SUPPORT vector machines, *CLASSIFICATION algorithms, *SURFACE cracks, *DEEP learning
Abstract

This research is focussed on detection of cracks in concrete surfaces. Existence of cracks shows that the surface is degrading and cause severe damage when it is continuously exposed to all climatic conditions. Generally an expert examines the crack but it is not surely known about the depth of the crack to be repaired. This research work can be the replacement and finds the spreading level of the concrete cracks accurately[2]. Image preprocessing techniques are used to extract the significant features of the input images. Classification algorithms such as Support Vector Machine and Convolutional Neural Network (CNN) are experimenting with the cracks features into different stages as initial level, medium level and advanced level[14]. CNN gives better accuracy, reduced training and testing time. By detecting the level-of crack, the necessary steps can be taken before any accident happens. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Structural characterization and anticancer studies of copper(II) and cobalt(III) complexes containing N,O-donor heterocyclic chelators.

Author

Lavanya, C., Parveen, S., Sabarinathan, D., Saju, Leena, and Kalaiarasi, G.

Subjects
COPPER, STAINS & staining (Microscopy), CHEMICAL synthesis, CANCER cells, UMBILICAL veins, OXALATES
Abstract

New Cu(II) and Co(III) complexes were synthesized from reaction of 3-((E)-1-(((E)-4-(diethylamino)-2-hydroxybenzylidene)hydrazono)ethyl)-8-methoxy-2H-chromen-2-one (HL) with Cu(NO3)2·3H2O and Co(NO3)2·6H2O in methanol. The synthesized compounds were characterized by various analytical and spectral (IR, UV-Vis, NMR, EPR, ESI-MS and HR-MS) techniques. From the spectral studies, we concluded that the formation of the ligand and complexes. In the copper complex with formula of [Cu2(L)(NO3)3(H2O)], HL coordinated to copper ion via azomethine nitrogen, lactone oxygen, azomethine nitrogen and oxalate oxygen atoms. In cobalt complex, the ligand coordinated to cobalt ion via ring carbonyl oxygen, azomethine nitrogen, oxalate oxygen and azomethine nitrogen, and the complex is of ML2 type with the general formula [CoL2]·2NO3. The cytotoxic nature of the compounds was analyzed with human liver cancer cells (HepG2) and human breast cancer cells (MM2), in which the complexes exhibited a comparable activity than the positive control cisplatin. The IC50 values of the compounds were greater than 200 μM for non-cancerous human umbilical vein endothelial (HUVEC) cells, indicating selectivity of the compounds towards cancer cells rather than normal cells. The apoptogenic natures of the complexes with HepG2 and MM2 cancer cells were confirmed using AO-EB and DAPI staining assays. These results revealed the possibility of developing highly active cobalt and copper complexes as anticancer agents. [ABSTRACT FROM AUTHOR]

Published
2024
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