Dr. Manikandan Ramachandran
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Dr. Manikandan Ramachandran

Senior Assistant Professor
Department of Information and Communication Technology, School of Computing, SASTRA University, India

Highest Degree
Ph.D. in VLSI Physical Design from Sastra University, India

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Biography

R.Manikandan obtained his Ph.D. in VLSI Physical design from SASTRA Deemed University, India in 2014. He received his Bachelor of Engineering in Computer Science from Bharathidasan University in 1996 and Master of Technology in VLSI Design from SASTRA Deemed University in 2002. He possesses three decades of academic and 17 years of research experience in the field of Computer Science and Engineering. He has more than 200 research contributions to his credit, which are published in referred and indexed journals, book chapters and conferences. He is presently working as Senior Assistant Professor at SASTRA Deemed University for the last 18 years. He has delivered many lectures and has attended and presented in International Conferences in India and Abroad. He has edited more than 300 research articles to his credit, which includes his editorial experience across refereed and indexed journals, conferences and book chapters at national and international levels. His contemporary research interests include Data Analytics, VLSI Design, IoT and Health Care Applications.

Area of Interest:

Computer Sciences
100%
VLSI Physical Design
62%
Data Mining
90%
VLSI Cell Routing
75%
VLSI Cell Placement
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
202
Abstracts
0

Selected Publications

  1. Prabakar, D., M. Sundarrajan, S.P.A. Surendhar, M. Ramachandran and D. Gupta, 2023. Trust Model Based Data Fusion in Explainable Artificial Intelligence for Edge Computing Using Secure Sequential Discriminant Auto Encoder with Lightweight Optimization Algorithm. In: Explainable Edge AI: A Futuristic Computing Perspective. Studies in Computational Intelligence, Hassanien, A.E., D. Gupta, A.K. Singh and A. Garg, (Eds.), Springer International Publishing, ISBN: 978-3-031-18292-1, pp: 139-160.
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  2. Kumar, A., T.M. Rajesh, M. Ramachandran and D. Gupta, 2023. Role of Explainable Edge AI to Resolve Real Time Problem. In: Explainable Edge AI: A Futuristic Computing Perspective. Studies in Computational Intelligence, vol 1072, Hassanien, A.E., D. Gupta, A.K. Singh and A. Garg, (Eds.), Springer International Publishing, ISBN: 978-3-031-18291-4, pp: 101-116.
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  3. Jagedish, S.A., M. Ramachandran, A. Kumar and T.H. Sheikh, 2023. Wearable Devices with Recurrent Neural Networks for Real-Time Fall Detection. In: International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 471, Gupta, D., A. Khanna, S. Bhattacharyya, A.E. Hassanien, S. Anand and A. Jaiswal, (Eds.), Springer Nature Singapore, ISBN: 978-981-19-2534-4, pp: 357–366.
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  4. Annadurai, C., I. Nelson, K.N. Devi, R. Manikandan and A.H. Gandomi, 2023. Image watermarking based data hiding by discrete wavelet transform quantization model with convolutional generative adversarial architectures. Appl. Sci., Vol 13. 10.3390/app13020804 .
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  5. Velliyangiri, G., V. Krishnamoorthy, C. Inbaraj, A. Venkatachalam, R. Rahim and M. Ramachandran, 2022. Blockchain and Artificial Intelligent for Internet of Things in e-Health. In: The Convergence of Artificial Intelligence and Blockchain Technologies, Goundar, S., G. Suseendran and R. Anandan, (Eds.), World Scientific, pp: 23-42.
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  6. Velliyangiri, G., A. Venkatachalam, M. Ramachandran, A. Kumar and M. Subramanian, 2022. Internet of Health Things (IoHT) Against COVID-19: A Review of Recent Development. In: Computational Intelligence for COVID-19 and Future Pandemics. Disruptive Technologies and Digital Transformations for Society 5.0, Kose, U., J. Watada, O. Deperlioglu, J.A.M. Saucedo, (Eds.), Springer Singapore, ISBN: 978-981-16-3782-7, pp: 267–279.
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  7. Subramanian, M., N. Iruthayanathan, A. Chinnamuthu, N.D. Kathamuthu, M. Ramachandran and A. Kuma, 2022. Plant Disease Detection Using Hybrid Deep Learning Architecture in Smart Agriculture Applications. In: Artificial Intelligence and Smart Agriculture Applications, Kose, V.B.U., S. Prasath, M.R.H. Mondal, P. Podder and S. Bharati, (Eds.), Auerbach Publications, New York, pp: 213-232.
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  8. Sekaran, R., A.P. Raveendran, A.K. Munnangi and M. Ramachandran, 2022. Reinforcement Learning Model for Pandemic Precautions in Healthcare Environment. In: Computational Intelligence for COVID-19 and Future Pandemics. Disruptive Technologies and Digital Transformations for Society 5.0, Kose, U., J. Watada, O. Deperlioglu and J.A.M. Saucedo, (Eds.), Springer, Singapore, ISBN: 978-981-16-3782-7, pp: 171–184.
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  9. Sekaran, R., A.K. Munnangi, M. Ramachandran and A.H. Gandomi, 2022. 3D brain slice classification and feature extraction using Deformable Hierarchical Heuristic Model. Comput. Biol. Med., Vol. 149. 10.1016/j.compbiomed.2022.105990.
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  10. Raveendran, A.P., J.A. Alzubi, R. Sekaran and M. Ramachandran, 2022. A high performance scalable fuzzy based modified Asymmetric Heterogene Multiprocessor System on Chip (AHt-MPSOC) reconfigurable architecture. J. Intell. Fuzzy Syst., 42: 647-658.
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  11. Ramamoorthy, M., S. Qamar, R. Manikandan, N.Z. Jhanjhi, M. Masud and M.A. AlZain, 2022. Earlier detection of brain tumor by pre-processing based on histogram equalization with neural network. Healthcare, Vol. 10. 10.3390/healthcare10071218.
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  12. Ramachandran, M., R. Kulandaivel, H. Kalyanaraman, M. Subramanian and A. Kumar, 2022. Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector. In: How COVID-19 is Accelerating the Digital Revolution, Anandan, R., G. Suseendran, P. Chatterjee, N.Z. Jhanjhi and U. Ghosh, (Eds.), Springer International Publishing, pp: 85-99.
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  13. Ramachandran, M., H. Kalyanaraman, P. Jayachandran, A. Kumar and M. Subramanian, 2022. Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm. In: How COVID-19 is Accelerating the Digital Revolution, Anandan, R., G. Suseendran, P. Chatterjee, N.Z. Jhanjhi and U. Ghosh, (Eds.), Springer International Publishing, ISBN: 978-3-030-98166-2, pp: 101-114.
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  14. Rahim, R., M. Ramachandran, P. Jayachandran, H. Kalyanaraman, V. Bhaskar and A. Kumar, 2022. Holistic feistel authenticated learning-`based authorization for protecting the internet of things from cyber attacks. Wireless Pers. Commun., 127: 3511-3532.
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  15. Prassanna, J., L.J. Anbarasi, P. Rukmani, J.C. Jackson, B. Rajesh and R. Manikandan, 2022. CNN based framework for intelligent diagnosis of tuberculosis using chest radiographs. Res. J. Pharm. Technol., 15: 4529-4532.
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  16. Ponnuramu, V., V. Vijayaraj, B.S. Kumar and M. Ramachandran, 2022. Sudden Cardiac Arrest Detection by Feature Learning and Classification Using Deep Learning Architecture. In: Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death, Nijalingappa, P., S. Kautish, M. Ghonge and R. Ravi, (Eds.), IGI Global, pp: 62-83.
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  17. Patan, R., R. Manikandan, R. Parameshwaran, S. Perumal, M. Daneshmand and A.H. Gandomi, 2022. Blockchain security using merkle hash zero correlation distinguisher for the IoT in smart cities. IEEE Internet Things J., 9: 19296-19306.
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  18. Padmavathy, T.V., M.N.V. Kumar, S. Masi, C.S. Nageswari, R. Kulandaivel and M. Ramachandran, 2022. Blockchain-Based Internet of Things (IoT) Security for Data Sharing in Smart City Environment. In: The Convergence of Artificial Intelligence and Blockchain Technologies, Goundar, S., G. Suseendran and R. Anandan, (Eds.), World Scientific, pp: 221-241.
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  19. Nithya, R., B. Santhi, R. Manikandan, M. Rahimi and A.H. Gandomi, 2022. Computer vision system for mango fruit defect detection using deep convolutional neural network. Foods, Vol. 11. 10.3390/foods11213483.
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  20. Murugan, S., K. Vijayakumar, V. Sivakumar, R. Manikandan, A. Kumar and K. Saikumar, 2022. Impact of Internet of Health Things (IoHT) on COVID-19 Disease Detection and Its Treatment Using Single Hidden Layer Feed Forward Neural Networks (SIFN). In: How COVID-19 is Accelerating the Digital Revolution, Anandan, R., G. Suseendran, P. Chatterjee, N.Z. Jhanjhi and U. Ghosh, (Eds.), Springer International Publishing, ISBN: 978-3-030-98166-2, pp: 31-50.
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  21. Murugan, S., A. Sampathkumar, S.K.S. Raja, S. Ramesh, R. Manikandan and D. Gupta, 2022. Autonomous Vehicle Assisted by Heads up Display (HUD) with Augmented Reality Based on Machine Learning Techniques. In: Virtual and Augmented Reality for Automobile Industry: Innovation Vision and Applications. Studies in Systems, Decision and Control, vol 412, Hassanien, A.E., D. Gupta, A. Khanna and A. Slowik, (Eds.)m Springer International Publishing, ISBN: 978-3-030-94101-7, pp: 45–64.
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  22. Munnangi, A.K., R. Sekaran, S. Rajeyyagari, M. Ramachandran, S. Kannan and S. Bharati, 2022. Nonlinear cosine neighborhood time series-based deep learning for the prediction and analysis of COVID-19 in India. Wireless Commun. Mobile Comput., 10.1155/2022/3180742.
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  23. Munnangi, A.K., R. Sekaran, A.P. Raveendran and M. Ramachandran, 2022. Computational Intelligence Using Big Data for Fight Against Covid-19 Pandemic in Healthcare Environment. In: How COVID-19 is Accelerating the Digital Revolution, Anandan, R., G. Suseendran, P. Chatterjee, N.Z. Jhanjhi and U. Ghosh, (Eds.), Springer International Publishing, ISBN: 978-3-030-98166-2, pp: 115-128.
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  24. Midasala, V., K.C. Janapati, S.V.N. Srinivasu, M. Ramachandran, M. Mousavi and A.H. Gandomi, 2022. Sensor-system-based network with low-power communication using multi-hop routing protocol integrated with a data transmission model. Electronics, Vol. 11. 10.3390/electronics11101541.
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  25. Manikandan, R., Indu, V.H.C. de Albuquerque, P. Tiwari, S.A. AlQahtani and M.S. Hossain, 2022. Quality of service-aware resource selection in healthcare IoT using deep autoencoder neural networks. Hum.-centric Comput. Inf. Sci., 12: 12-36.
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  26. Kathamuthu, N.D., A. Chinnamuthu, N. Iruthayanathan, M. Ramachandran and A.H. Gandomi, 2022. Deep Q-learning-based neural network with privacy preservation method for secure data transmission in internet of things (IoT) healthcare application. Electronics, Vol. 11. 10.3390/electronics11010157.
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  27. Karthickraja R., R. Kumar, S. Kirubakaran , L. J.A. Marcilin and R. Manikandan, 2022. COVID-19prediction and symptom analysis using wearable sensors and IoT. Int. J.Pervasive Comput. Commun., 18: 499-507.
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  28. Jawahar, M., L.J. Anbarasi, V. Ravi, J. Prassanna, S.G. Jasmine, R. Manikandan, R. Sekaran and S. Kannan, 2022. CovMnet–Deep Learning Model for classifying Coronavirus (COVID-19). Health Technol., 12: 1009-1024.
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  29. Jawahar, M., J. Prassanna, V. Ravi, L.J. Anbarasi, S.G. Jasmine, R. Manikandan, R. Sekaran and S. Kannan, 2022. Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier. Multimed. Tools Appl., 81: 40451-40468.
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  30. Jain, D.K., K.M. Lakshmi, K.P. Varma, M. Ramachandran and S. Bharati, 2022. Lung cancer detection based on kernel PCA-convolution neural network feature extraction and classification by fast deep belief neural network in disease management using multimedia data sources. Comput. Intell. Neurosci., 10.1155/2022/3149406.
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  31. Jacob, T.P., A. Pravin, M. Ramachandran, A. Kumar and D. Gupta, 2022. Human Body Interaction Driven Wearable Technology for Vital Signal Sensing. In: Wearable Telemedicine Technology for the Healthcare Industry, Jude, H.D., D. Gupta and A. Khamparia, (Eds.), Elsevier, pp: 1-16.
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  32. Hema, L.K., R. Manikandan, M. Alhomrani, N. Pradeep, A.S. Alamri, S. Sharma and M. Alhassan, 2022. Region-based segmentation and classification for ovarian cancer detection using convolution neural network. Contrast Media Mol. Imaging, Vol. 2022. 10.1155/2022/5968939.
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  33. Chandrasekar, T., S.K. Raju, M. Ramachandran, R. Patan and A.H. Gandomi, 2022. Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier. Appl. Soft Comput., Vol. 122. 10.1016/j.asoc.2022.108820.
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  34. Chakrapani, K., M. Kempanna, M.I. Safa, T. Kavitha, M. Ramachandran, V. Bhaskar and A. Kumar, 2022. An enhanced exploration of sentimental analysis in health care. Wireless Pers. Commun., 128: 901-922.
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  35. Brindha, G.R., B.S. Rishiikeshwer, B. Santhi, K. Nakendraprasath, R. Manikandan and A.H. Gandomi, 2022. Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis. Comput. Methods Programs Biomed., Vol. 224. 10.1016/j.cmpb.2022.107027.
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  36. Annadurai, C., I. Nelson, K.N.Devi, R. Manikandan, N.Z. Jhanjhi, M. Masud and Abdullah Sheikh, 2022. Biometric authentication-based intrusion detection using artificial intelligence internet of things in smart city. Energies, Vol. 15. 10.3390/en15197430.
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  37. Alzubi, J.A., O.A. Alzubi, A. Singh and M. Ramachandran, 2022. Cloud-IIoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Trans. Ind. Inf., 19: 1080-1087.
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  38. Ahmed, Q.W., S. Garg, A. Rai, M. Ramachandran, N.Z. Jhanjhi, M. Masud and M. Baz, 2022. AI-based resource allocation techniques in wireless sensor internet of things networks in energy efficiency with data optimization. Electronics, Vol. 11. 10.3390/electronics11132071.
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  39. Abolfazl Mehbodniya, J.L. Webber, R. Kumar and M. Ramachandran, 2022. Designing Machine Learning-Based Variable-Order Bayesian Network in Predicting Sudden Cardiac Arrest and Death. In: Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death, Nijalingappa, P., S. Kautish, M. Ghonge and R. Ravi, (Eds.), IGI Global, pp: 166-190.
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  40. Abdul Haleem, S.L., P.R. Rham, N. Manikandan and M. Ramachandran, 2022. Utilization of Artificial Intelligence-Based Wearable Sensors in Deep Residual Network for Detecting Heart Disease. In: Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death, Nijalingappa, P., S. Kautish, M. Ghonge and R. Ravi, (Eds.), IGI Global, pp: 191-217.
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  41. Vergin Raja Sarobin M, L. Jani Anbarasi, J. Prassanna, R. Manikandan, Fadi Al-Turjman 2021. Swarm intelligence-based optimal device deployment in heterogeneous Internet of Things networks for wind farm application. Int. J. Commun. Syst., 10.1002/dac.4779.
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  42. Sujarani, R., D. Manivannan, R. Manikandan and B. Vidhyacharan, 2021. Lightweight bio-chaos crypt to enhance the security of biometric images in internet of things applications. Wireless Pers. Commun., 119: 2517-2537.
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  43. Subramaniyan, M., A. Sampathkumar, D.K. Jain, M. Ramachandran, R. Patan and A. Kumar, 2021. Deep Learning Approach Using 3D-ImpCNN Classification for Coronavirus Disease. In: Artificial Intelligence and Machine Learning for COVID-19, Al-Turjman, F., (Ed.), Springer International Publishing, pp: 141–152.
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  44. Senthilkumar, G., J. Ramakrishnan, J. Frnda, M. Ramachandran, D. Gupta, P. Tiwari, M. Shorfuzzaman and M.A. Mohammed, 2021. Incorporating artificial fish swarm in ensemble classification framework for recurrence prediction of cervical cancer. IEEE Access, 9: 83876-83886.
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  45. Sekaran, R., V.K.M. Nagaraju, V. Jagadeesan, M. Ramachandran and A. Kumar, 2021. Medical Data Compression for Lossless Data Transmission and Archival. In: Internet of Medical Things, Hemanth, D.J., J. Anitha and G.A. Tsihrintzis, (Eds.), Springer International Publishing, pp: 55–74.
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  46. Sekaran, R., S.N. Goddumarri, S. Kallam, M. Ramachandran, R. Patan and D. Gupta, 2021. 5G integrated spectrum selection and spectrum access using aI-based frame work for IoT based sensor networks. Comput. Networks, Vol. 186. 10.1016/j.comnet.2020.107649.
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  47. Sekaran, R., M. Ramachandran, R. Patan and F. Al-Turjman, 2021. Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication. Sustainable Comput.: Inf. Syst., Vol. 30. 10.1016/j.suscom.2021.100522 .
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  48. Sekaran, R., A.K. Munnangi, S. Rajeyyagari, M. Ramachandran and F. Al‐Turjman, 2021. Ant colony resource optimization for Industrial IoT and CPS. Int. J. Intell. Syst., 37: 10513-10532.
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  49. Saminathan, M., M. Ramachandran, A. Kumar, K. Rajkumar, A. Khanna and P. Singh, 2021. A study on specific learning algorithms pertaining to classify lung cancer disease. Expert Syst. Vol. 39. 10.1111/exsy.12797.
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  50. Reddy, A.R., G.S.P. Ghantasala, R. Patan, R. Manikandan and S. Kallam, 2021. Smart Assistance of Elderly Individuals in Emergency Situations at Home. In: Internet of Medical Things, Hemanth, D.J., J. Anitha and G.A. Tsihrintzis, (Eds.), Springer International Publishing, ISBN: 978-3-030-63936-5, pp: 95–115.
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  51. Ramesh, S., S. Nirmalraj, S. Murugan, R. Manikandan and F. Al-Turjman, 2021. Optimization of energy and security in mobile sensor network using classification based signal processing in heterogeneous network. J. Sign. Process. Syst., 10.1007/s11265-021-01690-y.
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  52. Ramalingam, P., L. Gopalakrishnan, M. Ramachandran and R. Patan, 2021. Kinematic adaptive frequency sampling combined spatio temporal features for snow monitoring in aerospace applications. Expert Syst. Appl., Vol. 184. 10.1016/j.eswa.2021.115472.
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  53. Ramachandran, M., R. Patan, A. Kumar, S. Hosseini and A.H. Gandomi, 2021. Mutual informative MapReduce and minimum quadrangle classification for brain tumor big data. IEEE Trans. Eng. Manage., 10.1109/TEM.2021.3073018.
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  54. Rajkumar, K., M. Ramachandran, F. Al-Turjman and R. Patan, 2021. A reinforcement learning optimization for future smart cities using software defined networking. Int. J. Mach. Learn. Cybern., 12: 3221-3233.
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  55. Rahim, R., S. Murugan, R. Manikandan and A. Kumar, 2021. Efficient contourlet transformation technique for despeckling of polarimetric synthetic aperture radar image. J. Comput. Theor. Nanosci., 18: 1312-1320.
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  56. Pitchai, M.P., M. Ramachandran, F. Al-Turjman and L. Mostarda, 2021. Intelligent framework for secure transportation systems using software-defined-internet of vehicles. Comput., Mater. Continua, 68: 3947-3966.
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  57. Peneti, S., M.S. Kumar, S. Kallam, R. Patan, V. Bhaskar and M. Ramachandran, 2021. BDN-GWMNN: Internet of Things (IoT) enabled secure smart city applications. Wireless Pers. Commun., 119: 2469-2485.
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  58. Patan, R., S. Kallam, A.H. Gandomi, T. Hanne and M. Ramachandran, 2021. Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling. J. Oper. Res. Soc., 73: 2204-2215.
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  59. Mydukuri, R.V., S. Kallam, R. Patan, F. Al‐Turjman and M. Ramachandran, 2021. Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction. Expert Syst., Vol. 39. 10.1111/exsy.12694.
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  60. Murugan, S., R. Manikandan and A. Kumar, 2021. Multimodal Context‐Sensitive Human Communication Interaction System Using Artificial Intelligence‐Based Human‐Centered Computing. In: Human Communication Technology: Internet of Robotic Things and Ubiquitous Computing, Anandan, R., G. Suseendran, S. Balamurugan, A. Mishra and D. Balaganesh, (Eds.), Wiley, pp: 137-161.
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  61. Munnangi, A.K., R. Sekaran, G. Velliyangiri, M. Ramachandran and A. Kumar, 2021. Wearable Smart Devices for Remote Healthcare Monitoring to Detect Cardiac Diseases. In: Internet of Medical Things, Hemanth, D.J., J. Anitha and G.A. Tsihrintzis, G.A. (Eds.), Springer International Publishing, pp: 75–94.
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  62. Magesh, S., V.R. Niveditha, A. Kumar, R. Manikandan and P.S. Rajakumar, 2021. Comparative study on challenges and detection of brain tumor using machine learning algorithm. Intelligent Computing and Innovation on Data Science, Sep. 28, 2021, Springer Nature Singapore, pp: 21-30.
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  63. Kumar, A., R. Manikandan, S. Magesh, R. Patan, S. Ramesh and D. Gupta, 2021. Image analysis and data processing for COVID-19. In: Data Science for COVID-19, Kose, U., D. Gupta and A. Khanna, (Eds.), Elsevier, pp: 413-427.
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  64. Kumar, A. and R. Manikandan, 2021. Brain tumor detection using deep neural network-based classifier. Book cover Book cover International Conference on Innovative Computing and Communications, Aug. 18, 2021, Springer Singapore, pp: 173-181.
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  65. Jothiramalingam, R., A. Jude, R. Patan, M. Ramachandran, J.H. Duraisamy and A.H. Gandomi, 2021. Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal. Neural. Comput. Applic., 33: 4445-4455.
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  66. Jawahar, M., L.J. Anbarasi, P. Jayachandran, M. Ramachandran and F. Al-Turjman, 2021. Utilization of transfer learning model in detecting COVID-19 cases from chest x-ray images. Int. J. E-Health Med. Commun., Vol. 13. 10.4018/IJEHMC.20220701.oa2.
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  67. Jawahar, M., L.J. Anbarasi, J. Prassanna, C.J. Jackson, R. Manikandan, J.A. Alzubi and D. Dhanya, 2021. Diagnosis of COVID-19 using optimized PCA based local binary pattern features. Int. J. Curr. Res. Rev., 13: 37-41.
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  68. Jain, D.K., K. Srinivas, S.V.N. Srinivasu and R. Manikandan, 2021. Machine learning-based monitoring system with IoT using wearable sensors and pre-convoluted fast recurrent neural networks (P-FRNN). IEEE Sensors J., 21: 25517-25524.
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  69. Jacob, T.P., A. Pravin, M. Ramachandran and F. Al-Turjman, 2021. Differential spectrum access for next generation data traffic in massive-IoT. Microprocess. Microsyst., Vol. 82. 10.1016/j.micpro.2021.103951.
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  70. Hariharan, K., K. Rajkumar, R. Manikandan, A. Kumar and D. Gupta, 2021. Deep Learning for Optimization of E-Evidence. In: Cyber Crime and Forensic Computing: Modern Principles, Practices, and Algorithms, Shrivastava, G., D. Gupta and K. Sharma, (Eds.), Berlin, Boston: De Gruyter, pp: 111-136.
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  71. Balakrishnan, A., K. Ramana, K. Nanmaran, M. Ramachandran, V. Bhaskar and S. Kallam, 2021. RSSI based localization and tracking in a spatial network system using wireless sensor networks. Wireless Pers. Commun., 123: 879-915.
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  72. Babu, M.V., J.A. Alzubi, R. Sekaran, R. Patan, M. Ramachandran and D. Gupta, 2021. An improved IDAF-FIT clustering based ASLPP-RR routing with secure data aggregation in wireless sensor network. Mobile Networks Appl., 26: 1059-1067.
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  73. Umamaheswaran, R., S.R.S. Kumaran, R. Parameshwaran, G. Lakshminarayanan, S. Thiruppathirajan and R. Manikandan, 2020. An optimized VLSI based high performance architecture for lossless data compression. Int. J. Adv. Sci. Technol., 29: 9503-9510.
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  74. Thaventhiran, C., K.R. Sekar, R. Manikandan, R. Rahim and A. Kumar, 2020. Study on service oriented architecture for clinical evaluation using computational model. J. Comput. Theor. Nanosci., 17: 1931-1936.
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  75. Sekaran, R., R. Patan, A. Raveendran, F. Al-Turjman, M. Ramachandran and L. Mostarda, 2020. Survival study on blockchain based 6G-enabled mobile edge computation for IoT automation. IEEE Access, 8: 143453-143463.
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  76. Sekar, K.R., R. Parameshwaran, R. Patan, R. Manikandan and A. Kumar, 2020. Improved Deep Learning Techniques for Better Cancer Diagnosis. In: Deep Learning for Cancer Diagnosis. Kose, U. and J. Alzubi, Springer Singapore, New York, PP:111–133.
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  77. Sangeetha, V., J. Preethi, R. Krishankumar, K.S. Ravichandran and R. Manikandan, 2020. Cluster analysis of breast cancer data using modified BP-RBFN. In: Soft Computing Applications and Techniques in Healthcare, Mishra, A., G. Suseendran and Trung-Nghia Phung, CRC Press, pp: 255-276.
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  78. Ramachandran, M., R. Patan, M.R. Babu, A. Kumar and C. Thaventhiran, 2020. Big Data System based IoT Enabling Technologies: Ubiquitous Wireless Communication, Real-Time Analytics, Machine Learning, Deep Learning, Commodity Sensors. In: The Internet of Things and Big Data Analytics, Raj, P., T. Poongodi, B. Balusamy and M. Khari, Auerbach Publications, pp: 67-89.
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  79. Rahim, R., S. Murugan, S. Priya, S. Magesh and R. Manikandan, 2020. Taylor based grey wolf optimization algorithm (TGWOA) for energy aware secure routing protocol. Int. J. Comput. Networks Appl., 7: 93-102.
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  80. Rahim, R., R. Patan, R. Manikandan and S.R. Kumar, 2020. Introduction to Blockchain and Big Data. In: Introduction Blockchain Big Data, Rahim, R., R. Patan, R. Manikandan and S.R. Kumar, CRC Press, pp: 1-23.
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  81. Prassanna, J., R. Rahim, K. Bagyalakshmi, R. Manikandan and R. Patan, 2020. Effective Use of Deep Learning and Image Processing for Cancer Diagnosis. In: Deep Learning for Cancer Diagnosis, Kose, U. and J. Alzubi, Springer Singapore, pp: 147-168.
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  82. Prassanna, J., J.L. Anbarasi, R. Prabhakaran, V.K. Devi, R. Vincent, R. Manikandan and A. Kumar, 2020. Enhanced safe and secure distributed healthcare system through efficient duplication detection approach. Int. J. Curr. Res. Rev., 12: 101-105.
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  83. Patan, R., G.S.P. Ghantasala, R. Sekaran, D. Gupta and M. Ramachandran, 2020. Smart healthcare and quality of service in IoT using grey filter convolutional based cyber physical system. Sustainable Cities Soc., Vol. 59. 10.1016/j.scs.2020.102141.
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  84. Murugan, S., S. Jeyalaksshmi, B. Mahalakshmi, G. Suseendran, T.N. Jabeen, R. Manikandan, 2020. Comparison of ACO and PSO algorithm using energy consumption and load balancing in emerging manet and vanet infrastructure. J. Crit. Rev., 7: 1197-1204.
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  85. Manikandan, R., R. Patan, A.H. Gandomi, P. Sivanesan and H. Kalyanaraman, 2020. Hash polynomial two factor decision tree using IoT for smart health care scheduling. Expert Syst. Appl., Vol. 141. 10.1016/j.eswa.2019.112924.
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  86. Manikandan, R., A. Kumar and D. Gupta, 2020. Hybrid computational intelligence for healthcare and disease diagnosis. In: Hybrid Computational Intelligence Bhattacharyya, S., V. Snášel and A. Khanna, (Ed.)., Elsevier, Amsterdam, pp: 26.
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  87. Manikandan, P., R. Sekaran, G. Suseendran, T.N. Jabeen, A.P. Raveendran and R. Manikandan, 2020. An efficient detection and segmentation of brain tumor using robust active shape model. J. Crit. Rev., 7: 1205-1211.
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  88. Mallikarjuna, B., T.V. Ramana, S. Kallam, R. Patan and R. Manikandan, 2020. Visualizing Bitcoin Using Big Data. In: Blockchain, Big Data and Machine Learning, Kumar, N., N. Gayathri, M.A. Rahman and B. Balamurugan, CRC Press, pp: 150-171.
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  89. Kumar, A., R. Manikandan and R. Rahim, 2020. A study on brain tumor detection and segmentation using deep learning techniques. J. Comput. Theor. Nanosci., 17: 1925-1930.
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  90. Kumar, A., R. Manikandan and C. Thaventhiran, 2020. Pattern Recognition Concepts. In: Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications, Dulhare, U.N., K. Ahmad and K.A. Bin Ahmad, Wiley, pp: 131-152.
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  91. Kumar, A. and R. Manikandan, 2020. Recognition of brain tumor using fully convolutional neural network-based classifier. International Conference on Innovative Computing and Communications, Aug. 2, 2020 Springer Singapore, pp: 587-597.
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  92. Krishankumar, R., J. Premaladha, K.S. Ravichandran, K.R. Sekar, R. Manikandan and X.Z. Gao, 2020. A novel extension to VIKOR method under intuitionistic fuzzy context for solving personnel selection problem. Soft Comput., 24: 1063-1081.
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  93. Kavadi, D.P., R. Patan, M. Ramachandran and A.H. Gandomi, 2020. Partial derivative nonlinear global pandemic machine learning prediction of covid 19. Chaos Solitons Fractals, Vol. 139. 10.1016/j.chaos.2020.110056.
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  94. Kannan, C., M. Dakshinamoorthy, M. Ramachandran, R. Patan, H. Kalyanaraman and A. Kumar, 2020. Cryptography‐based deep artificial structure for secure communication using IoT‐enabled cyber‐physical system. IET Commun., 15: 771-779.
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  95. Jayaraman, S., M. Ramachandran, R. Patan, M. Daneshmand and A.H. Gandomi, 2020. Fuzzy deep neural learning based on goodman and kruskal's gamma for search engine optimization. IEEE Trans. Big Data, 8: 268-277.
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  96. Jain, D.K., X. Lan and R. Manikandan, 2020. Fusion of iris and sclera using phase intensive rubbersheet mutual exclusion for periocular recognition. Image Vision Comput., Vol. 103. 10.1016/j.imavis.2020.104024.
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  97. Jain, D.K., A. Mahanti, P. Shamsolmoali and R. Manikandan, 2020. Deep neural learning techniques with long short-term memory for gesture recognition. Neural Comput. Appl., 32: 16073-16089.
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  98. Anbarasi, L.J., J. Prassanna, V.R.M. Sarobin, S. Rajarajeswari, R. Prabhakaran and R. Manikandan, 2020. Autistic disorder analysis among children and adult. Int. J. Curr. Res. Rev., 12: 48-51.
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  99. Anbarasi, L.J., J. Prassanna, M. Abdul Quadir, J.C. Jackson, R. Manikandan, R. Rahim and G. Suseendran, 2020. Visual secret sharing: A review. J. Crit. Rev., 7: 1212-1216.
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  100. Alzubi, O.A., J.A. Alzubi, M. Alweshah, I. Qiqieh, S. Al-Shami and M. Ramachandran, 2020. An optimal pruning algorithm of classifier ensembles: dynamic programming approach. Neural Comput. Appl., 32: 16091-16107.
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  101. Alzubi, J.A., R. Manikandan, O.A. Alzubi, I. Qiqieh, R. Rahim, D. Gupta and A. Khanna, 2020. Hashed needham schroeder industrial IoT based cost optimized deep secured data transmission in cloud. Measurement, Vol. 150. 10.1016/j.measurement.2019.107077.
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  102. Jayakumar, S., P. Rajalingam, R. Patan and M. Ramachandran, 2019. Vedic arithmetic based high speed and less area MAC unit for computing devices. Recent Pat. Comput. Sci., 10.2174/2213275911666181114141832.
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  103. Srilakshmi, A., J. Rakkini, K.R. Sekar and R. Manikandan, 2018. A comparative study on internet of things (IoT) and its applications in smart agriculture. Pharmacogn. J., 10: 260-264.
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  104. Sethuraman, J., R. Manikandan and K.R. Sekar, 2018. A computational model to analyze medical web pages for optimized search. Biomed. Res. India, 29: 374-376.
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  105. Reddy, M.R., K.S. Ravichandran, B. Venkatraman, K.R. Sekar and R. Manikandan, 2018. Improved pharma education system in the field of medical images using compression techniques. Cluster comput. J., 10.1007/s10586-018-2496-1.
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  106. Rakkini, M.J.J., A. Srilakshmi, K.R. Sekar and R. Manikandan, 2018. Peripheral neuropathy disease analysis through optimization of scientific workflows: A survey. Int. J. Pure Applied Math., 119: 633-641.
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  107. Rajakumari, R.S., K. Hariharan, R. Manikandan and K.R. Sekar, 2018. A survey on various method used in hardware and software partitioning. Int. J. Pure Applied Math., 118: 2365-2386.
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  108. Pavithra, P.R., K.S. Ravichandran, K.R. Sekar and R. Manikandan, 2018. The effect of thermography on breast cancer detection. Syst. Rev. Pharm., 9: 10-16.
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  109. Nachiya, P.Y., K.R. Sekar, R. Manikandan and K.S. Ravichandran, 2018. Investigation of obsessive compulsive disorder through domain ontology construction: Survey. Int. J. Pure Applied Math., 119: 643-651.
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  110. Muhilan, M., S. Hariprasath, K.R. Sekar and R. Manikandan, 2018. Personalized recommendation of food products using rule-based techniques in hadoop environment. Int. J. Pure Applied Math., 119: 1409-1415.
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  111. Mary, M.V.A., K.R. Sekar, R. Manikandan and K.S. Ravichandran, 2018. Improvement of immune system analyzed through various cluster techniques: A survey. Pharmacogn. J., 10: 257-259.
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  112. Manikandan, R., K.R. Sekar and K. Harharan, 2018. A broad review on various VLSI CAD algorithms for circuit partitioning problems. Int. J. Mech. Prod. Eng. Res. Dev., 8: 945-956.
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  113. Amudhasurabi, J., K.R. Sekar, R. Manikandan and K.S. Ravichandran, 2018. Chronic kidney disease analyzed and estimated through specific classifiers: A survey. Int. J. Pure Applied Math., 119: 623-632.
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  114. Rajesh, K., K.R. Sekar, V. Ramaswamy, K. Kannan and R. Manikandan, 2017. Demand and supply analysis on manufacturing of cement for monsoon period using dvorak technique. Int. J. Mech. Prod. Eng. Res. Dev., 7: 187-194.
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  115. Rajendiran, P., K.R. Sekar, B. Srinivasan and R. Manikandan, 2017. Raw material captcha security, applied through zopfia technique. Int. J. Mech. Prod. Eng. Res. Dev., 7: 99-104.
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  116. Rajendiran, P., K.R. Sekar, B. Srinivasan and R. Manikandan, 2017. Customer relationship management in the manufacturing industry, using data mining techniques. Int. J. Mech. Prod. Eng. Res. Dev., 7: 63-70.
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  117. Manikandan, R., K. Harharan and R. Venkatesan, 2017. A power efficient multiplier architecture for FIR filters used for acoustic models in mechanical diagnostics. Int. J. Mech. Prod. Eng. Res. Dev., 7: 313-320.
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  118. Manikandan, R., 2017. A study on specific computation methods in VLSI physical design. Int. J. Innovations Sci. Eng. Res., 4: 218-220.
  119. Hariharan, K., N.R. Rajan, R. Manikandan and R. Venkatesan, 2017. An ultra low power 12T CAM cell based on voltage techniques. Int. J. Mech. Prod. Eng. Res. Dev., 7: 337-342.
  120. Hariharan, K., N. Raajan, R. Manikandan and K.R. Sekar, 2017. Design of Low power and area efficient full adder for ALU using 90NM process for industrial based CAD/CAM manufacturing units. Int. J. Mech. Prod. Eng. Res. Dev., 7: 71-78.
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  121. Sivaranjani, U. and R. Manikandan, 2016. A survey on reed solomon code in data transmission application. Int. J. Pharm. Technol., 8: 18498-18504.
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  122. Sekar, K.R., K.S. Ravichandran, J. Sethuraman and R. Manikandan, 2016. An investigation of software component for web application integration based on hybrid filtering. Int. J. Pharm. Technol., 8: 18621-18639.
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  123. Saraswathi, R. and R. Manikandan, 2016. A study on various issues in VLSI Low power testing. Int. J. Pharm. Technol., 8: 16653-16663.
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  124. Rajasekar, A. and R. Manikandan, 2015. Design of pipelined based DCT architecture for image processing application. Int. J. Applied Eng. Res., 10: 22677-22684.
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  125. Pradeepa, M.A. and R. Manikandan, 2015. Design of pipelined AES algorithm with area optimization for cryptographic applications. Int. J. Applied Eng. Res., 10: 23191-23197.
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  126. Gowri, K.M. and R. Manikandan, 2015. Efficient singular value decomposition using cordic for signal processing applications. Int. J. Applied Eng. Res., 10: 23199-23206.
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  127. Manikandan, R., P. Swaminathan and V. Vaithiyanathan, 2014. Analysis of utility theory on VLSI cell placement. Applied Math. Inf. Sci., 8: 1611-1616.
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  128. Manikandan, R. and V. Leela, 2014. Effective clustering algorithms for VLSI circuit partitioning problems. Contemp. Eng. Sci., 7: 923-929.
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  129. Manikandan, R. and S. Niranjani, 2014. Implementation on real time public transportation information using GSM query response system. Contemp. Eng. Sci., 7: 509-514.
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  130. Manikandan, R., P. Swaminathan and V. Vaithiyanathan, 2013. Study on markov alternative renewal reward process for VLSI cell partitioning. Int. J. Math. Anal., 7: 1949-1960.
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  131. Manikandan, R., P. Swaminathan and R. Sujitha, 2013. Unimodular hypergraph based clustering approaches for VLSI circuit partitioning. Int. J. Eng. Technol., 5: 2755-2758.
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  132. Manikandan, R., P. Swaminathan and R. Sujitha, 2013. A study on Vlsi physical design specific issues. J. Appl. Sci. Res., 9: 100-103.
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  133. Manikandan, R., G.S. Monolisa and K. Saranya, 2013. A cluster based segmentation of magnetic resonance images for brain tumor detection. Middle-East J. Sci. Res., 14: 669-672.
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  134. Manikandan, R. and P. Swaminathan, 2013. Analysis of markov renewal reward process on VLSI cell partitioning. Appl. Math. Sci., 7: 3281-3289.
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  135. Manikandan, R., S. Sathvi, C. Thenmozhi and R. Sujitha, 2012. Efficient markov clustering model for VLSI circuit partitioning. Aust. J. Basic Appl. Sci., 6: 31-35.
    Direct Link  |  
  136. Manikandan, R., M. Thiyagarajan and P. Swaminathan, 2012. Review of efficient and innovative mathematical on VLSI circuit partitioning. Res. J. Applied Sci. Eng. Technol., 4: 978-980.
  137. Manikandan, R., M. Thiyagarajan and P. Swaminathan, 2012. Deterministic and probabilistic models on VLSI cell placement-a survey. J. Theor. Appl. Inf. Technol., 37: 39-45.
    Direct Link  |  
  138. Manikandan, R., J. Sindura, M.D.L. SriRavali and P. Swaminathan, 2012. Efficient mathematical model on VLSI circuit partitioning. Res. J. Applied Sci. Eng. Technol., 4: 1978-1979.
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  139. Thiyagarajan, M. and R. Manikandan, 2011. Mini TCP/IP for 8-bit controllers. J. Theoretical Appl. Inf. Technol., 31: 109-112.
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  140. Thiyagarajan, M. and R. Manikandan, 2011. Hyper graph and coprocessor design for VLSI partitioning problems. J. Theoretical and Applic. Inf. Technol., 26: 107-111.
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  141. Thiyagarajan, M. and R. Manikandan, 2011. Area-time optimal VLSI integer multiplier with minimum computational time and weber problem. J. Theor. Applic. Inf. Technol., 25: 58-62.
    Direct Link  |  
  142. Manikandan, R., 2011. Improving efficiency of textual static web content mining using clustering techniques. J. Theor. Applic. Inf. Technol., 33: 193-196.
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  143. Thiyagarajan, M. and R. Manikandan, 2010. Application of hypergraph in VLSI cell partitioning. Icfai Univ. J.Comput. Math., 3: 59-67.
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  144. Thiyagarajan, M. and R. Manikandan, 2010. A novel graph theoretical algorithms for constrained placement problems. Int. J. Comput. Intell. Inf. Secu., 1: 26-33.
  145. Thiyagarajan, M. and R. Manikandan, 2009. Generalized weber problem. J. Phys. Sci., 13: 201-208.
    Direct Link  |  
  146. Thiyagarajan, M. and R. Manikandan, 2009. Data mining and wavelet tools for VLSI cell placement. Int. J. Comput. Appl., 4: 1-5.
  147. Thiyagarajan, M. and R. Manikandan, 2009. A novel hybrid stochastic VLSI design model. Icfai Univ. J. Comput. Math., 2: 46-55.
    Direct Link  |  
  148. Thiyagarajan, M. and R. Manikandan, 2008. Use of hopfield neural network algorithm in VLSI cell placement. Proceedings of the National Conference on Advanced in Applied Technologies, March 14-15, Kalasalingam University, KrishnanKoil, -.
  149. Thiyagarjan M. and R. Manikandan, 2007. Use of genetic algorithm in VLSI cell placement. Proceedings of the AICTE Sponsored National Conference on Discrete Mathematics and its Applications, Spetember 27-29, Thiyagarajar college of Engineering, Madurai, -.

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