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Co-Authors
- S.K. Srivastava
- S. K. Srivastava
- M. Ramkumar
- Amirtha Preeya
- T. Karthikeyan
- M. Punithavalli
- V. S. Akshaya4
- Shanmugaraj Madasamy
- J. Venkata Subramanian
- A. Pandian
- R. Ramakrishnan
- B. S. Sathishkumar
- G. Jayaseelan
- R. Vijayakumar
- P. Kanimozhi
- N. Balakrishnan
- V. Balasubramani
- D. Sudhakar
- V. Udayasuriyan
- Robbi Rahim
- S. Murugan
- S. Priya
- S. Magesh
- Muruganantham Ponnusamy
- Jayasri Subramaniam
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Manikandan, R.
- Habitat and Life form Analysis on Endemism in the Family Asteraceae from Karnataka State
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Indian Forester, Vol 139, No 1 (2013), Pagination: 49-52Abstract
The present article deals with detailed information on habit, habitat, distribution, flowering and fruiting phenology and conservation status of 39 taxa belonging to 16 genera which are endemic to India and confining their distribution in the state of Karnataka.Keywords
Habitat, Life Form, Endemic Taxa, Asteraceae, Karnataka.
- Diversity, Medicinal and Threatened Plants in Govind Pashu Vihar Wildlife Sanctuary, Western Himalaya
Abstract Views :155 |
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Authors
Affiliations
1 Botanical Survey of India, Northern Regional Centre Dehradun, Uttarakhand, IN
1 Botanical Survey of India, Northern Regional Centre Dehradun, Uttarakhand, IN
Source
Indian Forester, Vol 141, No 9 (2015), Pagination: 966-973Abstract
The paper deals with information on floristic composition of the Govind Pashu Vihar Wildlife Sanctuary compriseing 821 species, 8 subspecies, 11 varieties and a few cultivated species of Angiosperms, distributed over 479 genera and 125 families, of these, 9 species are critically endangered, 14 species are endangered, 9 species are vulnerable and 7 species are Least Concern. In addition, medicinal plants which form the basis for certain life saving drugs have also been incorporated.Keywords
Medicinal Plants, Threatened Plants, Govind Pashu Vihar Wildlife Sanctuary, Western Himalaya.- IRIS Detection For Biometric Pattern Identification Using Deep Learning
Abstract Views :147 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Presidency University, IN
3 Department of Computer Science, The Quaide Milleth College for Men, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Presidency University, IN
3 Department of Computer Science, The Quaide Milleth College for Men, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2610-2614Abstract
In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.Keywords
Iris Detection, Pattern Identification, Liveness Detection, Biometric, Deep LearningReferences
- Z. Zhao and A. Kumar, “A Deep Learning based Unified Framework to Detect, Segment and Recognize Irises using Spatially Corresponding Features”, Pattern Recognition, Vol. 93, pp. 546-557, 2019.
- S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- A. Khadidos, A.O. Khadidos and S. Kannan, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-18, 2020.
- K. Srihari, G. Dhiman and S. Chandragandhi, “An IoT and Machine Learning‐based Routing Protocol for Reconfigurable Engineering Application”, IET Communications, Vol. 23, No. 2, pp. 1-15, 2021.
- S.B. Sangeetha, R. Sabitha and B. Dhiyanesh, “Resource Management Framework using Deep Neural Networks in Multi-Cloud Environment”, Proceedings of International Conference on Operationalizing Multi-Cloud Environments, pp. 89-104, 2021.
- H. Proenca and J.C. Neves, “Deep-Prwis: Periocular Recognition without the Iris and Sclera using Deep Learning Frameworks”, IEEE Transactions on Information Forensics and Security, Vol. 13, No. 4, pp. 888-896, 2017.
- H. Proenca and J.C. Neves, “Segmentation-Less and NonHolistic Deep-Learning Frameworks for Iris Recognition”, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8, 2019.
- N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.
- I.J. Jacob, “Capsule Network based Biometric Recognition System”, Journal of Artificial Intelligence, Vol. 1, No. 2, pp. 83-94, 2019.
- M. Vatsa, R. Singh and A. Majumdar, “Deep Learning in Biometrics”, CRC Press, 2018.
- V. Maheshwari, M.R. Mahmood, S. Sravanthi and N. Arivazhagan, “Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-14, 2021.
- S. Umer, A. Sardar and B.C. Dhara, “Person Identification using Fusion of Iris and Periocular Deep Features”, Neural Networks, Vol. 122, pp. 407-419, 2020.
- S. Arora and M.P.S. Bhatia, “Presentation Attack Detection for Iris Recognition using Deep Learning”, International Journal of System Assurance Engineering and Management, Vol. 8, No. 2, pp. 1-7, 2020.
- Classification of Cervical Cancer in Women Using Convolutional Neural Network
Abstract Views :220 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science, The Quaide Milleth College for Men, IN
3 Department of Mechanical Engineering, Rathinam Technical Campus, IN
4 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
5 Department of Computer Science, Cork Institute of Technology, IE
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science, The Quaide Milleth College for Men, IN
3 Department of Mechanical Engineering, Rathinam Technical Campus, IN
4 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
5 Department of Computer Science, Cork Institute of Technology, IE
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2470-2474Abstract
Cervical cancer is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with cervical cancer. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses Convolutional Neural Network (CNN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a pre-processing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over cervical cancer image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models.Keywords
Machine Learning, Cervical Cancer, Classification, Diagnosis.References
- M. Guzman and G. Kouri, “Dengue and Dengue Hemorrhagic Fever in the Americas: Lessons and Challenges”, Journal of Clinical Virology, Vol. 27, No. 1, pp. 1-13, 2003.
- S. Kannan and S.N. Mohanty, “Survey of Various Statistical Numerical and Machine Learning Ontological Models on Infectious Disease Ontology”, Proceedings of International Conference on Data Analytics in Bioinformatics: A Machine Learning Perspective, pp. 431-442, 2021.
- N. Kousik, A. Kallam, R. Patan and A.H. Gandomi, “Improved Salient Object Detection using Hybrid Convolution Recurrent Neural Network”, Expert Systems with Applications, Vol. 166, pp. 114064-114075, 2021.
- N.V. Kousik, “Analyses on Artificial Intelligence Framework to Detect Crime Pattern”, Proceedings of International Conference on Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques and Applications, 119-132, 2021.
- K. Srihari, S. Chandragandhi, G. Dhiman and A. Kaur, “Analysis of Protein-Ligand Interactions of SARS-Cov-2 Against Selective Drug using Deep Neural Networks”, Big Data Mining and Analytics, Vol. 4, No. 2, pp. 76-83, 2021.
- K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.
- T. Karthikeyan and K. Praghash, “An Improved Task Allocation Scheme in Serverless Computing Using Gray Wolf Optimization (GWO) Based Reinforcement Learning (RIL) Approach”, Wireless Personal Communications, Vol. 80, No. 7, 1-19, 2020.
- J.L. San Martín, J.O. Solorzano and M.G. Guzman, “The Epidemiology of Dengue in the Americas over the Last Three Decades: A Worrisome Reality”, American Journal of Tropical Medicine and Hygiene, Vol. 82, No. 1, pp. 128-135, 2010.
- K. Srihari, G. Dhiman, K. Somasundaram and M. Masud, “Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking”, Mathematical Problems in Engineering, Vol. 2021, pp. 1-18, 2021.
- D.A. Thitiprayoonwongse, P.R. Suriyaphol and N.U. Soonthornphisaj, “Data Mining of Dengue Infection using Decision Tree”, Proceedings of International Conference on Latest Advances in Information Science and Applications, pp. 1-14, 2012.
- V. Nandini and R. Sriranjitha, “Dengue Detection and Prediction System using Data Mining with Frequency Analysis”, Proceedings of International Conference on Computer Science and Information Technology, pp. 1-12, 2016.
- G. Li, X. Zhou and J. Liu, “Comparison of Three Data Mining Models for Prediction of Advanced Schistosomiasis Prognosis in the Hubei Province”, PLoS Neglected Tropical Diseases, Vol. 12, pp. 1-22, 2018.
- V. Chang, B. Gobinathan, A. Pinagapani and S. Kannan, “Automatic Detection of Cyberbullying using Multi-Feature Based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 107186-107198, 2021.
- A. Shukla, G. Kalnoor and A. Kumar, “Improved Recognition Rate of Different Material Category using Convolutional Neural Networks”, Materials Today: Proceedings, Vol. 78, No. 1, pp. 1-5, 2021.
- S. Kannan, G. Dhiman and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoT-Based Bat Agents for Traffic Management”, Electronics, Vol. 7, No. 1, pp. 785-793, 2021.
- J. Gowrishankar, T. Narmadha and M. Ramkumar, “Convolutional Neural Network Classification On 2d Craniofacial Images”, International Journal of Grid and Distributed Computing, Vol. 13, No. 1, pp. 1026-1032, 2020.
- A. Khadidos, A.O. Khadidos and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image using DeepSense Model”, Frontiers in Public Health, Vol. 8, pp. 1-12, 2020.
- Siriyasatien Padet,Atchara Phumee, Phatsavee Ongruk, Katechan Jampachaisri and Kraisak Kesorn, “Analysis of Significant Factors for Dengue Fever Incidence Prediction”, BMC Bioinformatics, Vol. 17, No. 166, pp. 1-22, 2016.
- P. Vivekanandan, “An Efficient SVM Based Tumor Classification with Symmetry Non-Negative Matrix Factorization using Gene Expression Data”, Proceedings of International Conference on Information Communication and Embedded Systems, pp. 761-768, 2013.
- A. Daniel and K.M. Baalamurugan, “A Novel Approach to Minimize Classifier Computational Overheads in Big Data using Neural Networks”, Physical Communication, Vol. 42, pp. 101130-101135, 2020.
- K.M. Baalamurugan and S.V. Bhanu, “A Multi-Objective Krill Herd Algorithm for Virtual Machine Placement in Cloud Computing”, The Journal of Supercomputing, Vol. 76, No. 6, pp. 4525-4542, 2020.
- I. Kononenko, “Machine Learning for Medical Diagnosis: History, State of the Art and Perspective,” Artificial Intelligence in Medicine, Vol. 23, No. 1, pp. 89-109, 2001.
- D. Raval, D. Bhatt, M.K. Kumar, V. Parikh and D. Vyas, “Medical Diagnosis System using Machine Learning”, International Journal of Computer Science and Communication, Vol. 7, No. 1, pp. 177-182, 2016.
- M. Umar, D. Babu, K.M. Baalamurugan and P. Singh, “Automation of Energy Conservation for Nodes in Wireless Sensor Networks”, International Journal of Future Generation Communication and Networking, Vol. 13, No. 3, pp. 1-12, 2020.
- C. Saravanabhavan, T. Saravanan, D.B. Mariappan, S. Nagaraj and K.M. Baalamurugan, “Data Mining Model for Chronic Kidney Risks Prediction Based on Using NB-CbH”, Proceedings of IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 1023-1026, 2021.
- Personalized Cloud Storage for E-Mails
Abstract Views :145 |
PDF Views:2
Authors
Affiliations
1 SRM University, Chennai, IN
1 SRM University, Chennai, IN
Source
Wireless Communication, Vol 4, No 2 (2012), Pagination: 94-100Abstract
There has been a wide variety of cloud storage services for the file access and sharing, but it has been cost-effective. That makes it as less utile. People are not willing to use it. But as in our project the cloud storage services is offered for the web services such as Gmail and Yahoo! Even for uncharged accounts, this makes people to use it in the highly basis. As e-mail has become the powerful communication in our modern world. We have to provide the basic needs of giving the more security in preventing their mails from being destroyed. So we provide a backup storage or cloud storage for every individual account to access client own mails with our project. We are using the POP3 mail service to retrieve the mails. It is the more secured path to be accessed. A personal storage system leveraging online email service infrastructures would also benefit service providers as it extends their access to valuable customer data in terms of both volumes and variety. The major objective of the project is we have to connect to the mail server with the desired login information. As we are allowing the access for two mail services such as Yahoo and Gmail. The mails which have been accessed in our account can be retrieved in our My Mailbox using the Cloud storage services. As we are forming the personal cloud storage for every e-mail account. It makes easier to retrieve the relevant mails. As the mails are retrieved from our services, it can also be restored to our individual account. This makes it efficient for our web services. The main concern in our project is that, whenever the users or clients have deleted or lost their mails in the main server, it can be viewed here. And we also restrict the spam messages to the inbox. We will only retrieve the relevant mails which is present in the E-mail services with the help of the POP3 protocol.Keywords
Cloud Storage, POP3 Protocol, Web Services.- Video Object Extraction by Using Background Subtraction Techniques for Sports Applications
Abstract Views :199 |
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Authors
Affiliations
1 Tamilnadu Physical Education and Sports University, Chennai, IN
2 Department of Advanced Sports Training and Technology, Tamilnadu Physical Education and Sports University, Chennai, IN
1 Tamilnadu Physical Education and Sports University, Chennai, IN
2 Department of Advanced Sports Training and Technology, Tamilnadu Physical Education and Sports University, Chennai, IN
Source
Digital Image Processing, Vol 5, No 9 (2013), Pagination: 435-440Abstract
Segmenting out foreground object from its background is an interesting and important research problem in the video based applications. It has more importance in the field of computer vision due to its applications such as sports, security systems, video surveillance, etc. The background subtraction algorithms are used to analyze the player‟s activity in sports, to improve the performance of player by detecting the motion of the players in video sequences. The various algorithms like frame difference, approximate median, mixture of gaussian are compared and analyzed with real time sports videos. Mixture of Gaussian turns out to be best in reliability of extraction of moving objects, robust to noise, whereas the conventional algorithms result in noise and poor extraction of objects. The parametric analyzes of metric such as recall, precision, etc., gives the complete behavior of player.Keywords
Approximate Median, Frame Difference, Mixture of Gaussian, Motion Detection.- Enhancement of Iris Biometric Recognition System Using Cryptography and Error Correction Codes – A Review
Abstract Views :206 |
PDF Views:3
Authors
Affiliations
1 AVC College of Engineering, Mannmpamdal, Mayiladuthurai, IN
2 Department of Electronics and Communication Engineering, AVC College of Engineering, Mannmpamdal, Mayiladuthurai, IN
3 AVC College of Engineering, IN
1 AVC College of Engineering, Mannmpamdal, Mayiladuthurai, IN
2 Department of Electronics and Communication Engineering, AVC College of Engineering, Mannmpamdal, Mayiladuthurai, IN
3 AVC College of Engineering, IN
Source
Biometrics and Bioinformatics, Vol 4, No 7 (2012), Pagination: 273-278Abstract
The Main challenge on iris and most biometric identifier’s is the user variability in the acquired identifiers. The Iris of the same person captured in different time may differ due to the signal noise of the environment or the iris camera. In Error Correction Code, ECC is introduced to reduce the variability and noise of the iris data. To find best system performance, This paper reviews an approach is tested using 2 different distance metric measurement functions for the iris pattern matching identification process which are Hamming Distance and Weighted Euclidean Distance. An experiment with the CASIA version 1.0 iris database indicates that results can assure a higher security with a false acceptance rate (FAR).
Keywords
CASIA, ECC, FAR.- Screening of Antifouling Compound Producing Marine Actinobacteria against Biofouling Bacteria Isolated from Poultries of Namakkal District, South India
Abstract Views :224 |
PDF Views:6
Authors
Affiliations
1 Research Department of Microbiology, Bharathidasan University Constituent College, Kurumbalur - 621107, Perambalur District, Tamilnadu, IN
1 Research Department of Microbiology, Bharathidasan University Constituent College, Kurumbalur - 621107, Perambalur District, Tamilnadu, IN
Source
Research Journal of Science and Technology, Vol 8, No 2 (2016), Pagination: 83-89Abstract
Growth of biofouling microorganisms in surfaces of poultry, marine and other ecosystems is one of the major issues. Thus it is essential to control the growth of biofouling bacterial growth in surface of biological ecosystem. The present study has aimed to isolate novel antifouling compound from marine actinobacterial isolates. A total of 55 actinobacteria were isolated from Palk Strait coastal region (Bay of Bengal), Tamilnadu, India. All of them were preliminarily screened for their antifouling activity against six different biofouling bacterial (BFB) species isolated from poultry farms by cross streak plate method. Among them, 20 isolates possessed antibacterial activity against biofouling bacteria. From the 20 antifouling compound producers, 10 actinobacterial isolates were selected for further confirmation of antifouling compound production and their antifouling efficacies by shake flask culture method. Of them, one potential strain VS6 was found to be more active against all the six BFB. Thus, the result of the present study represents that the coastal areas of Tamil Nadu are rich in antifouling compound producing actinobacteria.Keywords
Palk Strait Coast, Marine Actinobacteria, Poultry, Antifouling Activity.- Transformation of Tobacco (Nicotiana tabaccum) with cry2AX1 Gene and Analysis of Transgenic Plants
Abstract Views :212 |
PDF Views:0
Authors
R. Manikandan
1,
P. Kanimozhi
1,
N. Balakrishnan
1,
V. Balasubramani
1,
D. Sudhakar
1,
V. Udayasuriyan
1
Affiliations
1 Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
1 Department of Plant Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
Source
International Journal of Forestry and Crop Improvement, Vol 7, No 1 (2016), Pagination: 79-85Abstract
A novel synthetic cry2AX1 gene was codon optimized and a sequence encoding cotton rbcS1b transit peptide was fused upstream of coding sequence. The fusion cry2AX1 gene, driven by maize ubiquitin1 promoter was cloned in a pUH plant transformation vector. Agrobacterium mediated transformation was carried out with pUH-ctp-2AX1 construct using leaf discs of tobacco as model plant. Screening by PCR revealed presence of cry2AX1 gene in all nine putative transformants and expression of cry2AX1 protein in PCR positive T0 tobacco plants ranged from 1.5 to 10.0 ng/g. The detached leaf bit bioassay of tobacco transformants with Helicoverpa armigera showed 30 per cent mortality even at lower level of cry2AX1 expression. The results indicated a newly developed construct was functionally expressed in tobacco plant.Keywords
Tobacco, Transformation, Insect Resistance, Helicoverpa armigera.- Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol
Abstract Views :306 |
PDF Views:4
Authors
Affiliations
1 Department of Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, ID
2 Department of Computer Science, Sri Aravindar Engineering College, Villupuram, Tamil Nadu, IN
3 Department of Computer Science, Sri Malolan College of Arts and Science, Kanchipuram, Tamil Nadu, IN
4 Maruthi Technocrat E Services, Chennai, Tamil Nadu, IN
5 School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, IN
1 Department of Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, ID
2 Department of Computer Science, Sri Aravindar Engineering College, Villupuram, Tamil Nadu, IN
3 Department of Computer Science, Sri Malolan College of Arts and Science, Kanchipuram, Tamil Nadu, IN
4 Maruthi Technocrat E Services, Chennai, Tamil Nadu, IN
5 School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 7, No 4 (2020), Pagination: 93-102Abstract
Wireless Sensor Network (WSN) design to be efficient expects better energy optimization methods as nodes in WSN are operated only through batteries. In WSN, energy is a challenging one in the network during transmission of data. To overcome the energy issue in WSN, Taylor based Grey Wolf Optimization algorithm proposed, which is the integration of the Taylor series with Grey Wolf Optimization approach finding optimal hops to accomplish multi-hop routing. This paper shows the multiple objective-based approaches developed to achieve secure energy-aware multi-hop routing. Moreover, secure routing is to conserve energy efficiently during routing. The proposed method achieves 23.8% of energy, 75% of Packet Delivery Ratio, 35.8% of delay, 53.2% of network lifetime, and 84.8% of scalability.Keywords
Taylor Series, Grey Wolf Optimization, Multi-hop Routing, Energy Efficiency, SecurityFull Text
References
- Deepti Gupta, “Wireless Sensor Networks ‘Future trends and Latest Research Challenges’”, IOSR Journal of Electronics and Communication Engineering, vol. 10, no. 2,pp.41-46, 2015.
- Khalaf, Osamah Ibrahim, and Bayan Mahdi Sabbar. "An overview on wireless sensor networks and finding optimal location of nodes", Periodicals of Engineering and Natural Sciences, vol.7, no. 3, pp: 1096-1101, 2019.
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- S.Murugan, S.Jeyalaksshmi, B.Mahalakshmi, G.Suseendran, T.NusratJabeen,R.Manikandan, “Comparison of ACO and PSO algorithm using energy consumption and load balancing in emerging MANET and VANET infrastructure”, JCR, vol. 7, no. 9, pp: 1197-1204, 2020.
- R. Elkamel, A. Cherif, R. Elkamel, A. Cherif, R. Elkamel, A. Cherif, “Energy-efficient routing protocol to improve energy consumption in wireless sensors networks: energy efficient protocol in WSN”, International Journal of Communication System, Vol. 30, no. 6, 2017.
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- Mohan, R., Ananthula, V.R., “Reputation-based secure routing protocol in mobile ad-hoc network using Jaya Cuckoo optimization”, International Journal of Modeling, Simulation, Science Computing, vol. 10, no. 3, 2019.
- Cengiz, K., Dag, T.,“Energy aware multi-hop routing protocol for WSNs”,IEEE Access,vol. 6, pp. 2622–2633, 2018.
- Shende, D. K., &Sonavane, S. S., “CrowWhale-ETR: CrowWhale optimization algorithm for energy and trust aware multicast routing in WSN for IoT applications”, Springer Wireless Networks, pp. 1-9,2020.
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- Mohan, R., Reddy, A.V., “T-Whale: trust and whale optimization model for secure routing in mobile Ad-Hoc network”, International Journal of Artificial Life Research (IJALR), vol. 8, no. 2, pp: 67–79, 2018.
- Ch, Ram & A, Venugopal, “M-LionWhale: Multi-objective optimization model for secure routing in mobile Ad-hoc network”, IET Communications, vol. 12, pp. 1-7, 2018.
- Kumar, R., Kumar, D., & Kumar, D., “EACO and FABC to multi-path data transmission in wireless sensor networks”, IET Communications, vol. 11, no. 4, pp. 522–530, 2017.
- Rajeev Kumar and Dilip Kumar, “Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks”,Hindawi journal of sensors, Article ID 5836913, pp. 1-19, 2016.
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- R. Pachlor, D. Shrimankar, “VCH-ECCR: a centralized routing protocol for wireless sensor networks”,Journal of Sensor, vol.1, pp. 1–10, 2017
- Srbinovska, M., Cundeva-Blajer, M., Optimization Methods for Energy Consumption Estimation in Wireless Sensor Networks, Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 7, no. 2, pp 261-274, 2019
- Carolina Del-Valle-Soto , Carlos Mex-Perera , Juan Arturo Nolazco-Flores, Ramiro Velázquez and Alberto Rossa-Sierra, “Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols”, Journal of Energies, vol. 13, no. 728, 2020.
- Trupti Mayee Behera, Sushanta Kumar Mohapatra, Umesh Chandra Samal, Mohammad. S. Khan, Mahmoud Daneshmand, and Amir H. Gandomi, “Residual Energy Based Cluster-head Selection in WSNs for IoT Application”, IEEE Internet of Things Journal,vol.6, no.3, pp. 5132-5139, 2019.
- Zhao, L., Qu, S. & Yi, Y. “A modified cluster-head selection algorithm in wireless sensor networks based on LEACH”, Journal of Wireless Communication Network, vol. 1, no. 287, 2018.
- Mathematical Morphology based Digital Image Enhancement Processing with Cross Separate Boundary Objects
Abstract Views :160 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2699-2703Abstract
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression.Keywords
Digital Image Processing, Computer Algorithms, Digital Images, Mathematical MorphologyReferences
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