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Kalaivaani, P. T.
- Improving Medical Image Processing Using an Enhanced Deep Learning Algorithm
Abstract Views :91 |
PDF Views:1
Authors
Affiliations
1 Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, IN
2 Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
3 Department of Data Science, School of Science, Jain University, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
1 Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, IN
2 Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
3 Department of Data Science, School of Science, Jain University, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2811-2816Abstract
The use of ML methods with the objective of selecting wheat varieties that have a higher level of rust resistance encoded in their genomes is referred to as rust selection. In addition to that, the categorization of wheat illnesses by means of machine learning It has been attempted to classify wheat diseases by making use of a wide variety of machine learning techniques. In this paper, we develop an enhanced deep learning model to classify the disease present in the wheat plant. The study uses an improved convolutional neural network to classify the plant disease using a series of layers. The simulation is conducted in terms of the accuracy, precision, recall and f-measure. The results show that the proposed method achieves higher rate of accuracy than its predecessor.Keywords
ML, Wheat Varieties, Rust Resistance, Disease.
References
- Z. Li and B. Wang, “Plant Disease Detection and Classification by Deep Learning-A Review”, IEEE Access, Vol. 9, pp. 56683-56698, 2021.
- B. Subramanian, V. Saravanan and S. Hariprasath, “Diabetic Retinopathy-Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal of Engineering and Advanced Technology, Vol. 9, pp. 618-627, 2019.
- A. Abbas and S. Vankudothu, “Tomato Plant Disease Detection using Transfer Learning with C-GAN Synthetic Images”, Computers and Electronics in Agriculture, Vol. 187, pp. 106279-106287, 2021.
- R.K. Nayak, R. Tripathy and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of 4th International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.
- M. Zia Ur Rehman and I. Hussain, “Classification of Citrus Plant Diseases using Deep Transfer Learning”, Computers, Materials and Continua, Vol. 70, No. 1, pp. 1-12, 2021.
- R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-8, 2022.
- J. Annrose and D.G. Immanuel, “A Cloud-Based Platform for Soybean Plant Disease Classification using Archimedes Optimization based Hybrid Deep Learning Model”, Wireless Personal Communications, Vol. 122, No. 4, pp. 2995-3017, 2022.
- J. Schuler, H. Rashwan and D. Puig, “Color-Aware Two-Branch Dcnn for Efficient Plant Disease Classification”, Nature, Vol. 28, No. 1, pp. 55-62, 2022.
- E. Akanksha and K. Gulati, “OPNN: Optimized Probabilistic Neural Network based Automatic Detection of Maize Plant Disease Detection”, Proceedings of International Conference on Inventive Computation Technologies, pp. 1322-1328, 2021.
- Z. Chen, S. Chen, Z. Yuan and X. Zou, “Plant Disease Recognition Model based on Improved Yolov5”, Agronomy, Vol. 12, No. 2, pp. 365-373, 2022.
- Identification and recognition of Leaf Disease Using Enhanced Segmentation Techniques
Abstract Views :93 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Siddhant College of Engineering, IN
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, IN
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
1 Department of Information Technology, Siddhant College of Engineering, IN
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, IN
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2825-2830Abstract
Segmenting refers to the technique of breaking up an image into its component parts one by one. When it comes to the process of segmenting photos, there is a plethora of choice available at current point in time. These options range from the easy thresholding approach to the complicated color image segmentation techniques. The bulk of the time, the parts that go into making up these sub-assemblies are items that individuals are able to easily identify and categorize as being distinct from one another. As a result of the limitation of computer lack of intelligence to differentiate between distinct items, a wide variety of techniques have been devised and utilized in the process of segmenting photographs. In order to complete its tasks, the image segmentation algorithm requires a wide range of image characteristics to be provided as input. This could be referring to the colors that are contained within an image, the borders that are included within the image, or a particular region that is contained within the image. In order to break down color images into their component elements, we make use of an algorithm that is inspired by natural selection. The research uses enhanced segmentation techniques to identify and recognize the leaf disease in plants. The study conducts extensive simulation to test the efficacy of the model. The results show that the proposed method achieves higher segmentation accuracy than other methods.Keywords
No Keywords.References
- R.K. Nayak, R. Tripathy and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of 4th International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.
- B. Subramanian, V. Saravanan and S. Hariprasath, “Diabetic Retinopathy-Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal of Engineering and Advanced Technology, Vol. 9, pp. 618-627, 2019.
- R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-8, 2022.
- V. Pooja and V. Kanchana, “Identification of Plant Leaf Diseases using Image Processing Techniques”, Proceedings of IEEE Technological Innovations in ICT for Agriculture and Rural Development, pp. 130-133, 2017.
- N. Krithika and A.G. Selvarani, “An Individual Grape Leaf Disease Identification using Leaf Skeletons and KNN Classification”, Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-5, 2017.
- P. Kantale and S. Thakare, “A Review on Pomegranate Disease Classification using Machine Learning and Image Segmentation Techniques”, Proceedings of International Conference on Intelligent Computing and Control Systems, pp. 455-460, 2020.
- T. Fang and B. Wang, “Crop Leaf Disease Grade Identification based on an Improved Convolutional Neural Network”, Journal of Electronic Imaging, Vol. 29, No. 1, pp. 1-14, 2020.
- P. Kaur, S. Bhatia and A.M. Alabdali, “Recognition of Leaf Disease using Hybrid Convolutional Neural Network by Applying Feature Reduction”, Sensors, Vol. 22, No. 2, pp. 575-583, 2022.
- S. Bashir and N. Sharma, “Remote Area Plant Disease Detection using Image Processing”, IOSR Journal of Electronics and Communication Engineering, Vol. 2, No. 6, pp. 31-34, 2012.
- V. Singh and A.K. Misra, “Detection of Unhealthy Region of Plant Leaves using Image Processing and Genetic Algorithm”, Proceedings of International Conference on Advances in Computer Engineering and Applications, pp. 1028-1032, 2015.
- P. Revathi and M. Hemalatha, “Classification of Cotton Leaf Spot Diseases using Image Processing Edge Detection Techniques”, Proceedings of International Conference on Emerging Trends in Science, Engineering and Technology, pp. 169-173, 2012.
- A.S. Tulshan and N. Raul, “Plant Leaf Disease Detection using Machine Learning”, Proceedings of International Conference on Computing, Communication and Networking Technologies, pp. 1-6, 2019.
- Wireless Traffic and Routing Enhancement Using Emperor Penguin Optimizer Guided by Conditional Generative Adversarial Nets
Abstract Views :62 |
PDF Views:2
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Prathyusha Engineering College, IN
2 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
3 Department of Information Technology, Vardhaman College of Engineering, IN
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, IN
1 Department of Electronics and Communication Engineering, Prathyusha Engineering College, IN
2 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
3 Department of Information Technology, Vardhaman College of Engineering, IN
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 4 (2023), Pagination: 3029-3036Abstract
The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.Keywords
Wireless Communication, Emperor Penguin Optimizer, Conditional Generative Adversarial Nets, Traffic Optimization, Routing Enhancement.References
- H. Kaur, S.S. Bhatia and G. Dhiman, “MOEPO: A Novel Multi-Objective Emperor Penguin Optimizer for Global Optimization: Special Application in Ranking of Cloud Service Providers”, Engineering Applications of Artificial Intelligence, Vol. 96, pp. 1-12, 2020.
- Saul Dobilas, “cGAN: Conditional Generative Adversarial Network - How to Gain Control Over GAN Outputs”, Available at https://towardsdatascience.com/cgan-conditional-generative-adversarial-network-how-to-gain-control-over-gan-outputs-b30620bd0cc8, Accessed at 2022.
- T. Lathies Bhasker, “A Scope for MANET Routing and Security Threats”, ICTACT Journal on Communication Technology, Vol. 4, No. 4, pp. 840-848, 2013.
- A.G. Ismaeel, K. Janardhanan, S.N. Mahmood and A.H. Shather, “Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network”, Sustainability, Vol. 15, No. 19, pp. 1-9, 2023.
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- G. Kirchgassner and J. Wolters, “Introduction to Modern Time Series Analysis”, Springer, 2007.
- B. Vijayalakshmi “Improved Spectral Efficiency in Massive MIMO Ultra-Dense Networks through Optimal Pilot-Based Vector Perturbation Precoding”, Optik, Vol. 273, pp. 1-8, 2023.
- M. Rajalakshmi, V. Saravanan and C. Karthik, “Machine Learning for Modeling and Control of Industrial Clarifier Process”, Intelligent Automation and Soft Computing, Vol. 32, No. 1, pp. 339-359, 2022.
- J. Gowrishankar, P.S. Kumar and T. Narmadha, “A Trust Based Protocol for Manets in IoT Environment”, International Journal of Advanced Science and Technology, Vol. 29, No. 7, pp. 2770-2775, 2020.
- Alberto Dainotti, Antonio Pescape and Kimberly C. Claffy, “Issues and Future Directions in Traffic Classification”, IEEE Network, Vol. 26, No. 1, pp. 35-40, 2012.
- Jochen W. Guck, Amaury Van Bemten, Martin Reisslein, Wolfgang Kellerer, “Unicast QoS Routing Algorithms for SDN: A Comprehensive Survey and Performance Evaluation”, IEEE Communications Surveys and Tutorials, Vol. 20, No. 1, pp. 388-415, 2017.
- C.D. Kumar, “Weighted Multi-Objective Cluster Based Honey Bee Foraging Load Balanced Routing in Mobile Ad Hoc Network”, International Journal of Applied Engineering Research, Vol. 13, No. 12, pp. 10394-10405, 2018.
- P. Vijayalakshmi and A.J. Dinakaran, “Mobile Ad Hoc Routing Protocols A Comparative Performance Analysis by Diversifying the Nodes”, International Journal of Computer Applications, Vol. 21, No. 5, pp. 42-47, 2011.
- M. Kandasamy and A.S. Kumar, “QoS Design using Mmwave Backhaul Solution for Utilising Underutilised 5G Bandwidth in GHz Transmission”, Proceedings of International Conference on Artificial Intelligence and Smart Energy, pp. 1615-1620, 2023.
- P. Ajay, R. Arunkumar and R. Huang, “Enhancing Computational Energy Transportation in IoT Systems with an Efficient Wireless Tree-Based Routing Protocol”, Results in Physics, Vol. 51, pp. 1-13, 2023.