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Yuvaraj, N.
- Design and Analysis of Robot Movement Control Using Intelligent Controller
Authors
1 Muthayammal Engg. College, Rasipuram, Tamilnadu, IN
2 Karpagam College of Engg., Coimbatore, Tamilnadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 3 (2010), Pagination:Abstract
In this paper, the intelligent controller is used to increase the parameters of the robot like accuracy and speed. The intelligent controller includes fuzzy logic and neural network systems it will be more applied for modern automation processing systems. The performance of the proposed system is examined by MATLAB software with its simulation results. Finally, the above parameters are found better off controlled with the addition of intelligent controller.Keywords
Robots, Intelligent Controller, Fuzzy Logic, MATLAB, DC Motor.- A Survey on Leaf Disease Prediction Algorithms using Digital Image Processing
Authors
1 Department of Computer Science, KPR Institute of Engineering and Technology, Coimbatore-641407, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 10 (2016), Pagination: 1-4Abstract
Objective: To investigate the plant leaf disease prediction algorithms that utilizes the digital image processing techniques in agricultural environments.
Findings: In digital image processing, the segmentation process of healthy and diseased tissue was mainly focused in order to detect and diagnose the plant leaf diseases accurately.Semi-automatic segmentation technique was mostly utilized among various segmentation methods, which was developed based on the grayscale histogram. However, the issue of accuracy in segmentation process was still not improved. In this paper, the leaf disease prediction algorithms are investigated briefly according to the digital image processing techniques and evaluated the performance effectiveness of different algorithms.
Results: In this paper, various segmentation algorithms are studied which are used to predict the leaf diseases through digital image processing techniques in terms of their merits and demerits to prove segmentation based on grayscale histogram is better than other segmentation techniques to predict the leaf diseases.
Application/Improvements: The finding of this study shows that the segmentation technique based on grayscale histogram is better than the other digital image processing techniques.
Keywords
Digital Image Processing, Plant Disease, Segmentation, Grayscale Histogram, Leaf Symptoms.References
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- S. Kumar,R. Kaur. Plant disease detection using image processing-A review. International Journal of Computer Applications, 2015; 124(16), 6-9.
- P. Karmuhil, L. Parthiban. An automatic road network extraction from satellite images using Modified SOFM approach. Indian Journal of Innovations and Developments.2016; 5(4), 1-6.
- K. Khairnar, R. Dagade. Disease detection and diagnosis on plant using image processing- A Review. International Journal of Computer Applications.2014; 108(13), 36-38.
- J.G.A.Barbedo. An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Disease.2014; 98(12), 1709-1716.
- M. Grand-Brochier, A.Vacavant, G. Cerutti, K. Bianchi, L. Tougne. Comparative study of segmentation methods for tree leaves extraction. In: Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications. ACM. 2013.
- R. Gao, H. Wu. Agricultural image target segmentation based on fuzzy set. Optik-International Journal for Light and Electron Optics, 2015; 126(24), 5320-5324.
- M. Guijarro, I. Riomoros, G. Pajares, P. Zitinski. Discrete wavelets transform for improving greenness image segmentation in agricultural images. Computers and Electronics in Agriculture.2015; 118, 396-407.
- J. Wang, J.He, Y. Han, C. Ouyang, D. Li. An adaptive thresholding algorithm of field leaf image. Computers and electronics in agriculture, 2013; 96, 23-39.
- M.G. Larese, R. Namías, R.M. Craviotto, M.R. Arango, C. Gallo, P.M. Granitto. Automatic classification of legumes using leaf vein image features. Pattern Recognition, 2014; 47(1), 158-168.
- L. Han, M. S. Haleem, M. Taylor. A novel computer vision-based approach to automatic detection and severity assessment of crop diseases. In:Science and Information Conference (SAI), IEEE.2015; 638-644.
- A. Aakif, M.F. Khan. Automatic classification of plants based on their leaves. Biosystems Engineering.2015; 139, 66-75.
- M. Solahudin, B. Pramudya, R. Manaf. Gemini virus attack analysis in field of chili (Capsicum annuum L.) using aerial photography and Bayesian segmentation method. Procedia Environmental Sciences, 2015; 24, 254-257.
- S.B. Patil, S. K. Bodhe. Leaf disease severity measurement using image processing. International Journal of Engineering and Technology, 2011; 3(5), 297-301.
- C.H. Teng, Y.T. Kuo, Y.S. Chen. Leaf segmentation, classification, and three-dimensional recovery from a few images with close viewpoints. Optical Engineering. 2011; 50(3), 1-13.
- J.G.A. Barbedo. A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing. Tropical Plant Pathology. 2016; 41(4), 210-224.
- A Survey on Crop Yield Prediction Models
Authors
1 M.E. (Computer Science and Engineering), KPR Institute of Engineering and Technology, Arasur, Coimbatore, IN
2 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, IN
Source
Indian Journal of Innovations and Developments, Vol 5, No 12 (2016), Pagination: 1-7Abstract
Objectives: To analysis various models to improve the prediction of crop yield production.
Methods: In this paper, there are different methods has been analyzed to predict the crop yield. The methods such as artificial neural network, Adaptive Neuro-Fuzzy inference System, Fuzzy Logic and Multi Linear Regression are analyzed to know the best methods for crop yield prediction. The prediction of crop yield varied by internal factors and external factors of crop an environment. The internal factors such as pesticides, water level, spacing and fertilizers and the external factors such as temperature, humidity, and rainfall. There are various models were developed to predict the crop yield prediction. This paper provides detailed information about the different models for crop yield prediction.
Findings: In this paper various models for crop yield prediction are compared through their parameters such as Root Mean Square Error (RMSE), R2,correlation coefficient and Mean Square Error (MSE) to prove Adaptive NeuroFuzzy Inference System (ANFIS)prediction model is better than other techniques.
Application/improvements: The findings of this work prove that the Adaptive Neuro Fuzzy Inference System (ANFIS) prediction model provides better result than other approaches.
Keywords
Crop Yield Prediction, Adaptive Neurofuzzy Inference System, Data Mining, Agriculture.References
- Wu Fan, Chen Chong, Guo Xiaoling, Yu Hua, Wang Juyun. Prediction of crop yield using big data. 8th International Symposium onComputational Intelligence and Design (ISCID).2015;1, 255-260.
- Monali Paul, Santosh K. Vishwakarma, Ashok Verma. Analysis of soil behaviour and prediction of crop yield using data mining approach. Computational Intelligence and Communication Networks (CICN). 2015; 766-771.
- Subhadra Mishra, Debahuti Mishra, GourHariSantra.Applications of machine learning techniques in agricultural crop production: a review paper.Indian Journal of Science and Technology.2016, 9(38), 1-14.
- Aliyu Muazu, Azmi Yahya, W.I.W. Ishak, S. Khairunniza-Bejo. Yield prediction modeling using data envelopment analysis methodology for direct seeding, wetland paddy cultivation. Agriculture and Agricultural Science Procedia. 2014; 2, 181-190.
- Kadir, MuhdKhairulzaman Abdul, MohdZakiAyob, NadarajMiniappan. Wheat yield prediction: Artificial neural network based approach. 4th International Conference onEngineering Technology and Technopreneuship (ICE2T). 2014; 161-165.
- Agus Buono. An implementation of fuzzy inference system for onset prediction based on Southern Oscillation Index for increasing the resilience of rice production against climate variability. Advanced Computer Science and Information Systems (ICACSIS). 2012; 281-286.
- O.K. Chaudhari, P.G. Khot, K.C. Deshmukh, N.G. Bawne. Anfis based model in decision making to optimize the profit in farm cultivation. International Journal of Engineering Science. 2012; 4(2), 442-448.
- Samad Emamgholizadeh, M. Parsaeian, Mehdi Baradaran. Seed yield prediction of sesame using artificial neural network. European Journal of Agronomy. 2015;68, 89-96.
- Keong, Yong Kian, Wong Mum Keng. Statistical modeling of weather-based yield forecasting for young mature oil palm. APCBEE Procedia. 2012; 4, 58-65.
- Benyamin Khoshnevisan, Shahin Rafiee, Mahmoud Omid, Hossein Mousazadeh. Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information Processing in Agriculture. 2014; 1(1), 14-22.
- Kefaya Qaddoum, Evor Hines, Daciana Illiescu. Adaptive neuro-fuzzy modeling for crop yield prediction.Recent Researches in Artificial Intelligence, Knowledge Engineering and Databases. 2011; 16(7), 1-6.
- Aditya Shastry, H.A. Sanjay, Madhura Hegde. A parameter based ANFIS model for crop yield prediction. Advance Computing Conference (IACC). 2015; 253-257.
- Jesus Soto, Patricia Melin, Oscar Castillo. Optimization of interval type-2 and type-1 fuzzy integrators in ensembles of ANFIS models with Genetic Algorithms. World Congress onNature and Biologically Inspired Computing (NaBIC).2013; 41-46.
- S.P. Srinivasan, P. Malliga. A new approach of adaptive Neuro Fuzzy Inference System (ANFIS) modeling for yield prediction in the supply chain of Jatropha. IEEE 17th International Conference onIndustrial Engineering and Engineering Management (IE&EM).2010, 1249-1253.
- Swati Hira, P.S. Deshpande. Data analysis using multidimensional modeling, statistical analysis and data mining on agriculture parameters. Procedia Computer Science. 2015; 54, 431-439.
- Design of Categorical Data Clustering Using Machine Learning Ensemble
Authors
1 Institute of Computer Science and Information Science, Srinivas University, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 4 (2022), Pagination: 2729-2734Abstract
Cluster analysis of data is a crucial tool for discovering and making sense of a dataset underlying structure. It has been put to use in many contexts and many different fields with great success. In addition, new innovations in the last decade have piqued the interest of clinical researchers, scientists, and biologists. As the number of dimensions in a data set grows, the consensus function of traditional ensemble clustering often fails to generate final clusters. The main problem with conventional ensemble clustering is exactly this. The proposed work employs a similarity measure between links to identify which clusters contain the unknown datasets. To this end, this study proposes employing an improved ensemble framework for clustering categorical datasets. More specifically, it employs ensemble machine learning methods to categorize data. Multiple machine learning algorithms are incorporated into this model. Objective performance indicators are used to compare a model to more traditional approaches to determine how effective each the proposed method is.Keywords
Base Clustering, Ensemble Clustering Clusters, Accuracy, PrecisionReferences
- L. Bai and J. Liang, “A Categorical Data Clustering Framework on Graph Representation”, Pattern Recognition, Vol. 128, pp. 1-13, 2022.
- R. Brnawy and N. Shiri, “Improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule Computing”, Proceedings of International Conference on Database and Expert Systems Applications, pp. 261-272, 2021.
- G. Pole and P. Gera, “Cluster-Based Ensemble Using Distributed Clustering Approach for Large Categorical Data”, Proceedings of International Conference on ICT Analysis and Applications, pp. 671-680, 2021.
- I. Khan and R. Hedjam, “Ensemble Clustering using Extended Fuzzy k-Means for Cancer Data Analysis”, Expert Systems with Applications, Vol. 172, pp. 114622-114633, 2021.
- D.T. Dinh, V.N. Huynh and S. Sriboonchitta, “Clustering mixed Numerical and Categorical Data with Missing Values”, Information Sciences, Vol. 571, pp. 418-442, 2021.
- I. Singh, N. Kumar and S. Jain, “A Multi-Level Classification and Modified PSO Clustering based Ensemble Approach for Credit Scoring”, Applied Soft Computing, Vol. 111, pp. 107687-107698, 2021.
- B.A. Hassan and T.A. Rashid, “A Multidisciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets”, Neural Computing and Applications, Vol. 33, No. 17, pp. 10987-11010, 2021.
- K. Parish Venkata Kumar and M. Jogendra Kumar, “Concept Summarization of Uncertain Categorical Data Streams Based on Cluster Ensemble Approach”, Proceedings of International Conference on Pervasive Computing and Social Networking, pp. 385-398, 2022.
- V. Shorewala, “Early Detection of Coronary Heart Disease using Ensemble Techniques”, Informatics in Medicine Unlocked, Vol. 26, pp. 1-16, 2022.
- I.B. Ayinla and S.O. Akinola, “An Improved Ensemble Model using Random Forest Branch Clustering Optimisation Approach”, University of Ibadan Journal of Science and Logics in ICT Research, Vol. 7, No. 2, pp. 8-19, 2021.