Abstract
Apparently, there has been abundant of data generation and transfer going on over a daily basis. This data can either be static, dynamic or transactional in nature. There is frequent appending of new data to the data that is being already existing. There occurs a need to explore and fetch knowledge from the newly added data. One of the solution can be executing the algorithm for the appended datasets, which turns out to be quiet complicated and time engulfing. Resultant technique of dimensionality reduction has been proposed that aids in minimizing data dimensionality for carrying out various data processing processes like machine learning, data mining, pattern recognition and text retrieval in an effect and weather condition, best crop production is being analyzed in the existing research by making use of the proposed algorithm of decision tree (DT). At last the research work recommends a technique to handle various stages such as pre-manner. The method of dimensionality reduction had been proposed and incorporated in the soil and agriculture domain. The research work recommends principle component analysis (PCA) which is a dimension reduction algorithm employed in dynamic environment in order to produce reduced set of attribute as dynamic reduce thereby learning from it and drawing out future prediction for weather forecasting. The proposed method aids in assessment of new datasets pertaining to agriculture and soil upon its availability and makes appropriate modification in reduce so as it fits the whole dataset. On the basis of soil processing, dimensionality reduction and prediction via DT algorithm. The recommended PCA helps in comprehending data semantics, making the writing of analytics agriculture applications quiet simplistic and employing the approach of dimensional reduction for enhancing the performance. To successfully accomplish this, the accuracy of the predictions is carried out by dimensional reduction that is configured using a number of variables and inputs. The results yield no information loss with least execution time.
Similar content being viewed by others
Data availability
We used our own data.
References
Aladeemy M, Adwan L, Booth A, Khasawneh MT, Poranki S (2020) New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows. Appl Soft Comput 86:105866
Anandharajan TRV, Hariharan GA, Vignajeth KK, Jijendiran R, Kushmita (2016) Weather monitoring using artificial intelligence. In: International conference on computational intelligence and networks, 2016, © IEEE, pp 106–111
Bhangale PP, Patil YS, Patil DD (2017) Improved crop yield prediction using neural network. IJARIIE 3(2):2395–4396
Deyasi A, Mukherjee S, Bhattacharjee AK, Sarkar A (2020) Classification of single and double-gate nanoscale MOSFET with different dielectrics from electrical characteristics using soft computing techniques. Int J Inf Technol 12(1):165–174
Gandge Y, Sandhya (2017) A study on various data mining techniques for crop yield prediction. In: ICEECCOT, 2017, © IEEE, pp 420–423
Hegde NG, Mujumdar S, Jambarmath SS, Madhavi R (2017) Survey paper on agriculture yield prediction tool using machine learning. Int J Adv Res Comput Sci Manag Stud 5(11):36–39
Jiang L, Jiang H, Wang HH (2020) Soft computing model using cluster-PCA in port model for throughput forecasting. Soft Comput 24:14167–14177
Jin X, Kumar L, Li Z, Feng H, Xu X, Yang G, Wang J (2018) A review of data assimilation of remote sensing and crop models. Eur J Agron 92:141–152
Teeda K, Vallabhaneni N, Sridevi T (2018) Analysis of Weather Attributes to Predict Crops for the Season Using Data Mining. IntJ Pure Appl Math 119(12):12515–12522
Juhi Reashma SRK, Pillai AS (2017) Edaphic factors and crop growth using Machine learning—a review. In: International conference on intelligent sustainable systems, 2017, © IEEE, pp 270–274
Majumdar J, Naraseeyappa S, Ankalaki S (2017) Analysis of agriculture data using data mining techniques: application of big data. J Big Data 4:20
Manjula E, Djodiltachoumy S (2017) A model for prediction of crop yield. Int J Comput Intell Inform 6(4):298–305
Mishra S, Paygude P, Chaudhary S, Idate S (2018) Use of data mining in crop yield prediction. In: International conference on inventive systems and control, 2018, © IEEE, pp 796–802
Padarian J, Minasny B, McBratney AB (2018) Using deep learning to predict soil properties from regional spectral data, © Elsevier, Geoderma Regional, vol 16. pp 1–9
Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Electron Agric 121:57–65
Papageorgiou EI, Aggelopoulou KD, Gemtos TA, Nanos GD (2013) Yield prediction in apples using fuzzy cognitive map learning approach. In: Computers and electronics in agriculture, 2013. © Elsevier, pp 19–29
Patel H, Patel D (2016) A comparative study on various data mining algorithms with special reference to crop yield prediction. Indian J Sci Technol 9(22):1–8
Ramesh D, Vishnu Vardhan B (2015) Analysis of crop yield prediction using data mining techniques. Int J Res Eng Technol 04(01):470–473
Salman MG, Kanigoro B, Heryadi Y (2015) Weather forecasting using deep learning techniques. In: ICACSIS, 2015, © IEEE, pp 281–285
Shakoor MT, Rahman K, Rayta SN, Chakrabarty A (2017) Agricultural production output prediction using supervised machine learning techniques. © IEEE
Shi X, Tian S, Yu L, Li L, Gao S (2017) Prediction of soil adsorption coefficient based on deep recursive neural network. Autom Control Comput Sci 51(5):321–330
Paul M, Vishwakarma SK, Verma A (2015) Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach. © IEEE, International Conference on Computational Intelligence and Communication Networks, pp 766–771
Subarna S (2020) Process mining error detection for securing the IoT system. J ISMAC 2(03):147–153
Jeong JH, Resop JP, Mueller ND et al (2016) Random forests for global and regional crop yield predictions. © PLOS ONE :1–15
Veenadhari S, Misra B, Singh CD (2014) Machine learning approach for forecasting crop yield based on climatic parameters. In: International conference on computer communication and informatics. © IEEE
Venkatesan R, Prabu S (2020) Feature extraction from hyperspectral image using decision boundary feature extraction technique. Soft computing for problem solving. Springer, Singapore, pp 927–940
Shakeel PM, Tolba A, Al-Makhadmeh Z, Jaber MM (2020) Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Comput Appl 32(3):777–790
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
All author states that there is no conflict of interest.
Human and or animals rights
Humans/Animals are not involved in this work
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Radhika, A., Masood, M.S. Effective dimensionality reduction by using soft computing method in data mining techniques. Soft Comput 25, 4643–4651 (2021). https://doi.org/10.1007/s00500-020-05474-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05474-7