Genetically Optimized Deep Neural Learning for Breast Cancer Prediction

Agrawal, Suchitra; Tiwari, Aruna; Goel, Ishan

doi:10.1007/978-981-15-3287-0_10

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Generalized Neuro-Fuzzy Models (GNFM) [ 60 ], ANN model [ 61 ], ANFIS model [ 62 ], MLP-NN [ 63 ] [ 113 ], ELM algorithm [ 64 ] [ 90 ], M5 Model Tree [ 113 ], LS-SVR , MARS, ELM, WNN and GANN [ 66 ]. The DL model and ML model are applied in various domains such the COVID-19 analysis [ 114 ], proposed a novel method based on artificial intelligence (AI) to identify COVID-19 disease [ 115 ], developed genetically optimized Deep Neural Network [ 116 ], Tripathy et al [ 117 ] investigated the performance of MARPUF approach and it is found to be better resistant to such modelling attacks, image classification using deep learning [ 118 ], Artificial Intelligence approaches used to classifying various types of cancer [ 119 ], enhanced the grip functionality of myoelectric hands based on deep learning [ 120 ], and classifiers for on-line handwriting recognition based on SVM and KNN algorithms [ 121 ], and a survey for software fault prediction [ 122 ]. Singh et al [ 123 ] presented the efficient results and reliable algorithm for optimal design of water distribution networks.The literature summary of ML and EA are given in Table 5 .…”

Section: Literature Of Irrigation Schedulingmentioning

confidence: 99%

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Saggi

Jain

2022

Arch Computat Methods Eng

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From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

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