Recognition of Noisy Numbers Using Neural Network

Nautiyal, Chanda Thapliyal; Singh, Sunita; Rana, U. S.

doi:10.1007/978-981-10-5699-4_13

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Artificial neural network and Machine learning algorithms have exhibited remarkable efficacy across diverse domains, exemplified by their prowess in character recognition [13] Nautiyal et al, 2018), image processing, and meteorological predictions.…”

Section: Artificial Intelligencementioning

confidence: 99%

Importance of Smart Agriculture and Use of Artificial Intelligence in Shaping the Future of Agriculture

Nautiyal,

Papnai

et al. 2024

JSRR

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India, the second-most populated country globally with 1.4 billion people, faces significant challenges in its agriculture sector, including the need to feed a growing global population, mitigate climate change impacts, and ensure sustainable resource management. To address these challenges, innovative techniques and advanced technologies are required. Modern farming techniques, coupled with artificial intelligence (A.I.), offer promising solutions. Technologies such as drones, biotechnology, genetics, and precision farming can enhance efficiency, productivity, and sustainability. Implementing these approaches can lead to a fiftyfold increase in yield and a 50% reduction in manpower, contributing to increased crop yields, reduced water usage, minimized chemical applications, and improved labor efficiency. Smart agriculture holds the potential to revolutionize the industry, significantly contributing to global food security and the preservation of natural resources.

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Section: Artificial Intelligencementioning

confidence: 99%

Importance of Smart Agriculture and Use of Artificial Intelligence in Shaping the Future of Agriculture

Nautiyal,

Papnai

et al. 2024

JSRR

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State-of-the-Art Object Recognition Techniques: A Comparative Study

Goel

Sharma

Kapoor

2020

Advances in Intelligent Systems and Computing

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New Structure of CCR with an AOANN Threshold

Rabehi

Djebbari

Hafaifa

et al. 2018

Journal of Optical Communications

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In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimator uses the weight (w) and the length (F) of the code word, the number of active users (Ν) and the signal to noise ratio as inputs to estimate the required optimal threshold. We have evaluated the proposed approach on a data set of 46,200 samples. We have found that it gives accurate results: 0.029 for the root mean square error, 0.37% for the relative root mean square error and 99.984% for the correlation coefficient (R), which reflects the efficiency of the proposed optimal threshold estimator.

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Recognition of Noisy Numbers Using Neural Network

Cited by 4 publications

References 2 publications

Importance of Smart Agriculture and Use of Artificial Intelligence in Shaping the Future of Agriculture

Importance of Smart Agriculture and Use of Artificial Intelligence in Shaping the Future of Agriculture

State-of-the-Art Object Recognition Techniques: A Comparative Study

New Structure of CCR with an AOANN Threshold

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