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Development and Assessment of SPM: A Sigmoid-Based Model for Probability Estimation in Non-Repetitive Unit Selection With Replacement

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Title Development and Assessment of SPM: A Sigmoid-Based Model for Probability Estimation in Non-Repetitive Unit Selection With Replacement
Not Available
 
Creator SAMARTH GODARA
G. AVINASH
RAJENDER PARSAD
SUDEEP MARWAHA
MUKHTAR AHMAD FAIZ
RAM SWAROOP BANA
 
Subject Probability estimation, sigmoid function, modeling, non-repetitive units selection, optimization.
 
Description Not Available
Probability estimation plays a pivotal role across diverse domains, particularly in scenarios
where the objective is to select non-repetitive units one at a time, with the option of replacement, from a
predefined set of units. Traditional probability calculations in this scenario pose three challenges: the number
of floating-point operations to be executed is directly proportional to the chosen set size, susceptibility to
floating-point precision errors, and exponential growth in storage needs with increasing number of chosen
units. In this scenario, the presented work aims to develop SPM: a sigmoid function-based model that
estimates probabilities for such problems with a fixed number of calculations (independent of the input
parameter), achieving a constant time complexity algorithm. The research methodology involves generating
probability data points, selecting the optimal sigmoid function, augmenting additional data to enhance
parameter estimation, identifying parameter estimation equations, and evaluating the model. Moreover, the
study’s second objective includes training and comparing six established machine learning-based models
(including Decision Tree, Random Forest, Support Vector, Linear Regression, Nearest Neighbour, and
Artificial Neural Network) against the proposed SPM. The rigorous assessment of the model’s performance,
utilising metrics including RMSE, MAE and r2 across a wide range of scenarios involving varying values
of the total units, affirms the model’s accuracy and resilience. The study findings can improve decision-
making processes in various domains, including statistics, cryptography, machine learning and optimisation,
by offering a faster, more adaptable solution for probability estimation in units’ selection with replacement.
Not Available
 
Date 2024-03-01T12:10:12Z
2024-03-01T12:10:12Z
2024-01-01
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/81532
 
Language English
 
Relation Not Available;
 
Publisher Not Available