Secure Communication Using Multi-Layer Perceptron Neural Network and the Adaptive-Network-Based Fuzzy Inference System in Wireless Network

Kamala, J.; Nawaz, G. M. Kadhar

doi:10.1007/s42979-023-02121-4

Cited by 1 publication

(1 citation statement)

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“…The monotone multi-layer perceptron neural network (monmlp) provide a flexible machine learning model that can identify complex patterns in input noise data by introducing monotonicity requirements into its design, expanding the conventional multi-layer perceptron-Eskandarian et al [36]. Its major objective is to simulate relationships between the input characteristics and the target noise variable in a way that ensures the predictions retain a steady directional trend, anticipating output consistently as the values of specific input attributes increase or decrease-Kamala and Nawaz [37]. The number of neurons in the hidden layer of the monotonic multi-layer perceptron model was controlled by the hidden1 hyperparameter, which inferred intricate patterns and representations from the input noise data.…”

Section: Machine Learning Prediction and Accuracy Assessmentmentioning

confidence: 99%

Prediction of Noise Levels According to Some Exploitation Parameters of an Agricultural Tractor: A Machine Learning Approach

Barač,

Radočaj,

Plaščak

et al. 2024

AgriEngineering

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The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further.

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Section: Machine Learning Prediction and Accuracy Assessmentmentioning

confidence: 99%