AN MS EXCEL TOOL FOR PARAMETER ESTIMATION BY MULTIVARIATE NONLINEAR REGRESSION IN ENVIRONMENTAL ENGINEERING EDUCATIONDEMR, SelamiKARADENZ, AykutYRKL, Hülya CVELEKDEMR, Neslihan MANAVSigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi...
1Introduction Detecting outliers in time series data, especially streaming data, has gained significant relevance due to the recent exponential growth in the amount of data captured in big-data and IoT applications (Ahmad et al.2017; Munir et al.2019; Macia̧g et al.2021). Particularly the ...
For OSC transformation and PLS regression analysis, the nonlinear iterative partial least square (NIPALS) algorithm was chosen, as the most appropriate algorithm for a smaller (n = 20–100) sample size [44]. 3. Results and discussion 3.1. Variability of potentially toxic elements in studied ...
(NNs),2 genetic algorithm (GA),3 and partial-least squares (PLS).4 Three types of MR models are generated: (1) the best possible MR models; (2) the MR model with ordered orthogonalized decriptors; (3) the nonlinear MR models (take into account linear descriptors, their squares and ...
This study uses the visualisation method of the boxplot, a data filtering technique in Excel and regression comparison using Cook’s distance method [33] to remove the outliers. Cook’s distance 𝐷𝑖Di of observation 𝑘k is given as: 𝐷𝑖=∑𝑛𝑖=1(𝑦̂𝑖−(𝑦̂𝑖(...
neural networks excel at capturing nonlinear relationships, albeit with variability that necessitates careful calibration. The inclusion of linear regression adds interpretability, serving as a transparent baseline for comparison. Together, these elements achieve a balance between simplicity, flexibility, and ...
RSM is valued for its structured approach to modeling complex processes, while ANNs excel in handling nonlinear relationships and large datasets. Combining RSM and ANNs offers a powerful, synergistic approach to improving predictive models, helping to preserve nutrients and extend shelf life. The ...
Machine learning algorithms, including both nonlinear techniques and ensemble learning techniques, have also been applied for time-series classification over the years. Traditional classifiers like Naïve Bayes, Decision Tree, and SVM are the most popular. Before using these algorithms, MTS data need ...
Each convolution incorporates BatchNorm1d for training stability and ReLU activation for nonlinear feature transformation. The intermediate stage consists of nine residual blocks (ResnetBlock_1 to ResnetBlock_9) that preserve feature dimensionality while preventing vanishing gradients. Each block contains ...