This research aimed to select essential Features for modeling the cost of pressurized irrigation systems using the data of 515 drip irrigation projects in four parts, including the cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and operation on-farm and pumping station (TCI), and total cost (TCT). In the first stage, a database including 39 Features influencing the cost of the mentioned sectors was prepared and the price of all projects (2006 to 2019) was updated for the base year of 2021. Then, Feature selection was done with different algorithms in MATLAB environment and in two parts including (1) all Features (39 Features before and after the design stage) and (2) 18 Features before the design phase (BD). The results showed that the amounts of RMSE and R2 for all the Features were equal to 0. 007 and 0. 92, respectively, and for the BD section, they were equal to 0. 003 and 0. 89, respectively. Among the different algorithms for Feature selection, support vector machine (SVM) and optimization algorithms (Wrapper) were identified as the best learner and Feature selection method, respectively. The results of the evaluation criteria showed that the two LCA and FOA algorithms achieved the best estimation, and their error criterion in all the Features were 0. 0020 and 0. 0018, respectively, while their correlations were 0. 94 and 0. 94. In the BD Features, these criteria were 0. 0006 and 0. 95 for both algorithms, respectively. Finally, in the all Features section, 10 out of 39 Features and for BD section, 8 out of 18 were selected as the most effective Features. The results of choosing the most effective Features that affect the cost of different parts of the drip irrigation system can make the cost modeling of the systems simpler and faster and, while being useful for research works, it facilitates estimation and management of costs before implementation of each project.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element Feature vector is formed. These four Features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the Feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent Features. Multi-label Feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent Features. To this aim, Features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group Features have more similarity to class and less similarity to other Features, and the second is redundant and less relevant Features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant Features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Feature selection is an important step in data preprocessing, which helps  reducing the dimensionality of data and simplifying the models. This process not only reduces the computational complexity of models, but also improves their accuracy by eliminating irrelevant Features and noise. The three most widely used approaches for Feature selection are filter, wrapper and embedded methods.  In this paper, first we review some support vector machine based Mixed-Integer Linear Programming (MILP) models and Supervised Infinite Feature Selection (Inf-FS$_s$) method.  Then, we propose three hybrid approaches based on them. The first approach involves solving the relaxed linear model of the underlying  MILP model and then solving the MILP model for those Features with nonzero weights, namely a smaller MILP. In the second approach, first the Inf-FS$_s$ method is applied to rank the Features. Then depending on the Features costs, either chooses the top Features from the ranked Features until budget parameter is reached  or solves a knapsack problem to select cost effective Features. The third approach applies the first approach to the top $20\%$ of Features ranked by Inf-FS$_s$ method. To evaluate the proposed approaches' performance, experiments are conducted on four high-dimensional benchmark datasets for fixed and random Features costs. Results demonstrate that using either of the proposed approaches can significantly reduce running time of MILP models with comparable accuracies with the original MILP models.