For maintaining of Zagros forests role in wild life, water and soil conservation, the suitable solutions and methods for assessing the existing conditions and planning for management of this forests should be given. This study was carried out in forest regions around Ilam city (Karzan region) in a forest area of 86 ha with coppice and seed origin. At first, a 100 percent forest inventory was implemented. The coordinates of each tree on the ground was recorded and their quantitative attributes (crown diameter, DBH of seed origin trees) were measured. Using Arcview software in GIS environment the eight cells sample plots for different inventory networks namely 100 m×100 m, 100 m×200 m and 200 m×200 m were simulated and statistical analysis were done. The mean value of trees density per hectare for all three networks were calculated and compared with their real data (100 percent inventory) by t-test (a=0.05).Results showed that there is no significant difference between mean values in three inventory networks and the real mean value.

The Fuzzy K-Nearest Neighbour (FKNN) method is a classification approach that integrates fuzzy theories with the K-Nearest Neighbour classifier. The algorithm computes the degree of membership for a given dataset within each class and then chooses the class with the highest degree of membership as the assigned classification outcome. This algorithm has several applications in regression problems. When the mathematical model of the data is not known, this method can be used to estimate and approximate the value of the response variable. This paper introduces a method, which incorporates a parameter distance measure to empower decision makers to make precise selections across several levels. Furthermore, we provide an analysis of the algorithm's strengths and shortcomings, as well as a comprehensive explanation of the distinctions between the closest neighbour approach in tasks of classification and regression. Finally, to further elucidate the principles, we present a range of examples that demonstrate the application of closest neighbour algorithms in the classification and regression of fuzzy numbers.

In this paper, we introduce an approximation for the $k$-Nearest neighbor graph ($k$-NNG) on a point set $P$ in $\mathbb{R}^d$. For any given $\varepsilon>0$, we construct a graph, that we call the \emph{approximate $k$-NNG}, where the edge with the $i$th smallest length incident to a point $p$ in this graph is within a factor of $(1+\varepsilon)$ of the length of the edge with the $i$th smallest length incident to $p$ in the $k$-NNG. For a set $P$ of $n$ moving points in $\mathbb{R}^d$, where the trajectory of each point $p\in P$ is given by $d$ polynomial functions of constant bounded degree, where each function gives one of the $d$ coordinates of $p$, we compute the number of combinatorial changes to the approximate $k$-NNG, and provide a kinetic data structure (KDS) for maintenance of the edges of the approximate $k$-NNG over time. Our KDS processes $O(kn^2\log^{d+1} n)$ events, each in time $O(\log^{d+1}n)$.

Guanidine hydrochloride has been widely used in the initial recovery steps of active protein from the inclusion bodies in aqueous two-phase system (ATPS). Knowledge of the guanidine hydrochloride effect on the liquid-liquid equilibrium (LLE) phase diagram behavior is still inadequate and no comprehensive theory exists for the prediction of the experimental trends. Therefore, the effect of the guanidine hydrochloride on the phase behavior of PEG4000+potassium phosphate+ water system at different guanidine hydrochloride concentrations and pH was investigated in this study. To fill the theoretical gaps, the typical support vector machines were applied was applied to the k-Nearest neighbor method in order to develop a regression model to predict the LLE equilibrium of guanidine hydrochloride in the above mentioned system. Its advantage is its simplicity and good performance, with the disadvantage of an increase in the execution time. The results of our method are quite promising; they were clearly better than those obtained by well-established methods such as Support Vector Machines, k-Nearest neighbor and Random Forest. It is shown that the obtained results are more adequate than those provided by other common machine learning algorithms.

As networking and communication technology become more widespread, the quantity and impact of system attackers have been increased rapidly. The methodology of intrusion detection (IDS) is generally classified into two broad categories according to the detection approaches: misuse detection and anomaly detection. In misuse detection approach, abnormal system behavior is defined at first, and then any other behavior is defined as normal behavior. The main goal of the anomaly detection approach is to construct a model representing normal activities. Then, any deviation from this model can be considered as an anomaly, and recognized to be an attack. Recently much more attention is paid to the application of lattice theory in different fields. In this work we propose a lattice based Nearest neighbor classifier capable of distinguishing between bad connections, called attacks, and good normal connections. A new nonlinear valuation function is introduced to tune the performance of the proposed model. The performance of the algorithm was evaluated by using KDD Cup 99 Data Set, the benchmark dataset used by Intrusion detection Systems researchers. Simulation results confirm the effectiveness of the proposed method.

Using vertical intakes is a common method of water withdrawal from rivers or storages to supply potable or agriculture water. Formation of vortex at vertical intakes is one of serious problems encountered in vertical intakes. Vortex formation reduces the efficiency of structure and causes loss of life. In this study, according to hydraulic conditions and geometry of vertical intakes, vortex formation was predicted by the K-Nearest neighbor modeling for different ratios of channel length to width (L/W). The efficiency and precision of the model was evaluated by some statistical coefficients. Appropriate values of the correlation coefficient 0.912, the efficiency of model 0.827 and the root mean square error 0.105 shows this model is suitable and efficient.