We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) BASED on simultaneous wireless INFORMATION and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. BASED on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation ALGORITHM that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed ALGORITHM (OPA) is evaluated against benchmark ALGORITHMs via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state INFORMATION (CSI) on the CBS-PU link.

Today, in many real-world applications, such as social networks, we are faced with data streams which new data is appeared every moment. Since the efficiency of most data mining ALGORITHMs decreases with increasing data dimensions, analysis of the data has become one of the most important issues recently. Online stream feature selection is an effective approach which aims at removing those of redundant features and keeping relevant ones, leads to reduce the size of the data and improve the accuracy of the online data mining methods. There are several critical issues for online stream feature selection methods including: unavailability of the entire feature set before starting the ALGORITHM, scalability, stability, classification accuracy, and size of selected feature set. So far, existing methods have only been able to address a few numbers of these issues simultaneously. To this end, in this paper, we present an online feature selection method called MMIOSFS that provides a better tradeoff between these challenges using MUTUAL INFORMATION. In the proposed method, first the feature set is mapped to a new feature using joint Random variables technique, then the MUTUAL INFORMATION of new feature with the class label is computed as the degree of relationship between the features set. The efficiency of the proposed method was compared to several online feature selection ALGORITHMs BASED on different categories. The results show that the proposed method usually achieves better tradeoff between the mentioned challenges.

Among all measures of independence between random variables, MUTUAL INFORMATION is the only one that is BASED on INFORMATION theory. MUTUAL INFORMATION takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions BASED on their MUTUAL INFORMATION. The distributions within each class have the same MUTUAL INFORMATION. These distributions have been used extensively as survival distributions of two component systems in reliability theory.

The intrusion detection system (IDS) manages a massive volume of data that comprises irrelevant and redundant features, leading to more significant resource consumption, long-time training and TESTing procedures, and low detection rate. Hence, feature selection is a crucial phase in intrusion detection. The aim of this paper is to introduce an intersection-BASED strategy that optimally selects the features for classification. This feature selection involves an intersection of simultaneous MUTUAL INFORMATION BASED on the transductive model (MIT-MIT), Anova F-TEST, and genetic ALGORITHM (GA) methods. A benchmark dataset, named NSL-KDD, is applied to evaluate the effectiveness of the proposed approach. This study includes accuracy, precision, recall, and F1 score as the evaluation metrics for IDS, which analyzes the proposed method with state-of-the-art classifiers. The evaluation results confirm that our feature selection ALGORITHM provides more essential features for IDS to achieve high accuracy, outperforming other comparative ALGORITHMs.

MUTUAL Funds are one of the important elements of financial markets which act as financial intermediation and convert investment of amateur investors from direct condition to indirect.Given the importance of funds, this study tries to evaluate the market timing ability of managers' funds using CONDITIONAL and unCONDITIONAL models (Treynor–Mazuy and Henriksson–Merton) and compares both CONDITIONAL and unCONDITIONAL approaches. Data relating to twenty-three funds are used during the period of 1388-1392, and generalized least squares regression analysis is performed by using EVIEWS6 software. The results indicate the inability of market timing in fund managers using both CONDITIONAL and unCONDITIONAL models. Moreover, the CONDITIONAL approach does not provide higher explanatory power than the unCONDITIONAL approach.

The purpose of the present study was to investigate the effect of generative learning teaching BASED to pattern cognitive Load on the memorization math of 7th grade students of in the academic year of 2021- 2022. The research method is quasi-experimental with pre-TEST and post-TEST with four groups: two control group and two experimental groups. The statistical population of the study consisted of all 7th grade male and female students of District 2 of Mashhad city that 112 person sample was selected by single stage cluster sampling method. The subjects in the experimental group involved in generative learning BASED to cognitive Load in 16 sessions and the control group did not receive any interventions. The research instrument consisted of a 20-questions researcher-made TEST. To analyze the obtained data, Multivariate analysis of covariance and SPSS version 24 software were used. The results showed that generative learning teaching BASED to approach cognitive Load have a positive and significant effect on 7th grade students' memorization math. BASED on the findings, it can be concluded that one of the effective training on math memorization students is the use of generative learning on the BASED load cognitive pattern.

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.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is BASED on a basic genetic ALGORITHM, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and BASED on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic ALGORITHM and multi-objective optimization goals. The genetic ALGORITHM's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the ALGORITHM suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial MUTUAL INFORMATION (PMI) ALGORITHM was used to determine efficient input variables in ANNs. The results of using the PMI ALGORITHM showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.