Recently designing an effective intrusion detection systems (IDS) within Mobile Ad Hoc Networks Security (MANETs) becomes a requirement because of the amount of indeterminacy and doubt exist in that environment. Neutrosophic system is a discipline that makes a mathematical formulation for the indeterminacy found in such complex situations. Neutrosophic rules compute with symbols instead of numeric values making a good base for symbolic reasoning. These symbols should be carefully designed as they form the propositions base for the neutrosophic rules (NR) in the IDS. Each attack is determined by membership, nonmembership, and indeterminacy degrees in neutrosophic system. This research proposes a MANETs attack inference by a hybrid framework of Self-Organized Features Maps (SOFM) and the genetic algorithms (GA). The hybrid utilizes the unsupervised learning capabilities of the SOFM to define the MANETs neutrosophic conditional variables. The neutrosophic variables along with the training data set are fed into the genetic algorithm to find the most fit neutrosophic rule set from a number of initial subattacks according to the fitness function. This method is designed to detect unknown attacks in MANETs. The simulation and experimental results are conducted on the KDD-99 network attacks data available in the UCI machine-learning repository for further processing in knowledge discovery. The experiments cleared the feasibility of the proposed hybrid by an average accuracy of 99.3608 % which is more accurate than other IDS found in literature.

Cardiotocography is a medical device that monitors fetal heart rate and the uterine contraction during the period of pregnancy. It is used to diagnose and classify a fetus state by doctors who have challenges of uncertainty in data. The Rough Neural Network is one of the most common data mining techniques to classify medical data, as it is a good solution for the uncertainty challenge. This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The comparison shows that the accuracy rates and time consumption of the proposed model are feasible and efficient.

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This paper is concerned with the application of hybrid fuzzy-JAYA optimization algorithm to find the solution of non-linear optimal reactive power dispatch (ORPD) problem in power systems. The proposed hybrid optimization algorithm combines the merits of fuzzy principle and the Jaya optimizer. Fuzzification of the ORPD variables is employed by pseudo goal strategy. Two technical objectives are minimized individually and simultaneously to enhance the overall power systems performance. These objectives are transmission active power losses and voltage deviation at load buses. The ORPD objectives are optimized considering both inequality and equality constraints that reflect the operation needs. The hybrid fuzzy-JAYA is established as efficient optimization method that is achieving the global optimal solution. The effectiveness of the proposed hybrid algorithm for solving the ORPD problem is proven by using three standard IEEE test networks. An assessment of the proposed hybrid algorithm is carried out compared with other optimization algorithms those reported in the literature. The simulation results assure that the fuzzy Jaya hybrid algorithm leads to significant power system performance enhancement for different scale power systems. INDEX TERMS Fuzzy logic, jaya optimization algorithm, hybrid strategy, ORPD, pseudo goal strategy, minimization of transmission power losses, enhancement of load buses voltage profile.

Cross-projects software defect prediction improves the quality of new software projects or projects with a shortage of historical data. Therefore, various data mining techniques are recommended in this field. The classification accuracy issue is considered one of the most significant problems due to the shortage and heterogeneous in historical data. To address this challenge, this research utilizes a spotted hyena optimizer algorithm as a classifier to predict defects through cross-projects. Confidence and Support are utilized as a multi-objective fitness function to look for the best classification rules. These classification rules are used to predict defects for new projects or other projects with insufficient data. The datasets of NASA such as JM1, KC1, and KC2 are used. By applying spotted hyena optimizer algorithm as a classifier on one dataset and predicting defects in the other two datasets, accuracy is reported 84.6, 92.0, 82.4, 90.7, 86.6 and 81.8 for JM1, KC1, and KC2 respectively. These accuracy values are better than the most significant data mining techniques in the field such as Support Vector Machine, Naïve Bayes, Boosting, C4.5, and Bagging. Also, the proposed research discusses other performance measures such as precision, recall, and f-measure. The conclusion proves that there are many features of McCabe and Halstead that have a strong impact to generate highly accurate predictors for defects such as McCabe's line count of code, McCabe's cyclomatic complexity, McCabe's essential complexity, McCabe's design complexity iv, Halstead's effort, Halstead's time estimator, Halstead's line count, Halstead's count of line of comments and total operators.

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