An effective genetic algorithm for network coding

The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm

Dual separable feedback systems govern firing rate homeostasis.

Firing rate homeostasis (FRH) stabilizes neural activity. A pervasive and intuitive theory argues that a single variable, calcium, is detected and stabilized through regulatory feedback. A prediction is that ion channel gene mutations with equivalent effects on neuronal excitability should invoke the same homeostatic response. In agreement, we demonstrate robust FRH following either elimination of Kv4/Shal protein or elimination of the Kv4/Shal conductance. However, the underlying homeostatic signaling mechanisms are distinct. Eliminating Shal protein invokes Krüppel-dependent rebalancing of ion channel gene expression including enhanced slo, Shab, and Shaker. By contrast, expression of these genes remains unchanged in animals harboring a CRISPR-engineered, Shal pore-blocking mutation where compensation is achieved by enhanced IKDR. These different homeostatic processes have distinct effects on homeostatic synaptic plasticity and animal behavior. We propose that FRH includes mechanisms of proteostatic feedback that act in parallel with activity-driven feedback, with implications for the pathophysiology of human channelopathies

Phishing Detection using Base Classifier and Ensemble Technique

Phishing attacks continue to pose a significant threat in today's digital landscape, with both individuals and organizations falling victim to these attacks on a regular basis. One of the primary methods used to carry out phishing attacks is through the use of phishing websites, which are designed to look like legitimate sites in order to trick users into giving away their personal information, including sensitive data such as credit card details and passwords. This research paper proposes a model that utilizes several benchmark classifiers, including LR, Bagging, RF, K-NN, DT, SVM, and Adaboost, to accurately identify and classify phishing websites based on accuracy, precision, recall, f1-score, and confusion matrix. Additionally, a meta-learner and stacking model were combined to identify phishing websites in existing systems. The proposed ensemble learning approach using stack-based meta-learners proved to be highly effective in identifying both legitimate and phishing websites, achieving an accuracy rate of up to 97.19%, with precision, recall, and f1 scores of 97%, 98%, and 98%, respectively. Thus, it is recommended that ensemble learning, particularly with stacking and its meta-learner variations, be implemented to detect and prevent phishing attacks and other digital cyber threats

double committee adaboost

Abstract In this paper we make an extensive study of different combinations of ensemble techniques for improving the performance of adaboost considering the following strategies: reducing the correlation problem among the features, reducing the effect of the outliers in adaboost training, and proposing an efficient way for selecting/weighing the weak learners. First, we show that random subspace works well coupled with several adaboost techniques. Second, we show that an ensemble based on training perturbation using editing methods (to reduce the importance of the outliers) further improves performance. We examine the robustness of the new approach by applying it to a number of benchmark datasets representing a range of different problems. We find that compared with other state-of-the-art classifiers our proposed method performs consistently well across all the tested datasets. One useful finding is that this approach obtains a performance similar to support vector machine (SVM), using the well-known LibSVM implementation, even when both kernel selection and various parameters of SVM are carefully tuned for each dataset. The main drawback of the proposed approach is the computation time, which is high as a result of combining the different ensemble techniques. We have also tested the fusion between our selected committee of adaboost with SVM (again using the widely tested LibSVM tool) where the parameters of SVM are tuned for each dataset. We find that the fusion between SVM and a committee of adaboost (i.e., a heterogeneous ensemble) statistically outperforms the most used SVM tool with parameters tuned for each dataset. The MATLAB code of our best approach is available at bias.csr.unibo.it/nanni/ADA.rar

Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respect to the classical controller, showing the feasibility of the proposed approach

Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines

This paper describes the utilization of artificial intelligence (AI) techniques to identify an optimal machine learning (ML) model for predicting dodecane fuel consumption in diesel combustion. The study incorporates sensitivity analysis to assess the impact levels of various parameters on fuel consumption, thereby highlighting the most influential factors. In addition, this study addresses the impact of noise and implements data cleaning techniques to ensure the reliability of the obtained results. To validate the accuracy of the predictions, the study performs several metrics and validation process, including comparisons with computational fluid dynamics (CFD) results and experimental data. Comprehensive comparisons are made among neural networks (NN), random forest regression (RFR), and Gaussian process regression (GPR) models, taking into account the complexity associated with fuel consumption predictions. The findings demonstrate that the GPR model outperforms the others in terms of accuracy, as evidenced by metrics such as mean absolute error (MAE), mean squared error (MSE), Pearson coefficient (PC), and R-squared (R2). The GPR model exhibits superior predictive ability, accurately detecting and predicting even individual data points that deviate from the overall trend. The significantly lower absolute error values also consistently indicate its higher accuracy compared with the NN and RFR models. Furthermore, the GPR model shows a remarkable speedup, approximately 1.7 times faster than traditional CFD solvers, and physically captures the momentum and thermal characteristics in a surface field prediction. Finally, the target optimization is assessed using the Euclidean distance as a fitness function, ensuring the reliability of predicted data

A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting

This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences

Effective Prostate Cancer Detection using Enhanced Particle Swarm Optimization Algorithm with Random Forest on the Microarray Data

Prostate Cancer (PC) is the leading cause of mortality among males, therefore an effective system is required for identifying the sensitive bio-markers for early recognition. The objective of the research is to find the potential bio-markers for characterizing the dissimilar types of PC. In this article, the PC-related genes are acquired from the Gene Expression Omnibus (GEO) database. Then, gene selection is accomplished using enhanced Particle Swarm Optimization (PSO) to select the active genes, which are related to the PC. In the enhanced PSO algorithm, the interval-newton approach is included to keep the search space adaptive by varying the swarm diversity that helps to perform the local search significantly. The selected active genes are fed to the random forest classifier for the classification of PC (high and low-risk). As seen in the experimental investigation, the proposed model achieved an overall classification accuracy of 96.71%, which is better compared to the traditional models like naïve Bayes, support vector machine and neural network