A Detection Method for Text Steganalysis Using Evolution Algorithm (EA) Approach

The ability of sending a secret message through a network nowadays has become a more challenging and complex process. One of the reasons why we need tools to detect the hidden message is because of security and safety. Secret messages can be both for good or bad, and subversive groups have been known to send secret messages to coordinate their terrorist activities. Thus,a technique such as steganalysis is one method example to detect a secret message. Much of the technical steganalysis work had been carried out on image, video, and audio steganalysis, but in any agency or organisation, all business documents generally uses the natural language in text form or document form. Therefore, this research employed a detection factor based on the evolution algorithm method for text steganalysis. The aim of this project was to detect a hidden message in an observed message using text steganalysis

Investigating feed mix problem approaches: An overview and potential solution

Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously.Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem

Evolutionary algorithms with average crossover and power heuristics for aquaculture diet formulation

The aquaculture farming industry is one of the most important industries in Malaysia since it generates income to economic growth and produces main source of food for the nation. One of the pillars in aquaculture farming industries is formulation of food for the animal, which is also known as feed mix or diet formulation. However, the feed component in the aquaculture industry incurs the most expensive operational cost, and has drawn many studies regarding diet formulation. The lack of studies involving modelling approaches had motivated to embark on diet formulation, which searches for the best combination of feed ingredients while satisfying nutritional requirements at a minimum cost. Hence, this thesis investigates a potential approach of Evolutionary Algorithm (EA) to propose a diet formulation solution for aquaculture farming, specifically the shrimp. In order to obtain a good combination of ingredients in the feed, a filtering heuristics known as Power Heuristics was introduced in the initialization stage of the EA methodology. This methodology was capableof filtering certain unwanted ingredients which could lead to potential poor solutions. The success of the proposed EA also relies on a new selection and crossover operators that have improved the overall performance of the solutions. Hence, three main EA model variants were constructed with new initialization mechanism, diverse selection and crossover operators, whereby the proposed EAPH-RWS-Avg Model emerged as the most effective in producing a good solution with the minimum penalty value. The newly proposed model is efficient and able to adapt to changes in the parameters, thus assists relevant users in managing the shrimp diet formulation issues, especially using local ingredients. Moreover, this diet formulation strategy provides user preference elements to choose from a range of preferred ingredients and the preferred total ingredient weights

A standard deviation selection in evolutionary algorithm for grouper fish feed formulation

Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment.Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price.However, the demand of grouper fish is still insufficient from the wild catch.Therefore, there is a need to farm grouper fish to cater to the market demand.In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available.Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm.Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation.Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable.The feasible and low fitness, quick solution can be obtained. These fitness can be further predicted to minimize cost in farming grouper fish

Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization

This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional–integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller

Meta-Learning Evolutionary Artificial Neural Networks

In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the comparative performance, we used three different well-known chaotic time series. We also present the state of the art popular neural network learning algorithms and some experimentation results related to convergence speed and generalization performance. We explored the performance of backpropagation algorithm; conjugate gradient algorithm, quasi-Newton algorithm and Levenberg-Marquardt algorithm for the three chaotic time series. Performances of the different learning algorithms were evaluated when the activation functions and architecture were changed. We further present the theoretical background, algorithm, design strategy and further demonstrate how effective and inevitable is the proposed MLEANN framework to design a neural network, which is smaller, faster and with a better generalization performance

Run-time prediction of business process indicators using evolutionary decision rules

Predictive monitoring of business processes is a challenging topic of process mining which is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process management systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía P12TIC-186

Text steganalysis using evolution algorithm approach

This study presents a new alternative of steganalysis method in order to detect hidden messages in text steganalysis called Evolution Detection Steganalysis System (EDSS) based on the evolution algorithm approach under Java Genetic Algorithms Package (JGAP). The result of the EDSS can be divided into two groups based on fitness values which are good fitness and bad fitness. Hopefully, this study can produce a good idea to other researchers for understanding the text steganalysis in order to develop a steganalysis system that can contribute a better performance in other domains

Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures

The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors.publishedVersio