Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Recovery prediction of copper oxide ore column leaching by hybrid neural genetic algorithm

In this paper, hybrid Artificial Neural Network and Genetic Algorithm (ANN-GA) approach for online monitoring of long-term voltage instability has been proposed. Thegenetic algorithm (GA) has been used to improve the accuracy of ANN by tuning its meta-parameters such as number of nodes in hidden layer, input and output activation function and learning rate. The proposed approach uses the voltage magnitude and phase angle obtained from phasor measurement units (PMUs) as the input vectors and the outputs is the Voltage Stability Margin Index (VSMI)vector. The effectiveness of the proposed approach is testedon New England 39-bus test system. The results of the proposed ANN-GA approach for voltage stability monitoring is compared with ANN model on same data set.

Fiber-reinforced concrete (FRC) is one of the efficient innovation in concrete industry that has the ability to enhance the mechanical properties significantly. To cope up with the increase in infrastructural activities which resulted in greater demand in production of different construction materials have a negative impact on the environment, this study aims to determine the mechanical performance of the optimum compressive and flexural strength of buntal fiber-reinforced concrete with surkhi as partial replacement for sand (BFRC-SS). Using 28th-day compressive and flexural strength, several mixtures were experimentally tested to derive a mix proportion that gave the best mechanical properties of BFRC-SS. From the results, best hybrid models of compressive and flexural strength were formulated using Artificial Neural Network (ANN). Results showed that ANN was able to establish the effects of surkhi and buntal (Corypha utan Lam) fiber to the mechanical properties of BFRC-SS. Furthermore, the multi-objective Genetic Algorithm (GA) model generated the optimum proportion for the best compressive and flexural strength. Fuzzy Inference System (FIS) and Multi-Linear Regression Analysis (MLRA) were also utilized to assess and validate the hybrid model through surface imaging. Utilizing least percent error, ANN hybrid model showed the most significant predictive model compared to other models generated by MLRA and FIS. This study adoptied the fusion of 4.0 Industrial Revolution and favoring creativity and integrity through artificial intelligence.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a classic control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem "cart-pole”. In this paper we have tried to train the Artificial Neural Network (ANN) with using Genetic Algorithms (program is written in MATLAB) which is compared with the output obtained using the Artificial Neural Network Toolbox provided in MATLAB. In proposed approach we have used both ANNs and Genetic Algorithm to get more optimal solution. Here we applied the approach for the Fuzzy logic technique to design a Fuzzy Logic Controllers (FLC) using ANNs and Genetic Algorithm (GA). The Fuzzy rules which are needed to control the problem will be framed with the combination of Artificial Neural Networks and Genetic Algorithm. It has been found that such a searching technique converges intelligently and much faster than conventional learning means. Performance of the presented neural network training using the genetic algorihtms is much better and providing more accurate results.

In this study, we proposed a new approach in estimating a minimum value of machining performance. In this approach, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated in order to search for a set of optimal cutting condition points that leads to the minimum value of machining performance. Three machining cutting conditions for end milling operation that were considered in this study are speed (v), feed (f) and radial rake angle (γ). The considered machining performance is surface roughness (R a). The minimum R a value at the optimal v, f and γ points was expected from this approach. Using the proposed approach, named integrated ANN–GA, this study has proven that R a can be estimated to be 0.139 µm, at the optimal cutting conditions of f = 167.029 m/min, v = 0.025 mm/tooth and γ = 14.769°. Consequently, the ANN–GA integration system has reduced the R a value at about 26.8%, 25.7%, 26.1% and 49.8%, compared to the experimental, regression, ANN and response surface method results, respectively. Compared to the conventional GA result, it was also found that integrated ANN–GA reduced the mean R a value and the number of iterations in searching for the optimal result at about 0.61% and 23.9%, respectively.

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

This paper presents a novel approach to solve the hydro plant dispatch problem based on the artificial neural network (ANN) and genetic algorithm (GA). In this work, the difficult water balance constraints are embedded and satisfied throughout the proposed encoding and decoding algorithms. The ANN is used as a pre-dispatch tool to generate raw hydro output for each hour temporarily ignoring time-dependent constraints. Then, the proposed decoding algorithm decodes the raw schedule of each plant into a feasible one. Finally, a GA is used to find the optimal schedule. The proposed approach is applied to an actual utility system of four hydro plants and 22 thermal units with great success. Results show that the new approach obtains a more highly optimal solution than the conventional dynamic programming method.

Since the last decade, several studies have shown the ability of Artificial Neural Networks (ANNs) in modeling of rainfall-runoff process. From methodological viewpoint, ANN belongs to more general paradigm, i.e., soft computing or computational intelligence, in which independent methodologies, mostly Fuzzy Logic (FL), ANN, and Genetic Algorithms (GAs), are combined together in order to provide an intelligent behavior in computational frameworks. Consequently, in the context of rainfall-runoff modeling, this question rises whether hybridization of ANNs with other soft computing-related methodologies improves the overall performance of modeling or not. In this study, based on the idea of structure and/or parameter identification of ANNs with GAs, the evolutionary neural networks modeling paradigm is examined for describing the rainfall-runoff process. One of the benchmark data set of current literature, i.e., Leaf River basin (near Collins, Mississippi) data set, is used for simulation. The results show that on the one hand, the overall accuracy is improved; but one the other hand, in evolutionary neural modeling, the computational time is increased significantly. Hence the modeler may be faced with a trade-off problem between accuracy and computational difficulties which may have different importance in a particular rainfall-runoff problem.

An inverse design method is proposed to achieve the pre-set control objectives of the aircraft cabin environment. The method combines the artificial neural network and the genetic algorithm, and the training and testing data of the artificial neural network is obtained by computational fluid dynamics analysis. Both of the thermal comfort and energy consumption are considered in the inverse design. The artificial neural network is used to identify the relationship between the thermal comfort and the air supply parameters (inlet velocity magnitude and angle, inlet air temperature). The genetic algorithm coupled with the well-trained artificial neural network is used to design the aircraft cabin environment. Numerical results show that the Bayesian regularization algorithm is proved to have better generalization capability than the other training algorithms for the artificial neural network. The increase of training data quantity improves the generalization capability of the artificial neural network, while it spends more simulation time. A computational fluid dynamics database with 60 datasets is shown to be suitable to the present inverse design, and the testing error of the artificial neural network is below 8.2%. Several groups of optimal air supply parameters are found with different trade-offs between the thermal comfort and energy consumption. The best solution of thermal comfort, i.e., the percentage of zone with |PMV| >0.5 in all cabin control domains, is less than 7.8%.

Though the high-speed railways are seen as a sustainable form of transportation, the fact that the rail wear in high-speed railways negatively affects the running safety and riding comfort, as well as the maintenance of railways, has drawn a wide range of concerns among researchers and scholars. In order to reduce the rail wear and achieve the goal of sustainable transportation, this paper proposes an ingenious optimization program of rail profiles based on the artificial neural network (ANN) and genetic algorithm (GA) coupled method. The candidate solutions of the nonlinear GA programming model are regarded as the inputs of the trained ANN model. Meanwhile, the outputs of the trained ANN model serve as the objective functions of the GA model. The computational results show that the optimized rail profile not only has superior performances in terms of the wheel/rail wear and contact conditions, but also maintains good dynamic performances. Therefore, this study can provide the theoretical and practical basis for the design and the preventive grinding of rails in the high-speed railways. Also, the ANN-GA coupled model can be extended and further employed on the optimization of other rail profiles.

Cladding is the most economical process used on the surface of low carbon structural steel to improve the corrosion resistance. The corrosion resistant property is based on the amount of ferrite present in the clad layer. Generally, the ferrite content present in the layer is expressed in terms of ferrite number (FN). The optimum range of ferrite number plays an instrumental role to bring adequate surface properties like chloride stress corrosion cracking resistance, pitting and crevice corrosion resistance and mechanical properties. For achieving maximum economy and enhanced life, duplex stainless steel (E2209T1-4/1) is deposited on the surface of low carbon structural steel of IS: 2062. The problem faced in the weld cladding towards achieving the required amount of ferrite number is the selection of an optimum combination of input process parameters (welding current, welding speed, contact tip to specimen distance and gun angle). This paper mainly concentrates on estimating FN and analysis of input process parameters on FN of heat treated duplex stainless steel cladding. To predict FN, mathematical equations was developed based on four factor five level central composite rotatable design with full replication using regression methods. In this paper, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated and labeled as ANN-GA to identify the optimum process parameters in FCAW to get maximum FN. From the results, the integrated ANN-GA (I OR II) is capable of giving maximum FN at optimum process parameters compared to that of experimental, regression and ANN modeling

Artificial neural network is an efficient and accurate fitting method. It has the function of self-learning, which is particularly important for prediction, and it could take advantage of the computer’s high-speed computing capabilities and find the optimal solution quickly. In this paper, four culture conditions: agar concentration, light time, culture temperature, and humidity were selected. And a three-layer neural network was used to predict the differentiation rate of melon under these four conditions. Ten-fold cross validation revealed that the optimal back propagation neural network was established with traingdx as the training function and the final architecture of 4-3-1 (four neurons in the input layer, three neurons in the hidden layer and one neuron in the output layer), which yielded a high coefficient of correlation (R2, 0.9637) between the actual and predicted outputs, and a root-mean-square error (RMSE) of 0.0108, suggesting that the artificial neural network worked well. According to the optimal culture conditions generated by genetic algorithm, tissue culture experiments had been carried out. The results showed that the actual differentiation rate of melon reached 90.53%, and only 1.59% lower than the predicted value of genetic algorithm. It was better than the optimization by response surface methodology, which the predicted induced differentiation rate is 86.04%, the actual value is 83.62%, and was 2.89% lower than the predicted value. It can be inferred that the combination of artificial neural network and genetic algorithm can optimize the plant tissue culture conditions well and with high prediction accuracy, and this method will have a good application prospect in other biological experiments.

Artificial neural network (ANN) has been commonly used to deal with many problems. However, since this algorithm applies backpropagation algorithms based on gradient descent (GD) technique to look for the best solution, the network may face major risks of being entrapped in local minima. To overcome those drawbacks of ANN, in this work, we propose a novel ANN working parallel with metaheuristic algorithms (MAs) to train the network. The core idea is that first, (1) GD is applied to increase the convergence speed. (2) If the network is stuck in local minima, the capacity of the global search technique of MAs is employed. (3) After escaping from local minima, the GD technique is applied again. This process is applied until the target is achieved. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is employed. The effectiveness of ANNPSOGA is assessed using both numerical models and measurement. The results demonstrate that ANNPSOGA provides higher accuracy than traditional ANN, PSO, and other hybrid ANNs (even a higher level of noise is employed) and also considerably decreases calculational cost compared with PSO.

Application of genetic optimized artificial immune system and neural networks in spam detection