Hybrid Artificial Neural Network Research Articles

Estimation of the Constituent Properties of Red Delicious Apples Using a Hybrid of Artificial Neural Networks and Artificial Bee Colony Algorithm

Non-destructive estimation of the constituent properties of fruits and vegetables has led to a dramatic change in the agriculture and food industry, allowing accurate and efficient sorting of the products based on their internal properties. Therefore, the present study utilized visible (VIS) and near-infrared (NIR) spectroscopy data in the range from 200 to 1100 nm for the estimation of several properties of Red Delicious apples, namely Brix minus acid (BrimA), firmness, acidity and starch content, using a hybrid of Artificial Neural Networks and Artificial Bee Colony (ANN–ABC) algorithm. Furthermore, the hybrid Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm was utilized to select the most effective properties to estimate these characteristics. The results indicated that there are different peaks within this spectral range, and the spectral range for each peak gives different results. To ensure the stability of the proposed method, 1000 replications were performed for each estimate. The highest coefficients of determination, R2, for estimating the studied properties among the 1000 replicates were 0.898 for BrimA, 0.8 for firmness, 0.825 for acidity and 0.973 for starch content. The selection of the most effective wavelengths for estimating the properties produced five effective wavelengths for BrimA, nine for firmness, seven for acidity and five for starch content. In this case, the best R2 of the hybrid ANN–ABC among the 1000 iterations were 0.828, 0.738, 0.9 and 0.923, respectively.

Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry

IntroductionBeing able to predict functional outcomes after a stroke is highly desirable for clinicians. This allows clinicians to set reasonable goals with patients and relatives, and to reach shared after-care decisions for recovery or rehabilitation. The aim of this study was to apply various machine learning (ML) methods for 90-day stroke outcome predictions, using a nationwide disease registry. MethodsThis study used the Taiwan Stroke Registry (TSR) which has prospectively collected data from stroke patients since 2006. Three known ML models (support vector machine, random forest, and artificial neural network), and a hybrid artificial neural network were implemented and evaluated by 10-time repeated hold-out with 10-fold cross-validation. ResultsML techniques present over 0.94 AUC in both ischemic and hemorrhagic stroke using preadmission and inpatient data. By adding follow-up data, the prediction ability improved to 0.97 AUC. We screened 206 clinical variables to identify 17 important features from the ischemic stroke dataset and 22 features from the hemorrhagic stroke dataset without losing much performance. Error analysis revealed that most prediction errors come from more severe stroke patients. ConclusionThe study showed that ML techniques trained from large, cross-reginal registry datasets were able to predict functional outcome after stroke with high accuracy. The follow-up data is important which can further improve the predictive models’ performance. With similar performances among different ML techniques, the algorithm's characteristics and performance on severe stroke patients will be the primary focus when we further develop inference models and artificial intelligence tools for potential medical.

A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images

A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.

Hybrid Artificial Neural Network with Extreme Learning Machine Method and Cuckoo Search Algorithm to Predict Stock Prices

This thesis aims to predict the stock prices, using artificial neural network with extreme learning machine (ELM) method and cuckoo search algorithm (CSA). Stock is one type of investment that is in great demand in Indonesia. The portion ownership of stock is determined by how much investment is invested in the company. In this case, stock is an aggressive type of investment instrument, because stock prices can change over time. In this case, ELM is used to determine forecasting values, while CSA is applied to compile and optimize the values of weights and biases to be used in the forecasting process. After obtaining the best weights and biases, the validation test process is then carried out to determine the level of success of the training process. The data used is the daily data of the stock price of PT. Bank Mandiri (Persero) Tbk. the total is 291 data. Furthermore, the data is divided into 70% for the training process is as many as 199 data and 30% for the validation test as many as 87 data. Then compiled pattern of training and validation test patterns is 198 patterns and 82 patterns. Based on the implementation of the program, with several parameter obtained the result of MSE training is 0.001304353, with an MSE of validation test is 0.0031517704. Because the MSE value obtained is relatively small, this indicates that the ELM-CSA network is able to recognize data patterns and is able to predict well.

Designing a Fruit Identification Algorithm in Orchard Conditions to Develop Robots Using Video Processing and Majority Voting Based on Hybrid Artificial Neural Network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.

Analysis of CT images for detection of Colorectal Cancers using Hybrid Artificial Neural Networks and Fire Fly Algorithm

Abstract The longest part of the large intestine or colon is called as rectum. The uncontrolled division of cells in the colon forming a polyp at initial stage is called as colorectal cancer. Initially the polyp formed may be benign and later it may turn out to be malignant. The advancements in the field of medicine has made the screening techniques to be an effective tool in identification of the colon cancer. Early detection of the colon cancer may lead to complete cure of the disease normally, but it is very difficult to diagnose at the start. It is because, it does not show any symptoms at initial stages. Colonoscopy, a type of computed tomography (CT) is usually recommended for the patients to detect the colon cancer and it is usually a painful test conduct to identify the disease. To relieve the patients from the suffering, image processing algorithms like Weighted Adaptive Scalable Invariant Transform (WASIT) is used for extraction of features like location, orientation and scale are used as inputs to train the Artificial Neural Network (ANN) using Back Propagation Algorithm (BPA). The optimal set of weights are obtained by adjusting the weights for BPA hybrid with Genetic Algorithm (GA) and Fire Fly Algorithm (FFA). The Likelihood Ratio (LR+) is found to optimal for the BPA tuned with FFA and is inferred to be in the range of 1.5 to 4 which varies with a deviation of ±0.4% from the nominal value.

A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization

Aluminum alloys have low weldability by conventional fusion welding processes. Friction stir welding (FSW) is a promising alternative to traditional fusion welding techniques for producing high quality aluminum joints. The quality of the welded joints is highly dependent on the process parameters used during welding. In this research, a new approach was developed to predict the process parameters and mechanical properties of AA6061-T6 aluminium alloy joints in terms of ultimate tensile strength (UTS). A new hybrid artificial neural network (ANN) approach has been proposed in which Henry Gas Solubility Optimization (HGSO) algorithm has been incorporated to improve the performance of Random Vector Functional Link (RVFL) network. The HGSO-RVFL model was constructed with four parameters; rotational speed, welding speed, tilt angle, and pin profile. The validity of the model was tested, and it was demonstrated that the HGSO-RVFL model is a powerful technique for predicting the UTS of friction stir welded (FSWD) joints. In addition, the effects of process parameters on UTS of welded joints were discussed, where a significant agreement was observed between experimental results and predicted results which indicates the high performance of the model developed to predict the appropriate welding parameters that achieve optimal UTS.

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

Energy systems around the world are undergoing substantial changes, with an increasing penetration of Renewable Energy Sources. For this reason, the availability of a pool of suitable forecasting models specific for the needed time horizon and task is becoming crucial in the grid operation. In addition, nowcasting techniques aiming at provideing the power forecast for the immediate future, are more often investigated due to the spread of micro-grids and the need of facing changing electrical market environments. In this paper a novel comprehensive methodology aiming at computing the PV power forecast on different time horizons and resolutions is introduced. Moving from the 24-hours ahead prediction provided by the Physical Hybrid Artificial Neural Network (PHANN), a technique to refine the power forecast for the following 3 hours with an hourly granularity is analyzed, leveraging on newer information available during the operations. Moreover, in order to provide the power forecast for the following 30 minutes on a minutely basis, an innovative modification of a statistical technique is proposed, the robust persistence. The proposed comprehensive approach allowed to greatly reduce the overall error committed when compared with the benchmark models. Finally, the proposed methodology is validated and tested on a freely available database consisting on different parameters recorded at both the meteorological and photovoltaic test facility at SolarTech <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LAB</sup> , Politecnico di Milano, Milan.

Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network

Brain Computer Interface (BCI) using Electrooencephalogram (EEG) is one of the versatile tools to measure the brain thoughts and convert them to operate the external devices in the deficiency of biological controls. These techniques are used to develop the rehabilitative devices for the individual person who affected with locked in syndrome (LIS). The main reason for LIS is due to death of motor neurons. To overcome the real world problem we conduct our experiment with eight normal patients for four tasks using three electrode system and ADI T26 bio amplifier with Lab chart. Four task signals are applied to statistical method to retrieve the twenty two features and trained with the feed forward neural network (FFNN) and feed forward neural network with wolf Grey optimization algorithm (FFNNWGOA) to see network model which was more perfectly supported to identify the tasks. The study showed that statistical features through feed forward neural network with grey wolf optimized algorithm classifier produced maximum performances of 94.06% compared to other feed forward neural network classifier model and also we identified that optimized features demonstrated maximum performances in minimum time duration during training in all the following twenty trials.

Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.

A variable mosquito flying optimization‐based hybrid artificial neural network model for the alarm tuning of process fault detection systems

AbstractChemical process systems are becoming extremely complex due to increased automation, heat and mass intensification, and expectation of higher efficiency. Many fault detection and diagnostic methods have been proposed for processing facilities. However, managing the missed alarm rate and the false alarm rate (FAR) in the detection and isolation of the fault is crucial in the complex process systems. This work presents a new data‐driven fault detection model using an artificial neural network (ANN) and variable mosquito flying optimization (V‐MFO) technique. The model is based on the optimization of the number of neurons in the hidden layer of the neural network. Subsequently, the model parameters have been tuned using the V‐MFO algorithm for maximizing the fault detection rate (FDR) while minimizing the FAR. The proposed fault detection method has been implemented on the Tennessee Eastman benchmark process. The performance of the proposed model has been evaluated in terms of accuracy, FDR and FAR against well‐known statistical‐based methods such as principal component analysis (PCA), kernel PCA, semiparametric PCA, modified independent component analysis, k nearest neighbors, linear discriminant analysis, support vector machine, and the ANN. The model is observed to be competitive for fault detection among the test algorithms. It recorded slightly improved accuracy and FDR. The proposed model also resulted in 0.6% improvement in the FAR and 8% improvement in missed detection rate compared to the simple ANN. This method provides an efficient fault detection tool for complex process systems.

MSSB to Prevent Cable Termination Faults for Long High Voltage Underground Cable Lines

Cable termination fault is one of the most important problems for high voltage underground cable lines (HVUCL). Harmonic current (HC) and the sheath current (SC) are causes of cable termination faults of HVUCL. The sheath voltage (SV) of cable increases due to SC and HC also. So, the electroshock risk for human occurs. In this study, cable termination faults are examined for long HVUCL that is connected to electric network with high harmonic distortion rate. In literature, solid bonding (SB) and sectional solid bonding (SSB) methods are used to reduce harmonic distortion rate and SC effect in HVUCL. However, when SB and SSB methods are used for bonding of long HVUCL, harmonic distortion limit and touch voltage limit are exceeded. Therefore, the modified SSB (MSSB) method is developed for bonding of long HVUCL. SV of HVUCL should be known to use MSSB for bonding of long HVUCL. Then SV of HVUCL is forecasted by using artificial neural network and hybrid artificial neural networks (HANN) before the long HVUCL is installed. MSSB parameters should be optimized, so that optimization methods are used for optimization of MSSB and HANN. When MSSB method is used for bonding of long HVUCL, harmonic distortion limit and touch voltage limit are not exceeded under high harmonic distortion conditions.

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.

Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting

Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin.The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0.3648 respectively, whereas for the WANN model the respective values were 0.1946 and 0.1695. Efficiencies during calibration and validation for ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0.9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hence hybrid ANN model is the promising tool to predict water table fluctuation as compared to ANN model.

Automated multi-objective optimization for thin-walled plastic products using Taguchi, ANOVA, and hybrid ANN-MOGA

In this paper, an automated two-staged multi-objective optimization tool for plastic injection molding (PIM) is designed, implemented, and applied to industrial product. In the first stage, Taguchi method is utilized for design of experiments (DOE). Process parameters include mold temperature, melt temperature, flow rate, cooling time, and four parameters related to variable pressure profile. Quality characteristics of plastic product containing warpage, weld line, and clamp force are obtained by simulation experiments. ANOVA and S/N ratio are utilized for data analysis to identify the importance of factors, provide an initial optimal combination of process parameters, and obtain quantitative factor’s contribution percentage. The number of process parameters is compressed from 8 to 5 considering factors’ contribution percentage for reduction in time and computation cost. In the second stage, hybrid artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) are utilized as further study to search for optimal solutions for synchronous reduction in three quality characteristics within compressed design space. Orthogonal array with Latin hypercube sampling (OA-LHS) is adopted for sampling with uniformity and stratification. ANN is utilized as a surrogate model to fit the surface between parameters and quality characteristics. MOGA is adopted to search the optimal solutions along the Pareto frontier. With the help of python, visual basic scripts, application programming interface (API) of Moldflow and DAKOTA, the two-stage optimization process is automated, which allows designers to study optimization for PIM, accurately and efficiently, without deep understanding of complicated optimization algorithms. The automated two-stage system is applied to an industrial plastic products and the result verifies that it is accurate and efficient in quality improvement.

A simple coupled ANNs‐RSM approach in modeling product distribution of Fischer‐Tropsch synthesis using a microchannel reactor with Ru‐promoted Co/Al 2 O 3 catalyst

A simple approach using hybrid artificial neural networks (ANNs)-response surface methodology (RSM) was developed to model the detailed product distribution using Ru-promoted cobalt-based catalyst with Al2O3 as the support in a microchannel reactor for Fischer-Tropsch (FT) synthesis. Using the independent process parameters for training, the established model is capable of predicting hydrocarbon production distributions, ie, paraffin formation rate (C2-C15) and olefin to paraffin ratio (OPR C2-C15) within acceptable uncertainties. The ANNs-RSM model and comprehensive mechanistic model using the Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach were compared to identify a few inherent advantages of the proposed model in modeling complex FT synthesis. The proposed ANNs-RSM model shows its appealing merits, ie, faster converge and higher accuracy (less than ±10% uncertainties was achieved from ANNs-RSM model, while LHHW model achieved less than ±15% uncertainties except a few exceptional high errors uncertainties at certain experimental conditions). The statistical significances to the product distributions and hydrocarbon formation rate during FT synthesis could be easily identified and quantitatively analyzed by the established ANNs-RSM model. The future effective alternative of kinetic study of the complex system such as FT synthesis in the microstructured reactor might follow the methods of using empirical reliable approach for process optimization together with mechanistic kinetic study for detailed reaction pathway discrimination.

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (Vp) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models.

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

Nondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.

Review of Meta-Heuristic Optimization based Artificial Neural Networks and its Applications

There are several meta-heuristic optimization algorithms developed on inspiration from nature. Artificial neural network proves to be efficient among other machine learning techniques. The efficiency of classification and prediction is improved by optimizing artificial neural network using the meta-heuristic optimization algorithms. The review of some of these hybrid artificial neural networks that are applied for benchmark datasets and to specific real-time experiments for classification and prediction are discussed. Upcoming sections cover the current trending research topics dealing with optimized artificial neural network concepts and provide some interesting insights for researchers to use in their respective applications domains of interest.