Backpropagation Training Algorithm Research Articles

Continuous and funnel-gate configurations of a permeable reactive barrier for reclamation of groundwater laden with tetracycline: experimental and simulation approaches

The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%.

Investigation of Vessel Manoeuvring Abilities in Shallow Depths by Applying Neural Networks

A set of planar motion mechanism experiments of the Duisburg Test Case Post-Panamax container model executed in a towing tank with shallow depth is applied to train a neural network to analyse the ability of the proposed model to learn the effects of different depth conditions on ship’s manoeuvring capabilities. The motivation of the work presented in this paper is to contribute an alternative and effective approach to model non-linear systems through artificial neural networks that address the manoeuvring simulation of ships in shallow water. The system is developed using the Levenberg–Marquardt backpropagation training algorithm and the resilient backpropagation scheme to demonstrate the correlation between the vessel forces and the respective trajectories and velocities. Sensitivity analyses were performed to identify the number of layers necessary for the proposed model to predict the vessel manoeuvring characteristics in two different depths. The outcomes achieved with the proposed system have shown excellent accuracy and ability in predicting ship manoeuvring with varying depths of shallow water.

Artificial neural networks in the retention of anthocyanins and total phenolics in the osmotic pre-treatment of Biloxi variety blueberry (Vaccinium corymbosum L.) jam

Blueberries are a fruit that is an important source of bioactive components beneficial to the human diet, such as anthocyanins and total phenolics, which are altered by the use of high temperatures during processing. This study aimed to evaluate the use of artificial neural networks in the optimization of sucrose concentration and time for the osmotic pre-treatment of blueberries of the Biloxi variety, to retain the greatest amount of anthocyanins and total phenolics in the subsequent preparation of jam. Artificial neural networks of the feedforward type were used, with a Backpropagation training algorithm with Levenberg-Marquardt weight adjustment, to achieve the optimal predicted combination that maximizes the retention of these bioactive components. The model achieved its best performance with 11 neurons in the hidden layer, achieving an R2 coefficient of 0.98 and a mean square error of 4.76, indicating a strong ability for generalization. Artificial neural networks allowed to obtain the best optimal combination of predicted multiple responses of factors consisting of a sucrose concentration of 1.64 M and a time of 211.52 min, which retained a higher content of total monomeric anthocyanins with 70.98 mg cyanidin-3-O-glucoside 100 g-1 of jam and total phenolics with 110.54 mg GAE g-1 of jam. On the other hand, through single-response optimization was obtained that the combination of experimental factors that maximized total anthocyanins (71.59 mg cyanidin-3-O-glucoside 100 g-1 of jam) was 1.54 M of sucrose and 232.73 min and for total phenols (111.06 mg GAE g-1 of jam) 1.79 M of sucrose and 196.36 min. The use of artificial neural networks is an excellent alternative for modeling phenomena, compared to traditional methods.

Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks.

Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by performing multiply-accumulate operations in parallel using nonvolatile analog memory. However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information-and therefore storage-of the partial derivatives of the weight values preventing suitable and scalable implementation in hardware. Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement. We experimentally demonstrate the in situ error calculation and the proposed progressive backpropagation method in a multilayer hardware-implemented neural network. We confirm identical learning characteristics and classification performance compared to conventional backpropagation in software. We show that our approach can be scaled to large and deep neural networks, enabling highly efficient training of advanced artificial intelligence computing systems.

Compare the Performance of Distinct Neural Networks Techniques to diagnose the kidney stone disease

Artificial Neural Networks are excellent at identifying patterns or trends in data, which makes them perfect for forecasting or prediction. Thus, neural networks have extensive application in biological systems. The application of neural networks to kidney stone diagnosis is emphasized in this article. Kidney stone issues can be diagnosed with neural networks by applying technological concepts such as MLP, SVM, RBF, and BPA. The purpose of this research is to use three different neural network algorithms—each with its own specific design and set of properties to identify kidney stone disease. The performance of the three neural networks is compared in this research with respect to training data set size, model creation time, and accuracy. Kidney stone sickness will be diagnosed using radial basis function (RBF) networks, two layers feed forward perceptrons trained with the back propagation training algorithm, and learning vector quantization (LVQ). However, determining the best approach for any particular diagnostic had never been an easy task. Like many other illnesses, kidney stones have already been diagnosed using neural network algorithms. The main objective of this work is to recommend the best medical diagnostic instrument, such as kidney stone detection, to reduce diagnosis times and improve accuracy and efficiency.

Modeling of deionization of aqueous compounds using electrodialysis process (data collection for enhanced clarity)

ABSTRACT The deionization process is increasingly recognized as a solution to global water shortages. This study aims to investigate the impact of various parameters on the deionization efficiency. Specifically, volume flow rates (F) were tested (1, 2.5, 5, 10, 15, and 20 ml min−1), solute concentrations (C) (200, 500, and 1000 mg l−1), temperatures (T) (50, 60, and 70°C), voltages (V) (10, 20, and 30 V), and pressures (P) (200, 400, and 800 Pa). In another experiment examined F (100, 250, and 500 mol l−1 m2), V (0.1, 0.5, and 1 V), C (100, 200, 300, 400, and 500 moles m3), and time durations (D) (16, 32, 48, 64, 80, and 96 min). These experiments aimed to determine the separation percentage of potassium chloride cations and anions and were based on data extracted from scientific articles. In the subsequent step, the ANFIS model with the FCM network creation method and the error backpropagation training algorithm was employed to evaluate the impact of each cell input on the separation percentage, output flux, and the separation percentage of potassium chloride cations and anions. This evaluation was conducted through 31 and 15 iterations, respectively. Based on the evaluation criteria, it was observed that the volume flow rate as the sole input yielded the highest prediction accuracy for the separation percentage and output flux. Additionally, the FCTVP model results indicated a slight reduction in the error criteria compared to the FCTV, FCT, FC, and single models. Consequently, it can be concluded that the cell pressure factor may play a decisive role, either reducing or increasing the separation process (R2 = 0.93,MBE = −0.3, and RMSE = 6.4), as well as the output flux (R2 = 0.85, MBE = 0.0003, and RMSE = 0.07). Furthermore, the statistical criteria values of the FVCT model for the separation percentage of potassium chloride cations and anions demonstrated a proper correlation (R2 = 0.96 and 0.95), slight underestimation, and low error MBE = −0.57 and −1.2) (RMSE = 15.2 and 18.5, respectively).

Slimmed Optical Neural Networks with Multiplexed Neuron Sets and a Corresponding Backpropagation Training Algorithm

Optical neural networks (ONNs) have recently attracted extensive interest as potential alternatives to electronic artificial neural networks, owing to their intrinsic capabilities in parallel signal processing with reduced power consumption and low latency. Preliminary confirmation of parallelism in optical computing has been widely performed by applying wavelength division multiplexing (WDM) to the linear transformation of neural networks. However, interchannel crosstalk has obstructed WDM technologies from being deployed in nonlinear activation on ONNs. Here, we propose a universal WDM structure called multiplexed neuron sets (MNS), which applies WDM technologies to optical neurons and enables ONNs to be further compressed. A corresponding backpropagation (BP) training algorithm was proposed to alleviate or even annul the influence of interchannel crosstalk in MNS-based WDM-ONNs. For simplicity, semiconductor optical amplifiers are employed as an example of MNS to construct a WDM-ONN trained using the new algorithm. The results show that the combination of MNS and the corresponding BP training algorithm clearly downsizes the system and improves the energy efficiency by a factor of 10 while providing similar performance to traditional ONNs.

Hybrid approach for gas viscosity in Yemeni oil fields

The estimation of a gas viscosity experimentally is often difficult. So, accurate determination of gas viscosity has been the main challenge in a gas reservoir development. There are many correlations to estimate this property. Often time, the results of these correlations do not agree with experimental data, thereby causing a considerable amount of error. The difficulty of these correlations can be propagated simply by tuning against some experimental data using artificial intelligent model. Currently, the achievements of artificial neural networks (ANN) techniques alone to predict gas viscosity open the door to use the hybrid system. In this model, the Particle Swarm Optimization (PSO) algorithm is employed to search for optimal connection weighs and thresholds for the neural networks (NN), then the back-propagation learning rule and training algorithm is used to adjust the final weights. A total of about 868 data points obtained from the laboratory measurements of gas viscosity were used. The data include measured gas viscosity, specific gas gravity, temperature, pressure, molecular weight, pseudo-critical temperature and pressure and non-hydrocarbon components (H2S, CO2, and N2). The performance of the PSONN model is compared with performance of ANN and other empirical model to show the most general and accurate model for predicting gas viscosity. From the results of this study, we found that the PSONN model is more reliable and accurate with the absolute present relative (APRE) error and mean square error (RMS) of 2.76 and 5.49 respectively.

Development of an energy prediction model for residential buildings using Artificial Neural Network

A model has been developed in this study for predicting the energy consumption of residential building sector using Artificial Neural Network. This model was based on the Multi-Layer Perceptron architecture using feed-forward back propagation algorithm for training. The Levenberg-Marquardt function and the Gradient Descent with momentum function were used as training and learning function, respectively. The mean squared error was used to check the overall performance of the developed model. Trials were performed to finalize the number of hidden layers required for the model. Regression analysis was done between the predicted and actual data to validate the proposed ANN model. The prediction for the same dataset was also performed using the traditional trend extrapolation method, and the predicted results of both were compared with the actual energy consumption data recorded by the electricity regulatory authority of the state. It has been concluded that the accuracy of predicted data using the proposed model was very high (i.e., of 99.54%) as compared to the traditionally used TEM (i.e., 91.07%). MSE achieved for the ANN model was 0.01767% and that of TEM was 0.13115%. The outcome of this study can be used at building level to achieve energy efficiency by predicting the energy consumption and at the level of distribution companies to predict the overall energy demand by the respective sector, and take measures accordingly.

Improving graphics processing unit performance based on neural network direct memory access controller

In this paper proposes the design and implementation of the back propagation algorithm based neural network DMA (Direct Memory Access) Controller for use of multimedia applications. The proposed DMA controller work with the back propagation-training algorithm. The advantages of the back propagation algorithm it will be work on the gradient loss w.r.t the network weights. So this back propagation algorithm is used as training algorithm for the DMA controller. The proposed method is test with the different workload characteristics like heavy workload, medium workload and normal workload. The performance parameters are considered here is like accuracy, precision, recall and F1 score etc. The proposed method is compared with existing methods like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Sort term Memory) and GRU (Gated Recurrent Unit) etc. Finally, the proposed design will give the better performance than existing methods.

Optimized artificial neural network model for accurate prediction of compressive strength of normal and high strength concrete

This study develops and presents an Artificial Neural Network (ANN) model employing the Levenberg-Marquardt Backpropagation (LMBP) training algorithm to predict the compressive strength of both normal and high strength concrete. The model's robustness was evaluated using an extensive dataset comprising 1637 samples. Eight input variables, including the cement content, blast furnace slag, fly ash, fine aggregate, coarse aggregate, water content, superplasticizer, and testing age, were considered. The optimal number of hidden layers and neurons in the layer were identified through analysis, and the effectiveness of the model was assessed through k-fold cross-validation and statistical measures, including correlation coefficient (R), coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MEA). Comparison with other models was carried out, and the perturbation/super-position method was employed for parametric studies to investigate the effect of each input variable on the output variable. The k-fold cross-validation confirmed the generalizability of the model, and statistical measures showed good results, with unit cement content and superplasticizers having the highest impact on compressive strength. The findings demonstrate that the suggested ANN model is an extremely precise, economical, and practical predictive tool for concrete compressive strength.

Artificial neural networks in predicting of the gas molecular diffusion coefficient

The diffusion coefficient is one of the most important parameters for designing two-phase operations between liquid and gas phases in refineries and petrochemical industries, as well as for the gas injection process in oil fields to enhance oil production. Accurate knowledge of this parameter is essential for the prediction of the dissolution rate of the gas phase into the liquid phase. Ideally, this parameter should be obtained experimentally. Given that setting up laboratory equipment and conducting experiments can be costly and time-consuming, mathematical modeling is used as an alternative. In some cases, this data is not either available or reliable, which poses a challenge for designs. Hence, empirically derived correlations are used to predict molecular diffusion coefficients. However, the success of empirical models depends mainly on the range of data at which they were originally developed. Empirical models are not comprehensive for applying to the other data. Recent studies demonstrated that the alternative approach to modeling complex processes and identifying the effective parameters is the artificial neural network (ANN), a suitable prediction method. This study presents a new model developed to predict the molecular diffusion coefficient of methane in crude oil. The model is developed using 172 data points collected from recent literature. Out of the total laboratory data, 90% (155 data points) were used for training the desired neural network, while 10% (17 data points) were reserved for testing and evaluating the performance of the network. The multi-layer perceptron (MLP) neural network architecture with back-propagation (BP) training algorithm was used successfully for the prediction of diffusion coefficients of methane in crude oil. The developed model is compared with the empirical data, which shows the developed model predicts the methane molecular diffusion coefficient in crude oil with an average absolute error of 4.18%.

Investigating and Modeling of the Scour Downstream of a Tree Trunk Deflector in a Straight Channel

Scouring depends on several factors, including the water flow of artificial obstacles, sections, piers, and foundations, the disturbance of bed materials, and soil permeability. The other factors are the non-parallelism between piers and the water flow, the type of river activity (static or dynamic), and the existence of a waterfall or an obstacle that forms a waterfall in natural bed materials, causing the underlying bed materials to be washed away. This study fully investigated how the movement of a tree trunk affects a river’s flow by considering different flow conditions using the artificial neural network (ANN) model. A feedforward optimal network with the error back-propagation training algorithm and sigmoid transfer functions was used for four models. To determine the number of neurons in the hidden layer, one and ten neurons were selected in the hidden layer according to verification indicators. In addition, a physical model was utilized to measure data. To verify and test the models, our data were gathered in a laboratory using the physical model. Considering the network structure of one neuron in the hidden layer, a comparison was made between dimensional and dimensionless parameter models that are effective in terms of the dimensions of the scour hole. The comparison between the results of the ANN and the measured data using nonlinear regression models demonstrated that the ANN was more accurate and capable of simulating phenomena. Additionally, R and RMSE values were between 0.93 and 0.98, as well as 0.18 and 0.013, respectively. Finally, the results related to the width, height, length, and depth of the scour revealed that the modified DOT model had the best agreement with Mahdavizadeh’s measured data.

Artificial neural networks for performance prediction of full-scale wastewater treatment plants: a systematic review.

Wastewater treatment plants (WWTPs) are complex systems that must maintain high levels of performance to achieve adequate effluent quality to protect the environment and public health. Artificial intelligence and machine learning methods have gained attention in recent years for modeling complex problems, such as wastewater treatment. Although artificial neural networks (ANNs) have been identified as the most common of these methods, no study has investigated the development and configuration of these models. We conducted a systematic literature review on the use of ANNs to predict the effluent quality and removal efficiencies of full-scale WWTPs. Three databases were searched, and 44 records of the 667 identified were selected based on the eligibility criteria. The data extracted from the papers showed that the majority of studies used the feedforward neural network model with a backpropagation training algorithm to predict the effluent quality of plants, particularly in terms of organic matter indicators. The findings of this research may help in the search for an optimum design modeling process for future studies of similar prediction problems.

Reduced precision floating-point optimization for Deep Neural Network On-Device Learning on microcontrollers

Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models in future TinyML applications. This paper tackles this challenge by introducing a novel reduced precision optimization technique for ODL primitives on MCU-class devices, leveraging the State-of-Art advancements in RISC-V RV32 architectures with support for vectorized 16-bit floating-point (FP16) Single-Instruction Multiple-Data (SIMD) operations. Our approach for the Forward and Backward steps of the Back-Propagation training algorithm is composed of specialized shape transform operators and Matrix Multiplication (MM) kernels, accelerated with parallelization and loop unrolling. When evaluated on a single training step of a 2D Convolution layer, the SIMD-optimized FP16 primitives result up to 1.72× faster than the FP32 baseline on a RISC-V-based 8+1-core MCU. An average computing efficiency of 3.11 Multiply and Accumulate operations per clock cycle (MAC/clk) and 0.81 MAC/clk is measured for the end-to-end training tasks of a ResNet8 and a DS-CNN for Image Classification and Keyword Spotting, respectively – requiring 17.1 ms and 6.4 ms on the target platform to compute a training step on a single sample. Overall, our approach results more than two orders of magnitude faster than existing ODL software frameworks for single-core MCUs and outperforms by 1.6× previous FP32 parallel implementations on a Continual Learning setup.

Human Walking Gait Classification Utilizing an Artificial Neural Network for the Ergonomics Study of Lower Limb Prosthetics

Prosthetics and orthotics research, studies, and technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics and orthotics usage in daily life. Prosthetics and orthotics demand is increasing due to certain factors. One of the factors is vascular-related disease, which leads to amputation. Prosthetic usage can increase an amputee’s quality of life. Therefore, studies of the ergonomic design of prosthetics are important. The ergonomic factor in design delivers prosthetic products that are comfortable for daily use. One way to incorporate the ergonomic design of prosthetics is by studying the human walking gait. This paper presents a multiclassification of human walking gait based on electromyography (EMG) signals using a machine learning method. An EMG sensor was attached to the bicep femoris longus and gastrocnemius lateral head to acquire the EMG signal. The experiment was conducted by volunteers during normal walking activity at various speeds and the movements were segmented as initial contact, which was labeled as initial gait; loading response to the terminal stance, which was labeled as mid-gait; and pre-swing to terminal swing, which was labeled as final gait. The EMG signal was then characterized using an artificial neural network (ANN) and compared to six training accuracy methods, i.e., the Levenberg–Marquardt backpropagation training algorithm, quasi-Newton training method, Bayesian regulation backpropagation training method, gradient descent backpropagation, gradient descent with adaptive learning rate backpropagation, and one-step secant backpropagation. The machine learning study performed well in the classification of three classes of human walking gait with an overall accuracy (training, testing, and validation) of 96% for Levenberg–Marquardt backpropagation. The gait data will be used to explore the design of lower limb prosthetics in future research.

Experimental study of bubble growth on novel fin structures during pool boiling

Boiling heat transfer associated with phase change is perhaps one of the most efficient cooling methodologies to manage extreme heat flux due to its large latent heat. Fin structures are used to further increase the magnitude of boiling heat transfer from the heated surface and have shown better performance than flat surface heat sinks. This work aims to experimentally investigate the heat transfer performance of two fin structures, namely regular and modified fins, in a pool boiling facility. The modified hollow fin structure is designed to enhance the regular fin's heat transfer performance by adding an artificial nucleation site. Heat transfer rates and heat transfer coefficients of the two fin structures are estimated in atmospheric pressure conditions using deionized water and compared with the literature. The results show that the regular fin heat sink shows a better heat transfer rate than the plane surface, while the modified fin structure shows higher heat transfer performance than the regular fin. This is attributed to the additional nucleation sites on the hollow fin, a better rewetting phenomenon, and therefore a favorable bubble growth and release mechanism. Also, a multilayer perceptron artificial neural network with a back-propagation training algorithm is applied for modeling the bubble departure diameter concerning wall superheat and subcooling level to predict the bubble behavior from the artificial nucleation site.

Artificial Neural Networks for Diagnosis of Kidney Stones Disease

The foundation of medical care for children with renal stone disease is the assessment of metabolic risk factors, which aims to stop the growth of preexisting calculi and subsequent stone occurrences. In this retrospective analysis, 90 children with kidney stone disease who had been sent to our institution and had undergone clinical testing in accordance with a defined procedure had their metabolic risk factors, clinical histories, and family histories assessed. Our pediatric patients were 10.7 years old on average, with a male to female ratio of 1.14:1.0. In 84.4% of the instances, biochemical abnormalities were discovered. Of the patients, 52.2% (n = 47) had only one urine metabolic risk factor, whereas the remaining 31.1% (n = 28) had several risk factors. Adrenal hypercalciuria. The aim of this work is to compare the performance of all three neural networks on the basis of its accuracy, time taken to build model, and training data set size. We will use Learning vector quantization (LVQ), two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF) networks for diagnosis of kidney stone disease. In this work we used Waikato Environment for Knowledge Analysis (WEKA) version 3.7.5 as simulation tool which is an open source tool

Dry Weight Prediction of Wedelia trilobata and Wedelia chinensis by Using Artificial Neural Network and MultipleLinear Regression Models

In China, Wedelia trilobata (WT) is among the top most invasive plant species. The prediction of its growth, using different efficient methods under different environmental conditions, is the optimal objective of ecological research. For this purpose, Wedelia trilobata and its native plant species Wedelia chinensis (WC) were grown in mixed cultures under different levels of submergence and eutrophication. The multiple linear regression (MLR) and artificial neural network (ANN) models were constructed, with different morphological traits as the input in order to predict dry weight as the output for both plant species. Correlation and stepwise regression analysis (SWR) were used to find the best input variables for the ANN and MLR models. Plant height, number of nodes, chlorophyll content, leaf nitrogen, number of leaves, photosynthesis, and stomatal conductance were the input variables for WC. The same variables were used for WT, with the addition of root length. A network with the Levenberg–Marquart learning algorithm, back propagation training algorithm, Sigmoid Axon transfer function, and one hidden layer, with four and six neurons for WC and WT, respectively, was created. The best ANN model for WC (7-4-1) has a coefficient of determination (R2) of 0.98, root mean square error (RMSE) of 0.003, and mean absolute error (MAE) of 0.001. On the other hand, the ANN model for WT (8-6-1) has R2 0.98, RMSE 0.018, and MAE 0.004. According to errors and coefficient of determination values, the ANN model was more accurate than the MLR one. According to the sensitivity analysis, plant height and number of nodes are the most important variables that support WT and WC growth under submergence and eutrophication conditions. This study provides us with a new method to control invasive plant species’ spread in different habitats.

Development of a Computerized Diagnostic System for Brain MRI Tumor Scanning Using a Robust Information Clustering Technique

This paper presents the development of a computerized diagnostic system for brain MRI tumor scanning using a robust information clustering technique. The method used for this study is data collection, data processing, feature extraction, artificial neural network, activation function, training algorithm, and classification. The method was modelled using a structural approach that developed the Artificial Neural Network (ANN) algorithm, using tansig activation function and back-propagation training algorithm. The brain tumor detection algorithm developed was implemented with MATLAB Simulink application, and tested with Mean Square Error (MSE) and Regression (R) analysis. The result showed that the MSE is 0.002488 and the Regression result is 0.9933. The algorithm was also comparatively compared with an existing system and the result showed that the new system achieved better regression performance than the others. Then it was deployed as a clinical decision system for the diagnosis of brain tumors and tested, the result showed that it was able to detect patients with brain MRI data. Keyword : Back-Propagation, Magnetic Resonance Imaging (MRI), Neural Network, Simulink, Tansig.