Multilayer Perceptron Approach Research Articles

Optimizing synthetic aperture radar image classification and change detection: a proportional factor firefly algorithm and multilayer perceptron approach

Optimizing synthetic aperture radar image classification and change detection: a proportional factor firefly algorithm and multilayer perceptron approach

Multi-method examination of contact mechanics in orthotropic layers under gravity

Analyzing the behavior of intelligent unconventional materials under diverse contact scenarios, in comparison to conventional materials, is a critical step in the initial design of engineering systems. This paper presents the development of analytical and numerical approaches for analyzing contact mechanics in a system comprising an orthotropic layer resting on a rigid foundation. Parametric analyses include the consideration of cylindrical and flat punch profiles. Analytical formulation utilizes integral transform methods to convert planar elasticity equations into a Cauchy-type singular integral equation of the second kind. A detailed description of the solution technique for the integral equation is provided, encompassing both the analytical formulation and the required discretization for obtaining the solution. Subsequently, a finite element approach (FEA) is employed to approximate contact bodies using a collection of finite elements, while contact boundaries are approximated by utilizing a set of polygons. Alternatively, the problem is addressed using the Multilayer Perceptron approach, a form of artificial neural network frequently applied in diverse machine learning applications, including scenarios involving contact problems. Finally, the resolution to the problem is achieved by employing the multilayer perceptron (MLP) method. The study yields determinations of contact stresses under the punch, the critical separation load resulting in the detachment of the orthotropic layer from the rigid foundation, and the corresponding separation distance. This analysis considers a range of dimensionless parameters and explored the behavior of diverse orthotropic materials. Comparisons between the results of the analytical method and computations from FEA and MLP reveal the exceptional accuracy attained by all three approaches. As the pioneer study employing three distinct approaches to examine continuous contact mechanics within an orthotropic layer under the influence of gravity, this research constitutes a valuable point of reference for upcoming scholars in the field.

Presentation of artificial neural network models based on optimum theories for predicting accident severity on rural roads in Iran

This study focuses on predicting the number and severity of rural accidents using nine accident-related variables by the multi-layer perceptron (MLP) approach as a type of artificial neural network modeling method. The models were developed using the input parameters of shoulder width, roadway width, roadside hazard, access density, passing zone ratio, speed limit, pavement condition index, shoulder rumble strips, and centerline rumble strips. MLP models were created based on accident data that occurred from 2019 to 2020 on the Tehran–Qom and Tehran-Saveh rural roads in Iran. In order to achieve the highest accuracy, twenty MLP models have been built with various structures. The MLP model, consisting of a multilayer feedforward network with hidden sigmoid and softmax output, has been used in this study. In this regard, an attempt was made to present an optimum theory to select the best model. The most accurate model was chosen based on the R-value and root mean square error (RMSE), mean absolute error (MAE), f, SD, and R2. Results indicated that the R-value obtained from the optimum model was 0.912, representing the accurate performance of the selected model. In addition, access density, roadside hazard, and roadway width were identified as the most significant variables.

Molding robust S-box design based on linear fractional transformation and multilayer Perceptron: Applications to multimedia security

This study introduces a novel and refined approach for generating exceptionally efficient S-boxes. The proposed methodology employs a hybrid approach that combines linear fractional transformation (LFT) with a multilayer perceptron (MLP) architecture. This method makes use of a perceptron with three layers: input, hidden, and output. Each layer's neuron count is fine-tuned to conform to the S-box layout. In addition, a threshold nonlinear transformation is utilized to increase nonlinearity, and a novel algorithm for boosting nonlinearity is introduced. The utilization of both LFT and MLP approaches has led to the development of S-boxes that possess nearly ideal average nonlinearity values, surpassing those that have been presented in literature. Notably, one S-box achieved an exceptional nonlinearity score of 114.50. Furthermore, to demonstrate how well the S-box works, this study also employs it in an image encryption application.

Modeling the decay of free residual chlorine in water distribution networks in Brazilian rural communities using artificial neural network

Disinfecting water with chlorine is crucial for maintaining sanitation and preventing waterborne diseases. However, managing Free Residual Chlorine (FRC) concentrations becomes challenging in water supply systems, as its levels can decrease throughout the distribution network, impacting water quality and public health. By leveraging water quality data from the Water Quality Surveillance Information System for Human Consumption (Sisagua) and employing the variable selection method t-Distributed Stochastic Neighbor Embedding, we crafted a Multilayer Perceptron (MLP) neural network. This MLP, fine-tuned with weighted mean square error (WMSE), was designed to evaluate the decline of free residual chlorine in water distribution networks within rural Brazilian communities. Training the model with the weighted mean squared error (WMSE) increased the average hit rate from 3.6 % to 5.0 %. In contrast to the traditional colorimetric method for predicting free residual chlorine, the proposed methodology showed superior performance, with accuracy rates of 85.45 %, 95.58 % and 99.84 % for equipment accuracies of 0.5 mg. /L, 1.0 mg/L and 2.0 mg/L, respectively. The MLP approach not only anticipates nonconformities related to FRC but also aids in adjusting chlorine dosage proactively.

Electricity theft detection in smart grid using machine learning

Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN).

Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network

The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer’s accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future.

Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

This paper proposes a deep learning method to predict cancer risk from gene symbols using a multilayer perceptron (MLP) feed forward neural network. The paper uses a data set of gene symbols and their corresponding cancer risk labels, obtained from a DNA microarray analysis. The paper then builds and compares different machine learning models, such as logistic regression, linear discriminant analysis, quadratic discriminant analysis, decision tree classifier, gaussian nb, ada boost classifier, gaussian process classifier, support vector machine, and random forest. Deep learning MLP model is built, tuned and optimized for hyperparameters which improves the accuracy significantly by 9.09% compared to the best machine learning model. The paper evaluates the performance of the MLP model on the data set using accuracy, precision, recall, and F1-score metrics. This paper contributes to the field of machine learning and bioinformatics by providing a novel and effective way to predict cancer risk from gene symbols.

Transferability of Temperature Evolution of Dissimilar Wire-Arc Additively Manufactured Components by Machine Learning.

Wire-arc additive manufacturing (WAAM) is a promising industrial production technique. Without optimization, inherent temperature gradients can cause powerful residual stresses and microstructural defects. There is therefore a need for data-driven methods allowing real-time process optimization for WAAM. This study focuses on machine learning (ML)-based prediction of temperature history for WAAM-produced aluminum bars with different geometries and process parameters, including bar length, number of deposition layers, and heat source movement speed. Finite element (FE) simulations are used to provide training and prediction data. The ML models are based on a simple multilayer perceptron (MLP) and performed well during baseline training and testing, giving a testing mean absolute percentage error (MAPE) of less than 0.7% with an 80/20 train-test split, with low variation in model performance. When using the trained models to predict results from FE simulations with greater length or number of layers, the MAPE increased to an average of 3.22% or less, with greater variability. In the cases of greatest difference, some models still returned a MAPE of less than 1%. For different scanning speeds, the performance was worse, with some outlier models giving a MAPE of up to 14.91%. This study demonstrates the transferability of temperature history for WAAM with a simple MLP approach.

Monthly rainfall prediction using artificial neural network (case study: Republic of Benin)

Abstract Complex physical processes that are inherent to rainfall lead to the challenging task of its prediction. To contribute to the improvement of rainfall prediction, artificial neural network (ANN) models were developed using a multilayer perceptron (MLP) approach to predict monthly rainfall 2 months in advance for six geographically diverse weather stations across the Benin Republic. For this purpose, 12 lagged values of atmospheric data were used as predictors. The models were trained using data from 1959 to 2017 and tested for 4 years (2018–2021). The proposed method was compared to long short-term memory (LSTM) and climatology forecasts (CFs). The prediction performance was evaluated using five statistical measures: root mean square error, mean absolute error, mean absolute percentage error, coefficient of determination, and Nash–Sutcliffe efficiency (NSE) coefficient. Furthermore, Taylor diagrams, violin plots, box error, and Kruskal–Wallis test were used to assess the robustness of the model’s forecast. The results revealed that MLP gives better results than LSTM and CF. The NSE obtained with the MLP, LSTM, and CF models during the test period ranges from 0.373 to 0.885, 0.297 to 0.875, and 0.335 to 0.845, respectively, depending on the weather station. Rainfall predictability was more accurate, with 0.512 improvement in NSE using MLP at higher latitudes across the country, showing the effect of geographic regions on prediction model results. In summary, this research has revealed the potential of ANN techniques in predicting monthly rainfall 2 months ahead, supplying valuable insights for decision-makers in the Republic of Benin.

Prediction of Consultation Wait Time in Outpatient Clinic: An Approach using Neural Network with Optimized Feature Selection

Outpatient clinics globally grapple with the uncertainty of patient wait times, a critical factor affecting patient satisfaction. Extended waiting periods are often perceived as a hindrance to timely care, generating stress for both patients and healthcare providers. Accurate prediction of patient wait times can substantially improve patient satisfaction by reducing uncertainty. This study aims to predict patient Consultation Wait Time efficiently in a multispecialty hospital outpatient clinic utilizing a Multilayer Perceptron approach. Feature Selection Methods were employed to enhance the predictive efficiency of the model. The study evaluated various performance metrics, including Accuracy, Recall, Precision, F-measure, and Area Under the Receiver Operating Characteristic Curve (AUC). Temporal features, specifically Visit Time and Consultation Start Time, emerged as significant predictors in the model. Additionally, Vitals Examination was identified as a key factor in predicting Consultation Wait Time. Notably, the model incorporating variables selected through Reciprocal Ranking exhibited robust performance in predicting Consultation Wait Times.

What determines the performance of pharmaceutical firms in India on account of COVID-19 interventions?

Purpose This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs). Design/methodology/approach This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms. Findings The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age. Originality/value The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

Azarshahr travertine compression strength prediction based on point-load index (Is) data using multilayer perceptron

Azarshahr County in the northwest of Iran is predominantly covered by Azarshahr travertine, a prevailing sedimentary rock. This geological composition has led to extensive open-pit mining activities, particularly in the western and southwestern parts of the county. The rock's drillability and resistance to excavation play a pivotal role in determining its overall durability and hardness, crucial factors that influence the mining process. These attributes are intimately tied to the compressive strength of the rock. Accurate assessment of rock strength is vital for devising reliable excavation methodologies at mining sites. However, conventional approaches for analyzing rock strength have limitations that undermine the precision of strength estimations. In response, this study endeavors to leverage artificial intelligence techniques, specifically the Multilayer Perceptron (MLP), to enhance the prediction of travertine's compressive strength. To formulate a robust model, a comprehensive database containing data from 150 point-load index (Is) tests on Azarshahr travertine was compiled. This dataset serves as the foundation for the development of the MLP-based predictive model, which proves instrumental in projecting rock compressive strength. The model's accuracy and efficacy were rigorously assessed using the Receiver Operating Characteristic (ROC) curve, employing both training and testing datasets. The modeling outcomes reveal impressive results. The estimated R-squared coefficient attained an impressive value of 0.975 for axial strength and 0.975 for diametral strength. The overall accuracy, as indicated by the Area Under the Curve (AUC) metric, stands at an impressive 0.968. These exceptional performance metrics underscore the efficacy of the MLP model in accurately predicting compressive strength based on the point-load index of samples. The implications of this study are substantial. The predictive model, empowered by the MLP approach, has profound implications for excavation planning and drillability assessment within the studied region's travertine deposits. By facilitating accurate forecasts of rock strength, this model equips mining endeavors with valuable insights for effective planning and execution.

Modeling Strategies Based on Multiple Neural Network Systems Applied for a Monopolar and Bipolar Electrocoagulation

The objective of this research was to evaluate the efficiency of an electrocoagulation system using iron and aluminum electrodes, arranged in both monopolar and bipolar arrangements, for the removal of acid red 18 dye. Experimental and modeling approaches were employed to investigate the system’s performance. The effects of operating parameters: including initial pH (3–9), current density (0.4–5.6 mA cm−2), charge passed (2.16–21.6 C cm−2), and initial dye concentration (50–300 mg l−1) were studied. The results demonstrated that an increase in electric current intensity and passed charge enhanced the removal of COD and dye. However, to minimize energy consumption, these parameters were optimized for different dye concentrations. The monopolar arrangement exhibited favorable performance for the both electrodes, primarily due to reduced ohmic drop effect, although the iron electrode generated sludge with better settling characteristics. The monopolar iron electrode consumed the least energy (38.3 kWh kg−1 COD). Experimental evaluation was conducted to assess the influence of key electrolysis process parameters on dye and COD removal. Additionally, neural network models, employing radial basis function and multilayer perceptron approaches, were utilized to predict system outputs based on initial characteristics (COD and dye) and operation conditions. The neural network models provided accurate predictions, offering practical insights for experimental applications.

Quadruple-star systems are not always nested triples: a machine learning approach to dynamical stability

ABSTRACT The dynamical stability of quadruple-star systems has traditionally been treated as a problem involving two ‘nested’ triples which constitute a quadruple. In this novel study, we employed a machine learning algorithm, the multilayer perceptron (MLP), to directly classify 2 + 2 and 3 + 1 quadruples based on their stability (or long-term boundedness). The training data sets for the classification, comprised of 5 × 105 quadruples each, were integrated using the highly accurate direct N-body code mstar. We also carried out a limited parameter space study of zero-inclination systems to directly compare quadruples to triples. We found that both our quadruple MLP models perform better than a ‘nested’ triple MLP approach, which is especially significant for 3 + 1 quadruples. The classification accuracies for the 2 + 2 MLP and 3 + 1 MLP models are 94 and 93 per cent, respectively, while the scores for the ‘nested’ triple approach are 88 and 66 per cent, respectively. This is a crucial implication for quadruple population synthesis studies. Our MLP models, which are very simple and almost instantaneous to implement, are available on Github, along with python3 scripts to access them.

Predicting Indian electricity exchange‐traded market prices: SARIMA and MLP approach

AbstractThis research investigates the short‐term (ST) forecasting performance of the daily prices of the Indian exchange‐traded day‐ahead (DAM) market, divided into 13 bid areas, each consisting of states with varied fundamentals. Forecasts are built employing SARIMA (seasonal autoregressive integrated moving average) and MLP (multilayer perceptron) methods. Moreover, the robustness and performance of the model is compared using the lowest error and the Diebold–Mariano (DM) test statistic values. The results indicates that the SARIMA model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%. However, validation by the DM test suggests no statistical significant difference between the two models. The power generators, distribution companies, traders, policymakers, strategists and managers could use the findings for effective power management through proper planning.

An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running

The smooth interaction and coordination between lower-limb amputees and their prosthetics is crucial when performing complex tasks such as running. To address this, simultaneous and proportional control (SPC) based on continuous estimation of joint angles through surface electromyogram (sEMG) signals offers a more seamless interaction between users and their prosthetic limbs than conventional pattern recognition-based control, which relies on recognizing pre-defined movement classes from specific sEMG patterns using classification algorithms. This study proposes a deep learning-based model (AM-BiLSTM) that integrates the attention mechanism (AM) and bidirectional long short-term memory (BiLSTM) network to provide an accurate and robust SPC for estimating knee joint angle during running from a minimal number of sEMG sensors. The sEMG signals of four muscles recorded from 14 subjects during treadmill running at various speeds were utilized by the AM-BiLSTM model to decode knee joint kinematics. To comprehensively investigate the generalizability of the proposed model, it was tested in intra-subject and speed, intra-subject and inter-speed, and inter-subject and speed scenarios and compared with BiLSTM, standard LSTM, and multi-layer perceptron (MLP) approaches. Normalized root-mean-square error and correlation coefficient were used as performance metrics. According to nonparametric statistical tests, the proposed AM-BiLSTM model significantly outperformed the BiLSTM network as well as classical techniques including LSTM and MLP networks in all three experiments (p-value < 0.05) and achieved state-of-the-art performance. Based on our findings, the AM-BiLSTM model may facilitate the deployment of intuitive user-driven deep learning-based control schemes for a wide variety of miniaturized robotic lower-limb devices, including myoelectric prostheses.

A Model for Robot Arm Pattern Identification using K-Means Clustering and Multi-Layer Perceptron

Predictive maintenance of industrial machines is one of the challenging applications in Industry 4.0. This paper presents a comprehensive methodology to identify robot arm (SCARA) movement patterns to detect the mechanical aging of the robot, which is determined by the abnormal movement of the robot arm. The dataset used is two robot arm movements that go from point A to B and then back to point A. Accelerometer data is used to measure the signal of SCARA actions, mainly focus on the non-linear movement. The identification of the movement pattern of the robot arm is made by combining k-means and multilayer perceptron. The proposed approach first extracts valuable features as characteristics of the two datasets from the time domain statistical value parameters. K-means clustering technique is initiated to label the training dataset. In this phase, the elbow curve is used to determine the number of clusters in the dataset, which is 2 clusters. Moreover, the assumption is used to determine which cluster is labeled as a normal and abnormal movement. Hence, a multilayer perceptron approach is proposed to predict the testing dataset. The proposed multilayer perceptron model yields an accuracy of 94.14%, whereas its cross-validation yields an accuracy of 96.12%.

MLP (multi-layer perceptron) and RBF (radial basis function) neural network approach for estimating and optimizing 6-gingerol content in Zingiber officinale Rosc. in different agro-climatic conditions

The principal bioactive phenolic component of ginger (Zingiber officinale Rosc.) rhizome is 6-gingerol, which is known to have several sensory and therapeutic properties. Due to its antimicrobial, anti-biofilm, anti-cancer, and anti-inflammatory properties, a sizable global market for ginger has been generated. The variation in yield of 6-gingerol is observed with changes in environmental, agronomic, and genetic conditions. The current study aims to examine how environmental and climatic factors affect 6-gingerol content using an artificial neural network (ANN) model and predict a new location to produce 6-gingerol. ANN-based models; MLP (multilayer perceptron) and RBF (radial basis function) with the desirable learning algorithms, transfer functions, number of neurons, and hidden layers were designed. The dataset of 60 unique Zingiber officinale rhizomes obtained from ten agro-climatic zones of Odisha was included in this study. The content of 6-gingerol was ascertained using a high-performance thin-layer chromatography (HPTLC) technique. The experimental dataset was randomly divided into 70 % for training, 15 % for testing, and 15 % for validation to carry out the optimization. In 60 accessions from various locations of Odisha, the concentration of 6-gingerol in ginger rhizomes ranged from 0.203 % to 0.813 % on a wet weight basis. It was revealed that the ANN model with the MLP algorithm was superior to the RBF algorithm. The findings showed that an ANN-MLP architecture with a single hidden layer of 11 neurons and a 16–11–1 topology could accurately predict the content of 6-gingerol, with an R2 (coefficient of determination) of 0.99. Altitude, pH, maximum temperature, and phosphorus were identified as the factors that influenced the model's prediction accuracy, which was 95 %. The content of 6-gingerol was enhanced from 0.363 % to 0.408 % by regulating the present model's sensitive parameters. The current study concluded that the developed ANN model efficiently predicts the best region for optimum 6-gingerol yield.

Using Deep Learning to Map Ionospheric Total Electron Content over Brazil

The low-latitude ionosphere has an active behavior causing the total electron content (TEC) to vary spatially and temporally very dynamically. The solar activity and the geomagnetic field have a strong influence over the spatiotemporal distribution of TEC. These facts make it a challenge to attempt modeling the ionization response. Single frequency GNSS users are particularly vulnerable due to these ionospheric variations that cause degradation of positioning performance. Motivated by recent applications of machine learning, temporal series of TEC available in map formats were employed to build an independent TEC estimator model for low-latitude environments. A TEC dataset was applied along with geophysical indices of solar flux and magnetic activity to train a feedforward artificial neural network based on a multilayer perceptron (MLP) approach. The forecast for the next 24 h was made relying on TEC maps over the Brazilian region using data collected on the previous 5 days. The performance of this approach was evaluated and compared with real data. The accuracy of the model was evaluated taking into account seasonality, spatial coverage and dependence on solar flux and geomagnetic activity indices. The results of the analysis show that the developed model has a superior capacity describing the TEC behavior across Brazil, when compared to global ionosphere maps and the NeQuick G model. TEC predictions were applied in single point positioning. The achieved errors were 27% and 33% lower when compared to the results obtained using the NeQuick G and global ionosphere maps, respectively, showing success in estimating TEC with small recent datasets using MLP.