Multilayer Perceptron Neural Network Research Articles

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Estimation of the mean effective pressure of a spark ignition internal combustion engine using a neural network, considering the wall-wetting dynamics

The management and development of internal combustion engines stand as critical pursuits within the automotive and related industries. Utilizing cylinder pressure as feedback, engine controllers rely on intricate systems to regulate performance. However, due to the inherent complexity and nonlinearity of engines, direct measurement of cylinder pressure through pressure sensors is costly and computationally demanding. Consequently, the need for accurate and detailed engine models becomes paramount. Neural networks offer a promising avenue for simulating internal combustion engines, combining speed and precision. By treating the engine as an enigmatic entity, neural networks can construct detailed models. This study aims to employ two types of neural networks—multilayer perceptron and radial basis functions—to train and build a model of an internal combustion engine. These networks will simulate and estimate the engine's mean suitable pressure, allowing for a comparison of their effectiveness. Prior to implementing the neural network architecture, an engine model was constructed in MATLAB to gather necessary training data. This preliminary step ensured a robust foundation for subsequent network design and implementation. In summary, this research focuses on leveraging neural networks to model internal combustion engines, utilizing both multilayer perceptron and radial basis functions to simulate engine behavior and estimate mean suitable pressure.

FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Abstract Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) encounter a critical scalability issue when applied to the performance evaluation task for frame structure designs. Specifically, a model of MLP or CNN is limited to structures of a particular topology type and fails immediately when applied to other topology types. In order to tackle this challenge, we propose a scalable performance evaluation method (called FrameGraph) for frame structure designs using Graph Neural Network (GNN), offering applicability to a wide range of topology types simultaneously. FrameGraph consists of two main parts: (1) Components and their connections in a frame structure are denoted as edges and vertices in a graph, respectively. Subsequently, A graph dataset for frame structure designs with different topologies is constructed. (2) A well-defined GNN design space is established with a general GNN layer, and a controlled random search approach is employed to derive the optimal GNN model for this performance evaluation task. In numerical experiments of car door frames and car body frames, FrameGraph achieved the highest prediction precisions (96.28% and 97.87%) across all structural topologies compared to a series of classical GNN algorithms. Furthermore, the comparison with MLP and FEM highlighted FrameGraph's significant efficiency advantage. This verifies the feasibility and optimality of FrameGraph for the performance evaluation task of frame structures with different topologies.

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F1 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.

Predicting the scouring depth around a rectangular pier with a round nose by artificial intelligence methods

Local scour around bridge piers constitutes a significant phenomenon in river engineering. The development of artificial intelligence models and their capabilities has resulted in the widespread adoption of these models. In the experiments, various factors were considered, encompassing three different pier angles (0, 5, and 10°), six submerged vanes with two angles (20 and 30°) aligned with the flow direction, three vane heights (0, 1.25, and 2.5 cm) on the bed, and a collar. The study evaluated the performance of distinct machine learning models, namely, the multi-linear regression (MLR) model, radial basis function neural network (RBF), multilayer perceptron neural network (MLP), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) in predicting the scouring depth. Application of these models (RBF, MLP, ANFIS, SVM, and MLR) yielded positive outcomes, with corresponding R2 values of 0.99, 0.98, 0.97, 0.96, and 0.90 for scour depth assessment. The results demonstrated that the RBF artificial neural network model exhibited excellent generalizability in comparison to the other models. It consistently provided accurate predictions with minimal errors across varying training set sizes. Conversely, the ANFIS model exhibited limited generalizability compared to the other models, as reducing the training set size resulted in a significant increase in prediction errors during testing. Furthermore, although the MLR model demonstrated good generalizability, its prediction error was relatively high compared to the alternative models.

Improving wind power forecast accuracy for optimal hybrid system energy management

Abstract Due to its renewable and sustainable features, the wind energy is growing up around the word. However, the wind speed fluctuation induces the intermittent character of the generated wind power. Thus, wind power estimation, through wind speed forecasting, is very inherent to ensure an effective power scheduling. Four wind speed predictors based on deep learning networks and optimization algorithms, were developed. The designed topologies are the multilayer perceptron neural network, the long short-term memory network, the convolutional short-term memory network and the bidirectional short-term neural network coupled with the Bayesian optimization. The models performance was evaluated through evaluation indicators mainly, the root mean squared error, the mean absolute error and the mean absolute percentage. Based on the simulation results, all of them show a considerable prediction results. Moreover, the combination of the the long short-term memory network and the optimization algorithm is more robust on wind speed forecasting with a mean absolute error equal to 0.23 m/s. The estimated wind power was investigated for an optimal Wind/PV/Battery/Diesel energy management. The handling approach lies on the continuity of the load supply through the renewable sources as priority, the batteries on second order and finally the diesels. The proposed management strategy respects the designed criteria with satisfactory contribution percentage of the renewable sources equal to 71%.

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities.

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach

ABSTRACT One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN’s parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental-based AI modeling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favorable growth conditions in greenhouse agriculture. Packed bed systems have emerged as effective tools for regulating humidity levels, yet accurately assessing their performance remains unexplored, especially for temperate oceanic climates. This paper presents a packed-bed system with water as the working fluid to increase the humidity level during the winter for greenhouse cultivation. Accordingly, an experimental setup is developed, and a detailed parametric study is conducted. Further, an artificial intelligence (AI)-based modeling approach is developed for evaluating the performance of packed bed systems for greenhouse applications under varying environmental conditions with various inlet air flowrates, such as 176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr. Operating with water at an average temperature of 15.7 °C and a flow rate of 12.8 kg/min, the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda, indicating its efficiency in elevating air humidity levels. The multi-layer perceptron neural network, trained with 112 non-repeated datasets and employing a 2-10-10-1 topology, demonstrates high accuracy and resilience in estimating Δωa values for the packed bed system, with predictions closely aligning with experimental data and exhibiting a maximum discrepancy within ± 2.5%. This research contributes to the advancement of precision agriculture practices by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems.

Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.

Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.

As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X-rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity-related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older. A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi-layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed. The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity-related variables were also used in constructing the model. The sarcopenia prediction model, which consisted of only physical characteristics and activity-related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; ••: ••-••.

A comparative study of neural network architectures for software vulnerability forecasting

Abstract The frequency of cyberattacks has been rapidly increasing in recent times, which is a significant concern. These attacks exploit vulnerabilities present in the software components that constitute the targeted system. Consequently, the number of vulnerabilities within these software components serves as an indicator of the system’s level of security and trustworthiness. This paper compares the accuracy, trainability and stability to configuration parameters of several neural network architectures, namely Long Short-Term Memory, Multilayer Perceptron and Convolutional Neural Network. These architectures are utilized for forecasting the number of software vulnerabilities within a specified timeframe for a specific software product. By evaluating these neural network models, our aim is to provide insights into their performance and effectiveness in vulnerability forecasting.

Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation

Accurate time series forecasting has become increasingly important across various domains such as finance, energy, and medicine. This study introduces an innovative hybrid model that leverages the power of neural networks, precisely Many To Many LSTM (MTM LSTM) and Multilayer Perceptron (MLP), to improve time series forecasting accuracy. In this new combination, we trained network MTM LSTM to approximate the target at each step, and finally, we used network MLP to combine these approximations. To perform the evaluation, we made a forecast for the amount of solar energy radiation in the city of Mashhad, Iran. The experiment results concerning MSE and MAE showed that the proposed method with five lags outperforms the standard models.We hypothesize that MTM LSTM can effectively capture solar radiation's intricate temporal dependencies and nonlinearity. At the same time, MLP can enhance function approximation by modeling complex interactions, resulting in improved forecast accuracy. By employing the hybrid MTM LSTM and MLP model, we achieved improved accuracy in predicting solar energy radiation, which has significant implications for the renewable energy sector and its energy management and planning applications. This research advances time series forecasting techniques, highlighting the effectiveness of combining neural networks to address complex and dynamic patterns in time-dependent data.Overall, our findings underscore the potential and efficacy of the proposed hybrid model as a robust tool for accurate time series forecasting in various domains, supporting effective decision-making and planning processes.

Dynamic viscosity of low GWP refrigerants in the liquid phase: An empirical equation and an artificial neural network

This study presents a simple correlation for describing the temperature and pressure dependence of the liquid dynamic viscosity of low GWP refrigerants, namely HydroFluoroOlefins (HFOs) and HydroChloroFluoroOlefins (HCFOs). The model has 3 input parameters (i.e., reduced temperature, reduced pressure, and acentric factor) and 6 coefficients which were regressed on 794 experimental data collated from the literature for 7 alternative refrigerants (i.e., R1233zd(E), R1234yf, R1234ze(E), R1234ze(Z), R1224yd(Z), R1336mzz(E), and R1336mzz(Z)). Moreover, a multi-layer perceptron neural network for the liquid dynamic viscosity of the studied fluids was developed from the selected dataset. The artificial network has the same 3 input parameters of the correlation and one hidden layer with 19 neurons. The results of the proposed correlation proved that it is an accurate model for calculating the dynamic viscosity of the studied liquid refrigerants, despite its simplicity. It ensured an average absolute relative deviation of the liquid dynamic viscosity (AARD(η)) of 2.88 %, lower than that given by other literature correlations. As expected, the multi-layer perceptron neural network provided the best results for all the selected refrigerants (AARD(η) = 0.86 % for the complete dataset), proving that it can be considered a reference for the development of other models.

A Comparative Analysis of Neural Network Architectures for Predicting Indian Rice Production

Rice (Oryza sativa) is one of the most important cereal crops in World and feeds more than a third of the world’s population. In Asian region, rice is a main source of nutrition and provides 30% to 70% of the daily calories for half of the world’s population. Here, in this study two different neural network models were used in prediction of rice production of India. It was observed that the accuracy score of Multi-layer perceptron neural network is better than Radial basis function in prediction of rice production. The loss/error value for Multi-layer perceptron (MLP) model is lower than Radial basis function (RBF) model. The relative error is found to be high for MLP.

Analysing LULC transformations using remote sensing data: insights from a multilayer perceptron neural network approach

ABSTRACT The study examines the complex dynamics of changes in LULC over three decades, focused on the years 1992, 2002, 2012, and 2022. The research highlights the significance of comprehending these alterations within the framework of environmental and socio-economic consequences. The changes in land use and land cover (LULC) have significant and far-reaching effects on ecosystems, biodiversity, and human livelihoods. This study offers useful information for politicians, conservationists, and urban planners by examining historical patterns and forecasting future changes. The study utilized a Multilayer Perceptron Neural Network (MLP-NN), a well-known machine learning technique that excels at collecting intricate patterns. This model’s design had three layers: input, hidden, and output. The model underwent 10,000 iterations during its training process, and a thorough statistical analysis was conducted to assess the impact of each driving component. The MLP-NN model demonstrated impressive performance, with a skill measure of 0.8724 and an accuracy rate of 89.08%. The accuracy of the LULC estimates for 2022 was verified by comparing them with observed data, ensuring the model’s reliability. Moreover, the presence of evidence likely was found to be a significant factor that had a substantial impact on the accuracy of the model. The study highlights the effectiveness of the MLP-NN model in accurately predicting changes in LULC. The model’s exceptional accuracy and proficiency make it a powerful tool for future LULC forecasts. Identifying the primary causes of model performance and understanding their implications may help to enhance land management strategies, encourage spatial planning, guide accurate decision-making, and facilitate the development of policies that align with sustainable growth and development.

Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction

Multilayer Perceptron Neural Network with Arithmetic Optimization Algorithm-Based Feature Selection for Cardiovascular Disease Prediction

Can machine learning efficiently predict symmetry breaking in physical problems like Bose-Einstein condensates?

In this work, our objective is to evaluate whether machine learning algorithms combined with computational methods used in physical problems such as spontaneous symmetry breaking in Bose-Einstein condensates are capable of efficiently predicting results obtained from solutions of nonlinear equations. Thus, we use K-means algorithms, support vector machines (SVM) and artificial neural network (ANN) multilayer perceptron to evaluate the efficiency of each algorithm in the process of predicting theoretical results in regions where computational calculations cannot be performed. either by numerical inaccuracy or computational cost. The results show that sophisticated machine learning algorithms can predict the behavior of represented physical systems with a satisfactory quality, where we especially highlight the correct interpretation of the physical problem in border regions and/or transitions.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06 %, 73.95 %, and 58.11 %, respectively for the SFS-NN-MLP, EHO–NN–MLP, and SMA–NN–MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.

Impact of Goods and Service Tax on MSME Sector: A Study Using Artificial Neural Network and Multivariate Analysis of Variance

Micro, small and medium enterprises (MSME) is one the vital sector of the developing economy. Changes in fiscal policy directly impact this sector the most. The present article aims to examine the impact of the recent Indian indirect system tax overhaul, that is, Goods and Service Tax (GST), on the MSME sector. We have employed multivariate analysis of variance and multi-layer perceptron neural network on the extensive data set for 60 months from April 2015 to March 2020. The empirical findings state that a significant increase has been observed in the MSMEs’ export, revenue generation and gross domestic product in the post-GST era. The findings may aid the emerging economies that are planning to alter tax systems to boost the MSME sector at the international level. JEL Codes: H20, L00, B22, C01, C45