Artificial Neural Network-based Model Research Articles

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g−1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin–Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R 2) and root mean square error (RMSE), the optimal network configuration was identified as a 5–11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

A prolegomenon to Design Input Energy Spectra for the Himalayan region

Energy-based design is becoming progressively significant as a new age of earthquake-resistant design unfolds. Thitherto, very little is known about energy-based design in India despite the fact that the Himalayan region is one of the most seismically active regions in the world. Therefore, the current work provides a preliminary analysis of the elastic input energy and equivalent velocity (Vea) spectra for the active regions in the Himalayas. In this regard, the ground motions of Himalayan databases are blended with NGA WEST2 to estimate the Vea spectra, an inertia-independent representation of the input energy spectra. Incipiently, an Artificial Neural Network-based ground motion model (GMM) is developed for Vea spectra based on the predictor variables as magnitude, distance, hypocentral depth and site conditions. Consequently, a Probabilistic Seismic Hazard analysis is performed to obtain the uniform hazard energy spectra for a 2475-year return period. In addition, a Design Input Energy Spectra is proposed for the active regions in the Indian Himalayas. In addition, the relationship between the Vea and pseudo-spectral velocity (PSV) is analyzed to develop a Vea-informed GMM for PSV. Furthermore, the PSA estimated from the Vea-PSV model is compared with the design spectra recommended by the Indian Standard Code of Practice (IS1893-1:2016).

Pore fractal characteristics and ANN model of polyacrylonitrile fiber reinforced concrete under the synergistic effects of freeze-thaw and erosion

The degradation of the concrete pore structure is a major concern among scholars due to its negative impact on durability. This study conducted a salt freezing test on polyacrylonitrile fiber concrete (PANFRC) using a composite freeze-thaw medium consisting of Na2SO4, MgSO4, and NaCl (5 % + 5% + 3.5 %). The pore volume fractal dimension was determined by analyzing the T2 spectrum obtained through nuclear magnetic resonance technology. The study explored the influence of different types of pores on frost resistance and developed an artificial neural network-based machine learning model to predict macroscopic frost resistance using the pore volume fractal dimension. The experimental findings indicate that plain concrete exhibits a density loss of 4.67 % upon failure, whereas the density loss in PANFRC is reduced to less than 3 %. The frost resistance durability index is observed to improve as the fiber mass fraction increases within specific ranges. Notably, when the fiber content surpasses 1.2 kg/m³, the durability factor (DF) can be effectively doubled. Drawing from the concept of freeze-thaw cycles, a novel frost-thaw erosion damage model was developed, which incorporates the influence of fiber content under the combined action of freeze-thaw cycles and erosion. The analysis of pore dimensions reveals that the fractal characteristics become more pronounced with the increase in pore size, although this does not extend to the micro-pore range (i.e., those smaller than 0.1 μm). In comparison to other machine learning algorithms, the ANN demonstrates its superiority by establishing a meso-macro transfer prediction model with enhanced generalization capabilities.

Artificial neural network-based positioning error modeling and compensation for low-cost encoders of four-wheeled vehicles

Artificial neural network-based positioning error modeling and compensation for low-cost encoders of four-wheeled vehicles

Artificial intelligence-based surrogate modeling for computational cost-effective optimization of hydrogen liquefaction process

The chemical processes are inherently complex, and obtaining desired product quality and high energy efficiency by optimizing the design variables is challenging. The progress in computing technologies can be a blessing since machine learning models can process the information and data for learning purposes and eventually help ease the simulation and optimization efforts. Against this backdrop, an artificial neural network-based surrogate model is developed in the present study and applied to the challenging process of hydrogen liquefaction. Optimization of hydrogen liquefaction processes is still an open challenge because of convergence issues and many design variables; only a handful of algorithms are considered successful. One of those algorithms, particle swarm optimization (PSO), is used here to compare computational effort and results accuracy. The percentage error of prediction of the ANN-based surrogate model compared to PSO was 4% for the minimum internal approach temperature, which is usually considered as a constraint, and 0.04% for specific energy consumption considered as the objective function. The optimization of the proposed model significantly reduced the time needed for PSO optimization of the existing model by more than 99.99%. As an extension of the current work, the ‘shallow' neural network model, with one hidden layer used in the present study, can be used to analyze the whole liquefaction process.

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

Determining the minimum data size for the development of artificial neural network-based prediction models for rice pests in Korea

Sudden outbreaks of crop pests (insect pests and diseases) are increasing in Korea due to climate change and globalization. To prevent such outbreaks, it is necessary to predict and control pest occurrences in advance. Crop pests have been predicted through process-based or statistical modeling; however, the limitations of these models, which rely solely on historically acquired domain knowledge and data, have become increasingly prominent owing to climate change and rapidly changing agricultural ecosystems. To overcome these limitations, artificial neural network (ANN)-based models that use continuous pest survey data in the field have been investigated over the last decade. However, because pest survey data are collected by humans through process-mediated methods, fundamental problems exist in terms of data quality and size that may hinder the performance of the resulting ANN-based models. In this study, to determine the minimum pest data size required to ensure the optimal performance of ANN-based models, we developed feed-forward neural network models for 19 rice pests using 23 pest datasets collected from 149 districts by the Rural Development Administration of Korea over 19 years (2002–2020). Using each ANN-based model, the minimum data size required for the highest model performance achieved in this study was determined for all 19 rice pests. Furthermore, we developed a decision-tree rule to estimate the minimum data size based on the selected characteristics of each pest. The final Decision tree rule, based on the distinction between diseases and insect pests and the balance of pest data (the relative ratio of pest occurrence data to non-occurrence data), showed a relatively good performance (70.24 %) in the 3-fold cross-validation test. Overall, these results indicate that the minimum data sizes required for ANN modeling vary among rice pests, depending on the pest data characteristics, as indicated by the Decision tree rule developed in this study.

Deep learning-based solar power forecasting model to analyze a multi-energy microgrid energy system

Multi-energy microgrids (MEM) are a new class of power grids focusing on the distributed form of generation and integrating different energy sectors. The primary idea of MEM is to increase renewable energy share in the final energy demand while maintaining the energy balance at all times. However, integrating renewable technology into the grid has some technical limitations that must be analyzed before being deployed in the real world. This study examines the impact of increasing renewable penetration and portfolio design on a multi-energy microgrid energy system from a technical standpoint. As the accuracy of the system analysis is primarily a factor of modeling accuracy, an artificial neural network-based model is trained and deployed to develop forecasts for solar power generation. The forecasting model is integrated with the EnergyPLAN simulation tool to analyze the multi-energy microgrid system regarding renewable share in primary energy consumption and import/export of energy from the primary grid. The Norwegian energy system is considered a case study, as the energy generation and consumption patterns are interesting from both renewable energy and demand contexts for a cold country. One interesting conclusion is that the portfolio and capacities of coupling components such as combined heat and power plants negatively impact renewable integration, while heat pumps positively impact renewable integration by increasing renewable energy utilization. Additionally, the photovoltaic system size has a high degree of correlation to imports and exports compared to wind generation systems.

A neural network-based predictive decision model for customer retention in the telecommunication sector

Acquiring a new customer is far more expensive than retaining a customer. Hence, customer retention is a key aspect of business for a firm to maintain and improve on its market share and profit. The paper analyses customer retention strategies by employing an artificial neural network-based decision model to a real-life dataset collected from 311 mobile service users in India. Seven linear and non-linear adaptive models are developed using features related to customer dissatisfaction (DSF), customer disloyalty (DLF) and customer churn (CF). Findings of this study suggest that non-linear models are most efficient in predicting customer churn, and both DSF and DLF variables significantly affect the retention strategy. Three groups of customers are discussed in this study in the order of least likelihood of churning to most likelihood. Finally, a priority matrix based on key performance indicators is proposed to help service providers target potential customers to retain.

Artificial neural network-based home energy management system for smart homes

<p>Energy efficiency is widely recognized as one of the most significant and economical ways to lower greenhouse gas (GHG) emissions. The aims and goals are that smart meters can evaluate and communicate in-depth real-time electricity usage, enable remote real-time monitoring and management of power consumption, and provide consumers with real-time pricing and analyzed usage information. The house energy management controller decides which loads will be powered based on the real home energy demands and the predefined load priorities. Artificial intelligence (AI) is being used increasingly in control applications due to its great effectiveness and efficiency. As a result, in this work, the author designed, simulated, and optimized an artificial neural network-based model simulation framework that simulates a home with a variety of home appliances and optimizes the total energy consumption of the home realistically through intelligent control of home appliances. The MATLAB application was used to model and examine the performance of four common household appliances: the water heater (WH), washing machine (WM), air conditioner (AC), and refrigerator (RG). The result shows a considerable reduction and savings in energy consumption without a decrease in consumer comfort.<strong></strong></p>

Experimental investigation and artificial neural network-based modelling of thermal barrier engine performance and exhaust emissions for methanol-gasoline blends

In recent years, due to environmental concerns and the depletion of fossil fuels, alternative fuel use and alternative emission reduction methods have gained importance in the automotive industry. In addition, methanol is used as an alternative fuel in gasoline engines with coated piston engines. This study first presents an experimental investigation of engine performance and exhaust emissions for a partially thermal barrier lined piston engine operating on methanol-gasoline blends. In the second phase the obtained data is then used to develop an Artificial Neural Network (ANN) based model to predict engine performance and exhaust emissions for methanol-gasoline blends. The developed ANN model was trained and validated using MATLAB. The results of the experimental study showed that the use of methanol-gasoline blended fuel in the engine provides better engine performance and reduced exhaust emissions compared to gasoline fuel. According to the results obtained, an increase of 3.7 % in effective power and a decrease in NOx and HC emissions by 19 % and 18 %, respectively, compared to the STD case when both coating and alternative fuel are used in the engine. With the established ANN models, engine performance parameters and exhaust emission parameters were predicted with 99 % and 98 % accuracy respectively.

Surrogate model-assisted seismic resilience assessment of the interdependent transportation and healthcare system considering a two-stage recovery strategy

In this study, a two-stage post-earthquake recovery strategy optimization model is proposed for determining resource inputs and obtaining the optimal restoration sequence after earthquakes. An artificial neural network-based surrogate model for predicting the functionality of the interdependent transportation and healthcare system (ITHS) is established to avoid the significant time consumption arising from traffic assignment and path selection in functionality evaluation and recovery optimization. It is applied to the two-stage optimization and resilience assessment under different seismic scenarios, which can greatly improve computational efficiency. The resilience-based two-stage optimization decision-making method assisted by the surrogate model is applied to a real example and provides practical support for recovery strategies of the ITHS.

Artificial neural network-based modelling and prediction of white layer formation during hard turning of steels

During hard machining, steels subjected to very high thermal and mechanical loads can result in microstructural/phase changes such as the formation of a white layer. This layer, which is often harder than the raw material, is considered detrimental to the fatigue performance and in-service life of machined parts. This paper proposes a comprehensive study of white layer formation during hard machining of steels using statistical analysis and artificial neural networks (ANN) modeling. To this end, two steals, named AISI 52100 and AISI 4340, commonly used in the manufacturing of structural machines’ components and extensively studied in the last decade, have been considered in this study. First, Taguchi method combined with response surface methodology (RSM) was applied to analyze and to optimize the machining parameters regarding the white layer thickness. Second, an ANN model is developed to predict the white layer thickness during hard machining of the studied steels using a large amount of machining data. Three training algorithms were tested to find the most robust configuration. The equivalent carbon parameter was introduced for the first time in machining modeling which make the proposed ANN-based model capable of predicting the white layer thickness for different hardened steels. The results show a significant agreement between predictions and experimental results, avoiding costly experimental machining tests.

Artificial neural network-based QSAR model for predicting degradation techniques of pharmaceutical contaminants in water bodies with experimental verification

The pharmaceutical industry has been increasing its production, manufacturing, and promotion of various products, resulting in a rise in contaminants in water.

Estimation of cohesion for intact rock materials using regression and soft computing analyses

Shear strength parameters such as cohesion (c) and internal friction angle (ϕ) are among the most critical rock properties used in the geotechnical design of most engineering projects. However, the determination of these properties is laboring and requires special equipment. Therefore, this study introduces several predictive models based on regression and artificial intelligence methods to estimate the c of different rock types. For this purpose, a comprehensive literature survey is carried out to collect quantitative data on the shear strength properties of different rock types. Then, regression and soft computing analyses are performed to establish several predictive models based on the collected data. As a result of these analyses, five different predictive models (M1–M5) were established. Based on the performance of the established predictive models, the artificial neural network-based predictive model (model 5, M5) was the most suitable choice for evaluating the c for different rock types. In addition, mathematical expressions behind the M5 model are also presented in this study to allow users to implement it more efficiently. In this regard, the present study can be declared a case study showing the applicability of regression and soft computing analyses to evaluate the c of different rock types. However, the number of datasets used in this study should be increased to get more comprehensive predictive models in future studies.

Detection of Malware Attacks using Artificial Neural Network

Malware attacks are increasing rapidly as the technology continues to become prevalent. These attacks have become extremely difficult to detect as they continuously change their mechanism for exploitation of vulnerabilities in software. The conventional approaches to malware detection become ineffective due to a large number of varying patterns and sequences, thereby requiring artificial intelligence-based approaches for the detection of malware attacks. In this paper, we propose an artificial neural network-based model for malware detection. Our proposed model is generic as it can be applied to multiple datasets. We have compared our model with different machine-learning approaches. The experimentation results show that the proposed model can outperform other well-known approach as it achieves 99.6\% , 98.9\% and 99.9\% accuracy on the Windows API call dataset, Top PE Imports Dataset and Malware Dataset, respectively.

Artificial neural network-based prediction model of elastic floor response spectra incorporating dynamic primary-secondary structure interaction

The evaluation of the Floor Response Spectrum (FRS) holds paramount significance in assessing the seismic behavior of secondary structures. Precise FRS prediction empowers engineers to make informed decisions concerning structural design, retrofitting, and safety precautions. This study aims to scrutinize the impact of dynamic interaction between primary and secondary structures on FRS. Both the elastic primary structure (PS) and elastic secondary structure (SS) employ a single-degree-of-freedom (SDOF) system. Governing motion equations for both coupled (with dynamic interaction) and uncoupled (without dynamic interaction) systems are formulated and solved numerically. The study investigates how variations in the vibration period of PS (Tp), tuning ratio (Tr), mass ratio (μ), and damping ratio (ξs) of SS influence FRS. The FRS impact remains minimal at μ = 0.001 (0.1%); however, with increasing mass ratio, PS-SS dynamic interaction significantly affects SS's spectral acceleration response. Coupled analysis is crucial only for secondary structures tuned to the primary structure's vibration period (0.8≤Tr≤1.2). This study utilizes two-layer feed-forward Artificial Neural Networks (ANNs) for FRS prediction. The Levenberg-Marquardt (LM) backpropagation (BP) algorithm trains the network using a comprehensive dataset. In summary, it is evident that the ANNs, once trained, enable accurate prediction of the FRS, exhibiting a R2 of 99%. Additionally, a design expression is formulated utilizing the ANN model and subsequently compared with the existing formulation.

Unfolding 3D Space into Binary Images for Daylight Simulation via Neural Network

Daylighting plays a crucial role in building science, impacting both occupants’ well-being and energy consumption in buildings. Balancing the size of openings with energy efficiency has long been a challenge. To address this, various daylight metrics have been developed to assess interior spaces’ daylight quality. Additionally, architects have been using simulation algorithms to predict postconstruction light conditions. In recent years, machine learning (ML) has revolutionized daylight simulations, offering a way to predict daylight conditions without cumbersome 3D modeling or heavy computational resources. However, accommodating architects’ creativity remains a challenge for current machine learning-based models. Specifically, the diversity of window shapes and their locations on facades poses difficulties for prediction accuracy. To overcome this limitation, this paper proposes a novel method that transforms wall information into matrices and uses them as input to train an artificial neural network-based model; this model can well predict the annual daylight simulation result generated by the Climate-Based Daylight Modeling tools. This method allows the model to adapt to various real-world design scenarios in real time, and its robust reliability has been demonstrated through evaluations of prediction accuracy concerning different annual daylight metrics. This approach caters to specific cases and opens possibilities for application in other machine learning and deep learning-based methods.

Artificial Neural Network-Based Prediction Model Back Propagation on Blood Demand and Blood Supply

The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition must be maintained to reduce unused or expired blood supplies. Despite the situation in UTD-PMI, where the blood supply exceeds demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying the forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the mean square error (MSE). Ultimately, the research results present the prediction model for the four types of blood with respect to the demand and supply. These findings can serve as a reference to regulate future blood donation activities carried out by the UTD-PMI.

Thermal displacement prediction of variable preload motorized spindles based on speed reduction experiments and IABC-BP optimization models

The thermal error caused during the high-speed operation of a high-speed motorized spindle has a significant impact on machine tool precision. In this paper, we firstly built, constructed an experimental platform for variable voltage preloaded motorized spindle, used the natural deceleration experimental method to ascertain the spindle's thermal displacement and temperature information when bearing preload is 1400 N, 1450 N, 1550 N and 1700 N, and combined with the thermoelasticity theoretical analysis, the thermal displacement of affected by the heat generation of motor and bearing was separated, and a new artificial bee colony algorithm-optimized BP neural network-based thermal error prediction model has been presented. The thermal displacements of the spindles are separated and a BP neural network optimization of a novel thermal error prediction model based on Improved Artificial Bee Colony Algorithm (IABC-BP) is proposed for transformer preloaded spindles. Combined with the experimental data of bearing preload of 1400 N, the thermal displacements of the spindle at preloads of 1450 N, 1550 N and 1700 N are predicted, and the evaluation indexes obtained from the simulation of the BP, ABC-BP, and IABC-BP models are compared. absolute error (MAE) and root-mean-square error (RMSE), higher goodness-of-fit (R2), and 20.37 % lower mean absolute error and 26.83 % lower root-mean-square error compared with the traditional thermal error prediction model. The IABC-BP prediction model proposed in this paper provides a more accurate compensation method for transformer preloaded spindles.