Artificial Neural Model Research Articles

Magnetic sludge-derived biochar for Cu (II) removal: RSM-based preparation and BP artificial neural network adsorption modeling

Magnetic sludge-derived biochar (MSDB) was synthesized using response surface methodology (RSM) optimization, with sludge as the raw material and sodium pyrophosphate as the modifier. The structure and surface properties of MSDB were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), vibrating sample magnetometer (VSM), and Brunauer-Emmett-Teller (BET) analysis. These analyses revealed that MSDB is rich in functional groups and exhibits magnetic properties, facilitating the adsorption of Cu(II) via complexation and easy separation of MSDB through magnetic means. Batch adsorption experiments showed that under conditions of 25 °C and pH 3.0, with an MSDB dosage of 0.75 g L−1, a Cu(II) removal efficiency of 95.67 % was achieved. Thermodynamic studies demonstrated maximum adsorption capacities of 173.69 mg g−1 at 25 °C, 183.26 mg g−1 at 35 °C, and 197.96 mg g−1 at 45 °C. Additionally, the BP neural network model showed a high coefficient of determination (R2 = 0.9396) and a low mean squared error (MSE = 0.0268) in simulating the MSDB-Cu(II) adsorption process, confirming the robustness of the model in both data simulation and predictive accuracy. This method offers a promising approach for kinetic modeling and predicting adsorption behavior in related fields of study.

A Plantar Pressure Detection and Gait Analysis System Based on Flexible Triboelectric Pressure Sensor Array and Deep Learning.

Gait detection is essential for the assessment of human health status and early diagnosis of diseases. The current gait analysis systems are bulky, limited in the scope of use, and cause interference with the movement of the measured person. Hence, it is necessary to develop a wearable gait detection system that is soft, breathable, lightweight, and self-powered. Here, a plantar pressure sensor array and gait analysis system based on a flexible triboelectric pressure sensor (FTPS) array is developed. Soft, breathable, and wearable electrospinning nanofiber film with excellent triboelectric properties is used as the plantar pressure sensor, achieving a high sensitivity of 45.1 mVkPa-1 in the range of 40-200 kPa and 19.4 mVkPa-1 in the range of 200-400 kpa. 32 FTPSs are integrated into an intelligent insole, which has the characteristics of soft, easy production, good air permeability, long-time stability, no external power supply, and etc. Based on the long short-term memory artificial neural network deep learning model, the accuracy of gait judgment can reach 94.23%. This work provides a feasible solution for real-time gait detection, which will have potential applications in human health assessment and early diagnosis of disease.

Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations

Artificial Neural Network Surrogate Model for Geochemical Calculations in Pore-Scale Reactive Transport Simulations

In-field performance evaluation of robotic arm developed for harvesting cotton bolls

Robotic harvesting of cotton bolls combines the advantages of manual picking and mechanical harvesting, including selective picking and reduced labor dependency. Thus, we designed, developed, and tested a cotton-picking robotic arm equipped with a depth camera, a 4-DoF manipulator, and a vacuum-type end-effector. Convolutional neural networks were implemented for cotton boll recognition, while an artificial neural network-assisted particle swarm optimization-based model was utilized for solving inverse kinematics. Linear actuators, controlled by a microcontroller and relays, positioned the end-effector for cotton boll harvesting. Laboratory testing resulted in picking 44–49 cotton bolls per hour, taking approximately 73.0 s per boll. During field testing, the robotic arm picked 40–42 bolls per hour with an average time of 86.0 s per boll. Picking efficiency ranged from 76.19% to 85.71% in the lab and 66.32% to 72.04% in the field. The complex structure of cotton plants sometimes hindered the end-effector’s path, resulting in lower picking efficiency during field testing. As observed in present study, manual pickers achieved a significantly higher picking rate, harvesting 1.381 kg of cotton per hour, nearly 4.7 times more than the robotic arm. Manual pickers achieved 98.04% picking efficiency, significantly higher than the robotic arm’s 68.25%. This significant difference in efficiency is due to the agility, adaptability, and decision-making abilities of human pickers. The developed robotic arm demonstrates competitive efficiency in the domain of harvesting robots, contributing to advancements in robotic agriculture. Future research and development efforts in this field hold the potential to enhance the efficiency and productivity of robotic cotton harvesters.

An artificial neural network visible mathematical model for predicting slug liquid holdup in low to high viscosity multiphase flow for horizontal to vertical pipes

AbstractSlug liquid holdup (SLH) is a critical requirement for accurate pressure drop prediction during multiphase pipe flows and by extension optimal gas lift design and production optimization in wellbores. Existing empirical correlations provide inaccurate predictions because they were developed with regression analysis and data measured for limited ranges of flow conditions. Existing SLH machine learning models provide accurate predictions but are published without any equations making their use by other researchers difficult. The only existing ML model published with actual equations cannot be considered optimum because it was selected by considering artificial neural network (ANN) structures with only one hidden layer. In this study, an ANN-based model for SLH prediction with actual equations is presented. A total of 2699 data points randomly divided into 70%, 15%, and 15% for training, validation, and testing was used in constructing 71 different network structures with 1, 2, and 3 hidden layers respectively. Sensitivity analysis revealed that the optimum network structure has 3 hidden layers with 20, 5, and 15 neurons in the first, second, and third hidden layers, respectively. The optimum network structure was translated into actual equations with the aid of the weights, biases, and activation functions. Trend analysis revealed that this study’s model reproduced the expected effects of inputs on SLH. Test against measured data revealed that this study’s model is in agreement with measured data with coefficient of determinations of 0.9791, 0.9727, 0.9756, and 0.9776 for training, testing, validation, and entire datasets, respectively. Comparative study revealed that this study’s model outperformed existing models with a relative performance factor of 0.002. The present model is presented with visible mathematical equations making its implementation by any user easy and without the need for any ML framework. Unlike existing ANN-based models developed with one hidden layered ANN structures, the present model was developed by considering ANN structures with one, two, and three hidden layers, respectively.

A 20-year (1998–2017) global sea surface dimethyl sulfide gridded dataset with daily resolution

Abstract. The oceanic emission of dimethyl sulfide (DMS) plays a vital role in the Earth's climate system and constitutes a substantial source of uncertainty when evaluating aerosol radiative forcing. Currently, the widely used monthly climatology of sea surface DMS concentration falls short of meeting the requirement for accurately simulating DMS-derived aerosols with chemical transport models. Hence, there is an urgent need for a high-resolution, multi-year global sea surface DMS dataset. Here we develop an artificial neural network ensemble model that uses nine environmental factors as input features and captures the variability of the DMS concentration across different oceanic regions well. Subsequently, a global sea surface DMS concentration and flux dataset (1° × 1°) with daily resolution spanning from 1998 to 2017 is established. According to this dataset, the global annual average concentration was ∼ 1.71 nM, and the annual total emissions were ∼ 17.2 Tg S yr−1, with ∼ 60 % originating from the Southern Hemisphere. While overall seasonal variations are consistent with previous DMS climatologies, notable differences exist in regional-scale spatial distributions. The new dataset enables further investigations into daily and decadal variations. Throughout the period 1998–2017, the global annual average concentration exhibited a slight decrease, while total emissions showed no significant trend. The DMS flux from our dataset showed a stronger correlation with the observed atmospheric methanesulfonic acid concentration compared to those from previous monthly climatologies. Therefore, it can serve as an improved emission inventory of oceanic DMS and has the potential to enhance the simulation of DMS-derived aerosols and associated radiative effects. The new DMS gridded products are available at https://doi.org/10.5281/zenodo.11879900 (Zhou et al., 2024).

A conceptual framework for exploring the use of a green marketing mix and brand image to attract customers for green services while visiting a hotel: A step toward marketing sustainability

Recently, the application of green marketing strategies to promote marketing sustainability has increased in response to the growing demand for eco-friendly products, Price, promotion, place, and service are all variables that influence purchasing decisions, and the current study aims to confirm this. as the brand plays a significant role as well because it is regarded as the driving force behind consumer attraction in society. For this study, a framework connecting brand image (brand benefits and competence, brand identity, brand personality, brand association, brand attitude, and behavior), the green marketing mix (green services, green place, green price, green promotion), and green purchasing decisions were created to better understand the relationships among these variables. Artificial neural network (ANN) analysis and structural equation modeling (SEM) based on deep learning will be conducted via the new hybrid analysis approach. The research employed a survey of hotel customers in Iraq, Additionally, the researchers conducted structured interviews with a group of hotel managers. The issue of green marketing and how it relates to environmentally conscious consumer choices has received much attention, which has made addressing sustainability a top priority in the hospitality industry, particularly in hotels. In this study, customers were the main focus since their choice is crucial regarding switching to green services. However, a group of these hotel managers were interviewed to validate the findings and establish credibility. The current study was limited since it focused on clients in general, without considering clients from outside Iraq. Future studies can examine the variable of organizational support and the degree of change acceptance among customers.

Construction of an artificial neural network diagnostic model and investigation of immune cell infiltration characteristics for idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a severe lung condition, and finding better ways to diagnose and treat the disease is crucial for improving patient outcomes. Our study sought to develop an artificial neural network (ANN) model for IPF and determine the immune cell types that differed between the IPF and control groups.MethodsFrom the Gene Expression Omnibus (GEO) database, we first obtained IPF microarray datasets. To conduct protein-protein interaction (PPI) networks and enrichment analyses, differentially expressed genes (DEGs) were screened between tissues of patients with IPF and tissues of controls. Afterward, we identified the important feature genes associated with IPF using random forest (RF) analysis, and then constructed and validated a prediction ANN mode. In addition, the proportions of immune cells were quantified using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, which was performed on microarray datasets based on gene expression profiling.ResultsA total of 11 downregulated and 36 upregulated DEGs were identified. PPI networks and enrichment analyses were carried out; the immune system and extracellular matrix were the subjects of the enrichments. Using RF analysis, the significant feature genes LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were identified. The nine feature gene scores were integrated into the ANN to develop a diagnostic prediction model. The receiver operating characteristic (ROC) curves demonstrated the strong diagnostic ability of the ANN in predicting IPF in the training and testing sets. An analysis of IPF tissues in comparison to normal tissues revealed a reduction in the infiltration of natural killer cells resting, monocytes, macrophages M0, and neutrophils; conversely, the infiltration of T cells CD4 memory resting, mast cells, and macrophages M0 increased.ConclusionLRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were determined as key feature genes for IPF. The nine feature genes in the ANN model will be extremely important for diagnosing IPF. It may be possible to use differentiated immune cells from IPF samples in comparison to normal samples as targets for immunotherapy in patients with IPF.

Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals.

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.

Experimental Exploration of Cellulose Material for Battery Separators and Artificial Neural Network-Driven Predictive Modeling for Enhanced Thermal Safety in Electric Vehicles

Abstract Lithium-ion batteries (LIBs) are the most reliable energy storage devices nowadays because of their high energy density, long life cycle, and low self-discharge rate. But still, the safety concern is a significant problem in the area. When talking about LIB safety, thermal effects come first; this leads to thermal runaway, fires, and explosions. The critical component of LIB that has a great role in safety is the separator, which serves the purpose of preventing direct contact between the positive and negative electrodes while enabling the movement of lithium ions. This work aimed to find naturally available cellulose material for the LIB separator and to predict the performance of the material by artificial neural network (ANN) for better control of thermal problems that happen with traditional polymer separator materials. The cellulose derived from banana peels is isolated and characterized for its potential use as a separator material. The study conducts the four selected characterization approaches, scanning electronics microscopy (SEM) with three different resolutions to assess the morphology of the extracted cellulose, differential scanning calorimetry (DSC) to measure the heat flow with temperature change on the cellulose and the value obtained 231.22 J/g at a maximum temperature of 323.18 °C, thermogravimetric analysis (TGA) was used to examine the weight loss of the cellulose with respect to temperature variation, which results in a weight loss of 59.37% when the temperature reaches 235 °C, which is considered favorable, and a differential thermal analysis (DTA) was used to know the temperature difference in the banana peel cellulose (BPC), which results in a temperature of 330.23 °C. This morphological and thermal analysis technique for the BPC is used to determine the heat-related properties of the BPC, including phase transitions, thermal stability, and reaction. In addition, these results show BPC as an alternative material for separators in comparison to the existing polymer-based materials. Furthermore, these experimental results are used to train an ANN to predict the performance of BPC material using a binary classification. Because of the training process, 97.58% accuracy was achieved.

Comparative computer simulation and empirical analysis of MIDAS and artificial neural network-UMIDAS models for short- and long-term US GDP forecasting

Comparative computer simulation and empirical analysis of MIDAS and artificial neural network-UMIDAS models for short- and long-term US GDP forecasting

Machine learning chain models for multi-response prediction of electrical equipment in substation subjected to earthquakes

Engineering structures often exhibit multiple potential vulnerable positions during strong earthquakes, such as porcelain insulators and connection flanges of electrical equipment in substations. This paper proposes two types of machine learning (ML) chain models, where multiple individual models are linked end to end, to predict multiple peak seismic responses of substation equipment at the same time using intensity measures (IMs). One is a simple chain where the next input is the previous output, while in another one, the next input is combined by IMs and the previous output. The training mechanisms of ML chain models are presented by means of bio-inspired multi-objective optimization techniques for the hyperparameters of multiple individual ML models. A case study on a 1100 kV transformer bushing is then conducted. Artificial neural network-gradient boosting regression and artificial neural network-kernel ridge regression chain models are respectively established for predicting the peak stresses at two most vulnerable positions. Both the evaluation indicators and shaking table tests show enough prediction accuracy of the two ML chain models. Such chain models are qualified for identifying the initial damage to equipment, which can be employed for assisting post-earthquake rapid judgement and relief.

Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise Prediction

Noise in large urban areas, which is mainly generated by road traffic and by the human activities carried out nearby and inside the area under study, is a relevant problem. The continuous exposure to high noise levels, in fact, can lead to several problems, largely documented in the scientific literature. The analysis and forecasting of the noise level in a given area are, then, fundamental for control and prevention, especially when field measurements present peculiar trends and slopes, which can be modeled with a Time Series Analysis approach. In this paper, a hybrid model is presented for the analysis and the forecasting of noise time series in urban areas: this technique is based on the application of a deterministic decomposition model followed in cascade by a predictor of the forecasting errors based on an artificial neural network. Two variants of the hybrid model have been implemented and presented. The time series used to calibrate and validate the model is composed of sound pressure level measurements detected on a busy road near the commercial port of an Italian city. The proposed hybrid model has been calibrated on a part of the entire time series and validated on the remaining part. Residuals and error analysis, together with a detailed statistical description of the simulated noise levels and error metrics describe in detail the method’s performances and its limitations.

Machine learning based methods for ratemaking health care insurance

In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model.

Harmonic Interference Prediction of Power Amplifiers by Artificial Neural Network Behavioral Model

Harmonic Interference Prediction of Power Amplifiers by Artificial Neural Network Behavioral Model

Artificial Neural Network-aided Mathematical Model for Predicting Soil Stress-strain Hysteresis Loop Evolution

This study presents a novel approach to forecasting the evolution of hysteresis stress-strain response of different types of soils under repeated loading-unloading cycles. The forecasting is made solely from the knowledge of soil properties and loading parameters. Our approach combines mathematical modeling, regression analysis, and Deep Neural Networks (DNNs) to overcome the limitations of traditional DNN training. As a novelty, we propose a hysteresis loop evolution equation and design a family of DNNs to determine the parameters of this equation. Knowing the nature of the phenomenon, we can impose certain solution types and narrow the range of values, enabling the use of a very simple and efficient DNN model. The experimental data used to develop and test the model was obtained through Torsional Shear (TS) tests on soil samples. The model demonstrated high accuracy, with an average R² value of 0.9788 for testing and 0.9944 for training.

Artificial neural network-assisted theoretical model to predict the viscoelastic–plastic tensile behavior of polyamide-6 multi-ply yarns

Artificial neural network-assisted theoretical model to predict the viscoelastic–plastic tensile behavior of polyamide-6 multi-ply yarns

Identification of epithelial-related artificial neural network prognostic models for the prediction of bladder cancer prognosis through comprehensive analysis of single-cell and bulk RNA sequencing

BackgroundBladder cancer (BLCA) presents as a heterogeneous epithelial malignancy. Progress in the early detection and effective treatment of BLCA relies heavily on the identification of novel biomarkers. Therefore, the primary goal of this study is to pinpoint potential biomarkers for BLCA through the fusion of single-cell RNA sequencing and RNA sequencing assessments. Furthermore, the aim is to establish practical clinical prognostic models that can facilitate accurate categorization and individualized therapy for patients. MethodsIn this research, training sets were acquired from the TCGA database, whereas validation sets (GSE32894) and single-cell datasets (GSE135337) were extracted from the GEO database. Single-cell analysis was utilized to obtain characteristic subpopulations along with their associated marker genes. Subsequently, a novel BLCA subtype was identified within TCGA-BLCA. Furthermore, an artificial neural network prognostic model was constructed within the TCGA-BLCA cohort and subsequently verified utilizing a validation set. Two machine learning algorithms were employed to screen hub genes. QRT-qPCR was performed to detect the gene expression levels utilized in the construction of prognostic models across various cell lines. Additionally, the cMAP database and molecular docking were utilized for searching small molecule drugs. ResultsThe results of single-cell analysis revealed the presence of epithelial cells in multiple subpopulations, with 1579 marker genes selected for subsequent investigations. Subsequently, four epithelial cell subtypes were identified within the TCGA-BLCA cohort. Notably, cluster A exhibited a significant survival advantage. Concurrently, an artificial neural network prognostic model comprising 17 feature genes was constructed, accurately stratifying patient risk. Patients categorized in the low-risk group demonstrated a considerable survival advantage. The ROC analysis suggested that the model has strong prognostic ability. Furthermore, the findings of the validation group align consistently with those from the training group. Two types of machine learning algorithms screened NFIC as hub genes. Forskolin, a small molecule drug that binds to NFIC, was identified by employing a cMAP database and molecular docking. ConclusionThe analysis results supplement the research on the role of epithelial cells in BLCA. An artificial neural network prognostic model containing 17 characteristic genes demonstrates the capability to accurately stratify patient risk, thereby potentially improving clinical decision-making and optimizing personalized therapeutic approaches.

Neural Network Approach Modelling Mechanical Characteristics of Fly ash, Pond ash and Bottom Ash integrated Geopolymer Composites.

<p style="text-align:justify"><span style="font-size:11pt"><span style="line-height:200%"><span style="tab-stops:.55in"><span style="font-family:Calibri,"sans-serif""><span lang="EN-IN" style="font-size:12.0pt"><span style="line-height:200%"><span style="font-family:"Times New Roman","serif"">Key features for the quality assertion of geopolymer composites are the vital strength characteristics of geopolymer composites. In this work, an artificial neural network data-driven model was employed for predicting 28-day vital strength characteristics for geopolymer composites incorporated with ingredients such as fly ash, manufactured sand, pond ash, bottom ash, coarse aggregate water, sodium hydroxide, and sodium silicate in diverse combinations. In this paper, the ANN model was created using investigational data, and the developed network was trained to fit inputs and the targeted output. ANN modelling was exploited for the prediction of 70 sets of data comprising a training phase of 80%, a testing phase of 10%, and a validation phase of the leftover 10%. To create an artificial neural network, input six parameters to attain one output. Artificial neural networks best exemplified the responses of individual ingredients in composites and established that an artificial neural network is a prospective methodology for predicting vital strength characteristics. It also found that the precision of the outcome primarily depends on the erudition matrix, and the number of data exploited in the training and testing of the network may have had the highest error from -0.0029 to 0.0034 on evaluation.</span></span></span></span></span></span></span></p>

Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis

BackgroundKeloid is a disease characterized by proliferation of fibrous tissue after the healing of skin tissue, which seriously affects the daily life of patients. However, the clinical treatment of keloids still has limitations, that is, it is not effective in controlling keloids, resulting in a high recurrence rate. Thus, it is urgent to identify new signatures to improve the diagnosis and treatment of keloids.MethodBulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids.ResultsIn the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion.ConclusionIn summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.