Multilayer Perceptron Algorithm Research Articles

Acoustic impedance prediction based on extended seismic attributes using multilayer perceptron, random forest, and extra tree regressor algorithms

AbstractAcoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels through it. Understanding this relationship is essential because low acoustic impedance values are closely associated with high porosity, facilitating the accumulation of more hydrocarbons. In this study, we estimate the acoustic impedance based on nine different inputs of seismic attributes in addition to depth and two-way travel time using three supervised machine learning models, namely extra tree regression (ETR), random forest regression, and a multilayer perceptron regression algorithm using the scikit-learn library. Our results show that the R2 of multilayer perceptron regression is 0.85, which is close to what has been reported in recent studies. However, the ETR method outperformed those reported in the literature in terms of the mean absolute error, mean squared error, and root-mean-squared error. The novelty of this study lies in achieving more accurate predictions of acoustic impedance for exploration.

A Hybrid MultiLayerPerceptron-ExtremeGradientBoost Approach for Precise State of Charge and State of Health Assessment

Lithium ion batteries are popularly used in various energy storage systems. Accurate battery's State of Charge (SoC) and State of Health (SoH) estimate is essential for energy storage system's safety. This manuscript proposes a hybrid approach for estimating SoC and SoH of lithium ion battery. The proposed hybrid technique is the joint execution of both the Multi-Layer Perceptron (MLP) with Extreme Gradient boosting (XGBoost) algorithm. Hence, it is named as MLP-XG Boost Algorithm. The major objective of the proposed system is to improve the safety of energy storage systems and to achieve the highly accurate SoH. The MLP algorithm is utilized to analyze and trained the battery data. The XG Boosting algorithm is utilized to preprocessed and estimate the SoC and SoH of a lithium-ion battery. The proposed method's effectiveness is assessed using the MATLAB platform and contrasted with other methods that are currently in use. In every system now in use, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), the proposed approach produces better results. From the result it is concluded that the proposed method based Mean Square Error and Mean Absolute Error Root is less than 1 at various temperatures such as 0∘C, 25∘Cand 45∘C and it is better and efficient.

Towards a Framework for Performance Management and Machine Learning in a Higher Education Institution

Towards a Framework for Performance Management and Machine Learning in a Higher Education Institution

Monitoring of Plant Ecological Units Cover Dynamics in a Semiarid Landscape from Past to Future Using Multi-Layer Perceptron and Markov Chain Model

Anthropogenic activities and natural disturbances cause changes in natural ecosystems, leading to altered Plant Ecological Units (PEUs). Despite a long history of land use and land cover change detection, the creation of change detection maps of PEUs remains problematic, especially in arid and semiarid landscape. This study aimed to determine and describe the changes in PEUs patterns in the past and present, and also predict and monitor future PEUs dynamics using the multi-layer perceptron-Markov chain (MLP-MC) model in a semiarid landscape in Central Zagros, Iran. Analysis of PEUs classification maps formed the basis for the identification of the main drivers in PEUs changes. First, an optimal time-series dataset of Landsat images were selected to derive PEUs classification maps in three periods, each separated by 16 years. Then, PEUs multi-temporal maps classified for period 1 (years 1986–1988) period 2 (years 2002–2004), and period 3 (years 2018–2020) were employed to analyze and predict PEUs dynamics. The dominant transitions were identified, and the transition potential was determined by developing twelve sub-models in the final change prediction process. Transitions were modeled using a Multi-Layer Perceptron (MLP) algorithm. To predict the PEU map for period 3, two PEUs classification maps of period 1 and period 2 were used using the MLP-MC method. The classified map and the predicted map of period 3 were used to evaluate and validate the predicted results. Finally, based on the results, transitions of future PEUs were predicted for the year 2036. The MLP-MC model proved to be a powerful model that can predict future PEUs dynamics that are the result of current human and managerial activities. The findings of this study demonstrate that the impact of anthropogenic processes and management activities will become visible in the natural environment and ecosystem in less than a decade.

Performance analysis of a blockchain enabled domestic power consumption monitoring and forecasting system

Purpose The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting. Design/methodology/approach The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms. Findings The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively. Originality/value A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

Utilizing Deep Neural Networks for Chrysanthemum Leaf and Flower Feature Recognition

Chrysanthemums, a significant genus within the Asteraceae, hold a paramount position in the global floricultural industry, second only to roses in market demand. The proliferation of diverse chrysanthemum cultivars presents a formidable challenge for accurate identification, exacerbated by the abundance of varieties, intricate floral structures, diverse floret types, and complex genetic profiles. Precise recognition of chrysanthemum phenotypes is indispensable to navigating these complexities. Traditional methods, including morphology studies, statistical analyses, and molecular markers, have fallen short due to their manual nature and time-intensive processes. This study presents an innovative solution employing deep learning techniques for image-based chrysanthemum phenotype recognition. Leveraging machine learning, our system autonomously extracts key features from chrysanthemum images, converting morphological data into accessible two-dimensional representations. We utilized Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms to construct frameworks for processing image data and classifying chrysanthemum cultivars based on color, shape, and texture. Experimental results, encompassing 10 cultivars, 10 flower colors, and five flower shapes, consistently demonstrated recognition accuracy ranging from 79.29% up to 97.86%. This tool promises streamlined identification of flower traits, and we anticipate its potential for real-time identification enhancements in future iterations, promising advances in chrysanthemum cultivation and exportation processes. Our approach offers a novel and efficient means to address the challenges posed by the vast diversity within chrysanthemum species, facilitating improved management, breeding, and marketing strategies in the floricultural industry.

Machine learning-based virtual screening of multi-target anti-obesity compounds from medicinal and edible plants: A combined in silico and in vitro study

In response to the limited effectiveness of existing weight loss food products, we sought to apply machine learning-based virtual screening methods to identify potential anti-obesity functional compounds from medicinal and edible plants and validate their in vitro activities. Firstly, we construct and evaluate the machine learning (ML) screening models using Multilayer Perceptron (MLP) and Random Forest (RF) algorithms. The receiver operating characteristic (ROC) curve demonstrates the high accuracy of MLP and RF models in screening for obese-related targets PL (pancreatic lipase) and AMPK (Adenosine 5′-monophosphate activated protein kinase). Subsequently, the tested ML models are employed to screen the constructed database, and Gypenoside LXVI (GYP) and alisol-b-23-acetate (ALI) are identified as compounds exhibiting favorable activity against both targets. The hit compounds are tested for their impact on lipase activity and lipid accumulation. The test results show that GYP and ALI have favorable inhibitory effects on pancreatic lipase (PL), with IC50 of 359.7 and 433.8 μg/mL. Furthermore, both GYP and ALI significantly reduced cellular lipid accumulation by 72.89% and 79.01% with the concentration increase to 40 μg/mL. The molecular docking results indicate that GYP and ALI can interact with several amino acid residues on the two target proteins, thereby affecting the activity of the target proteins. In conclusion, GYP and ALI can prevent and alleviate obesity by inhibiting PL activity and regulating AMPK signaling factors. We innovatively applied virtual screening based on ML to discover functional factors in food for anti-obesity purposes. This novel computational screening technique holds significant potential in the development of dietary supplements to combat obesity.

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

BackgroundThe aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors.MethodsThis study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson's correlation test was also conducted to assess the correlation between participants' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset.ResultsA statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%.ConclusionsCaries risk assessment using a simple questionnaire can identify individuals at risk of dental caries, determine the key risk factors, provide information to help reduce the risk of dental caries over time and ensure follow-up. In addition, it is extremely important to apply effective preventive treatments and to prevent the general health problems that are caused by the deterioration of oral health. The results of this study show the potential of machine learning algorithms for predicting caries risk groups, and these algorithms are promising for future studies.

Dynamic Fractional-Order Grey Prediction Model with GWO and MLP for Forecasting Overseas Talent Mobility in China

International students play a crucial role in China’s talent development strategy. Thus, predicting overseas talent mobility is essential for formulating scientifically reasonable talent introduction policies, optimizing talent cultivation systems, and fostering international talent cooperation. In this study, we proposed a novel fractional-order grey model based on the Multi-Layer Perceptron (MLP) and Grey Wolf Optimizer (GWO) algorithm to forecast the movement of overseas talent, namely MGDFGM(1,1). Compared to the traditional grey model FGM(1,1), which utilizes the same fractional order at all time points, the proposed MGDFGM(1,1) model dynamically adjusts the fractional-order values based on the time point. This dynamic adjustment enables our model to better capture the changing trends in the data, thereby enhancing the model’s fitting capability. To validate the effectiveness of the MGDFGM(1,1) model, we primarily utilize Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the evaluation criteria for the prediction accuracy, as well as standard deviation (STD) as an indicator of the model stability. Furthermore, we perform experimental analysis to evaluate the predictive performance of the MGDFGM(1,1) model in comparison to NAÏVE, ARIMA, GM(1,1), FGM(1,1), LSSVR, MLP, and LSTM. The research findings demonstrate that the MGDFGM(1,1) model achieves a remarkably high level of prediction accuracy and stability for forecasting overseas talent mobility in China. The implications of this study offer valuable insights and assistance to government departments involved in overseas talent management.

Elastic Net - MLP - SMOTE (EMS) Based Model for Enhancing Stroke Prediction

A stroke is a sudden disruption in the blood supply to the brain, affecting one or more blood vessels that nourish the brain. This results in a disturbance or deficiency in the brain's oxygen supply, causing damage or impairment to brain cells. In some cases, determining the timing and severity of a stroke can be challenging. This study proposes an EMS (Elastic Net - MLP - SMOTE) model built on artificial intelligence, specifically utilizing two machine learning algorithms, Elastic Net and Multilayer Perceptron (MLP) with using Synthetic Minority Over-sampling Technique (SMOTE). The Elastic Net algorithm was employed for feature selection to identify crucial features, followed by prediction using the Multilayer Perceptron algorithm. The Elastic Net algorithm was used due to its incorporation of both L2 and L1 regularization, providing good results in discerning influential features in model performance. The Multilayer Perceptron algorithm was employed for its reliance on deep learning techniques, which yield promising results in such cases. This algorithm classified data from a comprehensive dataset containing essential features related to stroke. SMOTE is used to increase the performance of the model. Notably, no previous research study has integrated these three techniques together (Elastic Net - MLP – SMOTE). EMS achieved a prediction accuracy of 95% and MSE = 0.05. This model facilitates predicting the occurrence of stroke by relying on the patient's historical data, mitigating the sudden onset of this serious disease.

Strengthening Wireless Network Security: Supervised Machine Learning-Based Intrusion Detection for Enhanced Threat Mitigation

With the wireless networks deemed vital to our laced and unetched world, it has become fundamental to safeguard these networks against the various cyber threats that are on the rise. As a research proposal I show concern about the application of a Supervised Machine Learning-Based Intrusion Detection system, making use of Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. The survey begins with the introduction of an intrusive inter-dataset technology that merges two catalytic datasets into a single efficient model that can address both model security and data sensitivity concerns. The evaluation of several machine learning algorithms within the proposed framework is a core component of the analysis. The accuracy measure as well as others such as recall, precision, and F1 score, are applied to evaluate the algorithms' level in detecting intrusions from different datasets.The investigation shows that the intra-dataset routing noticeably improves the detecting modules' efficiency. Notably, the model of MLP algorithm has enhanced recall which in turn shows the enhancement of the positive instance identification. SVM shows increased precision, which accounts for the improved accuracy since correct names of positive cases are produced more often. LR finds it overall enhance the precisions thus attesting to its efficiency in correct deductions. The research documents the multifaceted nature of IDS, prompting the intervention of the machine learning algorithms with ddoswdelcome intrusion detection system. A study which provides a practical guide for network administrators and security professionals hinting on how to select algorithms to meet the distinct security needs in their enterprises. These findings contribute to the open discourse on wireless network security and serve as a base for future study of this engaging research topic in constantly developing field of intrusion detection.

Synergizing LED Technology and Hydropriming for Intelligent Modeling and Mathematical Expressions to Optimize Chickpea Germination and Growth Indices

AbstractThe influence of hydropriming and Light Emitting Diodes (LED) on germination and growth indices, followed by optimizing and validation via artificial intelligence-based models was carried out in this research. White LEDs (W-LEDs) were more effective by yielding the most effective growth indices, such as mean germination time (MGT) (1.11 day), coefficient of variation of germination time (CVt) (20.72%), mean germination rate (MR) (0.81 day−1), uncertainty (U) (0.40 bit), and synchronization (Z values) (0.79); the optimum MGT (1.09 day), CVt (15.97%), MR (0.77 day−1), U (0.32 bit), and Z (0.55) values were found after 2 h of hydropriming, which was responsible for all efficient growth indicators. W-LEDs with 1 h hydropriming proved to be the ideal LED and hydropriming combination. Results on growth indices for in vitro seedlings were completely different from those on germination indices, and the most desirable germination indices were linked to red LEDs (R-LEDs). Whereas 4 h hydropriming was most effective for the post-germination process. Pareto charts, normal plots, contour plots, and surface plots were created to optimize the input variables. Finally, the data were predicted using Arificial Neural Network (ANN) inspired multilayer perceptron (MLP) and machine learning-based random forest (RF) algorithms. For both models, plant height was correlated with maximum R2 values. Whereas, all output variables had relatively low mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) scores, indicating that both models performed well. The results of this investigation disclosed a link between certain LEDs and hydropriming treatment for in vitro germination indices and plant growth. Graphical Abstract Graphical presentation of actual and predicted values for germination indices in chickpea

Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor

BackgroundArtificial intelligence is being used for rehabilitation, including monitoring exercise compliance through sensor technology. AI classification of shoulder exercise wearing an IMU sensor has only been reported in normal (i.e. painless) subjects. To prove the feasibility of monitoring exercise compliance, we aimed to classify 11 types of shoulder rehabilitation exercises using an AI (artificial intelligence) algorithm in patients with shoulder pain. We had the patients wear an IMU-based sensor, collected data during exercise, and determined the accuracy of exercise classification.MethodsData were collected from 58 patients (27 males, 31 females, age range 37–82 years) diagnosed with shoulder diseases such as adhesive capsulitis and rotator cuff disease. 11 types of shoulder pain rehabilitation exercise programs were developed and repeated each exercise ten times per session while wearing an IMU sensor. The study applied the Rectified Linear Unit (ReLU) and the SoftMax as the activation function for hidden layers, the output layer.ResultsThe acquired data was used to train a DNN model using the multilayer perceptron algorithm. The trained model was used to classify 11 types of shoulder pain rehabilitation exercises. The training accuracy was 0.975 and the test accuracy was 0.925.ConclusionThe study demonstrates that IMU sensor data can effectively classify shoulder pain rehabilitation exercises, providing more appropriate feedback for patients. The model can be utilized to establish a system for remotely monitoring patients’ exercise performance. The use of deep learning in patient monitoring and rehabilitation has significant potential to bring innovative changes to healthcare service delivery.

Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms

The effective development and design of pharmaceuticals hold fundamental importance in the fields of medicine and the pharmaceutical industry. In this process, the accurate prediction of drug molecule solubility is a critical factor influencing the bioavailability, pharmacokinetics, and toxicity of drugs. Traditionally, mathematical equations based on chemical and physical properties have been used for drug solubility prediction. However, in recent years, with the advancement of artificial intelligence and machine learning techniques, new approaches have been developed in this field. This study evaluated different modeling approaches consisting of Graph Neural Networks (GNN), Multilayer Perceptron (MLP), and traditional Machine Learning (ML) algorithms. The Random Forest (RF) model stands out as the optimal performer, manifesting superior efficacy through the attainment of minimal error rates. It attains a Root Mean Square Error (RMSE) value of 1.2145, a Mean Absolute Error (MAE) value of 0.9221, and an R-squared (R2) value of 0.6575. In contrast, GNN model displays comparatively suboptimal performance, as evidenced by an RMSE value of 1.8389, an MAE value of 1.4684, and an R2 value of 0.2147. These values suggest that the predictions of this model contain higher errors compared to other models, and its explanatory power is lower. These findings highlight the performance differences among different modeling approaches in drug solubility prediction. The RF model is shown to be more effective than other methods, while the GNN model performs less effectively. This information provides valuable insights into which model should be preferred in pharmaceutical design and development processes.

Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.).

Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.

Early prediction models and crucial factor extraction for first-year undergraduate student dropouts

PurposeThis study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.Design/methodology/approachThe real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.FindingsDT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”Practical implicationsThis work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.Originality/valueAlthough first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.

The study on predicting the differentiation grade of colorectal cancer (CRC) based on magnetic resonance imaging (MRI) has not been reported yet. Developing a non-invasive model to predict the differentiation grade of CRC is of great value. To develop and validate machine learning-based models for predicting the differentiation grade of CRC based on T2-weighted images (T2WI). We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023. Patients were randomly assigned to a training cohort (n = 220) or a validation cohort (n = 95) at a 7:3 ratio. Lesions were delineated layer by layer on high-resolution T2WI. Least absolute shrinkage and selection operator regression was applied to screen for radiomic features. Radiomics and clinical models were constructed using the multilayer perceptron (MLP) algorithm. These radiomic features and clinically relevant variables (selected based on a significance level of P < 0.05 in the training set) were used to construct radiomics-clinical models. The performance of the three models (clinical, radiomic, and radiomic-clinical model) were evaluated using the area under the curve (AUC), calibration curve and decision curve analysis (DCA). After feature selection, eight radiomic features were retained from the initial 1781 features to construct the radiomic model. Eight different classifiers, including logistic regression, support vector machine, k-nearest neighbours, random forest, extreme trees, extreme gradient boosting, light gradient boosting machine, and MLP, were used to construct the model, with MLP demonstrating the best diagnostic performance. The AUC of the radiomic-clinical model was 0.862 (95%CI: 0.796-0.927) in the training cohort and 0.761 (95%CI: 0.635-0.887) in the validation cohort. The AUC for the radiomic model was 0.796 (95%CI: 0.723-0.869) in the training cohort and 0.735 (95%CI: 0.604-0.866) in the validation cohort. The clinical model achieved an AUC of 0.751 (95%CI: 0.661-0.842) in the training cohort and 0.676 (95%CI: 0.525-0.827) in the validation cohort. All three models demonstrated good accuracy. In the training cohort, the AUC of the radiomic-clinical model was significantly greater than that of the clinical model (P = 0.005) and the radiomic model (P = 0.016). DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process. In this study, we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC. This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Machine Learning-Based Predictive Model to Assess Rheological Dynamics of Eco-Friendly Oils as Biolubricants Enriched with SiO2 Nanoparticles

Efficient machinery operation relies on the performance of high-quality lubricants. Currently, mineral oils of different grades are widely employed for lubricating machine components, but their environmental impact is a concern. Biolubricants are potential alternatives to mineral oils due to environmental factors. The present study focuses on assessing the rheological characteristics of SiO2 nanoparticle (NP)-enhanced ecofriendly biolubricants for near zero and high-temperature conditions. Pure neem oil, pure castor oil and a 50:50 blend of both oils were considered as the base oils. Nanobiolubricants with enhanced dispersion stability were prepared for varied concentrations of NPs using an ultrasonification method. Viscosity analysis was conducted using an MCR-92 rheometer, employing the Herschel Bulkley model to precisely characterize the viscosity behavior of bio-oils. Due to the fluid–solid interaction between SiO2 NPs and bio-oils, a crossover trend was observed in the flow curves generated for different base oils enriched with SiO2 NPs. For neem oil, a significant increase in viscosity was noted for 0.2 wt% of NPs. Using the multilayer perceptron (MLP) algorithm, an artificial neural network (ANN) model was developed to accurately predict the viscosity variations in nanobiolubricants. The accuracy of the predicted values was affirmed through experimental investigations at the considered nanoSiO2 weight concentrations.

Cross-physical field prediction method for smoke field distribution in commercial building fire based on distributed optical fiber sensor

In modern commercial building spaces, the smoke generated during indoor fires poses a substantial safety threat to individuals attempting to evacuate. This study leveraged a distributed optical fiber temperature sensing system as the technological foundation. A series of commercial building space models were constructed by employing fire dynamics simulations, and a comprehensive database of simulated fire field information was established. The study built upon this database and proposed a method to establish influence pathways from existing temperature field detection data to three fire risk assessment parameters: carbon dioxide, oxygen, and visibility. The study utilized various machine learning algorithms and focused on selecting and constructing cross-physical field prediction models that optimized the performance of fire risk assessment parameters at the fire key plane. The findings demonstrated that the Multilayer Perceptron (MLP) algorithm model yielded the most accurate fit for carbon dioxide volume fraction, with a Mean Absolute Percentage Error (MAPE) of 3.80% between the model predictions and actual values. The Artificial Neural Network (ANN) algorithm showed superior fitting effects for oxygen volume fraction and visibility, with MAPEs of 1.37% and 3.10%, respectively. Furthermore, the study validated the generalizability of these optimal models by altering spatial scales, revealing robust performance. This study utilized a single optical fiber sensor to achieve dynamic prediction across different physical fields during fire incidents, expanding the application scope of optical fiber sensors in monitoring gas and visibility fields in commercial building fires. This not only enhanced the level of fire monitoring but also effectively reduce equipment costs. It provided effective data support for planning evacuation routes for individuals and rescue arrangements for firefighters, further ensuring personnel safety.

Fault detection in wind turbines: a supervised learning approach with multilayer perceptron neural network

The expansion of energies aligning with sustainable development requirements is continually progressing. A particular focus on wind turbines (WT), characterized as a highly intricate system, has brought attention to the costs associated with operation and maintenance. This has prompted a quest for increasingly efficient maintenance procedures. This article proposes that deviations from expected behavior be detected and classified as failure using operational data from permanent magnet direct drive wind turbines, obtained through the supervisory control and data acquisition system. To achieve real-time fault detection and gather information on the state of faults, the CFS subset evaluator method was employed to extract the most relevant attributes from the dataset. Experimental results demonstrate that the strategy of selecting the most pertinent attributes for the Multilayer Perceptron algorithm (MLP), comprising four layers, in fault classification, resulted in a high recognition rate for detecting and classifying faults in wind turbines. Computational results validating the models are presented.