Application Of Machine Learning Techniques Research Articles

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

The air quality in the Jakarta area is examined in this study using artificial intelligence (AI) to assist a semi-supervised learning technique. The clustering approach is used in this article to separate air pollution into three main categories moderate, low, and high levels. This clustering helps identify shared characteristics among measures like PM10, SO2, NO2, and others, even when air quality labels are not always accessible. Using the Random Forest method, the air quality will be categorized in this experiment with an accuracy rate of 93%. Additionally, the results of variable significance analysis are examined on this article to identify the variables with the biggest effects on air quality, notably PM10, SO2, and NO2. This study demonstrates the enormous potential of applying machine learning techniques, particularly semi-supervised learning approaches, to assist sustainable environmental regulations while also monitoring and enhancing Jakarta's air quality. We describe the experimental procedures, the findings, and the implications of our research for comprehending and addressing urban air pollution in this article

Machine learning application for prediction of surgical site infection after posterior cervical surgery.

Surgical site infection (SSI) is one of the most common complications of posterior cervical surgery. It is difficult to diagnose in the early stage and may lead to severe consequences such as wound dehiscence and central nervous system infection. This retrospective study included patients who underwent posterior cervical surgery at The Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University from September 2018 to June 2022. We employed several machine learning methods, such as the gradient boosting (GB), random forests (RF), artificial neural network (ANN) and other popular machine learning models. To minimize the variability introduced by random splitting, the results underwent 10-fold cross-validation repeated 10 times. Five measurements were averaged across 10 repetitions with 10-fold cross-validation, the RF model achieved the highest AUROC (0.9916), specificity (0.9890) and precision (0.9759). The GB model achieved the highest sensitivity (0.9535) and the KNN achieved the highest sensitivity (0.9958). The application of machine learning techniques facilitated the development of a precise model for predicting SSI after posterior cervical surgery. This dynamic model can be served as a valuable tool for clinicians and patients to assess SSI risk and prevent it in clinical practice.

Composite Fins Subsonic Flutter Prediction Based on Machine Learning

In the present work, the potential application of machine learning techniques in the flutter prediction of composite materials missile fins is investigated. The flutter velocity data set required for different fin aerodynamic geometries and materials is generated using a hybrid data collection method: from the wind tunnel experiments at flows ranging from 5 to 30 m/s at Re = 300,000 to 500,000, whereas synthetic data is collected using modified NACA flutter boundary model. Once the flutter data are collected, different regression algorithms were investigated, and the results were compared in terms of accuracy (when compared to the experimentally obtained results and the numerical flutter models), training time (minimization), and R-squared values (maximization). The algorithms investigated and their performance analyzed are fast forest regression, SDCA regression (stochastic dual coordinate ascent), and the light GBM (light gradient boosting machine) regression algorithm that belongs to the gradient boosting regression algorithm family. It was found that the light GBM algorithm renders the most accurate results. Based on this research, it can be concluded that artificial intelligence (machine learning) techniques can be successfully deployed in the analysis of complex flutter phenomena.

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels.

Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.

Analyzing the impact of geosynthetic reinforcement on Sinkhole: A numerical investigation with Machine Learning approach

The present study describes the results of numerical investigations with implementation of Machine Learning approach to prevent the event of sinkhole formation into the ground by using geosynthetic reinforcement. A series of 2D finite-element analysis were carried out with emphasis on the effects of geosynthetics reinforcement parameters such as geogrid length, stiffness and spacing between the geogrid layers to evaluate the behavior of ground over the sinkhole. Moreover, a database was also generated from the simulations for the application of Machine Learning techniques to predict the behavior as well. The hyperparameters of the Machine Learning models were optimized using Bayesian optimization to enhance the prediction accuracy. The critical values of geogrid reinforcement parameters for maximum reinforcing effect are also suggested. The results indicate the effectiveness of geosynthetic reinforcement to significantly reduce the sinkhole deformations and also the potential of Machine Learning to predict ground subsidence.

Exploration of Alloying Elements of High Specific Modulus Al-Li Alloy Based on Machine Learning.

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum-lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy's components, a more generalized modulus prediction model for Al-Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al-Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm-3)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al-Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al-Li alloys.

The application of machine learning approaches to determine the predictors of anemia among under five children in Ethiopia

Health professionals need a strong prediction system to reach appropriate disease diagnosis, particularly for under-five child with health problems like anemia. Diagnosis and treatment delay can potentially lead to devastating disease complications resulting in childhood mortality. However, the application of machine learning techniques using a large data set provides scientifically sounded information to solve such palpable critical health and health-related problems. Therefore, this study aimed to determine the predictors of anemia among under-5 year’s age children in Ethiopia using a machine learning approach. A cross-sectional study design was done using the Ethiopian Demographic and Health Survey 2016 data set. A two-stage stratified cluster sampling technique was employed to select the samples. The data analysis was conducted using Statistical Package for Social Sciences/SPSS version 25 and R-software. Data were derived from Ethiopian Demographic and Health Survey. Boruta algorism was applied to select the features and determine the predictors of anemia among under-5 years-old children in Ethiopia. The machine learning algorism showed that number of children, distance to health facilities, health insurance coverage, youngest child’s stool disposal, residence, mothers’ wealth index, type of cooking fuel, number of family members, mothers’ educational status and receiving rotavirus vaccine were the top ten important predictors for anemia among under-five children. Machine-learning algorithm was applied to determine the predictors of anemia among under- 5 year’s age children in Ethiopia. We have identified the determinant factors by conducting a feature importance analysis with the Boruta algorithm. The most significant predictors were number of children, distance to health facility, health insurance coverage, youngest child’s stool disposal, residence, mothers’ wealth index, and type of cooking fuel. Machine learning model plays a paramount role for policy and intervention strategies related to anemia prevention and control among under-five children.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data.

Machine learning (ML) of kinetic energy functionals (KEFs), in particular kinetic energy density (KED) functionals, is a promising way to construct KEFs for orbital-free density functional theory (DFT). Neural networks and kernel methods including Gaussian process regression (GPR) have been used to learn Kohn-Sham (KS) KED from density-based descriptors derived from KS DFT calculations. The descriptors are typically expressed as functions of different powers and derivatives of the electron density. This can generate large and extremely unevenly distributed datasets, which complicates effective application of ML techniques. Very uneven data distributions require many training datapoints, can cause overfitting, and can ultimately lower the quality of an ML KED model. We show that one can produce more accurate ML models from fewer data by working with smoothed density-dependent variables and KED. Smoothing palliates the issue of very uneven data distributions and associated difficulties of sampling while retaining enough spatial structure necessary for working within the paradigm of KEDF. We use GPR as a function of smoothed terms of the fourth order gradient expansion and KS effective potential and obtain accurate and stable (with respect to different random choices of training points) kinetic energy models for Al, Mg, and Si simultaneously from as few as 2000 samples (about 0.3% of the total KS DFT data). In particular, accuracies on the order of 1% in a measure of the quality of energy-volume dependence B'=EV0-ΔV-2EV0+E(V0+ΔV)ΔV/V02 (where V0 is the equilibrium volume and ΔV is a deviation from it) are obtained simultaneously for all three materials.

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.

Improving Project Budgeting Systems by Developing Machine Learning Models

The lack of an efficient budgeting system makes it more difficult for a business to satisfactorily execute projects or gain new business. To improve the accuracy of budgeting using the classical approach, a dynamic system is required. Building dynamic systems that apply machine learning techniques can support companies in improving their budgeting system. This quantitative study built five machine learning regression models: multiple linear regression, artificial neural network, support vector machine, k-nearest neighbors, and random forest. The five built models were used to predict the closing costs of 552 industrial automation projects that were carried out in Africa and the Middle East. Using root mean square error, the model forecast precision was compared to that of the classical system. The outcome shows that there is a significant difference between machine learning models and classical systems. Therefore, the use of machine learning techniques can improve the accuracy for businesses of their budgeting system.

Application of machine learning technique to predict the energy performance of energy tunnels

This study investigates the application of a machine learning technique in the field of energy tunnels (i.e. tunnels equipped as ground heat exchangers), as a method to efficiently predict and optimise the thermal exchange during the operational phase and reduce the related computational time and resources. Artificial Neural Networks (ANN) models were trained based on short-term (i.e. some years) numerical simulation data. Then, the accuracy of the trained model was assessed by comparing its results for longer operational periods under the same or different thermal operation patterns with respect to those used for training. Numerical simulations were run for long-term periods, and the obtained numerical results were assumed to be the target to verify the accuracy of the prediction. The analyses under different scenarios indicate that the trained models are able to accurately and rapidly predict the thermal exchange of energy tunnels, not only for future years under the same operational pattern, but also for other thermal operation patterns. In this study, using the same computational machine, a 20-year numerical forecast of thermal output takes hours or days depending on complexity of the numerical model, but it can be reduced to minutes by using the proposed ANN model. This represents a first attempt demonstrating that with further development, machine learning techniques could be used in the practice, in combination with short-term monitoring data, as a tool for the assessment of long-term energy potential and optimisation of energy tunnels during operation.

Prediction of dyskinesia in Parkinson’s disease patients using machine learning algorithms

Dyskinesias are non preventable abnormal involuntary movements that represent the main challenge of the long term treatment of Parkinson’s disease (PD) with the gold standard dopamine precursor levodopa. Applying machine learning techniques on the data extracted from the Parkinson’s Progression Marker Initiative (PPMI, Michael J. Fox Foundation), this study was aimed to identify PD patients who are at high risk of developing dyskinesias. Data regarding clinical, behavioral and neurological features from 697 PD patients were included in our study. Our results show that the Random Forest was the classifier with the best and most consistent performance, reaching an area under the receiver operating characteristic (ROC) curve of up to 91.8% with only seven features. Information regarding the severity of the symptoms, the semantic verbal fluency, and the levodopa treatment were the most important for the prediction, and were further used to create a Decision Tree, whose rules may guide the pharmacological management of PD symptoms. Our results contribute to the identification of PD patients who are prone to develop dyskinesia, and may be considered in future clinical trials aiming at developing new therapeutic approaches for PD.

When magnetron sputtering deposition meets machine learning: Application to process anomaly detection

This paper demonstrates how the application of machine learning techniques can be used in Magnetron Sputtering (MS) processes, to detect anomalies and reduce their failure rate. Magnetron Sputtering is a widely used technique in materials science and engineering to deposit thin films of various materials for a range of applications. However, the process is complex and can be prone to various anomalies that can lead to defects in the deposited films, resulting in a non-negligible waste of coated objects. In this paper, we focus on the use of machine learning algorithms for both online and offline anomaly detection, which can help identify and diagnose process anomalies in real-time or post-process. Our results demonstrate that machine learning techniques can be used to develop anomaly detection systems, to limit failure in magnetron sputtering processes.

A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques.

The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.

Application of machine learning in the investigation of entropy generation incurred during electroosmotic flows of Casson nanofluid

The present research article delves into the application of machine learning techniques in the study of entropy generation arising from the electro‐MHD Casson nanofluid flow in a symmetric microchannel. The inertial effect on fluid flow through a non‐Darcy porous medium is considered where we have taken into account the velocity, thermal, and concentration slips at boundaries. The resulting system of PDEs is converted to system of ODEs using appropriate wave frame transformations. The metamorphosed system is solved using bvp4c routine of MATLAB. The impact of several factors represented by respective parameters on velocity, nanofluid temperature, fraction of nanoparticles, entropy generation, and Bejan number are shown through graphs. This study also employs a machine learning technique in which Particle Swarm Optimization (PSO) algorithm has been used in conjunction with an Artificial Neural Network (ANN) to develop a predictive model capable of optimizing entropy generation within this specific fluidic context. Such an analysis could be useful in determining the optimum entropy generation in pulsatile transport of physiological fluid through intestine. With the use of above mentioned machine learning technique, the considered model offers the minimum entropy generation for specific values of the electroosmotic parameter, dimensionless temperature difference, and velocity slip parameter. It is observed that the flow with minimum electroosmosis effect and maintained at a specific velocity slip gives the optimal entropy generation.

Transmission line impulse response modelling using machine learning techniques

<p>Conventional methods of circuit simulation such as full-wave electromagnetic fieldsolvers can be very slow. Machine learning is an emerging technology in modelling, simulation, optimization, and design that present attractive alternatives to the conventional methodologies because they can be trained with a small amount of data, and then used to perform fast circuit predictions within the same design space. In this paper, we present applications of machine learning techniques for the modelling of transmission lines from their impulse reponses. The standard multilayer perceptron (MLP) neural network and the gaussian process (GP) regression techniques are demonstrated, and<br />both models are successfully implemented to model the impulse responses of transmission lines with great accuracies. We show that the GP outperforms the MLP in terms of prediction accuracies and that the GP is more data efficient than the MLP. This is beneficial considering that each training sample is expensive, making the GP a good candidate for the task, compared to the more popular MLP.</p>

Improving Brain Tumor Classification Efficacy through the Application of Feature Selection and Ensemble Classifiers

Accurate brain tumor detection is crucial due to its high mortality rate. However, existing automated methods suffer from limited accuracy and high false-positive rates. In this study, we aimed to improve brain tumor classification by comparing 17 different classifiers organized into six groups: Decision Tree (DT) Model, Support Vector Machine (SVM), Naive Bayes Classifier, Logistic Regression, Generalized Linear Model (GLM) Classifier, and Neural Network. We utilized a dataset of 3,762 Magnetic Resonance Imaging (MRI) scans of brain tumors from Kaggle, with each image having dimensions of 240 × 240 pixels and labeled as tumor or non-tumor. Our approach involved three main steps: extracting visual information using 17 predictor classes, optimizing feature extraction through weight optimization, and comparing different sets of classifier models. We evaluated the models’ performance using the confusion matrix and Receiver Operating Characteristics (ROC) curves. Our results showed that optimizing feature selection and utilizing ensemble classifiers improved the accuracy of brain tumor classification. The DT Model with ensemble classifiers emerged as the best-performing classifier, achieving an accuracy of 98.11% and an AUC of 0.99. Notably, Random Tree (RT) exhibited the highest accuracy within the ensemble classifier set, with a significant increase compared to other models. Our proposed method outperformed the standard approach, demonstrating its potential for enhancing brain tumor detection accuracy. This study contributes to the field by providing a more accurate method for detecting brain tumors, potentially enabling earlier detection and improved patient outcomes. Future research should focus on further improving brain tumor diagnosis and treatment through the application of machine learning techniques.

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions, the dynamic compressive strength (DCS) of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering. Nevertheless, testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive. Therefore, this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques. Initially, a database of DCS for frozen-thawed rocks, comprising 216 rock specimens, was compiled. Three external load parameters (freeze-thaw cycle number, confining pressure, and impact pressure) and two rock parameters (characteristic impedance and porosity) were selected as input variables, with DCS as the predicted target. This research optimized the kernel scale, penalty factor, and insensitive loss coefficient of the support vector regression (SVR) model using five swarm intelligent optimization algorithms, leading to the development of five hybrid models. In addition, a statistical DCS prediction equation using multiple linear regression techniques was developed. The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes. A sensitivity analysis based on the cosine amplitude method has also been conducted. The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions. Among these models, the SVR model optimized with the chameleon swarm algorithm exhibited the best performance, with metrics indicating its effectiveness, including root mean square error (RMSE) = 3.9675, mean absolute error (MAE) = 2.9673, coefficient of determination (R2) = 0.98631, and variance accounted for (VAF) = 98.634. This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models. Notably, impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction. This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Machine Learning for Risk Prediction of Cardiovascular Disease: Current Advances and Future Prospects

One of the main causes of death worldwide is cardiovascular disease (CVD). Effective treatment of this global concern depends on early detection, as well as management. Currently, people with established heart issues are treated by physicians and other medical experts, with detection at later stages of CVD. However, the burden of cardiovascular disease treatment can be significantly reduced if it was possible to accurately estimate a patient's CVD risk at the initial stages. Machine learning techniques have emerged as a viable method for improving CVD risk prediction, which enables treatments to be more effectively tailored to each individual patient's needs. An in-depth analysis of current research on machine learning applications for CVD risk prediction is provided in this publication. The paper discusses the benefits of applying machine learning techniques, various prediction algorithms, performance assessment, and current research limits. Our findings suggest that machine learning methods are useful for predicting CVD risk and have the potential to improve clinical judgment, which may help to lessen the burden of cardiovascular disease in the future. This research also helps to shape the medical field by providing insights on treating similar deadly diseases using AI and machine learning models.