Machine Method Research Articles

Analyzing Twitter Sentiments on Booster Vaccination with Support Vector Machine (SVM) Method

The Indonesian government has implemented various measures to prevent the spread of the COVID-19 virus, one of which is through a vaccination program with two doses (the first dose and the second dose). However, new variants of the virus have emerged, reducing the effectiveness of the initial vaccinations. To address this, the government introduced a booster vaccination program aimed at enhancing immunity by up to 80%. The government's plan for booster vaccination has received both positive and negative opinions from the public through various media platforms, including Twitter. This study analyzes public opinions on the booster vaccination plan into three classes: positive, negative, and neutral. SVM is a classification method in machine learning categorized as supervised learning, which involves finding an optimal line (hyperplane) as a separator for two different data classes. The stages of this research include data collection, data cleaning, data transformation, and data classification using the Support Vector Machine (SVM) method. The results of this study indicate that the accuracy of the SVM model reaches 80.42%.

Comparative study of EDM on titanium diboride ceramic material using different electrode materials and dielectric fluid

Abstract Titanium diboride is a highly used material in aerospace industry due of its remarkable longevity. Its strength poses a problem to traditional machining methods. We want to improve the EDM process parameters in TiB2 material by comparing two different dielectric fluids and four different electrode materials. The input parameters were adjusted to find the optimal combination. By studying the fluctuations of output parameters such as MRR, TWR, SR and overcut when processing titanium diboride, researchers can gain valuable insights. This is achieved through experimental studies and the results are then verified using a theoretical regression model. Experimental results confirmed that discharge current, voltage and Ton had a remarkable influence on MRR, TWR and surface roughness in TiB2 material. It has been observed that tool wear rate when using kerosene-based dielectric fluids is higher than that of deionized water-based dielectric fluids. Tungsten copper electrodes cause slightly less tool wear than tungsten electrodes, and produce higher MRR overall, when compared to other three electrode materials. Using a dielectricand a copper electrode can help you obtain a smoother surface. When deionized water is used as adielectric fluid with tungsten electrodes, the crossover value drops dramatically. Brass electrodesproduce ahigher overcut value compared to the other three electrode materials. The microstructure of the EDM hole was distinguished by SEM analysis, and the material composition of TIB2 was determined by EDS analysis

Porosity identification using residual PPTransformer network

Precisely estimating the carbonate’s porosity is essential for subsurface reservoir characterization. However, conventional methods for obtaining porosity using either core measurements or logging interpretation are expensive and inefficient. Considering the sequence data feature of logging curves and the booming development of intelligent networks in geoscience, this study proposes a reliable and low-cost intelligent Porosity Prediction Transformer (PPTransformer) framework for reservoir porosity prediction using logging curves as inputs. PPTransformer network not only extracts global features through convolutional layers but also captures local features using Encoders and self-attention mechanisms. This proposed network is a data-driven supervised learning framework with a superior accuracy and robustness. The testing results demonstrate that compared to the Transformer network, Long Short-Term time series network, and support vector machine method, the PPTransformer framework exhibits the highest average correlation coefficient and determination coefficient indicators and the lowest root mean square error and absolute error indicators. Moreover, adding stratigraphic lithology as geological constraints to the PPTransformer framework further improves the prediction performance. This indicates that geological constraints will enhance network performance.

Study on nanosecond pulsed laser micro-milling of stainless steel miniature gears with intersecting-axes

The utilization rate of miniature gears has increased sharply with the widespread application of micro-pumps, electronic equipment, and measuring instruments, and therefore the design and processing of miniature gear has become a subject of intense research. Current research has predominantly focused on machining miniature parallel-axes gears, but well-established machining methods for miniature intersecting-axes gears are still lacking. This paper presented a nanosecond pulsed laser micro-milling technology for miniature intersecting-axes gear (MIAG). Firstly, the 3D models of driving MIAG and driven MIAG were established, and the machining schemes were proposed on the basis of the characteristics of the models. Then, the influences of processing parameters such as defocusing distance, pulse overlap, and scanning strategy on the output responses, including removal rate, surface roughness, and shape accuracy were analyzed. Afterward, some miniature structures, such as miniature grooves, cylinders and cones, were machined by combining these process parameters. Finally, the MIAGs were machined, and the accuracy evaluation and kinematics experiment were carried out. The diameter of driving MIAG was 1.21 mm, and the diameter of driven MIAG was 3.389 mm. The experimental results indicate that the proposed machining technology can process MIAG with relatively high precision and can also offer a valuable reference for the manufacturing of other miniature components.

A novel recast layer in situ thinning method for film cooling holes in fast EDM drilling

As the main machining method of film cooling holes, fast EDM (electrical discharge machining) drilling has the defect that the thickness of a recast layer is difficult to control, which can severely affect the service life of a turbine blade on the aero engine parts. In order to reduce the thickness of a recast layer in fast EDM drilling while ensuring machining precision and efficiency, a novel recast layer in situ thinning method, which uses the precision-controlled discharge erosion and tiny energy layer-by-layer thinning strategy, is proposed. Moreover, the recast layer removal mechanism, as well as the influence of this method on fast EDM drilling are systemically investigated, thereby, the optimal combination of machining parameters with different electrode diameters of the in situ thinning method is obtained. Machining results show that with the proposed method, the maximum and average thickness of the recast layer can be well controlled within 2.5 μm and 2 μm, with the highest reduction rate of the recast layer reaching 85.39%, which greatly improves the machining quality of film cooling holes of a turbine blade.

Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.

Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microbiology offers a promising avenue for enhancing precision and reliability in site inference. Our study investigates the application of microbiome analysis in inferring drowning sites, focusing on microbial diversity in water samples and lung tissues of drowned animals from different sites in the Northwest River. We utilized 16S rDNA sequencing to analyze microbial diversity in water samples and lung tissues, revealing distinct microbial signatures associated with drowning sites. Our findings highlight variations in species richness and diversity across different sampling points, indicating the influence of environmental factors on microbial community structure. Machine learning models trained on microbial data from lung tissues demonstrated high accuracy in predicting drowning sites, with cross-validation accuracy ranging from 83.53% ± 3.99% to 95.07% ± 3.17%. Notably, the Gradient Boosting Machine (GBM) method achieved a classification accuracy of 95.07% ± 3.17% for different sampling points at a submersion time of 1 day. Moreover, our cross-species site inference results revealed that utilizing data from drowned mice to predict the drowning sites of rabbits in location W5 achieved an accuracy of 72.22%. In conclusion, our study underscores the potential of microbiome analysis in forensic investigations of drowning incidents. By integrating microbial data with traditional forensic techniques, there is significant potential to enhance the reliability of scene inferences, thereby making substantial contributions to case investigations and judicial trials.IMPORTANCEBy employing advanced techniques like microbial profiling and machine learning, the study aims to enhance the accuracy of determining drowning sites, which is crucial for both legal proceedings. By analyzing microbial diversity in water samples and drowned animal lung tissues, the study sheds light on how environmental factors and victim-related variables influence microbial communities. The findings not only advance our understanding of forensic microbiology but also offer practical implications for improving investigative techniques in cases of drowning.

A Fault Diagnosis Method for Turnout Switch Machines Based on Sound Signals

The turnout switch machine, a vital outdoor component of railway signaling, controls train steering amidst complex operations and high frequencies. Its malfunction significantly disrupts train operations, potentially causing derailments. This paper proposes a sound-based fault diagnosis method, termed ERS (a method combining EMD, ReliefF, and SVM), for effective monitoring and detection of turnout switch machines. The method employs Eigenmode Decomposition (EMD) to smooth the sound signal, reduce noise, and extract key statistical parameters of both the time and frequency domains. To address redundant information in high-dimensional features, the ReliefF algorithm is utilized for feature selection, dimension reduction, and fault classification based on weighted parameters. Subsequently, the selected feature parameters are used to train the Support Vector Machine (SVM). A comparison with results obtained without ReliefF feature selection demonstrates the necessity of this step. The results show that the fault diagnosis accuracy reaches 98% in the positioning work mode and 95.67% in the reversing work mode, verifying the method’s effectiveness and feasibility.

Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China

The collapsibility of aeolian sand has hindered the development of oil and gas resources and the construction of oil and gas stations in the Mu Us Desert. This study considered aeolian sand on the southern edge of the Mu Us Desert as the research object. Based on a water immersion load test, standard penetration test, and indoor geotechnical test, four evaluation indicators were selected, the water content, dry density, void ratio, and saturation. Combined with the support vector machine method, we established a method for evaluating the collapsibility of aeolian sand based on basic physical indicators. The results showed the following: (1) The degree of collapsibility was slight, with a small portion showing no collapsibility. And the load-settlement curve (P-s) was divided into three stages: the linear elastic deformation stage, the elastic–plastic deformation stage, and the collapsible deformation stage. (2) There was a strong relationship between the collapsibility coefficient and the four evaluation indicators for aeolian sand. Based on these indicators, we could accurately predict and evaluate the collapsibility coefficient. (3) Machine learning methods, such as the support vector machine, can effectively solve prediction and evaluation problems between variables when there is no clear mathematical relationship between multiple independent variables and a single dependent variable.

New approach for a non-linear/multi-dimensional calibration method for dynamic forces materials testing machines

New approach for a non-linear/multi-dimensional calibration method for dynamic forces materials testing machines

Enhancing ultrasound-guided brachial plexus nerve localization with ResNet50 and support vector machine

<p>Medical image segmentation and classification plays a vital role in nerve block/region identification, particularly for anesthesiologists relying on instinctual judgments. However, due to patient-specific anatomical variations, these methods sometimes lack precision. This research focuses on addressing the problem, by incorporating novel ensembling method of ResNet-50 and support vector machine (SVM) to achieve segmentation of dataset images and classification of nerve blocks respectively. The said novel ensemble model is trained on a publicly available dataset consisting of more than 16,800 images. The sole purpose of this work is to address the problem of peripheral nerve blocking (PNB) with the usage of ensemble modelling, while achieving the highest possible accuracy. This research will help practitioners in accurately identifying the location of brachial plexus and distinguishing the type of nerve block to be injected – interscalene and supraclavicular. The model, which integrates ResNet50 and SVM classifier, achieved a commendable 99.27% accuracy in identifying and classifying the brachial plexus region.</p>

Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDB-GSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (ε), and kernel parameter (γ) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90% within the error range. When using FS5, the PSO-SVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDB-GSHP heating systems.

Tool condition monitoring using I-kaz enhanced kernel extreme learning machine

Abstract In order to improve the accuracy of tool condition monitoring (TCM) in milling, an I-kaz enhanced Kernel Extreme learning Machine (I-kaz_KELM) method is proposed by combining the KELM and I-kaz statistical algorithm. It uses the I-kaz angle function to replace the conventional kernel function to avoid the selection of kernel function and the pre-set of its parameters. A two-layer network model of the I-kaz_KELM is constructed to improve the KELM in feature learning of complex non-linear high-dimensional. Research and analysis of two milling TCM public benchmark datasets (PHM 2010 TCM dataset and NASA TCM dataset) confirmed that the monitoring accuracy of the proposed method is better than SVM and KELM under limited samples. The RESM of the proposed method is reduced by at least 10% compared to the other two methods, and the RESM fluctuation amplitudes of the proposed method with model parameters is reduced by 35%–95% than that of the two other methods. It can be demonstrated that the proposed method can significantly improve learning performance without significantly affecting the learning speed, and has stronger robustness due to reduced sensitivity to the model parameter to be optimized.

Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.

Cloud radon data for earthquake magnitude prediction using machine learning

<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</span>

Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications

AbstractSuperalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components.

Temperature extraction from Brillouin sensing based on temporal convolutional networks

The Brillouin optical time-domain sensing system has become a hot spot of research due to its ability to seamlessly monitor the temperature and strain variations in optical fibers along the line. Given its current limitations of low accuracy and inadequate real-time performance in long-distance monitoring, the Brillouin gain extraction temperature method based on temporal convolutional networks is proposed. On this basis, we established a Brillouin optical time-domain experimental system where comprehensive simulations and tests were conducted to assess the temperature extraction performance under different conditions. Besides, a comparison was made between the system and traditional methods like Lorentz fitting method and extreme learning machine method. The results have suggested that the temporal convolutional network exhibits remarkable measurement accuracy, even in scenarios with low signal-to-noise ratios and large sweep frequency steps.

Surface evolution mechanism for atomic-scale smoothing of Si via atmospheric pressure plasma etching

Atmospheric plasma etching-based machining methods generally suffer from surface roughness deterioration. To achieve an atomic-scale smooth surface of Si via purely plasma etching, clarifying the etching evolution and mechanism is essential. In this study, we study surface evolution and smoothening mechanisms from the perspective of plasma etching modes comprehensively. The morphology and roughness evolutions of isotropic, orientation-selective, and atom-selective etching are investigated, respectively. The semi-finishing effect is realized through the growing and merging of hemispherical pits during isotropic etching, with the surface roughness being reduced from 103 nm to 0.79 nm. Orientation-selective etching is a roughening process, transforming square-opening pits into pyramid structures. Under the atom-selective mode, an atomically smooth surface with Sa 0.17 nm can be obtained. The top-down smoothing process of atom-selective is much more efficient than isotropic etching. Atom-selective etching with a maximum removal rate of 22 μm/min enables the rapid thinning of Si substrate thickness from 715 μm to 90.4 μm within 45 min. Additionally, atom-selective etching is a universal polishing approach regardless of pre-processed methods and is a damage-less process. This paper provides a promising strategy for atomic and close-to-atomic scale manufacturing.

Two-step optimization algorithm operated by heuristic and machine learning methods

Heuristics aim to provide approximate solutions for NP-hard combinatorial optimization (CO) problems in a reasonable time. They are faster than deterministic methods, where the solving process may take days. This paper presents an attempt to combine a heuristic algorithm and some machine learning (ML) methods for solving the Multidimensional Knapsack Problem (MKP), which is a well-known CO case. The approach named Genetic Algorithm and Machine Learning (GAML) is a two-step algorithm that first uses four machine methods to produce four solutions and then integrates the best one within the GA. For this purpose, (1) This approach builds models by training four machine learning (ML) methods using the decision variables obtained from the optimal solutions of simple MKP instances. The decision variable of an item is equal to one if it is selected in the knapsack and zero otherwise. (2) The four models are applied to predict the decision variables for complex MKP instances. (3) A dummy algorithm constructs feasible solutions from the predictions by adding the selected items randomly while the constraints are not violated. (4) For each MKP instance, the best feasible solution among the four built is integrated with the initialization and the crossing operators of a genetic algorithm. An experiment has been conducted on well-known complex MKP benchmarks. The results proved that the models were able to provide high-quality solutions. Also, the proposed algorithm was faster than other approaches in the literature.

Design optimization of bimetal-modified biochar for enhanced phosphate removal performance in livestock wastewater using machine learning

Mg-modified biochar shows high adsorption performance under weakly acidic and neutral water conditions. However, its phosphate removal efficiency markedly decreases in naturally alkaline wastewater, such as that released in livestock farming (anaerobic wastewater with a high phosphate concentration). This research employed six machine learning models to predict and optimize the phosphate removal performance of bimetal-modified biochar (i.e., Mg–Ca/Al/Fe/La) to develop material design strategies suitable for achieving high removal efficiency in alkaline wastewater. Random forest, gradient boosting regressor, and extreme gradient boosting models achieved high prediction accuracy (R2 > 0.98). Model predictions and experimental validations indicated that Mg–Ca-modified biochar still maintained high adsorption capacity under acidic conditions and could effectively realize phosphate adsorption under alkaline conditions, with a removal rate of 99.33 %. Overall, this research focuses on material performance optimization using machine learning, offering insights and methods for developing biochar materials for practical water-treatment applications.

Machine Learning Methods in IoT Based Embedded Systems for Classifying Physical Faults in Water Distribution Networks

Water is the most important factor for the survival of living things on Earth. Although 70% of the Earth is water, the amount of drinkable water is approximately 0.3%. Therefore, creating a sustainable water policy and carrying out studies are very important for our world and our future. Most of the potable water resources are physical losses. In the evaluations made based on metropolitan municipalities, it was seen that the water loss rate was approximately 50%. The study aims to find water pipe faults using IoT (Internet of Things) based machine learning classifiers to prevent physical losses in water distribution networks. Within the scope of this study, an experimental environment was created and an IMU (Inertial Measurement Unit) sensor was fixed on plastic pipes of different diameters and lengths. Vibration data collected in different scenarios (pressure, etc. factors) were transferred to the ThingSpeak platform over the internet. The transferred data could be monitored in real-time on a server. Physical damage in the pipes was detected using signal pre-processing, feature extraction, and feature selection algorithms on vibration data. In the study, damages were classified using machine learning-based classification (Decision Trees, k-Nearest Neighbors, Linear Discriminant, Support Vector Machines) methods to predict the type of damage (solid, hole, multi-hole). The data set revealed within the scope of the study is thought to lead to scientific studies in this field. The results obtained are close to the state-of-the-art results.