Machine Approach Research Articles

Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

Classification of Productive Subject Value with Vector Machine Support Method

This research aims to determine student competency obtained from productive subjects, one of the subjects that has an important role in measuring student quality. With the development of technology, student evaluations of productive subject grades can use data mining methods, one of which uses the Support Vector Machine approach. This method aims to build a classification of students' productive subject scores by identifying the variables that influence them. Based on the research results, it can be concluded that the value of productive subjects at Public Vocation High School 1 Pasuruan can be predicted and evaluated using data mining techniques that utilize the Support Vector Machine algorithm. It aims to predict student grades in productive subjects by utilizing a logistic regression algorithm. The classification results illustrate that student grades in productive subjects are influenced by learning media variables, teacher teaching quality, classroom/laboratory facilities, interest, and motivation to learn. Apart from that, the Support Vector Machine algorithm has an accuracy value of 64.5001, precision of 56.0279, recall = 86.9952, and F1-Score = 67.882.

Explainable Boosting Machine approach identifies risk factors for acute renal failure

BackgroundRisk stratification and outcome prediction are crucial for intensive care resource planning. In addressing the large data sets of intensive care unit (ICU) patients, we employed the Explainable Boosting Machine (EBM), a novel machine learning model, to identify determinants of acute kidney injury (AKI) in these patients. AKI significantly impacts outcomes in the critically ill.MethodsAn analysis of 3572 ICU patients was conducted. Variables such as average central venous pressure (CVP), mean arterial pressure (MAP), age, gender, and comorbidities were examined. This analysis combined traditional statistical methods with the EBM to gain a detailed understanding of AKI risk factors.ResultsOur analysis revealed chronic kidney disease, heart failure, arrhythmias, liver disease, and anemia as significant comorbidities influencing AKI risk, with liver disease and anemia being particularly impactful. Surgical factors were also key; lower GI surgery heightened AKI risk, while neurosurgery was associated with a reduced risk. EBM identified four crucial variables affecting AKI prediction: anemia, liver disease, and average CVP increased AKI risk, whereas neurosurgery decreased it. Age was a progressive risk factor, with risk escalating after the age of 50 years. Hemodynamic instability, marked by a MAP below 65 mmHg, was strongly linked to AKI, showcasing a threshold effect at 60 mmHg. Intriguingly, average CVP was a significant predictor, with a critical threshold at 10.7 mmHg.ConclusionUsing an Explainable Boosting Machine enhance the precision in AKI risk factors in ICU patients, providing a more nuanced understanding of known AKI risks. This approach allows for refined predictive modeling of AKI, effectively overcoming the limitations of traditional statistical models.

Machine learning prediction of malaria vaccine efficacy based on antibody profiles.

Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled human malaria infection (CHMI). Humoral and cellular immunity contribute to vaccine efficacy but only limited information about the implicated Pf-specific antigens is available. Here, we examined Pf-specific antibody profiles, measured by protein arrays representing the full Pf proteome, of 40 placebo- and PfSPZ-immunized malaria-naïve volunteers from an earlier published PfSPZ-CVac dose-escalation trial. For this purpose, we both utilized and adapted supervised machine learning methods to identify predictive antibody profiles at two different time points: after immunization and before CHMI. We developed an adapted multitask support vector machine (SVM) approach and compared it to standard methods, i.e. single-task SVM, regularized logistic regression and random forests. Our results show, that the multitask SVM approach improved the classification performance to discriminate the protection status based on the underlying antibody-profiles while combining time- and dose-dependent data in the prediction model. Additionally, we developed the new fEature diStance exPlainabilitY (ESPY) method to quantify the impact of single antigens on the non-linear multitask SVM model and make it more interpretable. In conclusion, our multitask SVM model outperforms the studied standard approaches in regard of classification performance. Moreover, with our new explanation method ESPY, we were able to interpret the impact of Pf-specific antigen antibody responses that predict sterile protective immunity against CHMI after immunization. The identified Pf-specific antigens may contribute to a better understanding of immunity against human malaria and may foster vaccine development.

An advanced classification method for urban land cover classification

This manuscript presents a detailed comparative analysis of three advanced classification techniques that were used between 2018 and 2020 to classify land cover using Landsat8 imagery, namely Support Vector Machine (SVM), Maximum Likelihood Classification (MLSC), and Random Forests (RF). The study focuses on evaluating the accuracy of these methods by comparing the classified maps with a higher-resolution ground truth map, utilising 500 randomly selected points for assessment. The obtained results show that, compared to MLSC and RT, the Support Vector Machine (SVM) approach performs better. The SVM model demonstrates enhanced precision in land cover classification, showcasing its effectiveness in discerning subtle differences in landscape features. Furthermore, using the precise classification results produced by the SVM method, this study examines the temporal variations in land cover between 2018 and 2020. The results provide insight into dynamic land cover changes and highlight the significance of applying reliable classification techniques for thorough temporal analysis with Landsat8 images.

Minimization of residual stress, surface roughness and tool wear in Electro Discharge Machining of inconel 625

Electro Discharge Machining (EDM) is a well-known non-traditional machining technique which is widely used to make die castings and turbine blades out of difficult-to-cut materials like Inconel 625. The machined part's surface quality and dimensional accuracy are affected by the EDM electrode's wear. Moreover, surface roughness and residual stress augmentation of EDM-machined components can reduce the workpiece's lifespan by decreasing the fatigue life of machined parts. To enhance the precision and durability of EDM machined components, residual stress, tool wear, and surface roughness should be evaluated and minimized. The majority of published research works for the assessment and optimization of EDM machining parameters are based on experimental works which are limited to testing, workpiece materials, and machining conditions. In order to improve the quality of machined surfaces and reduce tool wear and residual stress during Inconel 625 EDM processes, a virtual machining approach has been developed in the research work. To predict the cutting temperature during EDM operations, the modified Johnson Cook model of Inconel alloys is used. The finite element approach is then used to calculate the generated residual stress during the EDM process. The proposed virtual machining approach is also applied in order to predict the surface roughness of EDM machined parts. To minimize the residual stress, tool wear, and surface roughness during EDM operations of Inconel 625, the machining parameters of gap voltage, peak current, pulse-off time, and pulse-on time are optimized using the Taguchi optimization approach. Experiments and simulations are then conducted to verify the developed virtual machining system in the study. So, using the optimal machining parameters, the residual stress, surface roughness of machined items and wear of EDM electrode are minimized by 24.5 %, 25.4 %, and 25.4 % respectively. Thus, the quality and reliability of components made using EDM processes may be enhanced by the suggested virtual machining system.

Embedded Restricted Boltzmann Machine Approach for Adjustments of Repetitive Physical Activities Using IMU Data

Embedded Restricted Boltzmann Machine Approach for Adjustments of Repetitive Physical Activities Using IMU Data

Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis

Abstract Naqadeh Region (NR) is one of the most sensitive regions regarding geo-hazards ‎occurrence in Northwest of Iran. The landslides triggering parameters that ‎identified for the studied region are classified as elevation, aspect, slope angle, ‎lithology, drainage density, distance to river, weathering, land-cover, ‎precipitation, vegetation, distance to faults, distance to roads, and distance to ‎the cities. These triggering factors are selected based on conducting field ‎survey, remote-sensing investigation, and historical development background ‎assessment. Regarding the investigations, 12 large-scale, 15 medium-scale, and 30 small-scale historical landslides ‎(57 in total) were recorded in the NR. The historical landslides were used to provide ‎sensitive area with high probability of ground movements. The objectives of this study are multifaceted, aiming to address critical gaps in understanding and predicting landslide susceptibility in the NR. First, the study seeks to evaluate and compare the effectiveness of ‎support-vector machine (SVM), multilayer perceptron (MLP), and decision tree ‎‎(DT) algorithms in predicting landslide susceptibility. So, as methodology, the ‎presented study used comparative models for landslide susceptibility based on ‎SVM, MLP, and DT approaches. The predictive models were compared based on model ‎accuracy as the area under the curve of the receiver operating characteristic ‎curve. According to the estimated results, MLP is the highest rank of overall ‎accuracy to provide susceptibility maps for landslides in NR. From a perspective of ‎the risk ability, the west and south-west sides of the county were identified within ‎the hazard area.

Chip breakage in silk microfibre production using elliptical vibration turning

To overcome the precision limitation and environmental impact of current chemical-based production methods for manufacturing silk microfibres used for targeted drug delivery, this paper presents a high-precision, scalable, eco-friendly mechanical machining approach to produce such microfibres in the form of discontinuous chips obtained through elliptical vibration turning of silk fibroin film using a diamond tool. The length and waist width of fabricated microfibres can be precisely controlled. As each vibration cycle will produce one silk microfibre, complete and deterministic chip breakage becomes an essential and challenging task in this approach due to its unique two-phase structure. Thus, the hybrid FE-SPH numerical simulations and machining experiments were conducted to gain a pioneering and in-depth exploration of the chip-breaking mechanism in this process. It was found that applying a low depth ratio (ratio of the nominal depth of cut to the tool path vertical amplitude) and a high horizontal speed ratio (the nominal cutting speed versus the critical workpiece velocity) could effectively reduce the average tool velocity angle (the angle from the deepest cut to the tool exit point along the cutting direction). A smaller angle would enhance the diamond tool's shearing action and led to the reduction of hydrostatic pressure in the cutting zone and a consequent decrease in the ductility of silk fibroin due to its unique structure dominated by beta-sheet crystallites. The above adjustments collectively facilitated chip breakage. This paper, therefore, established a governing rule for the controlled and repeatable formation of microfibres based on the average tool velocity angle for the first time and revealed that the cutting chips would undergo complete and deterministic breakages once the angle approached below 22.6°. On this basis, the high-precision and scalable manufacturing of silk microfibres with precisely controllable length and waist width was ultimately achieved.

Optimizing the surface quality of heat-treated 2Cr13 stainless steel via electrochemical machining

The improvement of mechanical properties and surface quality was of great significance for materials. In this study, 2Cr13 stainless steel was subjected to the quenching and tempering treatment. The electrochemical machining approach was adopted for surface treatment. The results showed that the quenching and tempering treatment improved the Vickers microhardness of 2Cr13 stainless steel. The enhancement of microhardness was facilitated by the formation of tempered sorbite and grain refinement. Electrochemical machining provided high quality surfaces. The heat-treated workpice had more advantages in electrochemical machining efficiency. The polarization curves proved a downward shift of the self-corrosion potential for the heat-treated 2Cr13 stainless steel, which was strongly related to the electrochemical behavior. A mechanism model for promoting anode electrochemical dissolution was proposed.

Three-Dimensional Finite Element Modeling of Ultrasonic Vibration-Assisted Milling of the Nomex Honeycomb Structure

Machining of Nomex honeycomb composite (NHC) structures is of critical importance in manufacturing parts to the specifications required in the aerospace industry. However, the special characteristics of the Nomex honeycomb structure, including its composite nature and complex geometry, require a specific machining approach to avoid cutting defects and ensure optimal surface quality. To overcome this problem, this research suggests the adoption of RUM technology, which involves the application of ultrasonic vibrations following the axis of revolution of the UCK cutting tool. To achieve this objective, a three-dimensional finite element numerical model of Nomex honeycomb structure machining is developed with the Abaqus/Explicit software, 2017 version. Based on this model, this research examines the impact of vibration amplitude on the machinability of this kind of structure, including cutting force components, stress and strain distribution, and surface quality as well as the size of the chips. In conclusion, the results highlight that the use of ultrasonic vibrations results in an important reduction in the components of the cutting force by up to 42%, improves the quality of the surface, and decreases the size of the chips.

Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks

Abstract Feature extraction from an recorded surface electromyography (sEMG) signal plays an important role in identifying and quantifying the characteristics of muscle activities. These features can be used for various applications like muscle function assessment, muscle fatigue detection, etc. Common features extracted from sEMG signal are time-domain or frequency-domain features. However, features which are sensitive to uncertainties in the signal like noise, movement artifacts, and outliers should be avoided. Autocorrelation function (ACF), which is a measure of similarity between a signal and its time delayed version, is considered in this work as a feature to overcome the impact of noise, artifacts, and outliers. An artificial neural network (ANN) is developed to differentiate between fatigue and non-fatigue conditions using the calculated ACF from sEMG segments. The performance of an ANN model that can be adapted by means of various regularization methods was investigated. The proposed ANN model achieved an accuracy of about 97.62 %, a precision of about 95.50 % and a sensitivity of about 100 % in the classification of fatigue and non-fatigue sEMG segments, outperforming k-means and linear support vector machine approaches that served as references.

Hybrid Sparrow-Fly Swarm Optimized Support Vector Machine Approach for Automated Lung Cancer Identification from CT Imaging

Hybrid Sparrow-Fly Swarm Optimized Support Vector Machine Approach for Automated Lung Cancer Identification from CT Imaging

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and approaches the realm of human translation quality. This research aims to critically assess the translation accuracy of ChatGPT-4 against a traditional open-source machine translation tool from Persian to English, highlighting the advancements in artificial intelligence-driven translation technologies. Using Bilingual Evaluation Understudy scores for a comprehensive evaluation, this study compares the translation outputs from ChatGPT-4 with MateCat, providing a quantitative basis for comparing their accuracy and quality. ChatGPT-4 achieves a BLUE score of 0.88 and an accuracy of 0.68, demonstrating superior performance compared to MateCat, with a 0.82 BLUE score and 0.49 accuracy. The results indicate that the translations generated by ChatGPT-4 surpass those produced by MateCat and nearly mirror the quality of human translations. The evaluation demonstrates the effectiveness of OpenAI's large language model algorithms in improving translation accuracy.

Knowledge Graph Construction and Digital Twin Modeling Integrating Multi-modal Data

Supply chain analytics is focusing more and more attention on industrial logistics. Optimal allocation of assets is severely restricted by the timing inconsistency and geographical instability of industrial logistics assets brought by unpredictability and variability. Unnecessarily lengthy drive distances and lengthy delays are caused by the incapacity to acquire and utilize industrial logistics asset spatial-and-temporal (ST) qualities rationally, which affects the processes' potential to operate sustainably. Thus, for efficient usage of resources in industrial logistics, a novel machine learning (ML)-assisted knowledge graph (ML-KG) design is provided in this work. For evaluating the multi-modal data produced by widely deployed IoT devices, a novel customizable diversified kernelized support vector machine (CDK-SVM) approach is also suggested. After establishing the suggested ML-KG framework for ST integrity in the representation of the digital twin version, links are carried out and reasoning is performed using job data related to industrial logistics. The graph mechanism effectively distributes the industrial logistics resources. Lastly, the outcome proves that the suggested process is successful in the distribution of industrial logistics assets.

Web Attack Detection Based on Honeypots and Logistic Regression Algorithm

Web security has emerged as one of the most prominent concerns in the realm of cybersecurity. Traditional rule-based methods for detecting web attacks often rely on manual rule definition and pattern matching, leaving them inadequate in accurately identifying new and intricate attack patterns. In the face of these challenges, machine learning techniques have demonstrated potential and advantages. This paper presents a web attack detection method based on honeypots and the logistic regression algorithm. It involves the cleansing, filtering, and analysis of web logs captured by honeypots, followed by the vectorization of the textual data contained in these logs. The logistic regression algorithm is employed to train and test the classification of the text vectors, generating a logistic regression model. This model is then used to predict newly generated web logs, enabling effective dynamic web attack detection. Experimental evaluations using collected datasets are conducted, comparing the proposed method with the support vector machine approach. The results demonstrate that this method achieves rapid and accurate detection and recognition of web attack behaviors while ensuring performance efficacy.

Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine

As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased. To recover quickly from flood damage and prevent further consequential damage, flood waste prediction is of utmost importance. Therefore, developing a rapid and accurate prediction of flood waste generation is important in order to reduce disaster. Several approaches of flood waste classification have been proposed by various researchers, however only a few focus on prediction of flood waste. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) approach is adapted to address these challenges. Two different raw datasets were obtained from the “Advancing Sustainable Materials Management: Facts and Figures 2015” source. The datasets were for 9 years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Municipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015. The waste types were grouped as paper and paperboard (PP), glass (GI), metals (Mt), plastics (PI), rubber and leather (RL), textiles (Tt), wood (Wd), food (Fd), yard trimmings (YT) and miscellaneous inorganic wastes (IW).

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm

ObjectiveIn this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm. MethodsA total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI). ResultsThe support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085). Conclusionthe combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

Precise surface machining of fused silica using high stability atmospheric pressure microwave plasma jet with a new internal electrode

Plasma is a promising approach for machining of fused glass substrates. Developing high-performance microwave torch is the core element to ensure the processing accuracy of this technology. In this study, a spline curve-based microwave torch internal electrode structure was proposed, which increased the internal electric field strength by 54 %. The discharge dynamics simulation based on this electrode shows that the plasma is excited at the inner electrode tip and the bottom of the resonant cavity, then it expands to form a linear discharge region and eventually fill the discharge tube. It was revealed by CCD images that with the increase of carrier gas flow rate, turbulent flow state appears in the tail of the jet. OES data shows that microwave Ar plasma can effectively dissociate CF4, and due to the oxidation effect of oxygen, CFx and C2 content decrease significantly with the addition of oxygen. The machining experiment on fused silica shows that the material removal stability of this torch reaches 3.69 %, and the removal rate increases nYarly with dwell time due to thermal effect. Finally, the machining performance of the atmospheric pressure microwave plasma jet was verified by figuring of a planar fused silica surface reducing the form error from 108.1 nm RMS to 16.5 nm RMS.

Supplementation of deep neural networks with simplified physics-based features to increase accuracy of plate fundamental frequency predictions

To improve predictive machine learning-based models limited by sparse data, supplemental physics-related features are introduced into a deep neural network (DNN). While some approaches inject physics through differential equations or numerical simulation, improvements are possible using simplified relationships from engineering references. To evaluate this hypothesis, thin rectangular plates were simulated to generate training datasets. With plate dimensions and material properties as input features and fundamental natural frequency as the output, predictive performance of a data-driven DNN-based model is compared with models using supplemental inputs, such as modulus of rigidity. To evaluate model accuracy improvements, these additional features are injected into various DNN layers, and the network is trained with four different dataset sizes. When evaluated against independent data of similar features to the training sets, supplementation provides no statistically-significant prediction error reduction. However, notable accuracy gains occur when independent test data is of material and dimensions different from the original training set. Furthermore, when physics-enhanced data is injected into multiple DNN layers, reductions in mean error from 33.2% to 19.6%, 34.9% to 19.9%, 35.8% to 22.4%, and 43.0% to 28.4% are achieved for dataset sizes of 261, 117, 60, and 30, respectively, demonstrating potential for generalizability using a data supplementation approach. Additionally, when compared with other methods—such as linear regression and support vector machine (SVM) approaches—the physics-enhanced DNN demonstrates an order of magnitude reduction in percentage error for dataset sizes of 261, 117, and 60 and a 30% reduction for a size of 30 when compared with a cubic SVM model independently tested with data divergent from the training and validation set.