Machine learning classifiers
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Using a cutting-edge net-shape manufacturing technique called Additive Layer Manufacturing (ALM), highly complex components that are not achievable with conventional wrought and cast methods can be produced. As a result, the aerospace sector is paying closer attention to using this technology to fabricate superalloys based on nickel to develop the holistic gas turbine. Because of this, there is an increasing need for the mechanical characterisation of such material. Conventional mechanical testing is hampered by the limited availability of material that has been processed, especially given the large number of process factors that need to be assessed. Thus, the present study focuses on manufacturing CM247LC Ni-based superalloy with exceptional mechanical characteristics by laser powder bed fusion (L-PBF). This study evaluates the effect of input process variables such as laser power, scan speed, hatch distance and volumetric energy density on the mechanical performance of the LPBF CM247LC superalloy. The maximum value of as-built tensile strength obtained in the study is 997.81 MPa. Plotting Pearson's heatmap and the Feature importance (F-test) was used in the data analysis to examine the impact of input parameters on tensile strength. The accuracy of the tensile strength data classification by machine learning algorithms, such as k-nearest neighbours, Naïve Baiyes, Support vector machine, XGBoost, AdaBoost, Decision tree, Random forest, and logistic regression algorithms, was 92.5%, 83.75%, 83%, 85%, 87.5%, 90%, 91.25%, and 77.5%, respectively.
Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approaches. However, correlations between process parameters, thermal profiles, and resulting microstructure in AFSD still need to be better understood. This hinders process optimization for properties. This work employs a framework combining supervised machine learning (SML) and physics-informed neural networks (PINNs) to predict peak temperature distribution in AFSD from process parameters. Eight regression algorithms were implemented for SML modeling, while four PINNs leveraged governing equations for transport, wave propagation, heat transfer, and quantum mechanics. Across multiple statistical measures, ensemble techniques like gradient boosting proved superior for SML, with the lowest MSE of 165.78. The integrated ML approach was also applied to classify deposition quality from process factors, with logistic regression delivering robust accuracy. By fusing data-driven learning and fundamental physics, this dual methodology provides comprehensive insights into tailoring microstructure through thermal management in AFSD. The work demonstrates the power of bridging statistical and physics-based modeling for elucidating AM process-property relationships.
Colorectal cancer remains a major health burden, and its early detection is crucial for effective treatment. This study investigates the use of a handheld Raman spectrometer in combination with machine learning to classify colorectal tissue samples collected during colonoscopy. A dataset of 330 spectra from 155 participants was preprocessed using a standardized pipeline, and multiple classification models were trained to distinguish between healthy and pathological tissue. Due to the strong class imbalance and limited data size, a custom grid search approach was implemented to optimize both model hyperparameters and preprocessing parameters. Unlike standard GridSearchCV, our method prioritized balanced accuracy on the test set to reduce bias toward the dominant class. Among the tested classifiers, the Decision Tree (DT) and Support Vector Classifier (SVC) achieved the highest balanced accuracy (71.77% for DT and 70.77% for SVC), outperforming models trained using traditional methods. These results demonstrate the potential of Raman spectroscopy as a rapid, non-destructive screening tool and highlight the importance of tailored model selection strategies in biomedical applications. While this study is based on a limited dataset, it serves as a promising step toward more robust classification models and supports the feasibility of this approach for future clinical validation.
- Klíčová slova
- Balanced accuracy, Colorectal cancer, Machine learning, Preprocessing pipeline, Raman spectroscopy, Spectral classification,
- MeSH
- kolorektální nádory * diagnóza diagnostické zobrazování MeSH
- lidé středního věku MeSH
- lidé MeSH
- Ramanova spektroskopie * metody MeSH
- rozhodovací stromy MeSH
- strojové učení * MeSH
- support vector machine MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Crop classification during the early stages is challenging because of the striking similarity in spectral and texture features among various crops. To improve classification accuracy, this study proposes a novel fusion-based deep learning approach. The approach integrates textural and spectral features from a fused dataset generated by merging Landsat 8-9 and Sentinel-2A data using the Gram-Schmidt fusion approach. The textural features were extracted using the multi-patch Gray Level Co-occurrence Matrix (GLCM) technique. The spectral features, namely the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), were obtained using the spectral index method. The five machine learning methods (deep neural network, 1D convolutional neural network, decision tree, support vector machine, and random forest) were trained using textural and spectral parameters to develop classifiers. The proposed approach achieves promising results using deep neural network (DNN), with an accuracy of 0.89, precision of 0.88, recall of 0.91, and F1-score of 0.90. These results demonstrate the effectiveness of the fusion-based deep learning approach in enhancing classification accuracy for early-stage crops.
- Klíčová slova
- Crop classification, Deep learning, Early-stage crop, Feature integration, Image fusion, Multi-patch GLCM,
- MeSH
- deep learning MeSH
- monitorování životního prostředí * metody MeSH
- neuronové sítě MeSH
- satelitní snímkování * MeSH
- strojové učení * MeSH
- support vector machine MeSH
- zemědělské plodiny * klasifikace růst a vývoj MeSH
- Publikační typ
- časopisecké články MeSH
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.
- Klíčová slova
- Adversarial examples, Genetic algorithms, Kernel methods, Neural networks, Supervised learning,
- MeSH
- algoritmy MeSH
- lidé MeSH
- neuronové sítě * MeSH
- řízené strojové učení * trendy MeSH
- rozpoznávání automatizované metody trendy MeSH
- strojové učení trendy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict the survival prospects of patients, but newer algorithms such as deep learning can be tested with the aim of improving the models and prediction accuracy. In this study, we used machine learning and deep learning approaches to predict breast cancer survival in 4,902 patient records from the University of Malaya Medical Centre Breast Cancer Registry. The results indicated that the multilayer perceptron (MLP), random forest (RF) and decision tree (DT) classifiers could predict survivorship, respectively, with 88.2 %, 83.3 % and 82.5 % accuracy in the tested samples. Support vector machine (SVM) came out to be lower with 80.5 %. In this study, tumour size turned out to be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.
- MeSH
- analýza přežití MeSH
- deep learning * MeSH
- demografie MeSH
- dospělí MeSH
- kalibrace MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- nádory prsu mortalita MeSH
- neuronové sítě MeSH
- rozhodovací stromy MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- support vector machine MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.
- Klíčová slova
- Biomarkers, Data analysis, Ensemble learning, Glioma, Machine learning,
- MeSH
- gliom * diagnóza MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- nádory mozku * diagnóza MeSH
- strojové učení * MeSH
- stupeň nádoru MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Early detection of malignant thyroid nodules is crucial for effective treatment, but traditional diagnostic methods face challenges such as variability in expert opinions and limited integration of advanced imaging techniques. This prospective cohort study investigates a novel multimodal approach, integrating traditional methods with advanced machine learning techniques. We studied 181 patients who underwent fine-needle aspiration (FNA) biopsy, each contributing one nodule, resulting in a total of 181 nodules for our analysis. Data collection included sex, age, and ultrasound imaging, which incorporated elastography. Features extracted from these images included Thyroid Imaging Reporting and Data System (TIRADS) scores, elastography parameters, and radiomic features. The pathological results based on the FNA biopsy, provided by the pathologists, served as our gold standard for nodule classification. Our methodology, termed ELTIRADS, combines these features with interpretable machine learning techniques. Performance evaluation showed that a Support Vector Machine (SVM) classifier using TIRADS, elastography data, and radiomic features achieved high accuracy (0.92), with sensitivity (0.89), specificity (0.94), precision (0.89), and F1 score (0.89). To enhance interpretability, we used hierarchical clustering, shapley additive explanations (SHAP), and partial dependence plots (PDP). This combined approach holds promise for enhancing the accuracy of thyroid nodule malignancy detection, thereby contributing to advancements in personalized and precision medicine in the field of thyroid cancer research.
- Klíčová slova
- Elastography, Hierarchical clustering, Interpretable machine learning, Nodule classification, Radiomics,
- MeSH
- dospělí MeSH
- elastografie * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- nádory štítné žlázy diagnostické zobrazování patologie MeSH
- prospektivní studie MeSH
- radiomika MeSH
- senioři MeSH
- strojové učení * MeSH
- support vector machine MeSH
- tenkojehlová biopsie MeSH
- uzly štítné žlázy * diagnostické zobrazování klasifikace patologie diagnóza MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Fragmented QRS (fQRS) is an electrocardiographic (ECG) marker of myocardial conduction abnormality, characterized by additional notches in the QRS complex. The presence of fQRS has been associated with an increased risk of all-cause mortality and arrhythmia in patients with cardiovascular disease. However, current binary visual analysis is prone to intra- and inter-observer variability and different definitions are problematic in clinical practice. Therefore, objective quantification of fQRS is needed and could further improve risk stratification of these patients. We present an automated method for fQRS detection and quantification. First, a novel robust QRS complex segmentation strategy is proposed, which combines multi-lead information and excludes abnormal heartbeats automatically. Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified signal averaging (PRSA) and the number of baseline-crossings of the ECG, were used to train a machine learning classifier (Support Vector Machine) to discriminate fragmented from non-fragmented ECG-traces using multi-center data and combining different fQRS criteria used in clinical settings. The best model was trained on the combination of two independent previously annotated datasets and, compared to these visual fQRS annotations, achieved Kappa scores of 0.68 and 0.44, respectively. We also show that the algorithm might be used in both regular sinus rhythm and irregular beats during atrial fibrillation. These results demonstrate that the proposed approach could be relevant for clinical practice by objectively assessing and quantifying fQRS. The study sets the path for further clinical application of the developed automated fQRS algorithm.
- MeSH
- algoritmy MeSH
- elektrokardiografie * metody MeSH
- fibrilace síní * diagnóza MeSH
- lidé MeSH
- strojové učení MeSH
- support vector machine MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Research has shown a link between depression risk and how gamers form relationships with their in-game figure of representation, called avatar. This is reinforced by literature supporting that a gamer's connection to their avatar may provide broader insight into their mental health. Therefore, it has been argued that if properly examined, the bond between a person and their avatar may reveal information about their current or potential struggles with depression offline. To examine whether the connection with an individuals' avatars may reveal their risk for depression, longitudinal data from 565 adults/adolescents (Mage = 29.3 years, SD = 10.6) were evaluated twice (six months apart). Participants completed the User-Avatar-Bond [UAB] scale and Depression Anxiety Stress Scale to measure avatar bond and depression risk. A series of tuned and untuned artificial intelligence [AI] classifiers analyzed their responses concurrently and prospectively. This allowed the examination of whether user-avatar bond can provide cross-sectional and predictive information about depression risk. Findings revealed that AI models can learn to accurately and automatically identify depression risk cases, based on gamers' reported UAB, age, and length of gaming involvement, both at present and six months later. In particular, random forests outperformed all other AIs, while avatar immersion was shown to be the strongest training predictor. Study outcomes demonstrate that UAB can be translated into accurate, concurrent, and future, depression risk predictions via trained AI classifiers. Assessment, prevention, and practice implications are discussed in the light of these results.
- Klíčová slova
- Artificial intelligence, Avatar, Depression, Internet gaming, Machine learning,
- MeSH
- avatar * MeSH
- deprese * MeSH
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- průřezové studie MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
