Support Vector Machine Regression Algorithm Research Articles

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Data-driven battery health state estimation based on charging and switching cabinets

Accurate evaluation of the battery’s state of health (SOH) in the charging and switching cabinet is crucial to ensure the battery operates safely and reliably, while also reducing maintenance costs for the battery system. A support vector regression technique, utilising the sparrow algorithm optimisation, is suggested to improve the precision of assessing the battery’s state of health in the charging and switching cabinet. This algorithm tackles the task of identifying parameters in the conventional model. Analysing the ageing dataset of lithium batteries is the initial stage to determine the health parameters that signal the battery’s health state. The support vector machine regression algorithm is employed to select the kernel function and penalty factor, which are fine-tuned using the sparrow optimisation technique. The data is utilised to create the SSA-SVR model. The battery health status of the charging and switching cabinet is assessed. The study shows that the enhanced support vector regression model can effectively monitor the status of lithium-ion batteries and achieve superior estimation results for different types of batteries.

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression

This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.

Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Assessing occupational hazards in welding operations: A machine learning-based approach for worker safety in Indian foundries.

Quantitatively analyzing the impact of UV radiation and noise during welding operations is essential to assess the exposure, identify potential hazards, and develop targeted safety protocols to ensure worker safety and adherence to safety regulations, especially in developing countries with inadequate adherence to safety standards and resources. This study employs machine learning for predicting ultraviolet radiation and noise levels during welding, emphasizing worker safety. The focus is on the Indian foundry sector to gauge actual exposure vis-á-vis safety standards. Ultraviolet radiation and noise emitted during the welding of a ferrous alloy were collected from three foundries in Agra, India. Five machine learning (ML) algorithms were applied for data analysis and prediction of UV radiation and noise levels, and a relative performance comparison was carried out on the compiled data against safety standards. Out of all the ML algorithms applied, the Support Vector Machine regression algorithm (RMSE = 356.93) obtained the best performance on UV radiation data, and the Random Forest algorithm (RMSE = 11.4) was found to give the best results for the noise level prediction task. This work represents the first known application of machine learning techniques for predicting UV radiation and noise levels in arc welding processes. The results show the efficacy of algorithms such as SVM regression and Random Forest for the problem. Further, the datasets and ML algorithms implemented in the work will be made openly available to support further research endeavors in this and related areas.

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

Study on prediction of early adverse events by CapeOX therapy in patients with colorectal cancer.

CapeOX is a regimen used as postoperative adjuvant chemotherapy for the treatment of advanced recurrent colorectal cancer. If early adverse events occur, treatment may not progress as planned and further dose reduction may be necessary. In this study, we investigated whether pre-treatment medical records could be used to predict adverse events in order to prevent adverse events caused by CapeOX treatment. The 178 patients were classified into two groups (97 in the adverse event positive group and 81 in the adverse event-negative group) based on withdrawal or postponement of four or fewer courses. In univariate analysis, age, height, weight, body surface area (BSA), creatinine clearance, muscle mass, and lean body mass were associated with early adverse events (P<0.05). The area under the receiver operating characteristic curve obtained by Stepwise logistic regression analysis using the Akaike information criterion method was 0.832. For nested k-fold cross validation, the accuracy rates of the support vector machine, random forest, and logistic regression algorithms were 0.71, 0.70, and 0.75, respectively. The results of the present study suggest that a logistic regression prediction model may be useful in predicting early adverse events caused by CapeOX therapy in patients with colorectal cancer. J. Med. Invest. 71 : 141-147, February, 2024.

Integrated analysis reveals the potential of cluster of differentiation 86 as a key biomarker in high-grade glioma.

This study aimed to evaluate the potential of cluster of differentiation 86 (CD86) as a biomarker in high-grade glioma (HGG). The TCGA and TCIA databases were used to obtain the CD86 expression value, clinical data, and MRI images of HGG patients. Prognostic values were assessed by the Kaplan-Meier method, Receiver operating characteristic curve (ROC), Cox regression, logistic regression, and nomogram analyses. CD86-associated pathways were also explored. We found that CD86 was significantly upregulated in HGG compared with the normal group. Survival analysis showed a significant association between CD86 high expression and shorter overall survival time. Its independent prognostic value was also confirmed. These results suggested the possibility of CD86 as a biomarker in HGG. We also innovatively established 2 radiomics models with Support Vector Machine (SVM) and Logistic regression (LR) algorithms to predict the CD86 expression. The 2 models containing 5 optimal features by SVM and LR methods showed similar favorable performance in predicting CD86 expression in the training set, and their performance were also confirmed in validation set. These results indicated the successful construction of a radiomics model for non-invasively predicting biomarker in HGG. Finally, pathway analysis indicated that CD86 might be involved in the natural killer cell-mediated cytotoxicity in HGG progression.

Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression

Focusing on frequency problems caused by wind power integration in ultra-high-voltage DC systems, an accurate assessment of the maximum generation capacity of large-scale new energy sources can help determine the available frequency regulation capacity of new energy sources and improve the frequency stability control of power systems. First, a random forest model is constructed to analyze the key features and select the indexes significantly related to the generation capacity to form the input feature set. Second, by establishing an iterative construction model of the polynomial fitting surface, data are maximized by the upper envelope surface, and an effective sample set is constructed. Furthermore, a new energy maximum generation capacity assessment model adopts the support vector machine regression algorithm under the whale optimization algorithm to derive the correspondence between the input features and maximum generation capacity of new energy sources. Finally, we validate the applicability and effectiveness of the new maximum energy generation capacity evaluation model based on the results of an actual wind farm.

Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.

Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics.

To generate an image-driven biomarker (Rad_score) to predict tumor-infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC). Overall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients. Landmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622-0.867) and 0.733 (95% CI 0.535-0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657-0.885) and 0.724 (95% CI 0.522-0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1. High Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment.

Retrieval of leaf chlorophyll content using drone imagery and fusion with Sentinel-2 data

Leaf chlorophyll is an important dynamic biochemical parameter to assess crop phenology, health stress, and yield. Optical remote sensing data is a widely used technique for the estimation of vegetation leaf chlorophyll. Drone technology is a promising solution for high-resolution monitoring of the leaf chlorophyll content and employed in this study. Image fusion was also accomplished to provide high-resolution multi-spectral images by combining Sentinel-2A and drone imagery. Support vector machine regression was adopted to determine the best vegetation indices for the retrieval of leaf chlorophyll content. Random forest regression and support vector machine regression algorithms were adopted to develop the best models for chlorophyll retrieval. The 34 models derived from drone data and the 46 models derived from fusion data were evaluated for chlorophyll retrieval. It was found that the best support vector machine model, based on anthocyanin content index and enhanced vegetation index (M31), had the best correlation coefficient (r = 0.732) and root mean square error (1.93). The random forest regression models M32 (based on chlorophyll vegetation index and visible atmospherically resistant index) and M16 (based on canopy chlorophyll content index and visible atmospherically resistant index) for drone data were found to be the best, which performed equally well in terms of correlation coefficients (r = 0.77) and root mean square error (1.51). The multiple vegetation indices of drone-based leaf chlorophyll content random forest regression models (M16 and M32) provided higher performance than single vegetation indices-based linear (simple linear fit) and nonlinear leaf chlorophyll content models. Out of 46 models of fusion products, the random forest regression M14 (green normalized difference vegetation index with narrow near infrared band and visible atmospherically resistant index) offered the best performance for leaf chlorophyll content retrieval, as can be observed through a comparison of correlation (r = 0.77) and RMSE (=1.71) values. It was also investigated that inversion performances (r ∼ 0.8 and RMSD ∼ 1.4 with standard deviation ∼ 2.6) of fusion data-based models in cross-applicability and transferability were found to be suitable for large-scale inversion of Sentinel-2A data products.

Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms.

Determination of live weight, which is one of the most important features that determine meat production, is a very important issue for herd management and sustainable livestock. In this context, the necessity of finding alternative methods has emerged, especially in rural conditions, due to the difficulties to be experienced in finding the weighing tool. Especially for conditions with no weighing tool, it has been tried to establish relations between the information obtained from body measurements and live weight. Since these studies will differ from species to species and breed to breed, the need for new studies is extremely high. For this aim, it is to evaluate the body measurement information obtained with the present study using several statistical approaches. To implement this aim, several data mining and machine learning algorithms such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), and support vector machine regression (SVR) algorithms were used for training (70%) and test (30%) sets. To predict final body weight, 280 hair sheep breeds (162 female and 118 male) ranging from 2 months to 3 years were used with different data mining and machine learning approaches. Various goodness-of-fit criteria were used to evaluate the performances of the aforementioned algorithms. Although the MARS and SVR algorithms gave the same and highest results in terms of R2 and r values for both the train and the test sets, the SVR algorithm is one of the methods to be recommended as a result of this study, especially when other goodness-of-fit criteria are evaluated. In conclusion, the usage of SVR algorithms may be a useful tool of machine learning approaches for detecting the hair sheep breed standards and may contribute to increasing the sheep meat quality in Mexico.

Comparison of SVMR and PLSR for ATR-IR data treatment: Application to AQC of mAbs in clinical solutions

Attenuated Total Reflectance Infrared spectroscopy (ATR-IR) enables rapid, preparation-free and cost-effective analysis of many clinically relevant samples. For instance, it can substitute traditional techniques like high performance liquid chromatography (HPLC) for Analytical Quality Control (AQC) of therapeutic solutions of anticancer drugs like monoclonal antibodies (mAbs). More reliable quantitative interpretation of the ATR-IR spectra is usually achieved with multivariate data mining protocols. In the present study, the analytical performance of ATR-IR was evaluated for four clinical solutions of mAbs: Bevacizumab (Avastin®), Trastuzumab (Ontruzant®), Cetuximab (Erbitux®) and Rituximab (Truxima®). As expected, the ATR-IR spectra were a combination of the bands of the antibody itself and those of the excipients. For quantitative analysis of the solutions, two types of regression methods have been compared namely, Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR). While both approaches were relevant, the latter gave better performances in terms of accuracy, linearity and the percent relative error that was below 15%. The results are thus promising for future applications of ATR-IR Spectroscopy coupled to the SVMR algorithm as a clinical tool for AQC of mAbs.

Mechanical properties of Lop Nor salt rock fillers for subgrade and its forecast model construction

A deep exploration was conducted on the evolution law of the mechanical properties of salt rock fillers under the influence of different forming parameters in the Lop Nor area to promote the engineering application process of salt rock. The accuracy of primary regression, square regression, and support vector machine (SVM) regression algorithms in predicting the mechanical properties of salt rock fillers was compared, and the most accurate prediction model for the California bearing ratio (CBR) and rebound modulus of salt rock fillers were recommended. The results showed that the optimal brine content of SC-40, SC-20, and SC-10 salt rock fillers was between 8.2% and 9.3%, with a dry density of approximately 1.69–1.76 g/cm3. The CBR of salt rock samples gradually decreased with an increase in brine content, and the rebound modulus was higher than 90.6 MPa. As the compaction degree increased, the CBR value increased significantly, and the rebound modulus increased by approximately 28.1 MPa. As the immersion time increased, the mechanical properties of salt rock gradually decreased. Among the various regression models, the SVM prediction model had the highest accuracy index coefficient of determination (R2), whereas the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) were the smallest. Therefore, the SVM prediction model was recommended to accurately estimate the mechanical properties of salt rock roadbed fillers and provide a reference for the regulation of compaction parameters and the guarantee of bearing capacity characteristics of salt rock roadbeds.

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed.

The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France.

Online detection of compensatory strategies in human movement with supervised classification: a pilot study.

Stroke survivors often compensate for the loss of motor function in their distal joints by altered use of more proximal joints and body segments. Since this can be detrimental to the rehabilitation process in the long-term, it is imperative that such movements are indicated to the patients and their caregiver. This is a difficult task since compensation strategies are varied and multi-faceted. Recent works that have focused on supervised machine learning methods for compensation detection often require a large training dataset of motions with compensation location annotations for each time-step of the recorded motion. In contrast, this study proposed a novel approach that learned a linear classifier from energy-based features to discriminate between healthy and compensatory movements and identify the compensating joints without the need for dense and explicit annotations. Six healthy physiotherapists performed five different tasks using healthy movements and acted compensations. The resulting motion capture data was transformed into joint kinematic and dynamic trajectories. Inspired by works in bio-mechanics, energy-based features were extracted from this dataset. Support vector machine (SVM) and logistic regression (LR) algorithms were then applied for detection of compensatory movements. For compensating joint identification, an additional condition enforcing the independence of the feature calculation for each observable degree of freedom was imposed. Using leave-one-out cross validation, low values of mean brier score (<0.15), mis-classification rate (<0.2) and false discovery rate (<0.2) were obtained for both SVM and LR classifiers. These methods were found to outperform deep learning classifiers that did not use energy-based features. Additionally, online classification performance by our methods were also shown to outperform deep learning baselines. Furthermore, qualitative results obtained from the compensation joint identification experiment indicated that the method could successfully identify compensating joints. Results from this study indicated that including prior bio-mechanical information in the form of energy based features can improve classification performance even when linear classifiers are used, both for offline and online classification. Furthermore, evaluation compensation joint identification algorithm indicated that it could potentially provide a straightforward and interpretable way of identifying compensating joints, as well as the degree of compensation being performed.

Battery SOH estimation based on decision tree and improved support vector machine regression algorithm

Battery state of health (SOH) estimation is crucial for the estimation of the remaining driving range of electric vehicles and is one of the core functions of the battery management system (BMS). The lithium battery feature sample data used in this paper is extracted from the National Aeronautics and Space Administration (NASA) of the United States. Based on the obtained feature samples, a decision tree algorithm is used to analyze them and obtain the importance of each feature. Five groups of different feature inputs are constructed based on the cumulative feature importance, and the original support vector machine regression (SVR) algorithm is applied to perform SOH estimation simulation experiments on each group. The experimental results show that four battery features (voltage at SOC = 100%, voltage, discharge time, and SOC) can be used as input to achieve high estimation accuracy. To improve the training efficiency of the original SVR algorithm, an improved SVR algorithm is proposed, which optimizes the differentiability and solution method of the original SVR objective function. Since the loss function of the original SVR is non-differentiable, a smoothing function is introduced to approximate the loss function of the original SVR, and the original quadratic programming problem is transformed into a convex unconstrained minimization problem. The conjugate gradient algorithm is used to solve the smooth approximation objective function in a sequential minimal optimization manner. The improved SVR algorithm is applied to the simulation experiment with four battery feature inputs. The results show that the improved SVR algorithm significantly reduces the training time compared to the original SVR, with a slight trade-off in simulation accuracy.

A New Hybrid Model to Predict Human Age Estimation from Face Images Based on Supervised Machine Learning Algorithms

Abstract Age estimation from face images is one of the significant topics in the field of machine vision, which is of great interest to controlling age access and targeted marketing. In this article, there are two main stages for human age estimation; the first stage consists of extracting features from the face areas by using Pseudo Zernike Moments (PZM), Active Appearance Model (AAM), and Bio-Inspired Features (BIF). In the second step, Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms are used to predict the age range of face images. The proposed method has been assessed utilizing the renowned databases of IMDB-WIKI and WIT-DB. In general, from all results obtained in the experiments, we have concluded that the proposed method can be chosen as the best method for Age estimation from face images.

Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients

Bronchial arterial chemoembolization (BACE) was deemed as an effective and safe approach for advanced standard treatment-ineligible/rejected lung cancer patients. However, the therapeutic outcome of BACE varies greatly and there is no reliable prognostic tool in clinical practice. This study aimed to investigate the effectiveness of radiomics features in predicting tumor recurrence after BACE treatment in lung cancer patients. A total of 116 patients with pathologically confirmed lung cancer who received BACE treatment were retrospectively recruited. All patients underwent contrast-enhanced CT within 2 weeks before BACE treatment and were followed up for more than 6months. We conducted a machine learning-based characterization of each lesion on the preoperative contrast-enhanced CT images. In the training cohort, recurrence-related radiomics features were screened by least absolute shrinkage and selection operator (LASSO) regression. Three predictive radiomics signatures were built with linear discriminant analysis (LDA), support vector machine (SVM) and logistic regression (LR) algorithms, respectively. Univariate and multivariate LR analyses were performed to select the independent clinical predictors for recurrence. The radiomics signature with best predictive performance was integrated with the clinical predictors to form a combined model, which was visualized as a nomogram. The performance of the combined model was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Nine recurrence-related radiomics features were screened out, and three radiomics signatures (RadscoreLDA, RadscoreSVM and RadscoreLR) were built based on these features. Patients were classified into the low-risk and high-risk groups based on the optimal threshold of three signatures. Progression-free survival (PFS) analysis showed that patients of low-risk group achieved longer PFS than patients of high-risk group (P<0.05). The combined model including RadscoreLDA and independent clinical predictors (tumor size, carcinoembryonic antigen and pro-gastrin releasing peptide) achieved the best predictive performance for recurrence after BACE treatment. It yields AUCs of 0.865 and 0.867 in the training and validation cohorts, with accuracy (ACC) of 0.804 and 0.750, respectively. Calibration curves indicated that the probability of recurrence predicted by the model fits well with the actual recurrence probability. DCA showed that the radiomics nomogram was clinically useful. The radiomics and clinical predictors-based nomogram can predict tumor recurrence after BACE treatment effectively, which allowing oncologists to identify potential recurrence and enable better patient management and clinical decision-making.