Support Vector Machine Research Articles

AI-driven canine cataract detection: a machine learning approach using support vector machine

ABSTRACT The pivotal role of the Support Vector Machine becomes evident in addressing the impact of aging and genetics on canine cataract development. Timely detection, crucial for preventing illness in dogs, is facilitated by Artificial Intelligence. This proves especially beneficial for veterinarians grappling with detection challenges. Within the realm of Artificial Intelligence, the subset of Machine Learning emerges as a promising avenue for tackling intricate tasks. The current study introduces a model for grading canine cataracts through images captured on standard mobile phone cameras. Notably, the proposed system centers around harnessing the power of the Support Vector Machine, a potent Machine Learning algorithm tailored for classifying (grading) cataracts based on images. Rigorous evaluation, employing K-fold Cross-validation, validates the images’ reliability. Both binary-class and multi-class experimentation contribute to the dataset. Impressively, the accuracy rates for the trained classification model stand at 83% (without Cross-validation) and 81% (with Cross-validation for K = 10), underscoring the robustness of the approach.

Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on 18F-FDG PET/CT

ObjectiveTo investigate the value of 18F-FDG PET/CT-based intratumoral and peritumoral radiomics in predicting the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer.Methods190 patients who met the inclusion and exclusion criteria from 2017 to 2022 were studied. Features were extracted from the PET/CT intratumoral and peritumoral regions, feature selection was performed through the correlation analysis, t-tests, and least absolute shrinkage and selection operator regression (LASSO). Four classifiers, support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), and naive bayes (NB) were used to build the prediction models. The receiver operating characteristic (ROC) curves were plotted to measure the predictive performance of the models. Concurrent stratified analysis was conducted to establish subtype-specific features for each molecular subtype.ResultsCompared to intratumoral features alone, intratumoral + peritumoral features achieved higher AUC values in each classifier. The SVM model constructed with intratumoral + peritumoral features achieved the highest AUC values in both the train and test set (train set: 0.95 and test set: 0.83). Subtype-specific features improve performance in predicting the efficacy of NAC (luminal group: 0.90; HER2 + group: 0.86; triple negative group: 0.92).ConclusionIntratumoral and peritumoral radiomics models based on 18F-FDG PET/CT can reliably forecast the efficacy of NAC, thereby assisting clinical decision-making.

Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.

Non-destructive estimation for Kyoho grape shelf-life using Vis/NIR hyperspectral imaging and deep learning algorithm

Grape shelf-life estimation is a substantial challenge for the grape industry. The objective of this study is to investigate the potential of grape shelf-life estimation using HSI technique and a deep learning algorithm. The visible and near-infrared (400.68–1001.61 nm) hyperspectral reflectance images data of grape samples was acquired and preprocessed with different spectral preprocessing methods. Additionally, a stacked denoising autoencoder (SDAE)-based deep learning algorithm was developed to extract deep features from pixel-level hyperspectral data of grapes, and then these features were used as inputs to establish support vector machine (SVM) models for estimating grape shelf-life. Furthermore, SVM, one-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) models were used as traditional machine learning and end to end models for comparison. The results demonstrated that the SDAE-SVM model achieved reasonable recognition accuracy of 100 % and 98.125 % for the shelf-life of grapes in the training and test sets, respectively. The overall results suggested that SDAE-based deep learning method can be used as a powerful tool to deal with large-scale hyperspectral data as well as this research confirms the feasibility of non-destructive estimation for grapes shelf-life by the combination of HSI technique and deep learning method, which would provide a valuable guidance for shelf-life estimation of other postharvest fruit.

Securing DNS over HTTPS traffic: a real-time analysis tool

DNS over HTTPS (DoH) is a developing protocol that uses encryption to secure domain name system (DNS) queries within hypertext transfer protocol secure (HTTPS) connections, thereby improving privacy and security while browsing the web. This study involved the development of a live tool that captures and analyzes DoH traffic in order to classify it as either benign or malicious. We employed machine learning (ML) algorithms such as K-Nearest Neighbors (K-NN), random forest (RF), decision tree (DT), deep neural network (DNN), and support vector machine (SVM) to categorize the data. All of the algorithms, namely KNN, RF, and DT, achieved exceptional performance, with F1 scores of 1.0 or above for both precision and recall. The SVM and DNN both achieved exceptionally high scores, with only slight differences in accuracy. This tool employs a voting mechanism to arrive at a definitive classification decision. By integrating with the Mallory tool, it becomes possible to locally resolve DNS, which in turn allows for more accurate simulation of DoH queries. The evaluation results clearly indicate outstanding performance, confirming the tool's effectiveness in analyzing DoH traffic for network security and threat detection purposes.

Predicting antioxidant activity of compounds based on chemical structure using machine learning methods.

Oxidative stress is a well-established risk factor for numerous chronic diseases, emphasizing the need for efficient identification of potent antioxidants. Conventional methods for assessing antioxidant properties are often time-consuming and resource-intensive, typically relying on laborious biochemical assays. In this study, we investigated the applicability of machine learning (ML) algorithms for predicting the antioxidant activity of compounds based solely on their molecular structure. We evaluated the performance of five ML algorithms, Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Random Forest (RF), and Deep Neural Network (DNN), using a dataset of over 1,900 compounds with experimentally determined antioxidant activity. Both RF and SVM achieved the best overall performance, exhibiting high accuracy (> 0.9) and effectively distinguishing active and inactive compounds with high structural similarity. External validation using natural product data from the BATMAN database confirmed the generalizability of the RF and SVM models. Our results suggest that ML models serve as powerful tools to expedite the discovery of novel antioxidant candidates, potentially streamlining the development of future therapeutic interventions.

Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors.

Maximizing safe resection in neuro-oncology has become paramount to improving patient survival and outcomes. Laser interstitial thermal therapy (LITT) offers similar survival benefits to traditional resection, alongside shorter hospital stays and faster recovery times. The extent of ablation (EOA) achieved using LITT is linked to patient outcomes, with greater EOA correlating with improved outcomes. However, the preoperative predictors for achieving supramaximal ablation (EOA ≥ 100%) are not well understood. By leveraging machine learning (ML) techniques, this study aimed to identify these predictors to enhance patient selection and therefore outcomes. The objective was to explore preoperative predictors for supramaximal EOA using ML in patients with glioblastoma. A retrospective study was conducted on the medical records of 254 patients undergoing LITT from 2013 to 2023 at a single tertiary center. Cohort criteria included age ≥ 18 years, diagnosis of glioblastoma, single-trajectory ablation, and a complete dataset. The study assessed preoperative clinical and radiographic factors, using EOA ≥ 100% as the endpoint. Five ML models were used: logistic regression, random forest (RF), gradient boosting, Gaussian naive Bayes, and support vector machine. Training and testing cohorts were subsequently assessed across ML models with fivefold cross-validation. Models were optimized using hyperparameter tuning. Performance was primarily quantified using the area under the curve (AUC) of the receiver operating characteristic curve. The final cohort consisted of 72 patients. Among the ML models, RF achieved the highest AUC (mean ± SD 0.94 ± 0.06). The leading models identified that lower preoperative volume, history of prior radiation therapy, history of prior craniotomy, preoperative neurological deficits, history of preoperative seizures, and distance from intracranial heat sinks were predictive of successful ablations in patients. Additionally, RF had the best mean metrics: accuracy 0.88, precision 0.87, specificity 0.87, and sensitivity 0.89. This is the first study to investigate the role of ML for optimizing ablation volumes in LITT. These ML models suggest that low preoperative volumes, previous craniotomy, previous radiation therapy, no previous neurological deficits, larger catheter-heat sink distance, and the presence of preoperative seizures are important prognostic factors for predicting successful supramaximal ablations with LITT.

Electroencephalogram Emotion Recognition via AUC Maximization

Imbalanced datasets pose significant challenges in areas including neuroscience, cognitive science, and medical diagnostics, where accurately detecting minority classes is essential for robust model performance. This study addressed the issue of class imbalance, using the ‘liking’ label in the DEAP dataset as an example. Such imbalances were often overlooked by prior research, which typically focused on the more balanced arousal and valence labels and predominantly used accuracy metrics to measure model performance. To tackle this issue, we adopted numerical optimization techniques aimed at maximizing the area under the curve (AUC), thus enhancing the detection of underrepresented classes. Our approach, which began with a linear classifier, was compared against traditional linear classifiers, including logistic regression and support vector machines (SVMs). Our method significantly outperformed these models, increasing recall from 41.6% to 79.7% and improving the F1-score from 0.506 to 0.632. These results underscore the effectiveness of AUC maximization methods in neuroscience research by offering a robust solution for managing imbalanced datasets, developing more precise diagnostic tools and interventions for detecting critical minority classes in real-world scenarios.

Prediction of Drug Pathway-based Disease Classes using Multiple Properties of Drugs

Background: Drug repositioning now is an important research area in drug discovery as it can accelerate the procedures of discovering novel effects of existing drugs. However, it is challenging to screen out possible effects for given drugs. Designing computational methods are a quick and cheap way to complete this task. Most existing computational methods infer the relationships between drugs and diseases. The pathway-based disease classification reported in KEGG provides us a new way to investigate drug repositioning as such classification can be applied to drugs. A predicted class of a given drug suggests latent diseases it can treat. Objective: The purpose of this study is to set up efficient multi-label classifiers to predict the classes of drugs. Methods: We adopt three types of drug information to generate drug features, including drug pathway information, label information and drug network. For the first two types, drugs are first encoded into binary vectors, which are further processed by singular value decomposition. For the third type, the network embedding algorithm, Mashup, is employed to yield drug features. Above features are combined and fed into RAndom k-labELsets (RAKEL) to construct multi-label classifiers, where support vector machine is selected as the base classification algorithm. Results: The ten-fold cross-validation results show that the classifiers provide high performance with accuracy higher than 0.95 and absolute true higher than 0.92. The case study indicates the novel effects of three drugs, i.e., they may treat new diseases. Conclusion: The proposed classifiers have high performance and are superiority to the classifiers with other classic algorithms and drug information. Furthermore, they have the ability to discover new effects of drugs.

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R2 = 0.902), IB-IBK (R2 = 0.82), IB-Kstar (R2 = 0.834), IB-MLP (R2 = 0.843), and DRF-AWV (R2 = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability. The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

Recognition of Heat-Damaged Corn Seeds Based on Fusion of Laser Ultrasonic Signal and Infrared Image Features

Corn is widely cultivated on a global scale. However, high temperatures during storage and transportation can lead to thermal damage to the kernels, negatively impacting their quality. Traditional methods for identifying heat-damaged grains primarily rely on manual inspection, which is characterized by low efficiency and accuracy. This study proposes a novel identification method that integrates laser ultrasonic signals with infrared image texture features. A pulsed laser stimulates the seeds to generate laser ultrasonic signals, while an infrared camera captures infrared images of the seeds. We extract time-domain, frequency-domain, and Hilbert-domain features from the laser ultrasonic signals, in addition to texture features from the infrared images. These features are combined using Canonical Correlation Analysis (CCA). Subsequently, the fused features are classified using a Backpropagation (BP) neural network, Support Vector Machine (SVM), and Particle Swarm Optimization–Support Vector Machine (PSO–SVM). The results indicate that the recognition rate achieved with the fused ‘signal-image’ features reaches 99.17%, providing a novel approach for detecting heat-damaged corn seeds.

An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates

This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR. This study utilized a cross-sectional dataset of 3,000 patients from the People’s Liberation Army General Hospital in 2021. Logistic regression was used as the baseline model to compare the prediction performance of the machine learning model and the related factors. The recursive feature elimination cross-validation (RFECV) algorithm was used to select features. Four machine learning models, support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boost decision tree (GBDT), were developed to predict DR. The models were optimized using grid search to determine hyperparameters, and the model with superior performance was selected. Shapley-additive explanations (SHAP) were used to analyze the important correlation factors of DR. Among the four machine learning models, the optimal model was GBDT, with predicted accuracy, precision, recall, F1-measure, and AUC values of 0.7883, 0.8299, 0.7539, 0.7901, and 0.8672, respectively. Six key correlates of DR were identified, including rapid micronutrient protein/creatinine measurement, 24-h micronutrient protein, fasting C-peptide, glycosylated hemoglobin, blood urea, and creatinine. The logistic model had 27 risk factors, with an AUC value of 0.8341. A superior prediction model was constructed that identified easily explainable key factors. The number of correlation factors was significantly lower compared to traditional statistical methods, leading to a more accurate prediction performance than the latter.

Integrating Multisource Data and Machine Learning for Supraglacial Lake Detection: Implications for Environmental Management and Sustainable Development Goals in High Mountainous Regions

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument(MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Investigating the Role of Artificial Intelligence in Predicting Consumer Preferences (A Little Study in the Tehran Market)

Accurately predicting consumer preferences is one of the key success factors in marketing strategies. This research investigated the effectiveness of different artificial intelligence models in predicting consumer preferences in the Tehran market. The required data was collected through a questionnaire including demographic characteristics and purchase preferences from 400 consumers in Tehran. After data preprocessing, random forest, support vector machine (SVM) and logistic regression models were implemented to predict consumer preferences. The results showed that the random forest model has the best performance among the investigated models with an accuracy of 87%. Also, the data analysis showed that the variables of age, income and education have the greatest influence in predicting purchase preferences. These findings can help local businesses to optimize their marketing strategies and achieve better results in attracting customers.

Leading Edge Erosion Classification in Offshore Wind Turbines Using Feature Extraction and Classical Machine Learning

Leading edge (LE) erosion is a type of damage that inhibits the aerodynamic performance of a wind turbine, resulting in high operation and maintenance (O&M) costs. This paper makes use of a small dataset consisting of 50 images of LE erosion and healthy blades for feature extraction and the training of four types of classifiers, namely, support vector machine (SVM), random forest, K-nearest neighbour (KNN), and multi-layer perceptron (MLP). Six feature extraction methods were used with these classifiers to train 24 models. The dataset has also been used to train a convolutional neural network (CNN) model developed using Keras. The purpose of this work is to determine whether classical machine learning (ML) classifiers trained with extracted features can produce higher-accuracy results, train faster, and classify faster than deep learning (DL) models for the application of LE damage detection of wind turbine blades. The oriented fast and rotated brief (ORB)-trained SVM achieved an accuracy of 90% ± 0.01, took 80.4 s to train, and achieved inference speeds of 63 frames per second (FPS), compared to the CNN model, which achieved an accuracy of 79.4% ± 2.07, took 4667.4 s to train, and achieved an inference speed of 1.3 FPS. These results suggest that classical ML models can be more accurate and efficient than DL models if the appropriate feature extraction method is used.

Prediction of the R3 Test-Based Reactivity of Supplementary Cementitious Materials: A Machine Learning Approach Utilizing Physical and Chemical Properties

This study utilized machine learning (ML) models to investigate the effect of physical and chemical properties on the reactivity of various supplementary cementitious materials (SCMs). Six SCMs, including ground granulated blast furnace slag (GGBFS), pulverized coal fly ash (FA), and ground bottom ash (BA), underwent thorough material characterization and reactivity tests, incorporating the modified strength activity index (ASTM C311) and the R3 (ASTM C1897) tests. A data set comprising 46 entries, derived from both experimental results and literature sources, was employed to train ML models, specifically artificial neural network (ANN), support vector machine (SVM), and random forest (RF). The results demonstrated the robustness of the ANN model, achieving superior prediction accuracy with a testing mean absolute error (MAE) of 9.6%, outperforming SVM and RF models. The study classified SCMs into reactivity classes based on correlation analysis, establishes a comprehensive database linking material properties to reactivity, and identifies key input parameters for predictive modeling. While most SCMs exhibited consistent predictions across types, GGBFS displayed significant variations, prompting a recommendation for the inclusion of additional input parameters, such as fineness, to enhance predictive accuracy. This research provided valuable insights into predicting SCM reactivity, emphasizing the potential of ML models for informed material selection and optimization in concrete applications.

Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study

BackgroundAsthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.MethodsClinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART.ResultsBART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years.ConclusionsBART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability.

Predictive Modelling of Mineral Prospectivity Using Satellite Remote Sensing and Machine Learning Algorithms

In today’s world of falling returns on fixed exploration budgets, complex targets, and ever-increasing volumes of multi-parameter datasets, the effective management and integration of existing data are essential to any mineral exploration operation. Machine learning (ML) algorithms like Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM) are powerful data-driven methods that are not implemented very often with remote sensing-derived hydrothermal alternation information and limited field datasets for mapping mineral prospectivity. The application of machine learning algorithms with satellite remote sensing data and limited field data, they have not been compared and evaluated together thoroughly in this field. A data science approach was applied to create nine predictor maps, incorporating limited field data and satellite remote sensing information. A confusion matrix, statistical measures, and a Receiver Operating Characteristic (ROC) curve were used to evaluate the prediction models efficacy on both the training and test datasets. The results suggested that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. These results imply that the RF model is the most suitable for Cu potential mapping in the Pakistan’s North Waziristan region. Consequently, all the models including the RF model were used to generate a prospectivity map, which contained low to very-high potential zones, to support further exploration in the region. The newly discovered deposit inside the predicted prospective areas demonstrates the robustness and efficacy of the prospectivity modelling approach as proposed in this research for generating exploration targets.

Unveiling soil coherence patterns along Etihad Rail using Sentinel-1 and Sentinel-2 data and machine learning in arid region

This research applies machine learning to predict soil coherence for Etihad Rail, marking the first comprehensive study in the United Arab Emirates (UAE)'s arid regions. By integrating Sentinel-1 SAR and Sentinel-2 data with MODIS Aerosol Optical Depth (AOD) observations, the study develops detailed models that depict complex soil coherence patterns crucial for urban planning and risk assessment. Findings show variations in soil coherence between operational and under-construction phases, influenced by seasonal changes in aerosol dynamics and sand dust levels. Higher soil coherence is linked with lower annual sand dust deposition and AOD measurements, emphasizing the importance of this data for informed decision-making. The study employs a unique combination of data sources and machine learning algorithms to predict soil coherence, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBOOST), Gaussian Process Regression (GPR), Random Forest (RF), and 1D Convolutional Neural Network (CNN), with the Random Forest model achieving the lowest root mean squared error (RMSE) of 0.0826. These contributions enhance our understanding and provide a valuable framework for infrastructure development in similar environments.