Test Dataset Research Articles

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

Compare three deep learning-based artificial intelligence models for classification of calcified lumbar disc herniation: a multicenter diagnostic study

ObjectiveTo develop and validate an artificial intelligence diagnostic model for identifying calcified lumbar disc herniation based on lateral lumbar magnetic resonance imaging(MRI).MethodsDuring the period from January 2019 to March 2024, patients meeting the inclusion criteria were collected. All patients had undergone both lumbar spine MRI and computed tomography(CT) examinations, with regions of interest (ROI) clearly marked on the lumbar sagittal MRI images. The participants were then divided into separate sets for training, testing, and external validation. Ultimately, we developed a deep learning model using the ResNet-34 algorithm model and evaluated its diagnostic efficacy.ResultsA total of 1,224 eligible patients were included in this study, consisting of 610 males and 614 females, with an average age of 53.34 ± 10.61 years. Notably, the test datasets displayed an impressive classification accuracy rate of 91.67%, whereas the external validation datasets achieved a classification accuracy rate of 88.76%. Among the test datasets, the ResNet34 model outperformed other models, yielding the highest area under the curve (AUC) of 0.96 (95% CI: 0.93, 0.99). Additionally, the ResNet34 model also exhibited superior performance in the external validation datasets, exhibiting an AUC of 0.88 (95% CI: 0.80, 0.93).ConclusionIn this study, we established a deep learning model with excellent performance in identifying calcified intervertebral discs, thereby offering a valuable and efficient diagnostic tool for clinical surgeons.

BreCML: identifying breast cancer cell state in scRNA-seq via machine learning

Breast cancer is a prevalent malignancy and one of the leading causes of cancer-related mortality among women worldwide. This disease typically manifests through the abnormal proliferation and dissemination of malignant cells within breast tissue. Current diagnostic and therapeutic strategies face significant challenges in accurately identifying and localizing specific subtypes of breast cancer. In this study, we developed a novel machine learning-based predictor, BreCML, designed to accurately classify subpopulations of breast cancer cells and their associated marker genes. BreCML exhibits outstanding predictive performance, achieving an accuracy of 98.92% on the training dataset. Utilizing the XGBoost algorithm, BreCML demonstrates superior accuracy (98.67%), precision (99.15%), recall (99.49%), and F1-score (99.79%) on the test dataset. Through the application of machine learning and feature selection techniques, BreCML successfully identified new key genes. This predictor not only serves as a powerful tool for assessing breast cancer cellular status but also offers a rapid and efficient means to uncover potential biomarkers, providing critical insights for precision medicine and therapeutic strategies.

Estimating the health of turbine engine based on the relationship between torque margin and density altitude

We present an anomaly detection method developed for the PHM North America 2024 Conference Data Challenge. This competition is aimed at estimating the health of helicopter turbine engines (PHM Society, 2024). The task includes the estimation of the torque margin (regression) and the health state (binary classification) of turbine engines. We developed an estimation model using a hybrid algorithm that combines data-based machine learning and domain knowledge-based processing. Our method achieved scores over 0.99 for both the testing and validation datasets. based on the calculation rules provided by PHM Society. These results were ranked first among all the participating teams.

Automated detection of bone lesions using CT and MRI: a systematic review.

The aim of this study was to systematically review the use of automated detection systems for identifying bone lesions based on CT and MRI, focusing on advancements in artificial intelligence (AI) applications. A literature search was conducted on PubMed and MEDLINE. Data were extracted and grouped into three main categories, namely baseline study characteristics, model validation strategies, and the type of AI algorithms. A total of 10 studies were selected and analyzed, including 2,768 patients overall with a median of 187 per study. These studies utilized various AI algorithms, predominantly deep learning models (6 studies) such as Convolutional Neural Networks. Among machine learning validation strategies, K-fold cross-validation was the mostly used (5 studies). Clinical validation was performed using data from the same institution (internal testing) in 8 studies and from both the same and different (external testing) institutions in 1 study, respectively. AI, particularly deep learning, holds significant promise in enhancing diagnostic accuracy and efficiency. However, the review highlights several limitations, such as the lack of standardized validation methods and the limited use of external datasets for testing. Future research should address these gaps to ensure the reliability and applicability of AI-based detection systems in clinical settings.

Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data.

Despite the end of the global Coronavirus Disease 2019 (COVID-19) pandemic, the risk factors for COVID-19 severity continue to be a pivotal area of research. Specifically, studying the impact of the genomic diversity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on COVID-19 severity is crucial for predicting severe outcomes. Therefore, this study aimed to investigate the impact of the SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status on the severity of COVID-19, and to develop accurate and robust prediction models. The training set (n = 12,038), primary testing set (n = 4,006), and secondary testing set (n = 2,845) consist of SARS-CoV-2 genome sequences with patient information, which were obtained from Global Initiative on Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods were employed to construct prediction models. By extracting SARS-CoV-2 genomic features, optimizing model parameters, and integrating models, this study improved the prediction accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied to analyze the interpretability of the model and to identify risk factors, providing insights for the management of severe cases. The proposed ensemble model achieved an F-score of 88.842% and an Area Under the Curve (AUC) of 0.956 on the global testing dataset. In addition to factors such as patient age, gender, and vaccination status, over 40 amino acid site mutation characteristics were identified to have a significant impact on the severity of COVID-19. This work has the potential to facilitate the early identification of COVID-19 patients with high risks of severe illness, thus effectively reducing the rates of severe cases and mortality.

Application of CT-based foundational AI and radiomics models for prediction of outcome for non-small-cell lung cancer patients treated on the NRG/RTOG 0617 clinical trial

Abstract Objectives To apply CT-based foundational artificial intelligence (AI) and radiomics models for predicting overall survival (OS) for patients with locally advanced non-small cell lung cancer (NSCLC). Methods Data for 449 patients retrospectively treated on the NRG Oncology/Radiation Therapy Oncology Group (RTOG) 0617 clinical trial were analyzed. Foundational AI, radiomics and clinical features were evaluated using univariate cox regression and correlational analyses to determine independent predictors of survival. Several models were fit using these predictors and model performance was evaluated using nested cross-validation and unseen independent test datasets via area under receiver-operator-characteristic curves, AUC’s. Results For all patients, the combined foundational AI and clinical models achieved AUCs of 0.67 for the Random Forest (RF) model. The combined radiomics and clinical models achieved RF AUCs of 0.66. In the low-dose arm, foundational AI alone achieved AUC of 0.67, while AUC for the ensemble radiomics and clinical models was 0.65 for the support vector machine (SVM). In the high-dose arm, AUC values were 0.67 for combined radiomics and clinical models and 0.66 for the foundational AI model. Conclusions This study demonstrated encouraging results for application of foundational AI and radiomics models for prediction of outcomes. More research is warranted to understand the value of ensemble models toward improving performance via complementary information. Advances in knowledge Using foundational AI and radiomics-based models we were able to identify significant signatures of outcomes for NSCLC patients retrospectively treated on a national cooperative group clinical trial. Associated models will be important for application toward prospective patients.

Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image

Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 × 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions.

Temporal Convolutional Network-based Approach for Forecasting Fluctuations Differential Pressure in Reverse Osmosis Systems

Providing forecasts of pressure fluctuations and changes will aid in selecting appropriate maintenance strategies to optimize efficiency and costs. This paper presents a deep-learning-based model to forecast the degradation evolution of membrane biological fouling in RO (Reverse Osmosis) systems. Although applying deep learning in forecasting still faces many challenges, applying convolutional operations in convolution 1D has yielded promising results for sequential data, particularly time series data. Thus, in this paper we study and develop the 1D convolution operation-based Temporal Convolutional Network (TCN) model to predict pressure dynamics at both ends of the RO vessel. In addition, since the deep learning technique has yet to be widely explored in this field, thus we also need to pre-process the data collected from the Carlsbad Desalination Plant in California, such as the proposed model can identify complex relationships between timestamps and pressure features. The experiment results were evaluated and compared with other existing models, such as LSTM, CNN & LSTM, and GRU. The obtain results show that the TCN-based prediction model had the slightest error in the test dataset.

Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis. Digital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample. A total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database. The development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.

Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning.

Automated segmentation software in optical coherence tomography (OCT) devices is usually developed for and primarily tested on common diseases. Therefore segmentation accuracy of automated software can be limited in eyes with rare pathologies. We sought to develop a semisupervised deep learning segmentation model that segments 10 retinal layers and four retinal features in eyes with Macular Telangiectasia Type II (MacTel) using a small labeled dataset by leveraging unlabeled images. We compared our model against popular supervised and semisupervised models, as well as conducted ablation studies on the model itself. Our model significantly outperformed all other models in terms of intersection over union on the 10 retinal layers and two retinal features in the test dataset. For the remaining two features, the pre-retinal space above the internal limiting membrane and the background below the retinal pigment epithelium, all of the models performed similarly. Furthermore, we showed that using more unlabeled images improved the performance of our semisupervised model. Our model improves segmentation performance over supervised models by leveraging unlabeled data. This approach has the potential to improve segmentation performance for other diseases, where labeled data is limited but unlabeled data abundant. Improving automated segmentation of MacTel pathology on OCT imaging by leveraging unlabeled data may enable more accurate assessment of disease progression, and this approach may be useful for improving feature identification and location on OCT in other rare diseases as well.

Digital twin for decision support system of industrial utility management

Manufacturing and industrial operations rely heavily on energy that is generated mostly from fossil fuels, such as coal, oil, and natural gas, which harm the environment and contribute to climate change. Thus, renewable energy is being integrated into industrial processes to lessen environmental effects and reduce fossil fuel usage. However, the renewable source performance process can be greatly affected by disturbances and constraints, such as ambient air temperature and relative humidity, minimum utility consumption, and the total energy required, making effective control difficult. This study proposes a digital twin built with machine learning regression techniques for load demand forecasting as a decision-support system for industrial utility management. From the results, the ensemble tree (ET) model produced the highest accuracy, based on validation and test dataset root mean squared error values of 23.164 and 27.558, respectively, and R2 values of 0.96 and 0.95, respectively. The digital twin and load demand forecasting approaches effectively created an efficient operating schedule for industrial utility management. The ET model had a total error of 23.86%, substantially lower than the average load demand's total error of 65.29%. Therefore, the ET model with weather conditions in four scenarios could be recommended to optimize energy utilization when creating the operating schedule.

Identification of novel markers for neuroblastoma immunoclustering using machine learning

BackgroundDue to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the condition of the tumor immune microenvironment.MethodsThe GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.ResultsIn the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. Additionally, the tumor microenvironment can also affect the therapeutic response of neuroblastoma. Six characteristic genes (BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM) were identified by machine learning, and these genes are associated with multiple immune-related pathways and immune cells in neuroblastoma.ConclusionsBATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM may serve as biomarkers that reflect the conditions of the immune microenvironment of neuroblastoma and hold promise in guiding neuroblastoma treatment.

Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.

Emerging and evolving Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages, adapted to changing epidemiological conditions, present unprecedented challenges to global public health systems. Here, we introduce an adapted analytical approach that complements genomic sequencing, applying a cost-effective quantitative polymerase chain reaction (qPCR)-based assay. Viral RNA samples from SARS-CoV-2 positive cases detected by diagnostic laboratories or public health network units in Ceará, Brazil, were tracked for genomic surveillance and analyzed by using paired-end sequencing combined with integrative genomic analysis. Validation of a key structural variation was conducted with gel electrophoresis for the presence of a specific open reading frame 7a(ORF7a) gene deletion within the "BE.9" lineages tracked. The analytical innovation of our method is the optimization of a simple intercalating dye-based qPCR assay through repositioning primers from the ARTIC v4.1 amplicon panel to detect large molecular patterns. This assay distinguishes between "BE.9" and "non-BE.9" lineages, particularly BQ.1, without the need for expensive probes or sequencing. The protocol was validated against lineage predictions from next-generation sequencing (NGS) using 525 paired samples, achieving 93.3% sensitivity, 95.1% specificity, and 92.4% agreement, as measured by Cohen's Kappa coefficient. Machine learning (ML) models were trained using the melting curves from intercalating dye-based qPCR of 1724 samples, enabling highly accurate lineage assignment. Among them, the support vector machine (SVM) model had the best performance and after fine-tuning showed ∼96.52% (333/345) accuracy in comparison to the test data set. Our integrated approach provides an adapted analytical method that is both cost-effective and scalable, suitable for rapid assessment of emerging variants, especially in resource-limited settings. In this work, the protocol is applied to improve the monitoring of SARS-CoV-2 sublineages but can be extended to track any key molecular signature, including large insertions and deletions (indels) commonly observed in pathogenic agent subtypes. By offering a complement to traditional sequencing methods and utilizing easily trainable machine learning algorithms, our methodology contributes to enhanced molecular surveillance strategies and supports global efforts in pandemic control.

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminated mussels based on near-infrared reflectance spectroscopy was studied. The spectral data of Cd-contaminated and non-contaminated mussels were collected in the range of 950-1700nm. The model based on a robust energy-based least squares twin support vector machine (RELS-TSVM) was established to detect Cd-contaminated mussels. The influence of parameters on the RELS-TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS-TSVM model in detecting Cd-contaminated mussels reached 99.92%, which was better than other twin support vector machine-derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS-TSVM model had a high robustness for noise disturbance. The results show that near-infrared spectroscopy combined with the RELS-TSVM model can realize the detection of Cd-contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish. PRACTICAL APPLICATION: The method of detecting Cd-contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

Machine learning – based approach for predicting pushover curves of low-rise reinforced concrete frame buildings

Reinforced Concrete (RC) frame buildings are prevalent worldwide due to their seismic performance and the broad availability of materials. Pushover analysis is regarded as one way to assess the seismic performance of these buildings, which is simple to interpret. The only downside of this method is that it requires developing nonlinear models, which can be time-consuming and computationally expensive. Recently, Machine Learning (ML) has emerged as an alternative to approximate the pushover response of RC frame buildings. However, there are still improvements in several areas, such as including relevant and easily obtainable parameters in structural design (e.g. reinforcement ratios), considering a wide range of building configurations, and comparing different ML techniques. This paper presents the development of two models based on Random Forest (RF) and Artificial Neural Network (ANN) to predict the pushover response of low-rise RC frame buildings, supported by a user-friendly graphical interface. First, a dataset of over 138000 Pushover analyses was performed on flexure-controlled RC frames ranging from two to five stories using OpenSeesPy, subsequently used to develop predictive ML models. These models are designed to offer a trilinear approximation of a building's Pushover curve, and they require input information that is available from the customary building design. The pushover predictions obtained with RF and ANN take 0.12 % and 3.05 % of the computing time compared to the OpensSeesPy nonlinear model. The proposed models demonstrate a high level of accuracy, with a coefficient of determination (R²) of over 0.88 and 0.81 in testing datasets for RF and ANN models, underscoring their potential to significantly streamline the seismic performance assessment process by providing quick Pushover curve estimations.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Deep learning-based ground motion inversion through recursive structural acceleration response using DRA-LSTM Net

Known ground motion time history is crucial for the precise numerical analysis of structural response and evaluation of its safety. This study introduces the Deep Recursive Attentive Long Short-Term Memory Network (DRA-LSTM Net), a framework designed for the inversion of ground motion (GM) from recorded structural response (SR) during an earthquake. By integrating an attention mechanism in network along with a strategic recursive approach for dataset management, the DRA-LSTM Net achieves high precision in GM inversion. The method incorporates a novel pre-processed matrix format to capture unique recursive behaviour patterns in GM data, enhancing the model’s training, validation efficiency, and prediction accuracy on unseen test datasets. Case studies on single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structures highlight the method’s potential for real-time and high-precision GM inversion. Additionally, transfer learning was implemented to fine-tune the pre-trained model with real-world sensor data and structural responses from different floors, demonstrating the model’s adaptability and ability to retain high prediction accuracy across diverse datasets. This approach is particularly useful for regions with limited seismic instrumentation but equipped with structural response sensors.

Enhanced Forecasting of Global Ionospheric Vertical Total Electron Content Maps Using Deep Learning Methods

The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu.

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.