Random Forest Classifier Research Articles

MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area

Lithology classification stands as a pivotal research domain within geological Remote Sensing (RS). In recent years, extracting lithology information from multi-source RS data has become an inevitable trend. Various classification image primitives yield distinct outcomes in lithology classification. The current research on lithology classification utilizing RS data has predominantly concentrated on pixel-level classification, which suffers from a long classification time and high sensitivity to noise. In order to explore the application potential of superpixel segmentation in lithology classification, this study proposed the Multi-scale superpixel Segmentation Integrating Multi-source RS data (MSIMRS), and conducted a lithology classification study in Duolun County, Inner Mongolia Autonomous Region, China combining MSIMRS and the Support Vector Machine (MSIMRS-SVM). In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. Among these methods, our proposed MSIMRS-SVM achieved the highest accuracy in lithology classification in a typical semi-arid area, Duolun County, with an overall accuracy and Kappa coefficient of 92.9% and 0.92. Moreover, the findings indicate that incorporating superpixel segmentation into lithology classification resulted in notably fewer fragmented patches and significantly improved the visualization effect. The results showcase the application potential of superpixel primitives in lithology information extraction within semi-arid areas.

Water Hyacinth Invasion and Management in a Tropical Hydroelectric Reservoir: Insights from Random Forest and SVM Classification

The rapid proliferation of water hyacinth (Eichhornia crassipes) in newly formed reservoirs poses a significant threat to aquatic ecosystems and hydroelectric operations. The objective of this study was to map and monitor the spatio-temporal distribution of water hyacinth in the Hidroituango reservoir in Colombia from 2018 to 2023, using Sentinel-2 satellite imagery and machine learning algorithms. The Random Forest (RF) and Support Vector Machine (SVM) algorithms were employed for image classification, and their performance was evaluated using various accuracy metrics. The results revealed that both algorithms effectively detected and mapped water hyacinth infestations, with RF demonstrating greater stability in capturing long-term trends and SVM exhibiting higher sensitivity to rapid changes in coverage. The study also highlighted the impact of the COVID-19 pandemic on control efforts, leading to a temporary increase in infestation. The findings underscore the importance of continuous monitoring and adaptive management strategies to mitigate the ecological and economic impacts of water hyacinth in the Hidroituango reservoir and similar environments.

The Parkinson’s Puzzle: Early Detection & Diagnosis

Parkinson's disease is a neurological disorder that affects movement and becomes progressively worse over time. Currently, there is no cure for the disease, but early detection and appropriate management can significantly improve the patient's quality of life. This project presents a web-based application designed to detect Parkinson's disease based on the analysis of audio recordings of patient's voices. The application utilizes a machine learning model that has been trained on a dataset consisting of audio recordings from both Parkinson's disease patients and healthy individuals. The web application extracts relevant features from the audio recordings, employing machine learning algorithms to predict the probability of Parkinson's disease. The machine learning models used in this research are Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, MLP Classifier, Gaussian NB, XGB Classifier, Random Forest Classifier, and Cat Boost Classifier and stacked three models (XG Boost, Random Forest, and Cat Boost) using Stacking CV Classifier out of which the model with the highest performance will be chosen to develop the web application. Keywords: Parkinson’s Disease, Web Application, Ensemble Learning, Stacked Model

Comparison of Tree Typologies Mapping Using Random Forest Classifier Algorithm of PRISMA and Sentinel-2 Products in Different Areas of Central Italy

The continuous development of satellite imagery, coupled with advancements in machine learning technologies, allows detailed mapping of terrestrial landscapes. This study evaluates the classification performance of tree typologies using Sentinel-2 and PRISMA data, focusing on central Italy’s different areas. The purpose is to assess the role of spectral and spatial resolution in land cover classification, contributing to forest management and conservation efforts. Random Forest Classifier was applied to classify tree typologies across two study areas: the Roman Coastal region and the Lake Vico Basin. Ground truth (GT) data, collected from a trial citizen survey campaign, were used for training and validation. PRISMA datasets, particularly when processed with PCA, consistently outperformed Sentinel-2. The PRISMA PCA dataset achieved the highest overall accuracy with 71.09% for the Roman Coastal region and 87.15% for the Lake Vico Basin, emphasizing the value of spectral resolution. However, Sentinel-2 showed comparative strength in spatially heterogeneous areas. Tree typologies with more uniform distribution, such as hazelnut and chestnut, achieved higher classification accuracy compared to mixed-species forests. The study assesses that Sentinel-2 remains a viable alternative where spatial resolution is critical also considering the limited PRISMA images’ availability. Moreover, the work explores the potential of combining satellites and accurate GT for improved land cover mapping.

P0149 Deep functional analysis of a defined sulphide-reducing diet (4-SURE diet) as a novel therapy for ulcerative colitis (UC) demonstrates all specific luminal micro environmental objectives were achieved

Abstract Background As a dietary approach to reducing inflammation in ulcerative colitis (UC), the 4-SURE diet was designed to correct pathogenic alterations of excessive protein fermentation and hydrogen sulphide (H2S) production in the distal colon. Specific dietary objectives were achieved (Day et al,J Nutr 2022;152:1690) but it is uncertain whether mechanistic objectives could be achieved with 8 weeks of diet. Therefore, we aimed to perform a deep functional analysis (microbial and metabolomic) of the faeces. Methods Faecal samples of 28 adults with mild-moderately active UC were collected at week 0 and 8 of diet intervention, subsampled and stored at−80 °C. Shotgun metagenomic sequencing was used to identify genes involved in H2S metabolism. Metagenomic reads were trimmed, reads mapping to the human genome removed. Gene identification was performed using Diamond v2.1.9 for alignment against a database of genes involved in global sulphur cycling. Gas-chromatography mass-spectrometry was used to characterise volatile organic compounds (VOCs) with specific analysis of products of protein fermentation. Microbiota capacity to produce H2S was assessed by anaerobic incubation of homogenates followed by spectrophotometric determination of total H2S production. Results Majority of microbiome members belonged to the Bacteroidota and Bacillota phyla, with no significant difference in bacterial diversity determined between time points (p=0.16). However, using a Random Forest Classifier, known H2S producers, Odoribacter and Peptostreptococcaceae, were identified as the most important taxa in discriminating between pre and post-diet samples, with abundances markedly lower after diet intervention. A shift in microbiota metagenomic profiles putatively involved in H2S metabolism was identified post-diet, with differences (p<0.05, Wilcoxon signed-rank with Benjamini-Hochberg correction) in 12 of 23 analysed genes involved in H2S cycling determined, including iscS, cysK (Fig), metC, luxS, asrC and sseA. 173 faecal VOCs were identified across all samples. Indole (a specific marker of protein fermentation) decreased from 0.42 [-0.25,0.61] at week 0 to -0.28 [-0.77,0.48] at week 8 (p=0.007, FDR correction; Fig). Faecal H2S reduced from median 2.61 (interquartile range 2–4.96)µmol to 1.41 (0.81-2.27)µmol of H2S/g stool (wet weight)(p=0.002)(Fig). Conclusion Deep functional analysis of the faeces demonstrated the micro-environmental objectives of the 4-SURE diet successfully reduced protein fermentation, changed microbial community sulphur-metabolic gene profile and reduced H2S production ex vivo. Applying functional analysis to a diet study is novel, highlighting exemplar framework for including biomarkers of pathogenic relevance in analysis of therapeutic diets.

Mapping of Fluvial Morphological Units from Sentinel-1 Data Using a Deep Learning Approach

The identification of ongoing evolutionary trajectories, the prediction of future changes in the functioning of riverine habitats, and the assessment of flood-related risks to human populations all depend on regular hydro-morphological monitoring of fluvial settings. This paper focuses on the satellite monitoring of river macro-morphological units (assemblages of water, sediment, and vegetation units) and their temporal evolution. In particular, we develop a deep-learning semantic segmentation method using Synthetic Aperture Radar (SAR) Sentinel-1 dual-polarized data. The methodology is executed and tested on the Po River, located in Italy. The training of a relatively deep convolutional neural network requires a large amount of ground-truth data, which is often limited and challenging to acquire. To address this limitation, the dataset is augmented using a random forest (RF) classification algorithm. RF parameters are trained with both Sentinel-1 (S1) and Sentinel-2 (S2) data. The RF classification algorithm is very robust and achieves excellent performance. To overcome the limitation linked with the scarce availability of contemporary acquisition by S1 and S2 sensors, the deep learning (DL) model is trained by using only the Sentinel-1 input data and the ground truth from the RF result. The proposed approach achieves promising results in the classification of water, sediments, and vegetation along rivers such as the Italian Po River with low computational costs and no concurrency constraints between S1 and S2.

Automatic Blood Disease Identification System Using Machine Learning Algorithms with Hemogram Reports

Objectives: The primary objective of this research is to create prediction models with machine learning algorithms that can detect blood illnesses in their early stages, including sickle cell disease, leukemia, anemia, and lymphoma. Methods: Reducing the amount of deaths linked to these illnesses and increasing the precision of disease detection. The study used machine learning algorithms: Decision Tree, Random Forest, and XGBoost classifier, and it used a dataset of complete blood count reports. Based on the characteristics of the total blood count, these algorithms were trained on the dataset to forecast the probability of blood diseases. Findings: According to the study, there is a better chance of a cure for blood problems since machine learning models can correctly detect them in their early stages. The findings imply that using predictive models can both lower the death rate and enhance the quality of life for those with these conditions. With a 99.10% accuracy rate, the Random Forest method outperforms other algorithms like Decision Tree, and XGBoost. Novelty: This research is unique because it uses a dataset of complete blood count reports results to use machine learning algorithms for blood condition prediction. Healthcare systems may detect blood problems early on and enhance patient outcomes by using classifier algorithms such as Decision Tree, Random Forest, and XGBoost. This opens up new avenues for illness identification. Keywords: Machine Learning, Decision Tree, Random Forest, XGBoost, Blood Diseases

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes

Windthrows significantly disrupt forest ecosystems, impacting biotic community life cycles. To ensure the reformation of the ecosystem chain, it is essential to rehabilitate the windthrow area as soon as possible. Therefore, it is mandotory to determine the success of the rehabilitation processes. In this study, the rehabilitation process of windthrow that occurred in the Düzce Tatlıdere Forest District (DTFD) was identified using vegetation indices calculated from Unmanned Aircraft System (UAS) images and Sentinel-2A satellite images between 2017 and 2022. The Normalized Difference Red Edge Index (NDRE), Plant Senescence Reflectance Index (PSRI), and Normalized Difference Vegetation Index (NDVI) were calculated from Sentinel-2A satellite images, and the most successful index for detecting reforested areas was identified. UAS images were used to create training data, and this data was used to classify Sentinel-2A images with the Random Forest (RF) algorithm. The classification’s accuracy was assessed using the Kappa Coefficient and Overall Accuracy (%). Results showed that NDVI had the lowest accuracy in both years, whereas NDRE succesfully detected windthrow area borders. PSRI was most successful in monitoring rehabilitation progress and detecting reforested areas between 2017 and 2022. This study, he effectiveness and limitations of the NDRE, PSRI and NDVI indices in the rehabilitation process of the windthrow area have been detected, and the most important Sentinel-2A bands were determined based on the results of the RF classification. This study is pioneering in the use of NDRE and PSRI to detect reforested areas post-windthrow.

A Comprehensive Review on Brain Tumor Detection Using Advanced Learning Algorithms

Early detection of brain tumor is of critical importance in medical imagery. It reviews the application of advanced learning algorithms to increase the accuracy and efficiency of brain tumor detection using noninvasive imaging (in particular MRI). In the last couple of years, recent machine learning and deep learning approaches like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid models have shown an overnight progress by automating the process of extraction, segmentation, and classification of brain tumors. Tumor sub-region identification and feature selection has been improved by using techniques such as Random Forest classifiers, unsupervised clustering, and ensemble methods. Secondly, detection performance has been improved by the integration of handcrafted and automatic features, including texture, shape, and intensity. Further feature selection process is dimensionality reduction based techniques like Principal Component Analysis (PCA) and Information Gain (IG). The focus of this review is on the increasing usefulness of these algorithms to achieve adequate diagnosis and discuss future trends in personalized diagnostics and treatment planning.

Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids.

Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)-to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three chemometric approaches-random forest, support vector machine, and k-nearest neighbor algorithms-were employed and optimized using a 5-fold cross-validation grid search for all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level). The enhanced performance of the high-level fusion approach (F1 score of 92%) is demonstrated, effectively leveraging the surface-enhanced Raman data with a 90% voting weight, without compromising prediction accuracy (92%) when combined with infrared spectral data. This highlights the viability of a multi-instrument approach using data fusion and random forest classification to improve the detection of various components in complex opioid samples in a point-of-care setting.

Machine Learning-based Prediction of Mortality Among Malnourished Patients Hospitalized With Inflammatory Bowel Disease.

Malnourished patients hospitalized with inflammatory bowel disease (IBD) have a high risk of morbidity and mortality. Risk stratification can help identify patients who are most in need of medical and nutritional intervention. This study aimed to develop a machine-learning model that accurately predicts mortality in hospitalized IBD patients with protein-calorie malnutrition (PCM). Hospitalized adults with IBD and PCM were identified in the 2016 to 2019 National Inpatient Sample (NIS). Random Forest Classifier (RFC) and Extreme Gradient Boosting (XGB) models were constructed using a 70% randomly sampled training set from the years 2016 to 2018, tested using the remaining 30% of 2016 to 2018 data, and externally validated using 2019 data. Patient characteristics were evaluated using weighted estimates that accounted for the complex sampling design of the NIS. Among 879,730 malnourished patients hospitalized for IBD, 1930 (0.2%) died. Compared with malnourished patients who survived, those who died were generally older, White, had ulcerative colitis with multiple comorbidities, and admitted on the weekend. The accuracy, precision, sensitivity, and specificity for both models were 0.99, 0.98, 0.99, and 0.99, respectively. The area under the receiver operating characteristic curve was 0.91 for both models. Machine learning models can accurately predict mortality in malnourished patients hospitalized with IBD, while solely relying on readily available clinical data. Further integration of these tools into clinical practice could improve risk stratification of IBD patients with PCM and potentially reduce mortality in this high-risk population by prompting earlier intervention.

The application of machine learning approaches to classify and predict fertility rate in Ethiopia

Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data. Phyton programming language was used to develop these models. Predictors of fertility rate were determined using the feature important techniques. The performance of models was evaluated using accuracy, area under the curve (AUC), precision, recall, F1-score, specificity, and sensitivity. The mean age of participants was 32.7 (± 5.6) years. The random forest classifier (accuracy = 0.901 and AUC = 0.961) followed by a one-dimensional convolutional neural network (accuracy = 0.899 and AUC = 0.958), logistic regression (accuracy = 0.874 and AUC = 0.937), and gradient boost classifier (accuracy = 0.851 and AUC 0.927) were the top performing ML models. Family size, age, occupation, and education with an average importance score of 0.198, 0.151, 0.118, and 0.081, respectively were the top significant predictors of the fertility rate. The best ML models to classify and predict fertility rates were random forest, one-dimensional convolutional neural network, logistic regression, and gradient boost classifier. The findings on important factors of fertility rate can inform targeted public health, programs that address disparities related to family size, occupation, education, and other socioeconomic factors.

ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest

In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. The key idea behind ADA-NAF is the incorporation of NAF into an autoencoder structure, where it implements functions of a compressor as well as a reconstructor of input vectors. Our approach introduces several technical advances. First, a proposed end-to-end training methodology over normal data minimizes the reconstruction errors while learning and optimizing neural attention weights to focus on hidden features. Second, a novel encoding mechanism leverages NAF’s hierarchical structure to capture complex data patterns. Third, an adaptive anomaly scoring framework combines the reconstruction errors with the attention-based feature importance. Through extensive experimentation across diverse datasets, ADA-NAF demonstrates superior performance compared to state-of-the-art methods. The model shows particular strength in handling high-dimensional data and capturing subtle anomalies that traditional methods often do not detect. Our results validate the ADA-NAF’s effectiveness and versatility as a robust solution for real-world anomaly detection challenges with promising applications in cybersecurity, industrial monitoring, and healthcare diagnostics. This work advances the field by introducing a novel architecture that combines the interpretability of attention mechanisms with the powerful feature learning capabilities of autoencoders.

Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW's predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities.

Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.

Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer's disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker samples. Both of these samples, obtained from participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), were independently analyzed utilizing machine learning (ML)-based multiclassifiers. This study applied novel machine learning-based data augmentation techniques to gene expression profile data that are high-dimensional, low-sample-size (HDLSS) and inherently highly imbalanced. The investigation obtained the highest multiclassification performance to date in the multistage diagnosis of Alzheimer's disease utilizing the blood gene expression profiles of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Based on the performance results obtained, and other factors such as early prediction capabilities, this study compares the efficacies of the two types of biomarkers for multistage diagnosis. This study presents the sole investigation in which multiclassification-based AD stage diagnosis was conducted utilizing blood gene expression data. We obtained the best multiclassification result in both modalities of the ADNI data in terms of F1-score and were able to identify new genetic biomarkers. Methods: The combination of the XGBoost and SFBS (Sequential Floating Backward Selection) methods was used to select the features. We were able to select the 95 most effective gene probe sets out of 49,386. For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages-cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD)/dementia. DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. Because of the high data imbalance in genomic data, borderline oversampling/data augmentation was applied in the model training and original samples for validation. Results: Utilizing clinical data, the highest ROC AUC scores attained were 0.989, 0.927, and 0.907 for the identification of the CN, MCI, and dementia stages, respectively. The highest F1 scores achieved were 0.971, 0.939, and 0.886. Employing gene expression data, we obtained ROC AUC scores of 0.763, 0.761, and 0.706 for the CN, MCI, and dementia stages, respectively, and F1 scores of 0.71, 0.77, and 0.53 for CN, MCI, and dementia, respectively. Conclusions: This represents the best outcome to date for AD stage diagnosis from ADNI blood gene expression profile data utilizing multiclassification techniques. The results indicated that our multiclassification model effectively manages the imbalanced data of a high-dimension, low-sample-size (HDLSS) nature to identify samples of the minority class. MAPK14, PLG, FZD2, FXYD6, and TEP1 are among the novel genes identified as being associated with AD risk.

Spatiotemporal analysis of urban expansion and its impact on farmlands in the central Ethiopia metropolitan area

Urban growth in sub-Saharan Africa presents significant challenges to sustainable development, food security, and environmental conservation. The rapid urban expansion and impact on agricultural land reduction in central Ethiopian metropolitan areas (Addis Ababa and Sheger city) exemplify these issues while simultaneously offering opportunities for sustainable development. This study aims to quantify and characterize the spatiotemporal dynamics of urban expansion in Addis Ababa and the surrounding Sheger city, explicitly focusing on understanding the impact of urban expansion on farmlands. The supervised random forest (RF) classification in the Google Earth Engine platform was used to prepare land use and land cover (LULC) for 1990, 2000, 2010, and 2023. The study employed an analytical framework incorporating multiple methodologies: intensity analysis at interval, categorical, and transitional levels to quantify urban growth trajectories; gradient direction and distance analyses to examine spatial expansion patterns; and Land Expansion Index (LEI) and Landscape Dynamic Typology (LDT) metrics to characterize the urban morphology and spatial dynamics of the study area. The results revealed that edge expansion is the predominant mode of urban development, primarily affecting farmlands in the eastern section. Built-up areas quadrupled between 1990 and 2023, whereas arable land declined. Intensity analysis revealed significant changes, particularly affecting farmlands. Our LDT analysis showed reduction in stable areas and increased in LULC changes from 1990 to 2023. The findings highlight the need for revised urban development strategies in Ethiopia to focus on compact and efficient growth while safeguarding agricultural lands, aligning with SDGs 2, 11, and 15 to promote balanced development that ensures urban and agricultural sustainability.

Synthetic [18F]FET-PET Generation and Hotspot Prediction Via Preoperative MRI Fusion of Glioma Lacking Radiographic High-Grade Characteristics

Abstract Background Limited amino acid availability for Positron Emission Tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic [18F]FET-PET and predicting high [18F]FET-uptake from magnetic resonance imaging (MRI). Methods We trained a deep learning (DL)-based model to segment tumors in MRI and extracted radiomic features using the pyradiomics package and classification with a Random Forest classifier. To generate [18F]FET-PET images, we employed a Generative Adversarial Network (GAN) framework and utilized a split-input fusion module for processing different MRI sequences through feature extraction, concatenation, and self-attention. Results We included MR and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [18F]FET-PET, 85% were classified as clinically useful by senior physicians. Peak Signal-to-Noise Ratio analysis indicated high signal fidelity with a peak at 40 dB, while Structural Similarity Index values showed structural congruence. Root Mean Square Error analysis demonstrated lower values below 5.6. Most Visual Information Fidelity scores ranged between 0.6 and 0.7. This indicates that synthetic PET images retain the essential information required for clinical assessment and diagnosis. Conclusion For the first time, we demonstrate that predicting high [18F]FET-uptake and generating synthetic PET images from preoperative MRI in LGG and HGG is feasible. Advanced MRI modalities and other generative AI models will be used to improve the algorithm further in future studies.

A Landscape-Clustering Zoning Strategy to Map Multi-Crops in Fragmented Cropland Regions Using Sentinel-2 and Sentinel-1 Imagery with Feature Selection

Crop mapping using remote sensing is a reliable and efficient approach to obtaining timely and accurate crop information. Previous studies predominantly focused on large-scale regions characterized by simple cropping structures. However, in complex agricultural regions, such as China’s Huang-Huai-Hai region, the high crop diversity and fragmented cropland in localized areas present significant challenges for accurate crop mapping. To address these challenges, this study introduces a landscape-clustering zoning strategy utilizing multi-temporal Sentinel-1 and Sentinel-2 imagery. First, crop heterogeneity zones (CHZs) are delineated using landscape metrics that capture crop diversity and cropland fragmentation. Subsequently, four types of features (spectral, phenological, textural and radar features) are combined in various configurations to create different classification schemes. These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. Furthermore, the crop mapping results from this strategy closely align with agricultural statistics at the county level, with an R2 value of 0.9006. In comparison with other traditional zoning strategies, such as topographic zoning and administrative unit zoning, the proposed strategy proves to be superior. These findings suggest that the landscape-clustering zoning strategy offers a robust reference method for crop mapping in complex agricultural landscapes.

Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos.

Virtual reality (VR) has become a transformative technology with applications in gaming, education, healthcare, and psychotherapy. The subjective experiences in VR vary based on the virtual environment's characteristics, and electroencephalography (EEG) is instrumental in assessing these differences. By analyzing EEG signals, researchers can explore the neural mechanisms underlying cognitive and emotional responses to VR stimuli. However, distinguishing EEG signals recorded by two-dimensional (2D) versus three-dimensional (3D) VR environments remains underexplored. Current research primarily utilizes power spectral density (PSD) features to differentiate between 2D and 3D VR conditions, but the potential of other feature parameters for enhanced discrimination is unclear. Additionally, the use of machine learning techniques to classify EEG signals from 2D and 3D VR using alternative features has not been thoroughly investigated, highlighting the need for further research to identify robust EEG features and effective classification methods. This study recorded EEG signals from participants exposed to 2D and 3D VR video stimuli to investigate the neural differences between these conditions. Key features extracted from the EEG data included PSD and common spatial patterns (CSPs), which capture frequency-domain and spatial-domain information, respectively. To evaluate classification performance, several classical machine learning algorithms were employed: ssupport vector machine (SVM), k-nearest neighbors (KNN), random forest (RF), naive Bayes, decision Tree, AdaBoost, and a voting classifier. The study systematically compared the classification performance of PSD and CSP features across these algorithms, providing a comprehensive analysis of their effectiveness in distinguishing EEG signals in response to 2D and 3D VR stimuli. The study demonstrated that machine learning algorithms can effectively classify EEG signals recorded during watching 2D and 3D VR videos. CSP features outperformed PSD in classification accuracy, indicating their superior ability to capture EEG signals differences between the VR conditions. Among the machine learning algorithms, the Random Forest classifier achieved the highest accuracy at 95.02%, followed by KNN with 93.16% and SVM with 91.39%. The combination of CSP features with RF, KNN, and SVM consistently showed superior performance compared to other feature-algorithm combinations, underscoring the effectiveness of CSP and these algorithms in distinguishing EEG responses to different VR experiences. This study demonstrates that EEG signals recorded during watching 2D and 3D VR videos can be effectively classified using machine learning algorithms with extracted feature parameters. The findings highlight the superiority of CSP features over PSD in distinguishing EEG signals under different VR conditions, emphasizing CSP's value in VR-induced EEG analysis. These results expand the application of feature-based machine learning methods in EEG studies and provide a foundation for future research into the brain cortical activity of VR experiences, supporting the broader use of machine learning in EEG-based analyses.