Multi-Class Machine Learning to Quantify the Impact of Nitrogen Management Practices on Grassland Biomass

Object detection, identification and classification techniques have seen many variants and improvements over past two decades. Together with Internet of Things (IoT) devices, improved computational algorithms and cloud support, real-time classification with low-cost devices has already been achieved. This paper discusses the real-time object detection and classification using Microsoft Custom Vision multi-class Machine Learning (ML) model operating at the Edge of IoT network. This paper further examines the use of virtual dockers or containers at the IoT edge devices for better security and isolation by decoupling physical hardware as well that supports multiple applications and services on a single hardware. The experiments are performed using emulated and simulated IoT devices on Microsoft Azure IoT platform for real-time object classification using Custom Vision Machine Learning (ML) models run directly from the edge device. The experimental results are further discussed to validate the model accuracy and its implementation in a future Smart City surveillance environment.

Objective To address the calibration and procedural challenges inherent in remote audiogram assessment for rehabilitative audiology, this study investigated whether calibration-independent adaptive categorical loudness scaling (ACALOS) data can be used to approximate individual audiograms by classifying listeners into standard Bisgaard audiogram types using machine learning (ML). Design Three classes of ML approaches—unsupervised, supervised, and explainable—were evaluated. Principal component analysis (PCA) was performed to extract the first two principal components, which jointly explained more than 50% of the variance. Seven supervised multi-class ML classifiers were trained and compared, alongside unsupervised and explainable methods. Study Sample A large auditory reference database (n = 847 ears) containing ACALOS data was used for model development and evaluation. Results The factor map showed substantial overlap between listeners, indicating that cleanly separating participants into six Bisgaard classes based solely on their loudness patterns is challenging. Nevertheless, the ML models demonstrated reasonable classification performance. Among the supervised classifiers, logistic regression achieved the highest accuracy. In addition, the SHAP and feature permutation analyses showed that the highest predictive power of the ML models was attributable to the minimum loudness levels at 1.5 and 4 kHz. Conclusions The findings demonstrate that ML models can predict standard Bisgaard audiogram types—within certain limits—from calibration-independent loudness perception data. This approach may support future hearing aid fitting in remote or resource-limited settings without requiring a traditional audiogram.

Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification

Applying data science and machine learning to transportation engineering is essential for addressing modern urban mobility challenges. This thesis investigates whether these technologies improve decision-making in mobility systems, aiming to create more efficient, sustainable, and cohesive mobility solutions. By leveraging data science and machine learning, the research introduces innovative tools across various urban mobility applications to support organizations, stakeholders, and policymakers in navigating the complexities of urban mobility. The research follows a comprehensive methodology encompassing domain understanding, data collection, data cleaning, data exploration, feature engineering, predictive modeling, and stakeholder communication. At its core is a compendium of four scientific publications, each providing empirical evidence and insights aligned with the broader research objectives. The specific objectives focus on: (i) identifying effective data sources and collection techniques for urban mobility analysis; (ii) determining the role of feature engineering in enhancing data quality and relevance; (iii) evaluating the suitability and added value of machine learning techniques for urban mobility challenges; and (iv) assessing model performance while defining policy recommendations for mobility stakeholders. These objectives are tested through three practical research applications addressing real-world urban mobility challenges: (i) promoting micromobility for urban shopping; (ii) setting prices in ride-hailing services; and (iii) forecasting moped scooter-sharing demand. The first research application examines factors influencing the adoption of bicycles and scooters (primarily active modes) for urban shopping in cities with low micromobility maturity. Using machine learning models applied to survey data, the study identifies barriers and provides policy recommendations to improve infrastructure and safety for micromobility, emphasizing potential growth post-COVID-19. The second research application explores pricing dynamics in ride-hailing services. One study analyzes fare patterns before and during the COVID-19 pandemic in Boston and Atlanta, uncovering fare dynamics during crises. Another examines determinants of fares in Madrid, considering factors such as weather conditions and peak hours. Together, these studies provide insights into efficient pricing strategies and policy recommendations for better regulation and integration of ride-hailing services. The third research application focuses on forecasting demand for moped scooter-sharing services. Using machine learning techniques, the study predicts travel demand based on comprehensive datasets, including sociodemographic characteristics, transportation network attributes, and policy-related variables. The results demonstrate the effectiveness of the random forest model and underscore the significance of neighborhood characteristics and short-distance trip preferences in urban planning and transportation management. This thesis demonstrates that data science and machine learning significantly enhance decision-making in mobility systems by improving prediction accuracy, optimizing data utilization, and providing actionable recommendations for sustainable urban mobility. Furthermore, it acknowledges persistent challenges such as the need for high-quality data, robust algorithms, and enhanced model interpretability. Addressing these limitations is critical for ensuring successful real-world implementation and maximizing the transformative potential of these technologies. RESUMEN La aplicación de la ciencia de datos y el aprendizaje automático a la ingeniería del transporte es esencial para abordar los desafíos modernos de la movilidad urbana. Esta tesis investiga si estas tecnologías mejoran la toma de decisiones en los sistemas de movilidad, con el objetivo de crear soluciones más eficientes, sostenibles y cohesionadas. Al aprovechar estas herramientas, la investigación introduce innovaciones en diversas aplicaciones de movilidad urbana para apoyar a organizaciones, partes interesadas y responsables políticos en la gestión de la movilidad urbana. La metodología incluye comprensión del dominio, recopilación, limpieza y exploración de datos, ingeniería de características, modelado predictivo y comunicación con partes interesadas. En su núcleo está un compendio de cuatro publicaciones científicas, cada una aportando evidencia empírica y conocimientos alineados con los objetivos generales. Los objetivos específicos son: (i) identificar fuentes de datos y técnicas de recopilación efectivas para el análisis de movilidad urbana; (ii) determinar el papel de la ingeniería de características en la mejora de la calidad y relevancia de los datos; (iii) evaluar la idoneidad y el valor añadido de las técnicas de aprendizaje automático en desafíos de movilidad urbana; y (iv) analizar el rendimiento de los modelos y definir recomendaciones de políticas para las partes interesadas de la movilidad. Estos objetivos se prueban a través de tres aplicaciones prácticas: (i) promover la micromovilidad para compras urbanas; (ii) establecer precios en servicios de vehículo de transporte con conductor (VTC); y (iii) predecir la demanda de motos compartidas. La primera aplicación examina los factores que influyen en la adopción de bicicletas y patinetes (principalmente modos activos) para compras urbanas en ciudades con baja madurez en micromovilidad. Usando modelos de aprendizaje automático aplicados a datos de encuestas, se identifican barreras y se proponen recomendaciones para mejorar infraestructura y seguridad, destacando su potencial de crecimiento post-COVID-19. La segunda aplicación explora la dinámica de precios en servicios de VTC. Un estudio analiza patrones tarifarios antes y durante la pandemia en Boston y Atlanta, identificando la dinámica de tarifas en tiempos de crisis. Otro examina los determinantes de tarifas en Madrid, considerando clima y horarios pico. Estos estudios ofrecen estrategias de precios y recomendaciones de regulación para integrar mejor los servicios VTC. La tercera aplicación se centra en predecir la demanda de motos compartidas. Usando técnicas de aprendizaje automático, se predice la demanda basándose en datos sociodemográficos, atributos de la red y variables políticas. Los resultados destacan la efectividad del modelo de random forest y la importancia de las características de los barrios y las preferencias por trayectos de corta distancia en la planificación urbana y gestión del transporte. Esta tesis demuestra que la ciencia de datos y el aprendizaje automático mejoran la toma de decisiones en sistemas de movilidad al incrementar la precisión de predicciones, optimizar el uso de datos y ofrecer recomendaciones prácticas para la movilidad sostenible. También reconoce desafíos como la necesidad de datos de alta calidad, algoritmos robustos y modelos más interpretables. Abordar estas limitaciones es clave para garantizar implementaciones exitosas y maximizar el impacto de estas tecnologías.

Although species richness has been hypothesized to be highest at 'intermediate' levels of disturbance, empirical studies have demonstrated that the disturbance-diversity relationship can be either negative or positive depending on productivity On the other hand, hypothesized productivity diversity relationships can be positive, negative or unimodal, as confirmed by empirical studies. However, it has remained unclear under what conditions each pattern is realized, and there is little agreement about the mechanisms that generate these diverse patterns. In this study, I present a model that synthesizes these separately developed hypotheses and shows that the interactive effects of disturbance and productivity on the competitive outcome of multispecies dynamics can result in these diverse relationships of species richness to disturbance and productivity The predicted productivity diversity relationship is unimodal but the productivity level that maximizes species richness increases with increasing disturbance. Similarly, the predicted disturbance diversity relationship is unimodal but the peak moves to higher disturbance levels with increasing productivity Further, these patterns are well explained by the opposite effects of productivity and disturbance on competitive outcome that are suggested by the change in community composition along these two environmental gradients: higher productivity favours superior competitors while higher disturbance levels favour inferior competitors.

Severe Disorders of Consciousness (DoC) are generally caused by brain trauma, anoxia or stroke, and result in conditions ranging from coma to the confused-agitated state. Prognostic decision is difficult to achieve during the first year after injury, especially in the pediatric cases. Nevertheless, prognosis crucially informs rehabilitation decision and family expectations. We compared four multi-class machine learning classification approaches for the prognostic decision in pediatric DoC. We identified domains of a neurobehavioral assessment tool, Level of Cognitive Functioning Assessment Scale, mostly contributing to decision in a cohort of 124 cases. We showed the possibility to generalize to new admitted pediatric cases, thus paving the way for real employment of machine learning classifiers as an assistive tool to prognostic decision in clinics.

A massive amount of data is emerging continuously in the era of big data; the missing value is a common yet challenging problem. Missing data or missing value can be described as the data whose values in any given dataset are unknown. It is a recurrent phenomenon and can give biased results from the data to be observed and affect the value of the learning process. Therefore, it is mandatory to use missing value imputation techniques. Missing value imputation provides (MVI) optimal solutions for missing values in a dataset. There are diverse missing value imputation techniques such as statistical method and machine learning methods proposed till date, each having their own significances and flaws. Reasonably, good imputation results may be produced by machine learning MVI approaches but they take greater imputation times than statistical approaches usually. In this paper, we have reviewed some of the missing value imputation approaches proposed. In order to show the efficiency of their proposed approaches, they had compared their proposed methods with some baseline imputation algorithms like mode/median, k-nearest neighbor, Naive Bayes, support vector machine. The outcome of each proposed method is analyzed and their extension scopes from the perspectives of research focus. The main idea of this literature is to give a general review of missing value imputation approaches.

This study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It also describes a robust testing methodology used to determine battery SoH accurately, considering discharge rates and using machine learning algorithms for analysis. Machine learning techniques, including classical regression models and Ensemble Learning methods, were developed and calibrated using experimental UAV data to predict SoH accurately. Evaluation metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) assess model performance, highlighting the balance between model complexity and generalization. The results demonstrated improved SoH predictions with machine learning models, though complexities may lead to overfitting challenges. The transition from simpler regression models to intricate Ensemble Learning methods is meticulously described, including an assessment of each model’s strengths and limitations. Among the Ensemble Learning methods, Bagging, GBR, XGBoost, LightGBM, and stacking were studied. The stacking technique demonstrated promising results: for Flight 92 an RMSE of 0.03% and an MAE of 1.64% were observed, while for Flight 129 the RMSE was 0.66% and the MAE stood at 1.46%.

This technical note describes recent efforts to integrate machine learning (ML) models, specifically long short‐term memory (LSTM) networks and differentiable parameter learning conceptual hydrological models (δ conceptual models), into the next‐generation water resources modeling framework (Nextgen) to enhance future versions of the U.S. National Water Model (NWM). We address three specific methodology gaps of this new modeling framework: (1) assess model performance across many ungauged catchments, (2) diagnostic‐based model selection, and (3) regionalization based on catchment attributes. We demonstrate that an LSTM trained on CAMELS catchments can make large‐scale predictions with Nextgen across the New England region and match the average flow duration curve observed by stream gauges for streamflow with low exceedance probability (high flows), but diverges from the mean in high exceedance probability (low flows). We demonstrate improvements in peak flow predictions when using δ conceptual model, but results also suggest that performance increases may come at a cost of accurately representing hydrologic states within the conceptual model. We propose a novel approach using ML to predict the most performant mosaic modeling approach and demonstrate improved distributions of efficiency scores throughout the large sample of basins. Our findings advocate for the future development of ML capabilities within Nextgen for advancing operational hydrological modeling.

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.

Machine Learning-Assisted Prediction of Persistent Incomplete Occlusion in Intracranial Aneurysms From Angiographic Parametric Imaging-Derived Features.

Cancer heterogeneity presents a major obstacle to effective drug treatment, emphasizing the need for personalized approaches that can accurately predict drug responses. Advances in high-throughput technologies have driven precision medicine initiatives toward integrating multi-omics data, enabling a more comprehensive understanding of tumor biology. However, integration of diverse omics layers poses challenges for computational modeling, as many traditional machine learning (ML) and statistical methods are not designed to capture complex, high-dimensional and multimodal data. This review examines the studies that integrate multi-omics datasets, aiming to enhance drug response prediction (DRP). Specifically, it outlines the most used omics types and computational approaches – classical ML models, as well as advanced deep learning and multimodal integration frameworks for improving DRP, detailing key methodologies and evaluation metrics, such as area under the dose–response curve, F1 score and mean square error, which assess model performance. By summarizing the integrated omics data, computational methods and challenges encountered, this review provides an in-depth overview of the existing landscape of precision medicine and future directions for advancing drug-response prediction.

Over the past decade, hail has been responsible for most financial losses associated with severe convective storms, with costs steadily increasing. As the population grows and increasingly invests in vulnerable infrastructure, the risk of economic damage from large hail also rises. Accurate hazard assessment is therefore essential for effective mitigation. While recent machine learning (ML) approaches have enabled the development of hail climatologies for hazard assessment, many existing datasets remain limited by regional biases and coarse spatial or temporal resolution.This study aims to produce a novel, high-resolution large hail (> 2.5 cm in diameter) hazard climatology for the Contiguous United States (CONUS) using the gradient-boosted decision tree algorithm XGBoost. Our model will integrate multiple remotely sensed hail proxy observations, including hourly satellite-derived brightness temperature, radar-reflectivity, and lightning counts, along with key hail-related environmental parameters derived from the ERA5 reanalysis. The high resolution of the remotely sensed data will enable us to train the ML model at an unprecedented grid spacing of 4 km × 4 km, enhancing the spatial detail of large hail climatologies derived in previous datasets.We will assess model performance using held out test data and compare regional calibration across different climate regimes in the CONUS. Our research will also explore which predictors are most influential for large hail estimation and how effectively the model captures spatial heterogeneities in hail occurrence. The resulting dataset will represent a refined tool for hail risk assessment in the CONUS. Moreover, with globally available satellite and reanalysis data, this framework also holds the potential for expanded application in other regions around the world.

Diabetes is a chronic disease that has been impacting an increasing number of people throughout the years. Each year, it results in a huge number of deaths. Since late diagnosis results in severe health complications and a significant number of deaths each year, developing methods for early detection of this pathology is critical. As a result, early detection is critical. Machine learning (ML) techniques aid in the early detection and prediction of diabetes. However, ML models do not perform well with missing values in the dataset. Imputation of missing values improves the outcome. The article proposes an ensemble method with a strong emphasis on missing value imputation. Numerous ML models have been used to validate the proposed framework. The experimentation uses the Pima Indian Diabetes Dataset, which contains information about people with and without diabetes. TPR, FNR, PPV, FDR, overall accuracy, training time, and AUC are used to evaluate the performance of the 24 ML methods. The collected results demonstrate that subspace KNN outperforms with an accuracy of 85%. The collected data are confirmed systematically and orderly utilizing receiver operating characteristic (ROC) curves. Using missing value imputation for data pre-processing and classification has been shown to beat state-of-the-art algorithms in the diabetes detection sector.KeywordsDiabetes predictionMissing value imputationML model

A class center based approach for missing value imputation