Supervised Machine Learning Approach Research Articles

Influence of school characteristics on computational thinking: A supervised machine learning approach

Influence of school characteristics on computational thinking: A supervised machine learning approach

Exposing and explaining fake news on-the-fly

AbstractSocial media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80% accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Investigating Role of Supervised Machine Learning Approach in Classification of Diabetic Patient

Objectives: Healthcare analytics requires classifying diabetic patient datasets for quicker diagnosis and personalized treatment. This study used SVM, Decision Trees, KNN, ANN, and Logistic Regression to predict type 1 and 2 diabetes. Our detailed performance research shows these algorithms' utility in handling diabetic patient data's complexity.  Methods: We compare SVM, Decision Trees, KNN, ANN, and Logistic Regression for diabetes patient dataset classification. While each approach has pros and cons, ANN and logistic regression are promising clinical possibilities. Diagnoses, proactive therapies, and diabetes patient outcomes increase with these breakthroughs. Results: SVM has 84.3% accuracy in type 1 and type 2 diabetics. SVM recognized complicated dataset patterns with great accuracy and recall. Decision trees were more interpretable and could record diverse choice limits, with accuracy rates of 86.15% for both types. With 90.6% accuracy, KNN predicted type 1 diabetes well. KNN was ideal for complex datasets because to its greater accuracy and recall using data point similarities. ANN and Logistic Regression had the highest accuracy for type 1 and type 2 diabetes patients at 96.1% and 97%, respectively. Novelty: Layered ANN and logistic regression identified complicated dataset relationships with accuracy, recall, and F1 scores exceeding 0.94. ANN with logistic regression may change diabetes patient classification with unequaled prediction power and accuracy.

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

Novel Machine Learning Identifies Five Asthma Phenotypes Using Cluster Analysis of Real-World Data

Asthma classification into different sub-phenotypes is important to guide personalized therapy and improve outcomes. This study sought to further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We employed k-prototype unsupervised ML to develop a clustering model where predictors were age, gender, race, body mass index (BMI), pre- and post-bronchodilator (BD) spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross validated F1 score to support their distinctiveness. Data from 13,498 patients with asthma with available post-BD spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young non-severe asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest BMI (mean (SD): 44.44 (7.83) kg/m2), and the highest proportion of female (77.5%) and African Americans (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower FEV1% of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest FEV1% of 68.08 (15.02), the highest post-BD reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/μL) (34.8%). Using real-world data and unsupervised ML, we classified asthma into 5 clinically important sub-phenotypes where group-specific asthma treatment and management strategies can be designed and deployed.

Applications of machine learning in phylogenetics

Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select substitution models, and to perform downstream inferences of introgression and diversification. Here, we review how researchers have used several promising machine learning approaches to make phylogenetic inferences. Despite the promise of these methods, several barriers prevent supervised machine learning from reaching its full potential in phylogenetics. We discuss these barriers and potential paths forward. In the future, we expect that the application of careful network designs and data encodings will allow supervised machine learning to accommodate the complex processes that continue to confound traditional phylogenetic methods.

Performance optimization of multipath K-AOMDV protocol using SVM against blackhole attack

<p>This research focuses on the Ad Hoc On-Demand Multi-Path Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in mobile ad hoc networks (MANETs). However, identifying attackers in such networks is a complicated task due to malicious nodes providing optimistic, forward-looking optimistic responses. In this study, the author proposes a novel security solution, the K-AOMDV (KNN- Ad Hoc On-Demand Multi-Path Routing protocol) that uses K-means clustering to prevent routing misbehaviour. The efficiency of the proposed K-AOMDV routing protocol is analyzed using supervised machine learning approach to predict optimal routes with minimal packet hops between nodes. The proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall. It communicates the black hole attacker’s node identification (ID) into the network, ensuring that the attacker will not participate in the routing method in the future. The privacy domain in MANET is the main focus of this research, and the proposed solution affiliates an effective approach to enhancing the security of MANETs.</p>

What Explains Interest Group Prominence in Parliamentary Speech? Policy Agenda, Partisanship, or Conflict Expansion

Gaining the attention of legislators in a crowded advocacy landscape is a key dilemma for organized interests. Yet, there has not been a great deal of direct analysis of whether groups are indeed recognized as important by politicians in the context of them advancing political arguments. In this article, we examine under what conditions interest groups achieve prominence among political elites. Drawing on a supervised machine learning approach to code prominence from legislative speech, we exploit variations in levels of prominence for the entire Australian interest groups system. We find that prominence is highly concentrated and that it covaries with the need to align with prevailing policy agendas and the logic of conflict expansion. Conversely, we do not find evidence of a strong partisan or ideological dimension of prominence. This contributes to our understanding of the responsiveness and representativeness of democratic political systems in which the interest group sector is expected to funnel public preferences into policymaking.

Hybrid supervised and reinforcement learning for the design and optimization of nanophotonic structures.

From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components. However, both data-driven and exploration-based machine learning strategies have limitations in their effectiveness for nanophotonic inverse design. Supervised machine learning approaches require large quantities of training data to produce high-performance models and have difficulty generalizing beyond training data given the complexity of the design space. Unsupervised and reinforcement learning-based approaches on the other hand can have very lengthy training or optimization times associated with them. Here we demonstrate a hybrid supervised learning and reinforcement learning approach to the inverse design of nanophotonic structures and show this approach can reduce training data dependence, improve the generalizability of model predictions, and significantly shorten exploratory training times. The presented strategy thus addresses several contemporary deep learning-based challenges, while opening the door for new design methodologies that leverage multiple classes of machine learning algorithms to produce more effective and practical solutions for photonic design.

A Predictive Algorithm for Discriminating Myeloid Malignancies and Leukemoid Reactions

Background: Adults presenting with a neutrophil-predominant leukocytosis (white cell count >50,000/μL) often necessitate urgent medical management. These patients are diagnosed with either acute presentations of chronic myeloid malignancies or leukemoid reactions, yet accurate models to distinguish between these entities do not exist. We used demographic and lab data to build a machine learning model capable of discriminating between these diagnoses.Methods: The medical record at a tertiary care medical center was queried to identify adults with instances of white counts greater than 50,000/μL and >50% neutrophils from 2000-2021. For each patient, a full set of demographic and lab values were extracted at the time of their first presentation with a white count >50,000/μL. We generated a series of models in which the parameters most predictive of myeloid malignancies were identified, and a supervised machine learning approach was applied to the dataset.Results: Our best model – using a support vector machine algorithm – produced a sensitivity of 96.0% and a specificity of 95.9% (area under the curve = 0.982) for identifying myeloid malignancies. We also identified a clinically meaningful and significant disparity in outcomes based on diagnosis – a six-fold increase in 12-month mortality in those diagnosed with leukemoid reactions.Conclusions: These findings need to be validated but fill an unmet need for timely and accurate diagnosis in the setting of profound, neutrophil-predominant leukocytosis and support the use of predictive models as a means to improve patient outcomes.

IMU-Based Approach to Detect Spastic Cerebral Palsy in Infants at Early Stages

INTRODUCTION: Cerebral Palsy (CP) is a non-progressive neurological disorder affecting muscle control in early childhood, leading to permanent alterations in body posture and movement. Early identification is crucial for accurate diagnosis and therapy-based interventions. In recent years, an automated monitoring system has been developed to facilitate the health assessment of infants, enabling early recognition of neurological dysfunctions in high-risk infants. However, the interpretation of these assessments lacks standardization and is subject to examiner bias.
 OBJECTIVES: Many infants with CP exhibit increased tonic stretch reflexes due to Upper Motor Neuron Syndrome (UMNS), resulting from motor neuron damage that disrupts muscle signalling.
 METHOD: To detect abnormal muscle reactions, our team employed an Inertial Measurement Unit (IMU) sensor, comprising three tri-axial sensors (accelerometer, gyroscope, magnetometer) that capture movement data continuously and unobtrusively. IMU sensors are compact, cost-effective, and have low processing requirements, requiring attachment to the infant's body to measure inter-body part angles. Our team analyzed muscle activity and posture using IMU sensors, collecting tri-axial data from 43 infants in real-time. Additional factors like age, stride length, and leg length were incorporated into the dataset.
 RESULTS: Our team has applied various supervised machine learning approaches to predict CP in infants due to the limited dataset size, validating models through k-fold cross-validation. Among the models, Naive Bayes (NB) outperformed Logistic Regression (LR), Decision Tree (DT), Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM), achieving an accuracy of 88%. CONCLUSION: This research contributes to the early detection and intervention of CP in infants, potentially improving their long-term outcomes.

Prediction of vasculitic neuropathy using supervised machine learning approaches

Vasculitic neuropathy is an inflammation-driven nerve condition that often goes undiagnosed until irreversible damage occurs. This study developed and validated a supervised machine learning model to predict future onset of vasculitic neuropathy using electronic health record data from 450 cases and 1,800 matched controls. The predictive algorithm analyzed 134 structured features related to diagnoses, medications, lab tests and clinical notes. Selected logistic regression model with L2 regularization achieved an AUC of 0.92 (0.89-0.94 CI) internally, and maintained an AUC of 0.90 (0.84-0.93 CI) in the temporal validation cohort. At peak operating threshold, external sensitivity was 0.81 and specificity 0.79. Among highest risk decile, positive predictive value reached 47%. Key features driving predictions included inflammatory markers, neuropathic symptoms and vascular imaging patterns. This methodology demonstrates feasibility of leveraging machine learning for early detection of impending vasculitic neuropathy prior to confirmatory biopsy to enable prompt treatment and improved outcomes.

Leveraging past information and machine learning to accelerate land disturbance monitoring

Near real-time (NRT) monitoring of land disturbances holds great importance for delivering emergency aid, mitigating negative social and ecological impacts, and distributing resources for disaster recovery. Many past NRT techniques were built upon examining the overall change magnitude of a spectral anomaly with a predefined threshold, namely the unsupervised approach. However, their lack of fully considering spectral change direction, change date, and pre-disturbance conditions often led to low detection sensitivity and high commission errors, especially when only a few satellite observations were available at the early disturbance stage, eventually resulting in a longer lag to produce a reliable disturbance map. For this study, we developed a novel supervised machine learning approach guided by historical disturbance datasets to accelerate land disturbance monitoring. This new approach consisted of two phases. For the first phase, the supervised approach applied retrospective analysis on historical Harmonized Landsat Sentinel-2 (HLS) datasets from 2015 to 2021, combined with several open disturbance products. The disturbance model was constructed for each condition of consecutive anomaly number, with the aim of enhancing the specificity for delineating early-stage disturbance regions. Then, these stage-based models were applied for an NRT scenario to predict disturbance probabilities with 2022 HLS images incrementally on a weekly basis. To demonstrate the capability of this new approach, we developed an operational NRT system incorporating both the unsupervised and supervised approach. Latency and accuracy were evaluated against 3000 samples that were randomly selected from the five most influential disturbance events of the United States in 2022, based on labels and disturbance dates interpreted from daily PlanetScope images. The evaluation showed that the supervised approach required 15 days (since the start of the disturbance event) to reach the plateau of its F1 curve (where most disturbance pixels are detected with high confidence), seven days earlier with roughly 0.2 F1 score improvement compared to the unsupervised approach (0.733 vs. 0.546 F1 score). Further analysis showed the improvement was mainly due to the substantial decrease in commission errors (17.7% vs 44.4%). The latency component analysis illustrated that the supervised approach only took an average of 4.1 days to yield the first disturbance alert at its fastest daily updating speed, owing to its decreased sensitivity lag. This finding highlighted the importance of using past knowledge and machine learning to reduce detection delays for an NRT monitoring task.

A new xG model for football analytics

The aim of this exploratory study is to refine and improve, in terms of prediction performance, the expected goal (xG) model, one of the emerging tools in the field of football analytics. With this final goal, we merged data from different sources: tracking data, match event data and some players’ performance composite indicators. Using an original sample of match data relying on the 2019/2020 season of the Italian Serie A, composed of 660 shots, one outcome (i.e. the GOAL) and 22 regressors, a supervised machine learning approach (logistic regression model with imbalanced training sample adjustment) was applied to different scenarios for sample balanced techniques. Results are interesting in terms of sensitivity and F1 metrics, compared with a benchmark (Understat). Other results concerning the classic imbalance framework significantly outperform the benchmark in terms of the AUC metric. In addition, some performance composite indicators and one original tracking variable are significant for the classification model, contributing to increasing the goal prediction probability compared with the benchmark.

IntroUNET: Identifying introgressed alleles via semantic segmentation.

A growing body of evidence suggests that gene flow between closely related species is a widespread phenomenon. Alleles that introgress from one species into a close relative are typically neutral or deleterious, but sometimes confer a significant fitness advantage. Given the potential relevance to speciation and adaptation, numerous methods have therefore been devised to identify regions of the genome that have experienced introgression. Recently, supervised machine learning approaches have been shown to be highly effective for detecting introgression. One especially promising approach is to treat population genetic inference as an image classification problem, and feed an image representation of a population genetic alignment as input to a deep neural network that distinguishes among evolutionary models (i.e. introgression or no introgression). However, if we wish to investigate the full extent and fitness effects of introgression, merely identifying genomic regions in a population genetic alignment that harbor introgressed loci is insufficient-ideally we would be able to infer precisely which individuals have introgressed material and at which positions in the genome. Here we adapt a deep learning algorithm for semantic segmentation, the task of correctly identifying the type of object to which each individual pixel in an image belongs, to the task of identifying introgressed alleles. Our trained neural network is thus able to infer, for each individual in a two-population alignment, which of those individual's alleles were introgressed from the other population. We use simulated data to show that this approach is highly accurate, and that it can be readily extended to identify alleles that are introgressed from an unsampled "ghost" population, performing comparably to a supervised learning method tailored specifically to that task. Finally, we apply this method to data from Drosophila, showing that it is able to accurately recover introgressed haplotypes from real data. This analysis reveals that introgressed alleles are typically confined to lower frequencies within genic regions, suggestive of purifying selection, but are found at much higher frequencies in a region previously shown to be affected by adaptive introgression. Our method's success in recovering introgressed haplotypes in challenging real-world scenarios underscores the utility of deep learning approaches for making richer evolutionary inferences from genomic data.

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.

P537 Analysis of immunological VAriables in ex vivo Tofacitinib-treated human biopsies from Active ulcerative colitis patients to predict clinical Response (the AVATAR study)

Abstract Background Ulcerative colitis (UC) is characterized by chronically relapsing inflammation of the colonic mucosa. Tofacitinib (TOFA) is a paninhibitor of the JAK kinases, a family of intracellular kinases in volved in the signal transduction pathway of multiple inflammatory signals. Despite the efficacy shown by TOFA in inducing and maintaining remission in moderately-to-severely active UC, rates of primary non response remains high. We propose a new method to identify predictors of responsiveness to TOFA based on "ex-vivo" challenge of organ cultures from biopsies collected from candidate patients. Methods Baseline biopsies from inflamed areas of UC patients with active disease and candidate to receive TOFA 10mg b.i.d. for 8 weeks, were used for "ex-vivo" organ culture and stimulated with TOFA 20μM and 200nM or left untreated. After 24 hours, proteomic analysis was performed and quantitative expression of 92 inflammation-related human proteins (Olink® Human Inflammation panel) was correlated with week 8 clinical response defined by Modified Mayo Score (MMS). Intestinal biopsies were also collected at baseline and at 8 weeks for proteomic analysis. To decipher the nature of TOFA's effects, both unsupervised and supervised machine learning (ML) methods were employed (sklearn module, Python). The K-means method was used for unsupervised learning followed by a supervised ML approach to classify week 8 responders and non-responders. Data were transformed in into a second-order polynomial format and logistic regression was used for modelling. Results Between Jannuary 2022 and January 2023, 17 UC patients were enrolled (Table 1). At week 8, 13/17 (76.5%) patients treated with TOFA showed clinical response. Unsupervised ML, utilizing proteomic data from organ cultures stimulated with TOFA 200nM, achieved a 90% success rate in predicting a positive clinical response. This prediction was based on forming two distinct clusters, with significant contributions from key variables such as CASP-8, CCL19, CCL20, CD5, CXCL9, EN-RAGE, GDNF, HGF, IL-18R1, MCP-1, MCP-2, MCP-4, MMP-10, OPG, PD-L1, STAMBP, TNFRSF9, TRANCE, TWEAK, and VEGFA. In our supervised ML approach, logistic regression was employed with 30 shuffle splits to refine the predictive model. We discovered that transforming the dataset into a second-order polynomial format significantly enhanced the model's performance. The most effective logistic regression model achieved a mean precision score of 0.82 (Fig.1). Conclusion Our predictive model demonstrates a high level of precision in identifying positive treatment responses, emphasizing the potential of further dataset expansion and the use of advanced machine-learning to predict response in TOFA-treated UC patients.

Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents a promising avenue. Our article offers comprehensive insights into recent advancements in air quality research, employing the PRISMA method as a cornerstone for the reviewing process, while simultaneously exploring the application of frequently employed ML methodologies. Focusing on supervised learning algorithms, the study meticulously analyzes air quality data, elucidating their unique benefits and challenges. These frequently employed ML techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), and SVR (Support Vector Regression), are instrumental in our quest for cleaner, healthier urban environments. By accurately predicting key pollutants such as particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and ozone (O3), these methods offer tangible solutions for society. They enable informed decision-making for urban planners and policymakers, leading to proactive, sustainable strategies to combat urban air pollution. As a result, the well-being and health of urban populations are significantly improved. In this revised abstract, the importance of frequently employed ML methods in the context of air quality is explicitly emphasized, underlining their role in improving urban environments and enhancing the well-being of urban populations.

Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography.

Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use.

On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms

The air pollution caused by greenhouse gas emissions, particularly carbon dioxide (CO2), is a significant environmental concern that impacts air quality and contributes to global warming. The transportation sector plays a pivotal role in this issue, being a major contributor to CO2 emissions. In light of this situation, this article proposes a methodology that utilizes a supervised learning algorithm to estimate CO2 emissions and compare vehicles fueled with ethanol and gasoline. Additionally, the solution adopts an online, unsupervised machine learning algorithm to identify data outliers and improve the confidence in the results. Furthermore, this work incorporates the concept of digital twins, using virtual models of vehicles to carry out more extensive pollution simulations and allowing the simulation of various types of vehicles and the modeling of realistic traffic scenarios. A supervised machine learning approach was adopted to infer emission data in the model, allowing more comprehensive and meaningful comparisons between real-world and simulated measurements. The performed analyses of pollution emissions for different speeds and sections of routes demonstrate that CO2 emissions from ethanol were significantly lower than those from gasoline, favoring more sustainable fuels even in combustion engine vehicles. Adopting cleaner fuels is perceived as crucial to mitigate the negative effects of climate change, with plant-based fuels like ethanol being crucial during the transition from fossil fuels to a more sustainable vehicular landscape.