Machine Learning Algorithms Applied Research Articles

Machine learning algorithms applied to wildfire data in California's central valley

This study focuses on using Machine Learning methods to predict wildfires within California's Central Valley. The specific areas within the Central Valley were Yosemite Valley, Sequoias, and Kings Canyon since these areas can be considered wildfire hotspots. This topic is relevant since California has seen an increase in wildfires with an increase in annual forest burned areas to +172 % from 1996 to 2021 (ABC 2024). The algorithms selected were based on previous research that conducted similar studies. From this research it is hypothesized that the best performing algorithm for predicting wildfires would be Random Forest. The novelty in this study stems from focusing on the specific areas mentioned above, which is where many wildfires have occurred throughout the years. The overall goal is to determine the best machine learning algorithm to predict wildfires in the Central Valley and take the results to improve upon wildfire prevention within these regions. The methods implemented included Decision Trees, Random Forest, Naïve Bayes, and Neural Networks. The dataset was gathered from the following satellite data which include MERRA-2 and USGS Landsat 8 along with fire history from 2012 to 2023 within these regions. Utilizing the dataset in the following two variations were a random split and a chronological split of training and testing sets. The best-performing algorithm using this dataset was Decision Trees at 550 maximum splits with an F1-Score of 0.689. The F1-Score ranges between 0 and 1 with a score of 0.7 or higher being deemed a good model to be used for predictions. The conclusion that could be determined from this result is that the randomized data has better predicting power over a chronologically split dataset. This can be seen in the confusion matrices for the chronological split dataset having zero true positive values in all the methods except for Naïve Bayes. Overall, the results show that Decision trees with a larger maximum split in the leaf nodes result in a more accurate prediction of whether a fire will occur within the given regions. The conclusion that can be made from this result is that Decision Trees can be a useful tool in predicting wildfires in California's Central Valley. The applications of this research would be the ability to use the information gained in this study to aid in optimizing resources in wildfire prevention within these areas.

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition.

The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome's composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome's composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome's influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host-microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome's composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.

A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment.

Androgenic alopecia (AGA) staging is still based on macroscopic scales, yet the introduction of trichoscopy is gradually bringing an important change, even though it remains an eye-based method. However, recently developed artificial intelligence-assisted programs can execute automated count of trichoscopic patterns. Nevertheless, to interpret data elaborated by these programs can be complex. Machine learning algorithms might represent an innovative solution. Among them, support vector machine (SVM) models are among the best methods for classification. Our aim was to develop a SVM algorithm, based on three trichoscopic patterns, able to classify AGA patients and to calculate a severity index. We retrospectively analyzed trichoscopic images from 200 AGA patients using Trichoscale Pro® software, calculating the number of vellus hair, empty follicles and single hair follicular units. Then, we elaborated a SVM model, based on these three patterns and on sex, able to classify patients as affected by mild AGA or moderate-severe AGA, and able to calculate the probability of the classification being correct, expressed as percentage (from 50% to 100%). This probability estimate is higher in patients with more AGA trichoscopic patterns and, thus, it might serve as a severity index. For training and test datasets, accuracy was 94.3% and 90.0% respectively, while the Area Under the Curve was 0.99 and 0.95 respectively. We believe our SVM model could be of great support for dermatologists in the management of AGA, especially in better assessing disease severity and, thus, in prescribing a more appropriate therapy.

Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC-MS Analysis, Lead to a New Index of Geographical Origin Authentication.

The scope of the current study was to monitor if semi-quantitative data of volatile compounds (volatilome) of citrus honey (ch) produced in different countries could potentially lead to a new index of citrus honey authentication using specific ratios of the identified volatile compounds in combination with machine learning algorithms. In this context, the semi-quantitative data of the volatilome of 38 citrus honey samples from Egypt, Morocco, Greece, and Spain (determined by headspace solid phase microextraction coupled to gas chromatography mass spectrometry (HS-SPME/GC-MS)) was subjected to supervised and unsupervised chemometrics. Results showed that honey samples could be classified according to the geographical origin based on specific volatile compounds. Data were further evaluated with additional nectar honey samples introduced in the multivariate statistical analysis model and the classification results were not affected. Specific volatile compounds contributed to the discrimination of citrus honey in different amounts according to geographical origin. These were lilac aldehyde D, dill ether, 2-methylbutanal, heptane, benzaldehyde, α,4-dimethyl-3-cyclohexene-1-acetaldehyde, and herboxide (isomer II). The numerical data of these volatile compounds was summed up and divided by the total semi-quantitative volatile content (Rch, Karabagias-Nayik index) of citrus honey, according to geographical origin. Egyptian citrus honey had a value of Rch = 0.35, Moroccan citrus honey had a value of Rch = 0.29, Greek citrus honey had a value of Rch = 0.04, and Spanish citrus honey had a value of Rch = 0.27, leading to a new hypothesis and a complementary index for the control of citrus honey authentication.

Parametric Validation of the Reservoir Computing–Based Machine Learning Algorithm Applied to Lorenz System Reconstructed Dynamics

A detailed parametric analysis is presented, where the recent method based on the reservoir computing paradigm, including its statistical robustness, is studied. It is observed that the prediction capabilities of the reservoir computing approach strongly depend on the random initialization of both the input and the reservoir layers. Special emphasis is put on finding the region in the hyperparameter space where the ensemble-averaged training and generalization errors together with their variance are minimized. The statistical analysis presented here is based on the projection on proper elements method.

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease.

PurposeAs global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan’s population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations.MethodsThis study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group – a major health screening center in Taiwan – including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors.ResultsThe LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods.ConclusionThe five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research.

PolyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics

Polymers, due to advantages such as low-cost processing, chemical stability, low density, and tunable design, have emerged as a powerhouse class of materials for a wide range of applications, including dielectrics. However, in certain applications, the performance of dielectrics is limited by insufficient electric breakdown strength. Using this real-world application as a technology driver, we describe a novel artificial intelligence (AI)-based approach for the design of polymers. We call this approach polyG2G. The key concept underlying polyG2G is graph-to-graph translation. Graph-to-graph translation solves the inverse problem. First, the subtle chemical differences between high- and low-performing polymers are learned. Then, the learned differences are applied to known polymers, yielding large libraries of novel, high-performing, hypothetical polymers. Our approach, with respect to a host of presently adopted design methods, exhibits a favorable trade-off between generation of chemically valid materials and available chemical search space. polyG2G finds thousands of potentially high-value targets (in terms of glass-transition temperature, band gap, and electron injection barrier) from an otherwise intractable search space. Density functional theory simulations of band gap and electron injection barrier confirm that a large fraction of the polymers designed by polyG2G are indeed of high value. Finally, we find that polyG2G is able to learn established structure–property relationships.

Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility.

(1) Background: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potential alien life forms. The technology was not advanced enough for onboard machine analysis of microscopic observations to be performed in past missions, but recent increases in computational power make the use of automated in-situ analyses feasible. (2) Methods: Here, we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. (3) Results: While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates for the selected species does not exceed 82%. (4) Conclusions: Motility is an excellent biosignature, which can be used as a tool for upcoming life-detection missions. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.

회전기기의 운전상태 분류를 위한 기계학습 알고리즘의 성능 비교

회전기기의 운전상태 분류를 위한 기계학습 알고리즘의 성능 비교

Machine Learning Algorithm Applied for Predicting the Presence of Mycobacterium Tuberculosis

Tuberculosis is a contagious disease that causes of death. The body's response to active TB infection produces inflammation that can damage the lungs. Areas affected by active TB gradually fill with scar tissue. It is spread from person-to-person. A person is often infected by inhaling the germs. Tuberculosis germs are spread into the air when a person with TB disease of the lungs or throat coughs, sneezes, speaks, or sings. These germs can stay in the air for several hours, depending on the environment. However, patients with chronic diseases, such as diabetes, chronic kidney disease, and silicosis, are at elevated risk. Finally, age younger than 4 years, long-term malnutrition, and substance abuse are independent risk factors for disease. The aim of this research work is to develop an adaptive Neuro-Fuzzy system for predicting the presence of Mycobacterium tuberculosis. The system is structured with inputs and one output of which rules were generated by the system with the help of three domain Medical expertise and are injected in to the knowledge based where the system would use this rules to make decisions and draw a conclusion. MATLAB7.0 was used to implement this experiment using fuzzy logic and Neural Network toolbox. In this experiment linguistic variables are evaluated using Gaussian membership function.

Machine Learning Algorithms Applied to System Security: A Systematic Review

Machine Learning Algorithms Applied to System Security: A Systematic Review

Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data

We propose that we estimate geographic markets in two steps. First, estimate clusters of transactions interchangeable in use. Second, estimate markets from these clusters. We argue that these clusters are subsets of markets. We draw on both antitrust cases and economic intuition. We model and estimate these clusters using techniques from machine learning and data science. WE model these clusters using Blei et al.’s (2003) Latent Dirichlet Allocation (LDA) model. And, we estimate this model using Griffiths and Steyvers’s (2004) Gibbs Sampling algorithm (Gibbs LDA). We apply these ideas to a real-world example. We use transaction-level scanner data from the largest supermarket franchise in Italy. We find fourteen clusters. We present strong evidence that LDA fits the data. This shows that these interchangeability clusters exist in the marketplace. Then, we compare Gibbs LDA clusters with clusters from the Elzinga-Hogarty (E-H) test. We find similar clusters. LDA has a few identifiable parameters. The E-H test has too many parameters for identification. Also, Gibbs LDA avoids the silent majority fallacy of the E-H test. Then, we estimate markets from the Gibbs LDA clusters. We use consumption overlap and price stationarity tests on the clusters. We find four grocery markets in Tuscany.

A Comparative Study of Machine Learning Algorithms Applied to Predictive Toxicology Data Mining

This paper reports results of a comparative study of widely used machine learning algorithms applied to predictive toxicology data mining. The machine learning algorithms involved were chosen in terms of their representability and diversity, and were extensively evaluated with seven toxicity data sets which were taken from real-world applications. Some results based on visual analysis of the correlations of different descriptors to the class values of chemical compounds, and on the relationships of the range of chosen descriptors to the performance of machine learning algorithms, are emphasised from our experiments. Some interesting findings relating to the data and the quality of the models are presented--for example, that no specific algorithm appears best for all seven toxicity data sets, and that up to five descriptors are sufficient for creating classification models for each toxicity data set with good accuracy. We suggest that, for a specific data set, model accuracy is affected by the feature selection method and model development technique. Models built with too many or too few descriptors are undesirable, and finding the optimal feature subset appears at least as important as selecting appropriate algorithms with which to build a final model.