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Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems., Objective: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits., Methods: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines., Results: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention., Conclusions: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions., (©Henry Mark Granger Glyde, Caitlin Morgan, Tom M A Wilkinson, Ian T Nabney, James W Dodd. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.09.2024.)

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.

Background: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios., Methods: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning., Results: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively., Conclusions: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions., (© 2024. The Author(s).)

Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential or non-essential for survival in each species. Recently, a range of features linked to gene essentiality have been inferred using a machine learning (ML)-based approach, allowing essentiality predictions within a species. Nevertheless, predictions between species are still elusive. Here, we undertake a comprehensive study using ML to discover and validate features of essential genes common to both C. elegans and D. melanogaster . We demonstrate that the cross-species prediction of gene essentiality is possible using a subset of features linked to nucleotide/protein sequences, protein orthology and subcellular localisation, single-cell RNA-seq, and histone methylation markers. Complementary analyses showed that essential genes are enriched for transcription and translation functions and are preferentially located away from heterochromatin regions of C. elegans and D. melanogaster chromosomes. The present work should enable the cross-prediction of essential genes between model and non-model metazoans.

Purpose: Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) - based methods for robust radiomics signatures development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

El Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep learning to predict the Niño 3.4 index by encoding non-linear sea surface temperature patterns in the tropical Pacific using an autoencoder neural network. The resulting encoded patterns identify crucial centers of action in the Pacific that serve as predictors of the ENSO mode. These patterns are utilized as predictors for forecasting the Niño 3.4 index with a lead time of at least 6 months using the Long Short-Term Memory (LSTM) deep learning model. The analysis uncovers multiple non-linear dipole patterns in the tropical Pacific, with anomalies that are both regionalized and latitudinally oriented that should support a single inter-tropical convergence zone for modeling efforts. Leveraging these encoded patterns as predictors, the LSTM - trained on monthly data from 1950 to 2007 and tested from 2008 to 2022 - shows fidelity in predicting the Niño 3.4 index. The encoded patterns captured the annual cycle of ENSO with a 0.94 correlation between the actual and predicted Niño 3.4 index for lag 12 and 0.91 for lags 6 and 18. Additionally, the 6-month lag predictions excel in detecting extreme ENSO events, achieving an 85% hit rate, outperforming the 70% hit rate at lag 12 and 55% hit rate at lag 18. The prediction accuracy peaks from November to March, with correlations ranging from 0.94 to 0.96. The average correlations in the boreal spring were as large as 0.84, indicating the method has the capability to decrease the spring predictability barrier. [ABSTRACT FROM AUTHOR]

In supervised learning, feature selection methods identify the most relevant predictors to include in a model. For linear models, the inclusion or exclusion of each variable may be represented as a vector of bits playing the role of the genetic material that defines the model. Genetic algorithms reproduce the strategies of natural selection on a population of models to identify the best. We derive the distribution of the importance scores for parallel genetic algorithms under the null hypothesis that none of the features has predictive power. They, hence, provide an objective threshold for feature selection that does not require the visual inspection of a bubble plot. We also introduce the eradication strategy, akin to forward stepwise selection, where the genes of useful variables are sequentially forced into the models. The method is illustrated on real data, and simulation studies are run to describe its performance. [ABSTRACT FROM AUTHOR]

BACKGROUND: The potential reappearance and/or expansion of vector‐borne diseases is one of the terrifying issues awaiting humanity in the context of climate change. The presence of competent Anopheles vectors, as well as suitable environmental circumstances, may result in the re‐emergence of autochthonous Malaria, after years of absence. In Morocco, international travel and migration movements from Malaria‐endemic areas have recently increased the number of imported cases, raising awareness of Malaria's possible reintroduction. Using machine learning we developed model predictions, under current and future (2050) climate, for the prospective distribution of Anopheles claviger, Anopheles labranchiae, Anopheles multicolor, and Anopheles sergentii implicated or incriminated in Malaria transmission. RESULTS: All modelled species are expected to find suitable habitats and have the potential to become established in the northern and central parts of the country, under present‐day conditions. Distinct changes in the distributions of the four mosquitoes are to be expected under climate change. Even under the most optimistic scenario, all investigated species are likely to acquire new habitats that are now unsuitable, placing further populations in danger. We also observed a northward and altitudinal shift in their distribution towards higher altitudes. CONCLUSION: Climate change is expected to expand the potential range of malaria vectors in Morocco. Our maps and predictions offer a way to intelligently focus efforts on surveillance and control programmes. To reduce the threat of human infection, it is crucial for public health authorities, entomological surveillance teams, and control initiatives to collaborate and intensify their actions, continuously monitoring areas at risk. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate β (t) was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, β (t) was approximately a piecewise quadratic function. In most regions, the removed rate γ (t) was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, γ (t) displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production. [ABSTRACT FROM AUTHOR]

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. The main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. The data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. The results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. The resulting model was cross-validated through the 10-fold approach. The best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. The findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. [ABSTRACT FROM AUTHOR]

Indian diet has changed over the years as a result of geographic, cultural, and traditional influences. However, Western influences have recently had a substantial impact, including changes in eating behaviors and an increase in the consumption of fast food. Adoption of these foods has also led to a shift from traditional Indian meals, which mostly consists of a variety of grains, vegetables, lentils, and spices. In contrast to fast food, which is frequently highly processed and deficient in important nutrients, these traditional diets are frequently more wholesome, balanced, and nutritious. Obesity rates in India have risen rapidly, with a significant increase in cases of diabetes, heart disease, and other diet-related illnesses. According to the World Health Organization (WHO), the occurrence of obesity tripled between 1975 and 2016 and is continually rising. This research work seeks to give customers a meal plan that complies with their calorie needs based on their BMI and TDEE. The meal plan will only include foods that are available in India. A dish may also be substituted if the user does not have access to it. The meal plan is generated using the random forest classifier and the alternatives are provided by using the KNN algorithm. The classifier has an accuracy of 91%. [ABSTRACT FROM AUTHOR]

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.

Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival. [ABSTRACT FROM AUTHOR]

The proposed solution presented in this report aims to address the critical issue of stress detection and management among IT professionals. Our innovative approach leverages machine learning and consists of four key components, three of which actively monitor and analyze an individual's stress levels through their keystroke dynamics, heart rate variability (HRV) via an external mouse (utilizing cost-effective IOT devices), and facial expressions captured by a webcam. The fourth component focuses on providing tailored recommendations and suggestions to help users reduce their detected stress levels. Stress is a prevalent concern among IT professionals, with potential long-term repercussions on both physical and mental health. Recognizing the urgency of addressing this problem, our system facilitates early stress detection and offers practical strategies to mitigate and maintain stress at manageable levels. The ultimate goal is to enhance the overall work experience, minimize health complications, and boost productivity among IT professionals who utilize our user-friendly approach, which integrates seamlessly with their everyday tools and equipment. This holistic solution holds the promise of a healthier, happier, and more productive workforce in the IT industry. Furthermore, our system is designed to be scalable and adaptable to various IT environments, allowing organizations to tailor it to their specific needs and preferences. It can be seamlessly integrated into existing IT infrastructure, making it a cost-effective and efficient solution for companies seeking to prioritize the well-being of their IT professionals. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indian diet has changed over the years as a result of geographic, cultural, and traditional influences. However, Western influences have recently had a substantial impact, including changes in eating behaviors and an increase in the consumption of fast food. Adoption of these foods has also led to a shift from traditional Indian meals, which mostly consists of a variety of grains, vegetables, lentils, and spices. In contrast to fast food, which is frequently highly processed and deficient in important nutrients, these traditional diets are frequently more wholesome, balanced, and nutritious. Obesity rates in India have risen rapidly, with a significant increase in cases of diabetes, heart disease, and other diet-related illnesses. According to the World Health Organization (WHO), the occurrence of obesity tripled between 1975 and 2016 and is continually rising. This research work seeks to give customers a meal plan that complies with their calorie needs based on their BMI and TDEE. The meal plan will only include foods that are available in India. A dish may also be substituted if the user does not have access to it. The meal plan is generated using the random forest classifier and the alternatives are provided by using the KNN algorithm. The classifier has an accuracy of 91%.

The evolving character of the environment makes it challenging to predict water levels in advance. Despite being the most common approach for defining hydrologic processes and implementing physical system changes, the physics-based model has some practical limitations. Multiple studies have shown that machine learning, a data-driven approach to forecast hydrological processes, brings about more reliable data and is more efficient than traditional models. In this study, seven machine learning algorithms were developed to predict a dam water level daily based on the historical data of the dam water level. Multiple input combinations were investigated to improve the model’s sensitivity, and statistical indicators were used to assess the reliability of the developed model. The study of multiple models with multiple input scenarios suggested that the bagged trees model trained with seven days of lagged input provided the highest accuracy. The bagged tree model achieved an RMSE of 0.13953, taking less than 10 seconds to train. Its efficiency and accuracy made this model stand out from the rest of the trained model. With the deployment of this model on the field, the dam water level predictions can be made to help mitigate issues relating to water supply.

IntroductionBedwetting, also known as enuresis, is the second most common chronic health problem among children and it affects their everyday life negatively. A first-line treatment option is the enuresis alarm. This method entails the child being awoken by a detector and alarm unit upon urination at night, thereby changing their arousal mechanisms and potentially curing them after 6–8 weeks of consistent therapy. The enuresis alarm treatment has a reported success rate above 50% but requires significant effort from the families involved. Additionally, there is a challenge in identifying early indicators of successful treatment.MethodsThe alarm treatment has been further developed by the company Pjama AB, which, in addition to the alarm, offers a mobile application where users provides data about the patient and information regarding each night throughout the treatment. The wet and dry nights are recorded, in addition to the actual timing of the bedwetting incidents. We used the machine learning model random forest to see if predictions of treatment outcome could be made in early stages of treatment and shorten the evaluation time based on data from 611 patients. This was carried out by using and analyzing data from patients who had used the Pjama application. The patients were split into training and testing groups to evaluate to what extent the algorithm could make predictions every day about whether a patient’s treatment would be successful, partially successful, or unsuccessful.ResultsThe results show that a large number of patient outcomes can already be predicted accurately in the early stages of treatment.DiscussionAccurate predictions enable the correct measures to be taken earlier in the treatment, including increasing motivation, adding pharmacotherapy, or terminating treatment. This has the potential to shorten the treatment in general, and to detect patients who will not respond to the treatment early on, which in turn can improve the lives of children suffering from enuresis. The results show great potential in making the treatment of enuresis more efficient.

Thanks to the growth in data storage capacity, nowadays, researchers can use years' worth of mathematical models and depend on past datasets. A pattern of all pandemics can be identified through the assistance of Machine Learning. The movement of the COVID-19 herd and any future pandemic can be predicted. These predictions will vary based on the dataset, but it will allow the preparation beforehand and stop the spreading of COVID-19. This study focuses on developing Spatio-temporal models using Machine Learning to produce a predictive visualized heat regional map of COVID-19 worldwide. Different models of Machine Learning are compared using John Hopkins University dataset. This study has compared well-known basic models like Support Vector Machine (SVM), Prophet, Bayesian Ridge Regression, and Polynomial Regression. Based on the comparison of various metrics of the Support Vector Machine, Polynomial Regression Model was found to be better and hence can be assumed to give good results for long-term prediction. On the other hand, ARIMA, Prophet Model, and Bayesian Ridge Reduction models are good for short-term predictions. The metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) are better for Support Vector Machines compared to other models. The metrics such as R2 Score and Adjusted R-Square are better for the polynomial Regression model. [ABSTRACT FROM AUTHOR]

In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation (R2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day‐to‐day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time‐series into a 6‐dimensional space with a corresponding EPB R2 (0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post‐sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset. Key Points: AI Prediction of EPBs (APE) can accurately predict the Swarm Ionospheric Bubble IndexAPE is an XGBoost regressor that outperforms similarly trained linear and random forest modelsGame theory techniques reveals the influence of solar and geomagnetic activity as well as geo‐location, time, and season [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate. [ABSTRACT FROM AUTHOR]

Hydropower plays an essential role in Europe's energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower's contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works. [ABSTRACT FROM AUTHOR]

The recent advancement and enhancement that optimized uncoded space-time labelling diversity (USTLD) have provided significant diversity gains. By adopting the use of evolutionary algorithms, labelling diversity (LD) mapper designs produced are near-optimal in quality. The only disadvantage to the use of evolutionary algorithms is that the produced solution is not always optimal. To ease the calculation of how much a mapper design had achieved LD, this paper proposes a machine learning-based analysis to predict the amount of LD achieved by a mapper. In this paper, only the 16QAM constellation is studied as a simple case. Six machine learning-based algorithms were proposed in this paper, namely multi-linear regression (MLR), support vector regression (SVR), decision trees (DT), random forest (RF), K-nearest neighbours (KNN) and a simple artificial neural network (ANN). From the results obtained from the experiments, it can be seen that the MLR algorithm is the least time complex while the ANN is the most time complex. It is also important to note that the DT and KNN algorithms take a comparatively short amount of time to execute. When compared in terms of machine learning metrics, it was shown that the ANN algorithm performed the best with the least amount of error while the MLR algorithm performed the worst with the highest amount of error. Thus, it could be seen that the results from this paper provide a positive outlook on applying machine learning algorithms to the LD problem.

Cardiotocography (CTG) is a biophysical method for assessing fetal condition that primarily relies on the recording and automated analysis of fetal heart activity. The quantitative description of the CTG signals is provided by computerized fetal monitoring systems. Even though effective conclusion generation methods for decision process support are still required to find out the fetal risk such as premature embryo, this proposed method and outcome data can confirm the assessment of the fetal state after birth. Low birth weight is quite possibly the main attribute that significantly depicts an unusual fetal result. These expectations are assessed in a constant experimental decision support system, providing valuable information that can be used to obtain additional information about the fetal state using machine learning techniques. The advancements in modern obstetric practice enabled the use of numerous reliable and robust machine learning approaches in classifying fetal heart rate signals. The Naïve Bayes (NB) classifier, support vector machine (SVM), decision trees (DT), and random forest (RF) are used in the proposed method. To assess these outcomes in the proposed method, some of the metrics such as precision, accuracy, F1 score, recall, sensitivity, logarithmic loss and mean absolute error have been taken. The above mentioned metrics will be helpful to predict the fetal risk. [ABSTRACT FROM AUTHOR]

Background: Predicting cis-regulatory modules (CRMs) in a genome and their functional states in various cell/tissue types of the organism are two related challenging computational tasks. Most current methods attempt to simultaneously achieve both using data of multiple epigenetic marks in a cell/tissue type. Though conceptually attractive, they suffer high false discovery rates and limited applications. To fill the gaps, we proposed a two-step strategy to first predict a map of CRMs in the genome, and then predict functional states of all the CRMs in various cell/tissue types of the organism. We have recently developed an algorithm for the first step that was able to more accurately and completely predict CRMs in a genome than existing methods by integrating numerous transcription factor ChIP-seq datasets in the organism. Here, we presented machine-learning methods for the second step. Results: We showed that functional states in a cell/tissue type of all the CRMs in the genome could be accurately predicted using data of only 1~4 epigenetic marks by a variety of machine-learning classifiers. Our predictions are substantially more accurate than the best achieved so far. Interestingly, a model trained on a cell/tissue type in humans can accurately predict functional states of CRMs in different cell/tissue types of humans as well as of mice, and vice versa. Therefore, epigenetic code that defines functional states of CRMs in various cell/tissue types is universal at least in humans and mice. Moreover, we found that from tens to hundreds of thousands of CRMs were active in a human and mouse cell/tissue type, and up to 99.98% of them were reutilized in different cell/tissue types, while as small as 0.02% of them were unique to a cell/tissue type that might define the cell/tissue type. Conclusions: Our two-step approach can accurately predict functional states in any cell/tissue type of all the CRMs in the genome using data of only 1~4 epigenetic marks. Our approach is also more cost-effective than existing methods that typically use data of more epigenetic marks. Our results suggest common epigenetic rules for defining functional states of CRMs in various cell/tissue types in humans and mice. [ABSTRACT FROM AUTHOR]

During on-site verification activities conducted by the Technical Secretariat of Organization for the Prohibition of Chemical Weapons, identification by gas chromatography retention indices (RI) data, in addition to mass spectrometry data, increase the reliability of factual findings. However, reference RIs do not cover all the possible chemical structures. That is why it is important to have models to predict RIs. Applicable only for narrow data sets of chemicals with a fixed scaffold (G- and V-series gases as example), the non-learning incremental method demonstrated predictive median absolute and percentage errors of 2–4 units and 0.1–0.2%; these are comparable with the experimental bias in RI measurements in the same laboratory with the same GC conditions. It outperforms the accuracy of two reported machine learning methods–median absolute and percentage errors of 11–52 units and 0.5–2.8%. However, for the whole Chemical Weapons Convention (CWC) data set of chemicals, when a fixed scaffold is absent, the incremental method is not applicable; essential machine learning methods achieved accuracy: median absolute and percentage errors of 29–33 units and 0.5–2.2%, depending on the machine learning method. In addition, we have developed a homology tree approach as a convenient method for the visualization of the CWC chemical space. We conclude that non-learning incremental methods may be more accurate than the state-of-the-art machine learning techniques in particular cases, such as predicting the RIs of homologues and isomers of chemicals related to CWC. [ABSTRACT FROM AUTHOR]

In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the "St. Spiridon" Hospital of Iași, Romania. Using these datasets, different classification problems were proposed. The evaluation of glaucoma progression was conducted based on parameters such as VFI (Visual field index), MD (Mean Deviation), PSD (Pattern standard deviation), and RNFL (Retinal Nerve Fiber Layer). As classification tools, the following algorithms were used: Multilayer Perceptron, Random Forest, Random Tree, C4.5, k-Nearest Neighbors, Support Vector Machine, and Non-Nested Generalized Exemplars. The best results, with an accuracy of over 90%, were obtained with Multilayer Perceptron and Random Forest algorithms. The NNGE algorithm also proved very useful in creating a hierarchy of the input values according to their influence (weight) on the considered outputs. On the other hand, the decision tree algorithms gave us insight into the logic used in their classification, which is of practical importance in obtaining additional information regarding the rationale behind a certain rule or decision. [ABSTRACT FROM AUTHOR]

Banana is one of the main fruit crops in the world as it is a rich source of nutrients and has recently become popular for its fibre, particularly as a raw material in many industries. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. Therefore, we reviewed 75 full-text articles published between 1985 and 2021 for information on mathematical models related to banana growth and, fruit and fibre yield. We analysed results in order to provide a descriptive synthesis of selected studies. According to the co-occurrence analysis, most studies were conducted on the mathematical modelling of banana fruit production. Modellers often used multiple linear regression models to estimate banana plant growth and fruit yield. Existing models incorporate a range of predictor variables, growth conditions, varieties, modelling approaches and evaluation methods, which limits comparative evaluation and selection of the best model. However, the banana process-based simulation model 'SIMBA' and artificial neural network have proven their robust applicability to estimate banana plant growth. This review shows that there is insufficient information on mathematical models related to banana fibre yield. This review could aid stakeholders in identifying the strengths and limitations of existing models, as well as providing insight on how to build novel and reliable banana crop-related mathematical models. [ABSTRACT FROM AUTHOR]

Prediction of wetland greenhouse gas (GHG) fluxes has been a challenging undertaking. Machine learning techniques such as the artificial neural network (ANN) has a strong potential to provide high quality predictions of the wetland GHG fluxes. We developed eight different ANN models and investigated their suitability to predict the major GHG fluxes (CO2 and CH4) in coastal salt marshes (dominated by Spartina alterniflora) of Waquoit Bay, Massachusetts, USA. Based on the dominant environmental drivers, the daytime net uptake fluxes of CO2 were predicted as a function of photosynthetically active radiation, soil temperature (ST), and porewater salinity (SS). The net emission fluxes of CH4 were predicted as a function of ST and SS. Our models with the radial basis function neural network (RBNN) provided the most accurate and least-biased predictions of the net CO2 uptake (Nash-Sutcliffe Efficiency, NSE = 0.98) and CH4 emission (NSE = 0.90-0.92). The linear layer neural network generated the least successful and most biased predictions of the GHG fluxes (NSE = 0.48-0.80). Other ANNs, including the commonly-used feed forward neural network (FFNN), provided less accurate and more biased predictions of the CO2 (NSE = 0.86-0.97) and CH4 (NSE = 0.73-0.89) fluxes than the RBNN. We, therefore, recommend using RBNN as the first choice and FFNN (or its variant) as the second choice for predicting the GHG fluxes in coastal salt marshes. Our findings and tools would help derive plausible scenarios and guidelines for restoration, monitoring, and maintenance of coastal salt marshes in the U.S. and beyond. [ABSTRACT FROM AUTHOR]

Population ageing is considered to be one of the most significant social phenomena that is transforming economies and societies around the world. According to the World Health Organization (WHO), ageing is on the rise. In Colombia, demographic growth exhibits a natural increase, which shows a notable difference between birth and general mortality rates. According to DANE, in Colombia, natural growth rates denote a precipitous decline over time. The Central and local governments can help with decision-making in order to establish sexual and reproductive health policies. Machine Learning (ML) therefore appears as a support tool, in which there are algorithms that allow creating models to learn from data and identify patterns that aid in supporting government entities in the decision-making process. Based on the above, this work proposes a method for ensemble ML algorithms, which supports decision-making regarding demographic control focused on birth. The prediction method made it possible to show that the decrease in births in Colombia in recent years is due to the change in the priorities of women and men. Women face discrimination and difficulty in accessing and staying in employment due to maternity. Consequently, it is difficult for them to articulate their professional life with the job market. Women have to assume a disproportionate burden of care, which is why they want to have fewer children, namely one or two at most. [ABSTRACT FROM AUTHOR]