Ability Of Machine Learning Techniques Research Articles

Knowledge-based and Expert Systems in Prognostics and Health Management: a Survey

Prognostics and Health Management (PHM) has become increasingly popular in recent years, and data-driven methods and artificial intelligence have emerged as dominant tools within the PHM field. This trend is mainly due to the increasing use of sensors and the ability of machine learning techniques to leverage condition monitoring data. However, despite their utility and effectiveness, these techniques are not without drawbacks. One major issue is that data-driven methods often lack transparency in their reasoning, which is crucial for understanding fault occurrences and diagnostics. Additionally, the availability of data can be a challenge. In some cases, data are scarce or hard to obtain, either due to the cost of installing necessary sensors or the rarity of the required information. Lastly, the insights derived from data can sometimes diverge from those obtained through expert analysis and established norms. This contrasts with knowledge-based approaches such as expert systems, which formally organize the knowledge acquired from norms and experts, and then deduce the desired conclusion. While research is increasingly exploring data-driven techniques, industry tends to still frequently employ knowledge-based methods. To fill this gap, this paper offers a detailed survey of knowledge-based and expert systems in PHM, examining methodologies such as propositional logic, fuzzy logic, Dempster-Shafer theory and Bayesian networks. It assesses the integration and impact of these techniques in PHM for fault detection, diagnosis and prognosis, highlighting their strengths, limitations, and potential future developments. The study provides a thorough evaluation of current developments and contributes significant insights into the current capabilities and future directions of knowledge-based techniques in enhancing decision-making processes in PHM.

Prediction of esports competition outcomes using EEG data from expert players

Considerable efforts have focused on predicting sports event outcomes to enhance spectating, coaching, and betting. However, accurately predicting match results across various competitive scenes is challenging. Many researchers have achieved reasonable prediction accuracy using machine learning (ML) models and static data, such as player statistics up to the last match, to perform binary win/loss classification. However, the inherent uncertainty of sports often leads to unexpected outcomes. For example, narrowing the skill-level gap between players may negatively impact prediction accuracy. Moreover, accurately predicting “upsets,” where lower-skilled players defeat higher-skilled opponents, remains challenging. Conventional win/loss prediction techniques rely on static attributes derived from past match statistics, which fail to capture the player's condition immediately before the match. To address this limitation, this study focused on dynamic information reflecting the pre-match player's condition, particularly electroencephalography (EEG) data, recognized as a potent mental conditioning biomarker. We collected pre-match EEG data from esports experts and trained ML models on these data, aiming to assess the ability of ML techniques trained on EEG data to predict match outcomes, particularly in cases subject to high unpredictability. The findings revealed that the light gradient boosting machine (LightGBM) algorithm trained on EEG data achieved the highest prediction accuracy (80%), with parietal beta activity being the most relevant feature. Furthermore, predictive performance remained consistent even in match scenarios involving similar-level players and upset situations. Hence, this approach extends the applicability of win/loss prediction to traditionally unpredictable sports scenes, enhancing the quality of spectator experiences.

Optimizing Micropropagation and Rooting Protocols for Diverse Lavender Genotypes: A Synergistic Approach Integrating Machine Learning Techniques

This study comprehensively explored the micropropagation and rooting capabilities of four distinct lavender genotypes, utilizing culture media with and without 2 g/L of activated charcoal. A systematic examination of varying concentrations of BAP for micropropagation and IBA for rooting identified an optimal concentration of 1 mg/L for both BAP and IBA, resulting in excellent outcomes. Following robust root development, the acclimatization of plants to external conditions achieved a 100% survival rate across all genotypes. In addition to the conventional techniques employed, integrating machine learning (ML) methodologies holds promise for further enhancing the efficiency of lavender propagation protocols. Using cutting-edge computational tools, including MLP, RBF, XGBoost, and GP algorithms, our findings were rigorously examined and forecast using three performance measures (RMSE, R2, and MAE). Notably, the comparative evaluation of different machine learning models revealed distinct R2 rates for plant characteristics, with MLP, RBF, XGBoost, and GP demonstrating varying degrees of effectiveness. Future studies may leverage ML models, such as XGBoost, MLP, RBF, and GP, to fine-tune specific variables, including culture media composition and growth regulator treatments. The adaptability and ability of ML techniques to analyze complex biological processes can provide valuable insights into optimizing lavender micropropagation on a broader scale. This collaborative approach, combining traditional in vitro techniques with machine learning, validates the success of current micropropagation and rooting protocols and paves the way for continuous improvement. By embracing ML in lavender propagation studies, researchers can contribute to advancing sustainable and efficient plant propagation techniques, thereby fostering the preservation and exploitation of genetic resources for conservation and agriculture.

Efficient Inverse Fractional Neural Network-Based Simultaneous Schemes for Nonlinear Engineering Applications

Finding all the roots of a nonlinear equation is an important and difficult task that arises naturally in numerous scientific and engineering applications. Sequential iterative algorithms frequently use a deflating strategy to compute all the roots of the nonlinear equation, as rounding errors have the potential to produce inaccurate results. On the other hand, simultaneous iterative parallel techniques require an accurate initial estimation of the roots to converge effectively. In this paper, we propose a new class of global neural network-based root-finding algorithms for locating real and complex polynomial roots, which exploits the ability of machine learning techniques to learn from data and make accurate predictions. The approximations computed by the neural network are used to initialize two efficient fractional Caputo-inverse simultaneous algorithms of convergence orders ς+2 and 2ς+4, respectively. The results of our numerical experiments on selected engineering applications show that the new inverse parallel fractional schemes have the potential to outperform other state-of-the-art nonlinear root-finding methods in terms of both accuracy and elapsed solution time.

House price prediction using hedonic pricing model and machine learning techniques

SummaryThe problem with property valuation is that it is extremely complex. It is difficult to objectively model the pricing process or fairly estimate a property value. Many factors can contribute to this complexity such as spatial and time factors. Evaluators and researchers have been trying to model the process for centuries. Up until recently, when computer‐aided valuation systems provided better solutions in the data evaluation and real estate valuation. Nevertheless, they may suffer from low transparency, inaccuracy, and inefficiency. This work explores the ability of machine learning techniques (MLTs) in enhancing economic activities by increasing the accuracy of house price prediction. In this article, XGBoost algorithm has been integrated with outlier sum‐statistic (OS) approach. In the real estate industry, the price of property plays a crucial role in economic growth. The research attempts to predict the price of a house using MLTs. Here, the price of the property is predicted using Extreme Gradient (XG) boosting algorithm and hedonic regression pricing. Both XGBoost and hedonic pricing models use 13 variables as inputs to predict house prices. The contribution of this research lies in the practicality of using XGboost technique to predict house prices. Finally, the accuracy of the prediction algorithms is reported with XGBoosting showing the highest accuracy of 84.1% while the accuracy of the hedonic regression algorithm is 42%.

Comparison of multiple machine learning algorithms for urban air quality forecasting

Environmental air pollution has become one of the major threats to human lives nowadays in developed and developing countries. Due to its importance, there exist various air pollution forecasting models, however, machine learning models proved one of the most efficient methods for prediction. In this paper, we assessed the ability of machine learning techniques to forecast NO2, SO2, and PM10 in Amman, Jordan. We compared multiple machine learning methods like artificial neural networks, support vector regression, decision tree regression, and extreme gradient boosting. We also investigated the effect of the pollution station and the meteorological station distance on the prediction result as well as explored the most relevant seasonal variables and the most important minimal set of features required for prediction to improve the prediction time. The experiments showed promising results for predicting air pollution in Amman with artificial neural network outperforming the other algorithms and scoring RMSE of 0.949 ppb, 0.451 ppb, and 5.570 µg/m3 for NO2, SO2, and PM10 respectively. Our results indicated that when the meteorological variables were obtained from the same pollution station the results were better. We were also able to reduce the time by reducing the set of variables required for prediction from 11 down to 3 and achieved major time improvement by about 80% for NO2, 92% for SO2, and 90% for PM10. The most important variables required for predicting NO2 were the previous day values of NO2, humidity and wind direction. While for SO2 they were the previous day values of SO2, temperature, and wind direction values of the previous day. Finally, for PM10 they were the previous day values of PM10, humidity, and day of the year.

Application of Machine Learning Techniques for Okra Shelf Life Prediction

The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Na&#239ve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Na&#239ve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Na&#239ve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.

Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data

AbstractTornadoes pose a forecast challenge to National Weather Service forecasters because of their quick development and potential for life‐threatening damage. The use of machine learning in severe weather forecasting has recently garnered interest, with current efforts mainly utilizing ground weather radar observations. In this study, we investigate machine learning techniques to discriminate between nontornadic and tornadic storms solely relying on the Rapid Update Cycle (RUC) sounding data that represent the pre‐storm atmospheric conditions. This approach aims to provide for early warnings of tornadic storms, before they form and are detectable by weather radar observations. Feature analysis of a Random Forest machine learning model uncovers that the pressure variable has little impact on the classification process, which is consistent with known key physical attributes of tornado formation, demonstrating the ability of machine learning techniques to provide insight solely based on the data.

A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

Machine Learning Against Terrorism: How Big Data Collection and Analysis Influences the Privacy-Security Dilemma

Rapid advancements in machine learning techniques allow mass surveillance to be applied on larger scales and utilize more and more personal data. These developments demand reconsideration of the privacy-security dilemma, which describes the tradeoffs between national security interests and individual privacy concerns. By investigating mass surveillance techniques that use bulk data collection and machine learning algorithms, we show why these methods are unlikely to pinpoint terrorists in order to prevent attacks. The diverse characteristics of terrorist attacks—especially when considering lone-wolf terrorism—lead to irregular and isolated (digital) footprints. The irregularity of data affects the accuracy of machine learning algorithms and the mass surveillance that depends on them which can be explained by three kinds of known problems encountered in machine learning theory: class imbalance, the curse of dimensionality, and spurious correlations. Proponents of mass surveillance often invoke the distinction between collecting data and metadata, in which the latter is understood as a lesser breach of privacy. Their arguments commonly overlook the ambiguity in the definitions of data and metadata and ignore the ability of machine learning techniques to infer the former from the latter. Given the sparsity of datasets used for machine learning in counterterrorism and the privacy risks attendant with bulk data collection, policymakers and other relevant stakeholders should critically re-evaluate the likelihood of success of the algorithms and the collection of data on which they depend.

A Novel Data Mining on Breast Cancer Survivability Using MLP Ensemble Learners

Abstract Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients.

Use of machine learning techniques to model wind damage to forests

This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory plots damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle-Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested.All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results.Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage.

Predicting ratings of perceived exertion in Australian football players: methods for live estimation

Abstract The ability of machine learning techniques to predict athlete ratings of perceived exertion (RPE) was investigated in professional Australian football players. RPE is commonly used to quantifying internal training loads and manage injury risk in team sports. Data from global positioning systems, heart-rate monitors, accelerometers and wellness questionnaires were recorded for each training session (n=3398) from 45 professional Australian football players across a full season. A variety of modelling approaches were considered to investigate the ability of objective data to predict RPE. Models were compared using nested cross validation and root mean square error (RMSE) on RPE predictions. A random forest model using player normalised running and heart rate variables provided the most accurate predictions (RMSE ± SD = 0.96 ± 0.08 au). A simplification of the model using only total distance, distance covered at speeds between 18-24 km·h−1, and the product of total distance and mean speed provided similarly accurate predictions (RMSE ± SD = 1.09 ± 0.05 au), suggesting that running distances and speeds are the strongest predictors of RPE in Australian football players. The ability of non-linear machine learning models to accurately predict athlete RPE has applications in live player monitoring and training load planning.

The analysis of simulated sow herd datasets using decision tree technique

The ability of machine learning techniques, especially the decision tree method, to analyse pig production data was investigated. The C4.5-classification algorithm was used to detect threshold values of management decisions relating to sows’ replacement. In order to generate side-effect-free data, three pig production herds, each on a different performance level, were created using a Monte Carlo simulation. The results of applying the C4.5-algorithm on simulated datasets showed different threshold values within the trees depending on sow herd performance. The evaluation parameters differed due to the adjusted number of instances per class and the dataset size. The sensitivity reached a value of 42.1–72.7%, the kappa value was 44.5–78.1%, and the error rate fluctuated between 6.0 and 31.3%. The overall classification accuracy ranged from 85.8 to 93.4% and the specificity reached a value of 93.7–98.8%. The generated decision trees, visualising the threshold values, varied their number of leaves between 2 and 31 and the number of nodes from 3 to 61.