Machine learning algorithms based on generalized Gibbs ensembles

The authentication via accelerometer-based biometric gait recognition offers a user-friendly alternative to common authentication methods on smartphones. It has the great advantage that the authentication can be performed without user interaction. When the user is walking, his walk-pattern can be extracted from the accelerations measured using the integrated sensors of the smartphone. This pattern can be used for authentication. A study showed that users often deactivate the authentication methods of their mobile devices because they consume too much time. Because all steps necessary to perform biometric gait recognition can be executed in the background, no user interaction is necessary for the presented technique. Performing a continuous authentication while the user is walking, an up-to-date authentication result is available at any point in time. During log-in, no calculations are necessary anymore, hence there is no delay. Only in cases where the user is not walking, an alternative authentication method has to be used. This is a great benefit for the user because he has the advantages of a phone which is protected by authentication but without the disadvantages common methods impose. This high user-friendliness is likely to increase the number of smartphones for which the screen-lock is linked with an authentication. Therefore, a higher security of the data stored in smartphones can be achieved. A misuse of the stored information by an unauthorized user can be prevented. Due to the growing distribution of powerful smartphones, the number of available applications is increasing as well. These applications result in a growing amount of data stored on the devices, which make the protection of the device necessary. These data comprise e.g. addresses, appointments or GPS-information. Additionally, some applications, e.g. of e-mail-providers or social networks, require the user to authenticate himself. Often these credentials are stored by the user on the phone, such that it is not necessary to enter them each time. In case an unauthorized person has access to such a phone he can use these services without restrictions and therefore substantially harm the user. The objective of this thesis was to develop methods for accelerometer-based biometric gait recognition which achieve sufficient low error rates, as well as to demonstrate that their computational effort is low and allows for an execution on current smartphones. Because the basis of existing methods is the extraction of gait cycles (i.e. two steps) from the accelerometer data, a cycle-based method was developed and evaluated in a scenario test. This method uses raw data of the gait cycles as feature vectors and accomplishes the classification using distance functions. In addition, a further approach was selected, which does not need the time-costly and error-prone gait cycle extraction. Instead, it is using overlapping segments of a fixed time length. Several features are extracted from these segments and combined to feature vectors. Machine learning algorithms are used for classification. A benchmark of the approaches on a challenging database showed that these methods yield low equal error rates between 6% and 7% and are outperforming the cycle-based methods. These error rates were achieved under the realistic conditions that training and probe data are not collected on the same day. It was shown that five minutes of gait data are sufficient to thoroughly train the models. It should be regarded that the training data contain the different walking velocities at which the user should be recognized later on. To obtain low false rejection rates, the classification should be based on around three minutes walk data. Two of the developed methods were implemented on a smartphone. It was shown that both methods are able to perform the classification fast enough to allow for an authentication without delay for the user.

The way through which change of weather parameters is measured can be called as weather forecasting. Weather forecast plays important role in predicting natural calamities, agricultural sectors, some industrial sectors, etc. Heat index is a vital part of weather which depends on some weather parameters. Human body temperature, evaporation, etc. depends on heat index. Previously, several researchers worked on this issue to determine whether it rains or not for two to three days. Some of them worked just to determine the effect of change of heat index in different regions. A number of works were based on machine learning algorithms and most of them had either low accuracy or high error rate. But in this paper, we proposed a combined deep learning model. It can predict five most important weather parameters up to 30 days with very low error rate by using single cell long short term memory (LSTM). Along with this, the output from LSTM was further used to predict heat index using artificial neural network (ANN) which also gave a very high accuracy. The dataset provided to the model was pre-processed properly by using Gaussian filter, Median filter and scaling. It increased the sensitivity and performance of the model. We got the mean absolute percentage error rate (MAPE) ranging from 0.02%-8.53% with LSTM model and 94.68% of accuracy from ANN model. Further judging parameters of ANN are precision (94.57%), recall (94.57%), f1 score (94.57%), loss (12.75%). The other error rates of LSTM model are MSE (1%-16%), RMSE (10.72%-39.44%), R2 (99%-100%), MAE (8.17%-34.59%).

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments.KeywordsInductive Logic ProgrammingData Mining AlgorithmSubgraph IsomorphismOuterplanar GraphGraph MiningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper proposes a novel approach for achieving sustainable energy systems in unexpected sports event management by integrating machine learning and optimization algorithms. Specifically, we used reinforcement learning for peak load forecasting and bat evolutionary algorithm for optimization, since the energy management problem in sports events is typically non-linear. Machine learning algorithms, specifically reinforcement learning, are used to analyze historical data and provide accurate peak load forecasts. This information can then be used to optimize energy consumption during the event through the use of algorithms such as the bat evolutionary algorithm, which can effectively solve non-linear optimization problems. The integration of these algorithms in unexpected sports event management can lead to significant improvements in sustainability and cost-effectiveness. This paper presents a case study of the implementation of reinforcement learning and bat evolutionary algorithms in an unexpected sports event management scenario, demonstrating the effectiveness of the proposed approach in achieving sustainable energy systems and reducing overall energy consumption. Overall, this paper provides a roadmap for integrating machine learning and optimization algorithms, such as reinforcement learning and bat evolutionary algorithm, in unexpected sports event management to achieve sustainable energy systems, promoting a more sustainable future for the sports event industry and the planet as a whole.

Wind energy has the possibility for bringing out energy in a very constant and sustainable manner being a notable and eligible source. Although, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and therefore, the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which will also aid in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day to day wind speed info using machine learning and deep learning algorithms. A way is put forward by us to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term these algorithms may be used for wind generation rate with historical relation to wind speed info. Index Terms: Wind turbine,machine learning algorithm

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

Background:Since the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Due to heightened risks for developing mental illness, this trend is likely to continue during the post-pandemic period. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states.Objective:Studies using machine learning classification of mental well-being are scarce in Asian populations. This investigation aims to develop reliable machine learning classifiers based on health behavior indicators applicable to university students in South-East Asia.Methods:Using data from a large, multi-site cross-sectional survey, this research work models mental well-being and reports on the performance of various machine learning algorithms, such as generalized linear models, k-nearest neighbor, naïve-Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. Prediction models were evaluated using various metrics such as accuracy, error rate, kappa, sensitivity, specificity, Area Under the recursive operating characteristic Curve (AUC), and Gini Index.Results:Random forest and adaptive boosting algorithms achieved the highest accuracy of identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include body mass index, number of sports activities per week, grade point average (GPA), sedentary hours, and age.Conclusions:Based on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model–agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering the impact of variation in sample sizes for theprediction of soil nutrients. In this context, this study evaluates the impact of sample size on the prediction performance of five ML algorithms for mapping 14 soil properties, including key soil physico-chemical properties (soil organic carbon, pH, and electrical conductivity), and multiple macro (available nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur) and micronutrients (available iron, manganese, copper, zinc, and boron) at a geographical extent of 8303 km2 located in eastern India. A total of 1024 surface soil samples were collected, of which 800 were used for model training, while the remaining 224 were reserved for independent validation (IV) of the resultant maps. The original training data set (800 samples) was reduced by random selection into six different sample sizes, i.e., 800, 400, 200, 100, 50, and 25. An exhaustive set of 574 environmental variables, derived from digital terrain derivatives and Sentinel-2 satellite imagery, was used as predictors. Popular statistical indicators, such as Lin's concordance correlation coefficient (CCC) and root mean squared error (RMSE) were employed to evaluate the predictive capability of the algorithms under different sample size scenarios. The results showed that prediction accuracy and reliability of prediction performance across multiple target variables improved with sample size. However, beyond a certain point, improvement in predictive performance became substantially negligible compared to the efforts in additional sampling. All the ML algorithms performed well (mean IV-CCC varied between 0.26 and 0.64 for different soil properties) to increase insample size, except MLP, which exhibited a poorer prediction performance(mean IV-CCC varied between 0.14 and 0.29 for different soil properties). Micronutrients in general responded well to the increase in sample sizes. The uncertainty analysis revealed that increasing sample size generally reduced prediction uncertainty, though the extent varied by soil property and ML algorithm. Concisely, the results presented herein showed an effective manner of selecting an appropriate sample size and a suitable ML algorithm to predict multiple soil nutrients accurately, which would be recommended to achieve optimal accuracy for a project.

In the exploration and production of fossil resources, the characterization of rock facies is critical for estimations of rock physical properties, such as porosity and permeability, and for reservoir detection and simulation. We propose a new machine learning (ML) algorithm for characterizing rock facies using a convolutional neural network (CNN) with feature engineering and data padding strategies. In the new ML algorithm, we extend rock feature data from 1-dimensional “profile” to 2-dimensional maps by padding the original dataset. The 2-dimensional padded rock facies map enables the CNN to capture the inherent geological features while keeping the local continuities. In this new ML algorithm, we only need a simple CNN design and structure to efficiently achieve accurate classification of rock facies. We test the feasibility of applying this new algorithm using a verifiable well logging dataset from the Panoma gas field in southwest Kansas. The results show that our new ML algorithm with a simple CNN structure has achieved higher accuracy in classifications of rock facies in comparison with the CNN results of the 2016 SEG ML contest. This new ML algorithm has application potential in automatic rock facies characterization with high accuracy and efficiency.

The integration of machine learning (ML) algorithms into audit processes represents a significant advancement in the field of auditing, offering substantial benefits in terms of efficiency, accuracy, and risk management. This review examines the transformative potential of ML in auditing, highlighting its key benefits and the challenges that must be addressed to fully leverage its capabilities. Machine learning algorithms, with their ability to analyze large datasets and identify patterns, enhance the accuracy and thoroughness of audits. Traditional auditing methods often rely on sampling and manual checks, which can miss anomalies and fraudulent activities. In contrast, ML algorithms can process entire datasets, uncovering subtle patterns and irregularities that may indicate fraud or errors. This comprehensive analysis reduces the risk of oversight and improves the reliability of audit findings. One of the primary benefits of ML in auditing is its capacity for anomaly detection. ML models can be trained on historical data to understand normal financial behavior and flag deviations that might signify irregularities. This ability to detect anomalies in real-time enables auditors to identify potential issues promptly, reducing the time lag between occurrence and detection of fraud. Predictive analytics, powered by ML, further enhances audit processes by forecasting future risks based on historical data. This proactive approach allows auditors to anticipate and mitigate risks before they materialize, contributing to more robust risk management strategies. Despite these advantages, integrating ML into audit processes presents several challenges. Ensuring data quality and integrity is crucial, as ML algorithms are only as good as the data they analyze. Poor-quality data can lead to inaccurate predictions and conclusions. Additionally, the "black box" nature of some ML algorithms can pose transparency issues, making it difficult for auditors to explain how specific conclusions were reached, which is critical for stakeholder trust and regulatory compliance. Another significant challenge is the potential for algorithmic bias. ML models can inadvertently perpetuate existing biases in the data, leading to unfair or skewed audit outcomes. Continuous monitoring and validation of ML algorithms are necessary to detect and mitigate such biases. In conclusion, while integrating machine learning algorithms into audit processes offers substantial benefits in terms of accuracy, efficiency, and risk management, it also necessitates careful attention to data quality, transparency, and bias mitigation. Addressing these challenges is essential to fully realize the potential of ML in enhancing audit practices. Keywords: Benefits, Challenges, Audit Processes, Algorithms, ML.

Satellite-based aerosol optical property retrieval over land, especially size-related parameters, is challenging. This study proposed a novel two-stage machine learning (ML) algorithm for retrieving aerosol optical depth (AOD), Ångström exponent (AE), fine mode fraction (FMF), and fine mode AOD (FAOD)) over land using MODIS observed reflectance. The new ML algorithm consists of three steps: (1) first, all samples extracted from AERONET measurements were used to train the ML model, (2) then, to reduce the extreme estimation bias of the model, divided low-value and high-value samples were used to train low-value and high-value ML models, respectively, and (3) finally, the three ML models were integrated into the final retrieval based on the weight interpolation. Independent site network validation results show that the new ML algorithm has a Pearson correlation coefficient (R) of 0.894 (0.638, 0.661, 0.865) and root mean square error (RMSE) of 0.146 (0.258, 0.245, 0.153) for the AOD (AE, FMF, FAOD) retrieval, which significantly outperforms the validation metrics of MODIS operational products, with AOD (AE, FMF, FAOD) RMSE of 0.130-0.156 (0.536-0.569, 0.313, 0.191). The inter-comparison of aerosol products shows that the spatial patterns of AOD, AE, FMF, and FAOD of the new ML algorithm are in good agreement with those of the MODIS and POLDER products. These results illustrate that the new ML algorithm has good performance and transferability and indicate the ability of ML methods to be applied to multispectral instruments (such as MODIS) to retrieve multiple aerosol properties.

Constructing the outer crust of the neutron stars requires the knowledge of the Binding Energy (BE) of the atomic nuclei. Although the BE of a lot of the nuclei is experimentally determined and can be obtained from the AME data table, for the others we need to depend on theoretical models. There exist a lot of physical theories to predict the BE, each with its own strengths and weaknesses. In this paper we apply Machine Learning (ML) algorithms on AME2016 data set to predict the Binding Energy of atomic nuclei. The novel feature of our work is that it is model independent. We do not assume or use any nuclear physics model but use only ML algorithms directly on the AME2016 data set. Our results are further refined by using another ML algorithm to train the errors of the first algorithm, and repeating this process iteratively. Our best algorithm gives σrms ∼ 0.58 MeV for Binding Energy on randomized testing sets. This is comparable to all physics models or ML improved physics models studied in literature till date. Using the predictions of our Machine Learning algorithm, we construct the outer crust equation of state (EoS) of a neutron star and show that our model is comparable to existing models. This work also demonstrates the use of various ML algorithms and a detailed analysis on how we arrived at our best algorithm. It will help the physics community in understanding how to choose an ML algorithm which would be suited for their data set. Our algorithms and best fit model is also made publicly available for the use of the community.