A machine learning methodology to forecast well production: illustration with the Volve dataset

We investigated cognitively demanding tasks on patterns of human gait in healthy adults with a deep learning methodology that learns from raw gait data. Age-related differences were analyzed in dual-tasks in a cohort of 69 cognitively healthy adults organized in stratified groups by age. A novel spatio-temporal deep learning methodology was introduced to effectively classify dual-tasks from spatio-temporal raw gait data, obtained from a unique tomography floor sensor. The approach outperformed traditional machine learning approaches. The most favorable classification F-score obtained was of 97.3% in dual-tasks in a young age group experiment for a total of 12 users. The deep machine learning methodology outperformed classical machine learning methodologies by 63.5% in the most favorable case. Finally, a 2D manifold representation was obtained from trained deep learning models' data, to visualize and identify clusters from features learned by the deep learning models. This study demonstrates a novel approach to dual-task research by proposing a data-driven learning methodology with stratified age-groups.

Hospitals are in search of industry-proven processes through which an increasing gamut of complicated operations can be managed most prudently and also reduce the operative costs to a considerable degree coupled with better care for quality. The Agile methodologies provide adequate in healthcare using machine learning. The Agile is now ubiquitous in many work and organization. The ‘Agile’ redesign deals with improving system responsiveness to the patient through an improvised source of flexibility and coordination. Machine learning help in healthcare to diagnose disease, recommend the treatment, online consultations improve, speeding up drug development and improve the training of doctor and medical student. In hospital healthcare services are good but the quality of service provided in rural areas as a comparison to urban is not up to the mark. In this research an actuarial model of healthcare system is build which is very cost efficient. The adoptions of a higher number of computerized tools have resulted in the advantage identified with the procedure for taking care of patient. According to W.H.O less than one (0.8) doctor assigned for 1000, patients that’s why heavy workload on doctors in India and has placed 112th position among 191 countries of the world. In this research work design smart and secure healthcare service for rural areas to provide better healthcare service and healthcare cost estimation using Agile Methodology in machine learning.

Towards UCI+: A mindful repository design

Machine learning research often has a large experimental component. While the experimental methodology employed in machine learning has improved much over the years, repeatability of experiments and generalizability of results remain a concern. In this paper we propose a methodology based on the use of experiment databases. Experiment databases facilitate large-scale experimentation, guarantee repeatability of experiments, improve reusability of experiments, help explicitating the conditions under which certain results are valid, and support quick hypothesis testing as well as hypothesis generation. We show that they have the potential to significantly increase the ease with which new results in machine learning can be obtained and correctly interpreted.KeywordsDataset SizeExperiment DatabaseMachine Learning ResearchDefault Parameter SettingOptimal Experiment DesignThese 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.

BackgroundImprovements in the delivery of intensive care have increased survival among even the most critically ill children, thereby leading to a growing number of children with chronic complex medical conditions in the pediatric intensive care unit (PICU). Some of these children are at a significant risk of recurrent and prolonged critical illness, with higher morbidity and mortality, making them a unique population described as having chronic critical illness (CCI). To date, pediatric CCI has been understudied and lacks an accepted consensus case definition.ObjectiveThis study aims to describe the protocol and methodology used to perform a scoping review that will describe how pediatric CCI has been defined in the literature, including the concept of prolonged PICU admission and the methodologies used to develop any existing definitions. It also aims to describe patient characteristics and outcomes evaluated in the included studies.MethodsWe will search four electronic databases for studies that evaluated children admitted to any PICU identified with CCI. We will also search for studies describing prolonged PICU admission, as this concept is related to pediatric CCI. Furthermore, we will develop a hybrid crowdsourcing and machine learning (ML) methodology to complete citation screening. Screening and data abstraction will be performed by 2 reviewers independently and in duplicate. Data abstraction will include the details of population definitions, demographic and clinical characteristics of children with CCI, and evaluated outcomes.ResultsThe database search, crowd reviewer recruitment, and ML algorithm development began in March 2021. Citation screening and data abstraction were completed in April 2021. Final data verification is ongoing, with analysis and results anticipated to be completed by fall 2021.ConclusionsThis scoping review will describe the existing or suggested definitions of pediatric CCI and important demographic and clinical characteristics of patients to whom these definitions have been applied. This review’s results will help inform the development of a consensus case definition for pediatric CCI and set a priority agenda for future research. We will use and demonstrate the validity of crowdsourcing and ML methodologies for improving the efficiency of large scoping reviews.International Registered Report Identifier (IRRID)DERR1-10.2196/30582

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible. Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the incentive for the authors to complete a series of three textbooks entitled Learning in Medicine Part and Three, Springer Heidelberg Germany, 2012-2013, describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details. For this reason we produced a 100 page cookbook, entitled Machine Learning in Medicine - Cookbook One, with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled Machine Learning in Medicine - Cookbook Two is complementary to the first and also intended for providing a more balanced view of the field and thus, as a must-read not only for physicians and students, but also for any one involved in the process and progress of health and health care. Similarly to Learning in Medicine - Cookbook the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies:Cluster methodologies (Chaps. 1-3)Linear methodologies (Chaps. 4-11)Rules methodologies (Chaps. 12-20)In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8. We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing general purposes, main scientific questions and conclusions are given in place. Finally, we will demonstrate that modern machine learning performs sometimes better than traditional statistics does. learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.

Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into subsurface aquifers. This technology improves water storage, reduces over-extraction, and ensures water security in water-scarce or variable environments. MAR systems are complex, encompassing various components such as water storage, soil, meteorological factors, groundwater management (GWM), and receiving bodies. Over the past decade, the utilization of machine learning (ML) methodologies for MAR modeling and prediction has increased significantly. This review evaluates all supervised, semi-supervised, unsupervised, and ensemble ML models employed to predict MAR factors and parameters, rendering it the most comprehensive contemporary review on this subject. This study presents a concise and integrated overview of MAR’s most effective ML approaches, focusing on design, suitability for water quality (WQ) applications, and GWM. The paper examines performance measures, input specifications, and the variety of ML functions employed in GWM, and highlights prospects. It also offers suggestions for utilizing ML in MAR, addressing issues related to physical aspects, technical advancements, and case studies. Additionally, previous research on ML-based data-driven and soft sensing techniques for MAR is critically evaluated. The study concludes that integrating ML into MAR systems holds significant promise for optimizing WQ management and enhancing the efficiency of groundwater replenishment strategies.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-024-35529-3.

In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.

The term "artificial intelligence" (AI) refers to computer programs equipped with numerous competencies, such as making calculations, grouping and categorizing data, or communicating with the user in ethnic languages. On the other hand, artificial intelligence systems do not have certain properties, and among them, apart from the lack of "creative abilities", is indifference to the moral aspect of actions when searching and compiling data or cataloging phenomena. This study aims to discuss selected reasons for this state of affairs in the context of machine learning (ML) methodology, including the issues and applications of artificial intelligence from the perspective of scientific communication that occurs in the educational process. Given this goal, a research problem was formulated in the form of a question: How are ethical competencies developed in the machine learning process in the context of communication occurring in the educational process? In order to answer this research question, the text analysis method and the synthesis method were used. As a result of the research, it was determined that conducting machine learning with human participation, as well as using artificial intelligence systems previously learned with human participation, may enable the transmission of moral content in the normative sense to a cybernetic machine. Since human participation allows supervised learning of cybernetic machines, this type of learning, used as the sole method or in combination with another method, offers the opportunity to provide applications with the desired information about socio-cultural rules. Fully independent training of cybernetic machines does not ensure they collect information on ethical aspects desirable in communication during the educational process because open data sets on which machine learning takes place may contain harmful content, amplifying negative social phenomena.

The present research project entitled "Development of a method for predicting the optimal maturity level of avocado using machine learning" aims to establish an accurate and efficient approach to assess the maturity of avocados using machine learning methodologies. This research specifically focuses on identifying the relevant physical and chemical characteristics of avocados, creating a dataset containing categorized images of maturity levels, and creating machine learning models capable of accurately predicting fruit maturity. The studies reveal that the application of machine learning, in particular convolutional neural networks and multisensor models, has the potential to transform the prediction of avocado maturity and thus improve product quality and customer satisfaction. The findings indicate that the proposed techniques achieve an accuracy rate of over 90%, demonstrating their viability for integration into mobile applications that can benefit growers and suppliers in their decision-making processes.

Today in the medical science, the clinicians require huge information to diagnose and treatment of diseases by analyzing the data in cost- and time-efficient manner. Nowadays, machine learning methodology has vided applications in the field of finance and medical science. The medical science uses the machine learning methods to predict the diseases on the based on pattern recognition from the huge medical data set which is beyond to a human capability. This may be helpful to physician for planning of diagnosis, caring, best possible outcomes, cost reduction to diagnose and customer satisfaction. So machine learning and artificial intelligence become more powerful tool in the field of health care sectors and also become most study of point in the research as well. In this paper, we have presented the research that has been carried out in the field of medical sciences with using the machine learning. From whole discussion, it is defined that artificial intelligence and machine learning solve the problem of managing health care data in efficient way with respect to time and cost.

Over the years, computational fluid dynamics (CFD) has undergone significant evolution, establishing it as an essential tool for simulating complex fluid flow phenomena. The integration of deep learning (DL) and machine learning (ML) methodologies with CFD has recently emerged as a promising approach to enhance the accuracy, efficiency, and automation of simulations. This comprehensive review article explores the potential of ML and DL in advancing CFD, providing valuable insights into their transformative impact on the field. In this study, the large eddy simulation (LES), reduced-order models (ROMs), Reynolds-averaged Navier-Stokes (RANS), and direct numerical simulation (DNS) techniques, along with the role of ML in improving these models within CFD, are thoroughly discussed. It was demonstrated that ML and DL techniques can accelerate high-fidelity simulations, provide turbulence models with varying levels of precision, and generate ROMs that surpass the accuracy achieved through conventional methods. Additionally, this paper presents a broad perspective, highlighting recent advancements, opportunities, and unresolved challenges in the field.

Classifying Bangla news is still a big problem in processing languages with few resources, however most recent studies emphasize performance metrics instead of model explainability. This study introduces an explainable AI-based system for the multiclass classification of Bangla news articles into four categories: Sports, International, Entertainment, and National, utilizing a dataset of 11,904 articles. We conducted a comprehensive comparison of classical machine learning (ML) and deep learning (DL) methodologies. We used Ridge Classifier, SGD Classifier, Multinomial Naive Bayes, and XGBoost to help us learn how to use machines. We got the best score of 92.55% accuracy by using a hard-voting group of the four best linear models. We make deep learning models like CNN-LSTM, Simple LSTM, Stacked LSTM, and GRU. When they work together, the three best models get 95.76% right, and when they work alone, they get 95.46% right. We used LIME (Local Interpretable Model-agnostic Explanations) to make the Ridge Classifier and Multinomial Naive Bayes models more accurate and easier to understand. The results show that DL ensembles are better at finding the right answer than more common ML models. However, linear ML models are faster and easier to learn, train, and use. This change made Bangla NLP systems more open and reliable. This is a great way to stay up to date on the news and learn about what's going on in places where Bengali is spoken.

Maritime trade accounts for approximately 90% of global trade, and most global supply chains incorporate some form of maritime travel (Cheraghchi et al., 2017). Optimising operations at commercial maritime ports is therefore of significant importance worldwide, and impacts global trade. While damage to vessels and cargo has been studied extensively, as has optimising portside operational efficiency, investigations of damage to shipping containers themselves and the resultant disruption of port-side efficiency remains unstudied. The application of machine learning (ML) techniques to uncover causes of shipping container damage allows for more efficient handling of the service-disruptions they cause, as well as insights into the veracity of the current wisdom held by domain experts in the industry. Further, the application of ML methodologies for dynamic algorithm selection (per level 4 of the JDL/DFIG data fusion model) allows for significant improvements to the overall performance of the aforementioned ML methodologies.