Machine learning applications in large particle accelerator facilities: review and prospects

Large accelerator facilities for scientific research and particle accelerators for radiotherapy have been constructed in Korea. The environments requiring radiological protection procedures have changed substantially and the legal requirements have also increased. However, the same regulatory criteria that are applied to small radiation-generating devices also apply to large accelerator facilities. This study evaluates an approach that uses new criteria based on radiological risk to classify different types of large accelerator facilities. To inform this study, regulations and licensing procedures adopted in various countries were reviewed. The radiological risks from different types of particle accelerators and different types of accelerated particles were investigated in relation to current and planned large accelerators in Korea. The investigation was based on neutron yield and induced radioactivity calculations undertaken using the Monte-Carlo code FLUKA. Based on this analysis, we propose two categories for large accelerator facilities. In the process of the facility construction through to getting an operation permit, we also propose a graded approach, which would require changes in the Korean Nuclear Safety Act. A two-step licensing procedure with a prior review and a final permit for accelerator operation is very practical and conservative in the view of radiation protection. The review process for the revision of the Nuclear Safety Act is currently in progress and a major aspect of this revision reflects the results of this study.

Purpose: Traffic control in large cities is extremely tough. To alleviate costs associated with traffic congestion, some nations of the world have implemented Intelligent Transportation Systems (ITS). This paper reviews the application of artificial neural network (ANN) and machine learning (ML) techniques and also their implementation issues in TFP. Techniques other than ML and ANN have also been discussed. Methodology: The survey of literature on TFP (TFP) and ITS was conducted using several secondary sources of information such as conference proceedings Journals, Books, and Research Reports published in various publications, and then the kinds of literature that are reported as promising have been included. The collected information is then reviewed to discover possible key areas of concern in the TFP and ITS. Findings/Results: Traffic management in cities is important for smooth traffic flow. TFP and ITS are drawing much attention from researchers these days. Application of ML, ANN, and other techniques are being tried to alleviate the traffic flow problem in cities. TFP using ITS employing ML techniques to overcome the problem of traffic congestion looks promising. Originality: This review of literature is conducted using secondary data gathered from various sources. The information acquired will be useful to expand on existing theories and frameworks or to develop a new technique or modify to improve the accuracy of TFP. Tables containing categories of prediction, ML Pipelining, open-source ML tools available, standard datasets available have been included. Paper Type: Literature Review.

Machine learning (ML) techniques discover knowledge from large amounts of data. Modeling in ML is becoming essential to software systems in practice. The accuracy and efficiency of ML models have been focused on ML research communities, while there is less attention on validating the qualities of ML models. Validating ML applications is a challenging and time-consuming process for developers since prediction accuracy heavily relies on generated models. ML applications are written by relatively more data-driven programming based on the black box of ML frameworks. All of the datasets and the ML application need to be individually investigated. Thus, the ML validation tasks take a lot of time and effort. To address this limitation, we present a novel quality validation technique that increases the reliability for ML models and applications, called MLVal. Our approach helps developers inspect the training data and the generated features for the ML model. A data validation technique is important and beneficial to software quality since the quality of the input data affects speed and accuracy for training and inference. Inspired by software debugging/validation for reproducing the potential reported bugs, MLVal takes as input an ML application and its training datasets to build the ML models, helping ML application developers easily reproduce and understand anomalies in the ML application. We have implemented an Eclipse plugin for MLVal that allows developers to validate the prediction behavior of their ML applications, the ML model, and the training data on the Eclipse IDE. In our evaluation, we used 23,500 documents in the bioengineering research domain. We assessed the ability of the MLVal validation technique to effectively help ML application developers: (1) investigate the connection between the produced features and the labels in the training model, and (2) detect errors early to secure the quality of models from better data. Our approach reduces the cost of engineering efforts to validate problems, improving data-centric workflows of the ML application development.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

With the rapid development of the Internet and the rise of e-commerce, commercial enterprises are faced with a large amount of data and a complex market environment. In this situation, machine learning, as a powerful tool, is widely used in the field of business analysis. In this dissertation, we take Amazon and eBay as examples to study the application of machine learning in the company's business analytics, focusing on its role in market prediction, customer behavior analysis and operation optimization. By analyzing the relevant cases, we find that machine learning technology plays an important role in helping companies make more accurate decisions and improve efficiency. Studying the application of Amazon machine learning in business analytics can promote in-depth research on the application of machine learning in business in academia, and promote the application and development of machine learning technology in other business scenarios. Overall, the application of machine learning in business analytics can help companies understand customer behavior, optimize operations, and improve sales results. However, there are still some challenges, such as data quality, algorithm selection and privacy protection. Therefore, further research and innovation are necessary to advance the development of machine learning applications in business analytics.

Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.

Purpose The purpose of this review is to evaluate the transformative potential of machine learning (ML) in predicting fatigue behavior. The review seeks to highlight current progress, practical applications, and emerging challenges, offering a roadmap for leveraging ML to improve reliability, accuracy, and efficiency in fatigue-related engineering analyses. Design/methodology/approach This review systematically examines the application of machine learning (ML) techniques to fatigue behavior prediction, encompassing material properties, fatigue life, and crack growth. It employs a comprehensive survey of recent advancements, focusing on data preprocessing, regression methods, deep learning architectures, and hybrid approaches that integrate physics-based models with ML. Multiscale modeling and ensemble techniques are also analyzed for their potential to enhance prediction accuracy and reliability. Findings The review reveals that ML techniques significantly enhance fatigue behavior prediction by addressing complex, multiscale, and nonlinear characteristics of materials. Case studies demonstrate successful ML applications in aerospace and civil engineering, underscoring its practical value. However, challenges such as limited data quality, model interpretability, and computational scalability persist, necessitating further innovation and interdisciplinary collaboration to fully realize ML’s potential. Originality/value This review provides a comprehensive and up-to-date analysis of machine learning (ML) applications in fatigue behavior prediction, addressing material properties, fatigue life, and crack growth. It bridges the gap between traditional fatigue analysis methods and data-driven approaches, emphasizing the integration of physics-based insights with ML for enhanced accuracy and reliability.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Machine learning in medical applications is one of the focus areas of the researchers these days. Machine Learning with the application of Artificial Intelligence is not only giving solutions to the complex problems but also revolutionised the medical field. The main motive of machine learning is to improve its learning process over time by taking all the relevant data and information in the form of different inputs and observations. This study reviews different medical disease prediction and detection techniques with the help of distinct deep learning & machine learning models. The problems related to medical diseases, like cancer related diseases, heart, lung, thyroid and kidney diseases are being discussed in this article. Detection and analysing of medical diseases is one of the prominent applications of machine and deep learning. Deep learning as a technology offers a huge set of different and innovative tools which are relevant to different issues faced in the field of medical image processing. This study will discuss about the applications of Machine Learning, and then discuss some of the advancements done in different diseases like breast cancer, heart disease, skin disease, kidney disease etc.

Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

Machine learning applications in healthcare sector: An overview

Nowadays, digitalization and automation in both industrial and research activities are driving forces of innovations. In recent years, machine learning (ML) techniques have been widely applied in these areas. A paramount direction in the application of ML models is the prediction of the material service time in heating devices. The results of ML algorithms are easy to interpret and can significantly shorten the time required for research and decision-making, substituting the trial-and-error approach and allowing for more sustainable processes. This work presents the state of the art in the application of machine learning for the investigation of MgO-C refractories, which are materials mainly consumed by the steel industry. Firstly, ML algorithms are presented, with an emphasis on the most commonly used ones in refractories engineering. Then, we reveal the application of ML in laboratory and industrial-scale investigations of MgO-C refractories. The first group reveals the implementation of ML techniques in the prediction of the most critical properties of MgO-C, including oxidation resistance, optimization of the C content, corrosion resistance, and thermomechanical properties. For the second group, ML was shown to be mostly utilized for the prediction of the service time of refractories. The work is summarized by indicating the opportunities and limitations of ML in the refractories engineering field. Above all, reliable models require an appropriate amount of high-quality data, which is the greatest current challenge and a call to the industry for data sharing, which will be reimbursed over the longer lifetimes of devices.

Application of machine learning on the design of acoustic metamaterials and phonon crystals: a review

Applications of machine learning in acute care research.

Simple SummaryNon-invasive imaging modalities are commonly used in clinical practice. Recently, the application of machine learning (ML) techniques has provided a new scope for more detailed imaging analysis in esophageal cancer (EC) patients. Our review aims to explore the recent advances and future perspective of the ML technique in the disease management of EC patients. ML-based investigations can be used for diagnosis, treatment response evaluation, prognostication, and investigation of biological heterogeneity. The key results from the literature have demonstrated the potential of ML techniques, such as radiomic techniques and deep learning networks, to improve the decision-making process for EC patients in clinical practice. Recommendations have been made to improve study design and future applicability.Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.