Machine Learning Applications Research Articles

Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation.

Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on systems of particle shapes is still underexplored. To address this gap, we propose a simple, physics-agnostic, yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for systems of particle shapes, using input features such as particle distances and orientations. Our MLP classifier is trained in a supervised manner with a shape symmetry-encoded data augmentation technique without the need for any conventional roto-translations invariant symmetry functions. We evaluate the performance of our classifiers on four different scenarios involving self-assembly of cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and flexible, enabling easy application of the classifier to other processes involving particle orientations. Our work thus presents a valuable tool for investigating self-assembly processes on systems of particle shapes, with potential applications in structure identification of any particle-based or molecular system where orientations can be defined.

Decision Making on a Software Upgrade or Decommission with Data Mining and Machine Learning Techniques in Information Technology Industry

The Organizations have been investing more in Technology and Infrastructure spends like software upgrades, software renewals, software replacements, platform migrations etc., apart from investment in Business, People, and Processes. In this context, it is not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with another software. There is no unified approach or solution to consolidate data and relationships of Information Technology Assets, Software Upgrades, Software costs, Software defects, Software Performance Metrics, Security issues, IT system versions, service level objectives etc. Due to this, the decision making of software upgrades and software decommissioning is a tedious process and takes more time and effort. There is a need to build a solution that can integrate and validate the information like software assets, software upgrade success and failure likelihoods, cost benefit analysis of Cloud Computing, software metrics for fault prediction, software maintainability prediction results, Digital Transformation readiness and other related factors. There is an opportunity to apply Machine Learning techniques in defining and deriving the success likelihoods on the following data: Systems and data integration, software assets compatibility, operational service level agreement breaches, quality assurance metrics, security issues, number of open defects, number of defect fixes, number of priority incidents, mean time to resolve critical incidents, expected cost increase in software maintenance, potential cost reduction with the software or hardware replacement etc. This Research Proposal outlines the above mentioned to build a recommendation system aka decision tree namely Software Upgrades or Decommissions Life Cycle.

Prototyping a Reusable Sentiment Analysis Tool for Machine Learning and Visualization

Evaluating customer satisfaction is very significant in all organizations to get the perspective of users/customers/stakeholders on products and/or services. Part of the data obtained during the evaluation are observations and comments of respondents and these are very rich in insights as they provide information on the strengths as well as the areas needing improvement. As the volume of textual data increases, the difficulty of analyzing them manually also increases. With these concerns, text analytics tools should be used to save time and effort in analyzing and interpreting the data. The textual data being processed in sentiment analysis problems vary in so many ways. For instance, the context of textual data and the language used vary when data are sourced from different locations and areas or fields. Thus, machine learning was utilized in this study to customize text analysis depending on the context and language used in the dataset. This research aimed to produce a prototype that can be used to explore three vectorization techniques and selected machine learning algorithms. The prototype was evaluated in the context of features for the application of machine learning in sentiment analysis. Results of the prototype development and the feedback and suggestions during the evaluation were presented. In future work, the prototype shall be improved, and the evaluators' feedback will be considered.

Trend analysis of machine learning application in the study of soil and sediment contamination

Trend analysis of machine learning application in the study of soil and sediment contamination

A New Insight on Augmented Lagrangian Method with Applications in Machine Learning

A New Insight on Augmented Lagrangian Method with Applications in Machine Learning

"Federated Learning: Advancements, Applications, and Future Directions for Collaborative Machine Learning in Distributed Environments"

Federated Learning (FL) has become widely recognized as a feasible method for training machine learning models on decentralized devices, ensuring the preservation of data privacy. This study offers an extensive overview of the latest progress in federated learning methods, their applications, and the challenges they entail. We begin by introducing the concept of federated learning and its significance in distributed environments. Next, we delve into a range of methodologies aimed at improving the effectiveness, scalability, and confidentiality of federated learning. These encompass optimization algorithms, communication protocols, and mechanisms designed to uphold privacy. Moreover, we investigate the broad spectrum of applications where federated learning finds utility, spanning healthcare, the Internet of Things (IoT), and edge computing. This exploration illuminates tangible scenarios and advantages in real-world settings. Top of Form
 Additionally, we analyze the challenges and limitations inherent in federated learning, including communication overhead, non-IID data distribution, and model heterogeneity. We review recent research efforts aimed at addressing these challenges, such as federated averaging variants, adaptive client selection, and robust aggregation techniques. Finally, we outline future research directions and potential avenues for the advancement of federated learning, emphasizing the need for standardized benchmarks, federated learning frameworks, and interdisciplinary collaborations.

Gait Analysis—A Tool for Medical Inferences

Gait analysis is a valuable tool for making medical inferences and improving the diagnosis and treatment of mobility issues. This project aims to leverage gait analysis in addressing two important challenges: detecting knock knees and monitoring patients with Parkinson’s disease for falls. The project proposes the integration of gait analysis with yoga therapy to provide a unique and effective approach for correcting knock knees. A web user interface is developed to enable individuals to access the system, receive accurate feedback on their gait, and access yoga postures tailored to target knock knees. Additionally, a fall detection system is designed to monitor patients with Parkinson’s disease and notify caregivers or guardians in case of a fall. The implementation involves utilizing deep learning models, such as OpenPose model, a widely adopted deep learning framework for pose estimation and MediaPipe, another recognized framework used for building multimodal applied machine learning pipelines, to analyze gait patterns and detect knock knees and falls. The project aims to empower individuals in improving their gait, correcting knock knees, and enhancing their physical health, ultimately improving their quality of life and well-being.

Prediction of Degraded Infrastructure Conditions for Railway Operation

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks).

Application of machine learning methods for predicting childhood anaemia: Analysis of Ethiopian Demographic Health Survey of 2016.

Childhood anaemia is a public health problem in Ethiopia. Machine learning (ML) is a growing in medicine field to predict diseases. Diagnosis of childhood anaemia is resource intensive. The aim of this study is to apply machine learning (ML) algorithm to predict childhood anaemia using socio-demographic, economic, and maternal and child related variables. The study used data from 2016 Ethiopian demographic health survey (EDHS). We used Python software version 3.11 to apply and test ML algorithms through logistic regression, Random Forest (RF), Decision Tree, and K-Nearest Neighbours (KNN). We evaluated the performance of each of the ML algorithms using discrimination and calibration parameters. The predictive performance of the algorithms was between 60% and 66%. The logistic regression model was the best predictive model of ML with accuracy (66%), sensitivity (82%), specificity (42%), and AUC (69%), followed by RF with accuracy (64%), sensitivity (79%), specificity (42%), and AUC (63%). The logistic regression and the RF models of ML showed poorest family, child age category between 6 and 23 months, uneducated mother, unemployed mother, and stunting as high importance predictors of childhood anaemia. Applying logistic regression and RF models of ML can detect combinations of predictors of childhood anaemia that can be used in primary health care professionals.

Design of Modified Polymer Membranes Using Machine Learning.

Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.

GANs in the Panorama of Synthetic Data Generation Methods

This paper focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning applications (ML), using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models’ performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review.

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering.

In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC's exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.

Machine Learning-Based Prediction of Elastic Properties Using Reduced Datasets of Accurate Calculations Results

In this paper, the applicability of machine learning for predicting the elastic properties of binary and ternary bcc Ti and Zr disordered alloys with 34 different doping elements is explored. The original dataset contained 3 independent elastic constants, bulk moduli, shear moduli, and Young’s moduli of 1642 compositions calculated using the EMTO-CPA method and PAW-SQS calculation results for 62 compositions. The architecture of the system is made as a pipeline of a pair of predicting blocks. The first one took as the input a set of descriptors of the qualitative and quantitative compositions of alloys and approximated the EMTO-CPA data, and the second one took predictions of the first model and trained on the results of the PAW-SQS calculations. The main idea of such architecture is to achieve prediction accuracy at the PAW-SQS level, while reducing the resource intensity for obtaining the training set by a multiple of the ratio of the training subsets sizes corresponding to the two used calculation methods (EMTO-CPA/PAW-SQS). As a result, model building and testing methods accounting for the lack of accurate training data on the mechanical properties of alloys (PAW-SQS), balanced out by using predictions of inaccurate resource-effective first-principle calculations (EMTO-CPA), are demonstrated.

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. Following the PRISMA recommendations, this systematic review outlines ML models employed in the prediction and detection of NOAF in ICU patients and compares the ML-based approach with clinical-based methods. Inclusion criteria comprised randomized controlled trials (RCTs), observational studies, cohort studies, and case-control studies. A total of five articles published between November 2020 and April 2023 were identified and reviewed to extract the algorithms and performance metrics. Reviewed studies sourced 108,724 ICU admission records form databases, e.g., MIMIC. Eight prediction and detection methods were examined. Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.

Prediction and Elucidation of Physical Properties of Polycrystalline Materials Using Multichannel Machine Learning of Electron Backscattering Diffraction

AbstractThe application of machine learning in materials science has yielded several benefits, including the prediction of physical properties and the improvement of experimental efficiency. However, with complex models, such as convolutional neural networks (CNN), learning has become a black box, from which no universal physical knowledge can be obtained. In this study, a highly accurate prediction of the electrical properties of polycrystalline semiconductor thin films is achieved by learning multichannel CNN models from electron backscattering diffraction (EBSD) data, that is band contrasts, grain boundaries, and inverse pole figures. In addition, it examines how the CNN model learned the correlation between the crystallinity, grain boundaries, crystallographic orientation, and carrier mobility by polarizing certain EBSD data and checking the predicted changes in carrier mobility. Physical parameters affecting carrier mobility can be extracted, which is challenging via human image recognition. The methods proposed in this study will not only enable the prediction of electrical properties from EBSD data for all materials but also will contribute to the discovery of complex physical phenomena beyond the limits of human analysis.

Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy

Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing microRNA expression data. In this study, we implemented this approach using a panel of oral cancer-associated microRNAs sourced from standard incisional biopsy specimens to identify cases of oral squamous cell carcinomas (OSCC). For the model development process, we used a dataset comprising 30 OSCC and 30 histologically normal epithelium (HNE) cases. We initially trained a logistic regression prediction model using 70 percent of the dataset, while reserving the remaining 30 percent for testing. Subsequently, the model underwent hyperparameter tuning resulting in enhanced performance metrics. The hyperparameter-tuned model exhibited high accuracy (0.894) and ROC AUC (0.898) in predicting OSCC. Testing the model on cases of potentially malignant disorders (OPMDs) revealed that leukoplakia with mild dysplasia was predicted as having a high risk of progressing to OSCC, emphasizing machine learning’s advantage over histopathology in detecting early molecular changes. These findings underscore the necessity for further refinement, incorporating a broader set of variables to enhance the model’s predictive capabilities in assessing the risk of oral potentially malignant disorders.

Applying Machine Learning Algorithms to Predict the Size of the Informal Economy

Applying Machine Learning Algorithms to Predict the Size of the Informal Economy

Artificial Lift Selection Methods in Conventional and Unconventional Wells: A Summary and Review from Old Techniques to Machine Learning Applications

Artificial lift (AL) selection is an important process in enhancing oil and gas production from reservoirs. This article explores the old and current states of AL selection in conventional and unconventional wells, identifying the challenges faced in the process. The role of various factors such as production and reservoir data and economic and environmental considerations is highlighted. The article also examines the use of machine learning (ML) techniques in the AL selection process, emphasising their potential to increase the accuracy of selection and reduce data analysis time. The findings of this article provide valuable insights for researchers and practitioners in the oil and gas industry, as well as for those interested in the development of AL selection methods.

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.