Machine Learning Applications Research Articles

Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review

Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals. We did not consider articles introducing signal filtering or decomposition algorithms without previous identification of corrupted segments. Studies on MA-detecting approaches utilizing multiple channels and additional sensors such as accelerometers were also excluded. Despite its promising results, the literature on this topic shows several limitations and inconsistencies, particularly those regarding the model development and testing process and the measures used by authors to support the method’s suitability for real-time applications. Moreover, there is a need for broader exploration and validation across different body parts and a standardized set of experiments specifically designed to test and validate MA detection approaches. It is essential to provide enough elements to enable researchers and developers to objectively assess the reliability and applicability of these methods and, therefore, obtain the most out of them.

Research on the integrated application of machine learning in Unity

Abstract. In recent years, the introduction of Machine Learning Agents has enabled more individuals to apply machine learning within the renowned Unity game engine. As application development has become increasingly complex, traditional artificial intelligence (AI) struggles to keep pace with the growing demands of modern applications. However, machine learning offers new avenues for developing smarter, more dynamic game characters. This paper aims to provide a comprehensive review of the various applications of machine learning within the Unity ecosystem, including game AI, intelligent character behavior, and reinforcement learning. Through a literature review and case studies, this paper explores the specific applications of machine learning algorithms in the Unity engine and analyzes the current state of development in different fields. The research findings indicate that the application of machine learning in Unity exhibits numerous developmental trends and leaves ample room for further application development in its strong areas. This technology can be innovatively applied across many fields, from implementing advanced NPCs and third-person game agents to anti-cheat mechanisms, from image recognition-based health systems to the design of complex constrained environments. The integrated application model of machine learning and Unity3D will bring more innovation and possibilities to future application development.

Application of machine learning algorithms in resource allocation for wireless communications

Abstract. The accelerated advances of wireless communication technologies in recent years has highlighted the necessity of efficient resource allocation in 5G and upcoming 6G networks, particularly in large-scale, high-density network implementations such as the Internet of Things (IoT) and ultra-dense cellular networks. The application of machine learning offers a number of significant advantages over traditional resource allocation algorithms, including enhanced adaptability, robustness, scalability, and predictive power. Therefore, the paper aims to examine the process of selecting the optimal machine learning algorithm for a specific resource management task. To this end, it provides an overview of the fundamental concepts of machine learning, including deep reinforcement learning (DRL), graph neural networks (GNN), and joint learning. Furthermore, this paper examines the potential applications of machine learning in the field of wireless resource management. The research presented in this paper provides a crucial theoretical foundation and guidance for further exploration and application of machine learning capabilities in the domain of wireless communication resource management. Overall, the research elucidates the potential of machine learning in wireless communication resource management and its applications, thereby advancing knowledge in this field and providing valuable references for the development of efficient and intelligent wireless communication networks.

Application of machine learning in ground-based gravitational wave transient noise data processing

Application of machine learning in ground-based gravitational wave transient noise data processing

Applications of machine learning in quantitative trading

Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

Application status and prospect of machine learning in the field of enterprise development planning

Abstract. In today's digital information background, data is already an important means of production, various kinds of data continue to grow, and it is difficult to rely on the manpower to deal with such a huge amount of data. Machine learning can learn the law of data change from a large number of data and build a model for analysis, to give accurate and efficient program opinions. With the development of machine learning, deep machine learning in daily life is gradually widespread, and several successful commercial applications such as Google Translate have been born. Based on machine learning technology, this paper studies its application status and prospects in enterprise development planning. The research results show that more and more enterprises now apply machine learning to the planning and summary of the enterprise, at the same time, there are many functions of machine learning to be developed in the enterprise, through more development, enterprises can carry out more reasonable development and planning. The significance of this research is that it can provide specific application programs and solutions for enterprises, and also provide important references and guidance for the future development of enterprises, which is conducive to the healthy development of enterprises. In the future, with the continuous increase in the amount of data, the continuous progress of algorithms and the improvement of computing power, the application of machine learning in enterprise development planning will be more extensive and in-depth.

Fault-tolerant graph embeddings in Archimedean networks

Fault tolerance refers to the ability of a system to continue functioning even if some of its components fail. This concept is fundamental in several domains because reliability and continuous operation are essential in carrying out their functions. Graph embedding means mapping a graph into some other structure that preserves some of the properties or relations held by the original graph. Applications in machine learning, data mining, and network analysis make fault-tolerant embedding of graphs a crucial topic. In this paper, fault-tolerant embeddings are proposed in Archimedean tessellations. The path in the embeddings does not intersect or overlap.

Case Article—Humanitarian Supply Chains and Inventory Management: Planning Medical Supply at the International Committee of the Red Cross

This case is based on an actual project with the International Committee of the Red Cross. The rich context allows instructors to cover important topics in inventory management, supply chain management, and humanitarian logistics at a level appropriate for different student backgrounds. At a foundational level, the case presents opportunities to practice concepts like cycle stock, safety stock, and service level. Technical audiences can appreciate applications of machine learning and optimization for inventory management. Practice-oriented audiences learn about the challenges of applying commercial operations metrics and heuristics like ABC analysis to humanitarian logistics. Realistic data and models are provided. Funding: B. Thakur-Weigold acknowledges funding from the Engineering for Humanitarian Action initiative launched by the ICRC, ETH Zurich, and EPFL. International Committee of the Red Cross (ICRC), Eidgenössische Technische Hochschule (ETH) Zürich, and École Polytechnique Fédérale de Lausanne (EPFL). Supplemental Material: Supplemental materials are available at https://www.informs.org/Publications/Subcribe/Access-Restricted-Materials .

Machine Learning in 3D and 4D Printing of Polymer Composites: A Review

The emergence of 3D and 4D printing has transformed the field of polymer composites, facilitating the fabrication of complex structures. As these manufacturing techniques continue to progress, the integration of machine learning (ML) is widely utilized to enhance aspects of these processes. This includes optimizing material properties, refining process parameters, predicting performance outcomes, and enabling real-time monitoring. This paper aims to provide an overview of the recent applications of ML in the 3D and 4D printing of polymer composites. By highlighting the intersection of these technologies, this paper seeks to identify existing trends and challenges, and outline future directions.

Empowering big data analytics through machine learning: Applications, challenges, and future directions

Abstract. This paper explores the transformative role of machine learning (ML) methodologies in big data analytics, highlighting supervised learning, unsupervised learning, and deep neural networks' contributions to diverse sectors. Supervised learning, with regression analysis at its core, provides accurate forecasting in finance and healthcare by modeling relationships between variables. Unsupervised learning, through techniques like k-means and hierarchical clustering, uncovers patterns within data, offering insights for retail and biological analysis. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), excel in complex tasks like image recognition and sequential data processing, driving advances in fields from autonomous driving to language translation. The application of ML in enhancing business intelligence, innovating fintech, and advancing healthcare analytics is discussed, alongside the challenges of data privacy, security, ethical considerations, and the skills gap. This paper underscores the need for advanced cryptographic techniques, bias mitigation strategies, and education to address these challenges. It concludes by emphasizing the importance of interdisciplinary education and AI advancements in bridging the skills gap, ensuring the ethical use of ML, and making these technologies accessible for future innovations.

A study of machine learning applications in healthcare

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.

Enhancing investment strategies through machine learning: A comprehensive analysis across market sectors

Abstract. This study presents a detailed exploration of the application of machine learning (ML) models to optimize investment strategies across various market sectors, including equity markets, fixed income and derivatives, and the volatile cryptocurrency markets. We evaluate three primary ML models: linear regression, decision trees, and neural networks, based on their predictive accuracy, computational efficiency, and robustness to market volatility. A rigorous process involving backtesting and cross-validation assesses each model's performance. Our framework encompasses data preprocessing, feature engineering, model implementation, and a nuanced approach to risk assessment integrating Value at Risk (VaR) and the Sharpe Ratio. We demonstrate the models' effectiveness in predicting stock prices, interest rates, and cryptocurrency price movements. The application of our ML framework led to the development of dynamic portfolio optimization strategies that significantly outperform traditional methods. This study contributes to the understanding of ML's potential to revolutionize investment strategies, providing a foundation for future research and practical applications in financial markets.

Harnessing Machine Learning to Model Intuitive Physics: Insights from Game Engines and Cartoon Simulations

Abstract. This paper explores the application of machine learning (ML) in modeling intuitive physics, focusing on insights derived from game engines and cartoon simulations. Intuitive physics refers to the human capability to predict physical phenomena in everyday situations, a skill that can be replicated and enhanced in machines using ML algorithms. By integrating ML with game engines, which provide dynamic, controllable environments, and cartoon simulations, known for their exaggerated physical scenarios, the research aims to develop models that better understand and predict physical interactions. The study analyzes the efficiency of current ML approaches, including deep learning and reinforcement learning, in capturing the complex, often non-intuitive physics depicted in cartoons, and how these models can be generalized to understand real-world physics. The findings suggest potential improvements in the algorithms' ability to interpret and anticipate physical events, leading to advancements in robotics and AI-driven simulation systems.

Optimization of sewage treatment processes: Process control based on artificial intelligence

Abstract. The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.

Application of Machine Learning for the Prediction of Absorption, Distribution, Metabolism and Excretion (ADME) Properties from Cichorium intybus Plant Phytomolecules

Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study.

It is projected that drug-resistant infections will lead to 10 million deaths annually by 2050 if left unabated. Despite this threat, surveillance data from resource-limited settings are scarce and often lack antimicrobial resistance (AMR)-related clinical outcomes and economic burden. We aim to build an AMR and antimicrobial use (AMU) data warehouse, describe the trends of resistance and antibiotic use, determine the economic burden of AMR in Uganda, and develop a machine learning algorithm to predict AMR-related clinical outcomes. The overall objective of the study is to use data-driven approaches to optimize antibiotic use and combat antimicrobial-resistant infections in Uganda. We aim to (1) build a dynamic AMR and antimicrobial use and consumption (AMUC) data warehouse to support research in AMR and AMUC to inform AMR-related interventions and public health policy, (2) evaluate the trends in AMR and antibiotic use based on annual antibiotic and point prevalence survey data collected at 9 regional referral hospitals over a 5-year period, (3) develop a machine learning model to predict the clinical outcomes of patients with bacterial infectious syndromes due to drug-resistant pathogens, and (4) estimate the annual economic burden of AMR in Uganda using the cost-of-illness approach. We will conduct a study involving data curation, machine learning-based modeling, and cost-of-illness analysis using AMR and AMU data abstracted from procurement, human resources, and clinical records of patients with bacterial infectious syndromes at 9 regional referral hospitals in Uganda collected between 2018 and 2026. We will use data curation procedures, FLAIR (Findable, Linkable, Accessible, Interactable and Repeatable) principles, and role-based access control to build a robust and dynamic AMR and AMU data warehouse. We will also apply machine learning algorithms to model AMR-related clinical outcomes, advanced statistical analysis to study AMR and AMU trends, and cost-of-illness analysis to determine the AMR-related economic burden. The study received funding from the Wellcome Trust through the Centers for Antimicrobial Optimisation Network (CAMO-Net) in April 2023. As of October 28, 2024, we completed data warehouse development, which is now under testing; completed data curation of the historical Fleming Fund surveillance data (2020-2023); and collected retrospective AMR records for 599 patients that contained clinical outcomes and cost-of-illness economic burden data across 9 surveillance sites for objectives 3 and 4, respectively. The data warehouse will promote access to rich and interlinked AMR and AMU data sets to answer AMR program and research questions using a wide evidence base. The AMR-related clinical outcomes model and cost data will facilitate improvement in the clinical management of AMR patients and guide resource allocation to support AMR surveillance and interventions. PRR1-10.2196/58116.

Exploiting speech tremors: machine learning for early diagnosis of amyotrophic lateral sclerosis

Abstract Neurodegenerative diseases pose significant challenges in healthcare, with Amyotrophic Lateral Sclerosis (ALS) being one such rare yet debilitating condition affecting motor neurons. Machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in healthcare, offering insights and solutions for various medical conditions. This study investigates the application of ML to enhance early ALS diagnosis through the analysis of tremors in sustained speech. By focusing on tremor detection as a diagnostic marker, the research employs ML algorithms to develop predictive models capable of distinguishing ALS patients from healthy controls. The dataset comprises 54 patients from the Republican Research and Clinical Centre of Neurology and Neurosurgery in Belarus, Minsk. The study adopts a two-faceted approach: (1) Exploratory voice analysis to identify tremors associated with ALS in speech samples. (2) Development of ML algorithms to construct predictive models for early ALS diagnosis based on the identified tremors. The ML models exhibit promising results in distinguishing ALS patients from healthy controls based on speech analysis. Tremor detection in sustained speech proves to be an effective marker for early ALS diagnosis. While initial findings are encouraging, larger-scale studies are required to validate the clinical applicability of this approach. The successful application of ML and AI in early ALS diagnosis by leveraging innovative approaches, such as tremor detection in sustained speech, we can enhance early diagnosis and improve patient outcomes in neurodegenerative diseases like ALS on a broader scale.

Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front

Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations.

A machine learning assisted preliminary design methodology for bolted composite joints in large structures

Abstract Damage initiation hotspots around features, such as bolts and ply drops, must be investigated during the preliminary design phase of large composite structures, such as composite airframes. A global-local modelling approach is commonly employed to perform this investigation, whereby a global low-fidelity model is used to drive high-fidelity local models around the features of interest. However, this methodology is slow, repetitive and expert-dependent. In this investigation, we address these issues by applying machine learning techniques to this global-local modelling framework and demonstrate the time-saving benefit when predicting damage initiation of bolted composite joints. Feature engineering of model inputs and outputs, and appropriate customisation of machine learning methods enables damage initiation prediction. Special consideration is given to the boundary conditions that must be varied to simulate the response of the bolted composite joints. Results show over three orders of magnitude time-saving benefit and satisfactory accuracy of the proposed methodology. This indicates its potential to be developed further into a rapid design and optimisation tool.

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.