Limitations Of Machine Learning Research Articles

The Role of Machine Learning in Detecting and Preventing Data Breaches

Data breaches pose a growing threat to individuals, organizations, and public safety. In response, machine learning (ML) has emerged as a powerful tool for both detecting and preventing these breaches. This research paper analyses the multifaceted role of ML in data security, exploring its strengths, limitations, and potential future directions. Through a comprehensive literature review, this paper examines how different ML models and algorithms can be employed to detect anomalous behavior, predict cyberattacks, and mitigate vulnerabilities. The importance of ethical considerations surrounding privacy and user rights is also addressed, emphasizing responsible data handling practices. Furthermore, the paper investigates the challenges and best practices of integrating ML into existing security infrastructure, providing recommendations for organizations seeking to leverage its benefits. Finally, it assesses the current limitations of ML in data breach detection and prevention, identifying promising research avenues for advancing data security through ML advancements. In conclusion, this research highlights the crucial role of ML in the ongoing battle against data breaches. By effectively utilizing its capabilities while maintaining ethical considerations, organizations can significantly enhance their data security posture and safeguard valuable information in our increasingly digital world.

A Review on Financial Fraud Detection using AI and Machine Learning

This study thoroughly explores advanced approaches for addressing financial fraud, focusing on the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI). Recognizing the drawbacks of outdated methods, the examination aims to analyze the current situation, closely examining the efficiency and limitations of ML and AI techniques while mapping out intricate directions for future research. We delve into the intricate history of financial fraud, uncovering the inherent constraints embedded in conventional rule-based and manual detection approaches. Then, machine learning (ML) and artificial intelligence (AI) are discussed, highlighting significant research and successful implementations that have transformed the field of fraud detection. While analyzing the assessment metrics, we use various measures such as accuracy, precision, recall, F1 score, and the enigmatic ROC-AUC. Then, diverse ML and AI algorithms are introduced, including the mysterious Random Forest, the reliable Support Vector Machines (SVM), and the complex neural networks. As comparative analysis unfurls, uncovering the strengths and weaknesses inherent in distinct ML and AI systems. Beyond the limits of performance measures, our interpretation transcends, diving into the real-world ramifications and the labyrinth of possible routes for refinement and advancement.

Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics

Microplastics are an emerging contaminant that can persist in the environment for extended periods， posing risks to ecological systems. Recently， microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollution， it is of paramount importance to improve the simplicity and reliability of microplastic identification， systematically analyze the pollution characteristics of microplastics in various environmental media， and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplastics， provide scientific support for tracing the sources of microplastic pollution， and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermore， the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas.

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.

Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients. This study aimed to develop a prediction model for depression risk within the first month of cancer treatment. We included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use. Among 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients. The results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups.

Current State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review

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.

Machine learning and artificial intelligence in neuroscience: A primer for researchers

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields.For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues.Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.

Can we use machine learning to improve the interpretation and application of urodynamic data?: ICI-RS 2023.

A "Think Tank" at the International Consultation on Incontinence-Research Societymeeting held in Bristol, United Kingdom in June 2023 considered the progress and promise of machine learning (ML) applied to urodynamic data. Examples of the use of ML applied to data from uroflowmetry, pressure flow studiesand imaging were presented. The advantages and limitations of ML were considered. Recommendations made during the subsequent debate for research studies were recorded. ML analysis holds great promise for the kind of data generated in urodynamic studies. To date, ML techniques have not yet achieved sufficient accuracy for routine diagnostic application. Potential approaches that can improve the use of ML were agreed and research questions were proposed. MLis well suited to the analysis of urodynamic data, but results to date have not achieved clinical utility. It is considered likely that further research can improve the analysis of the large, multifactorial data sets generated by urodynamic clinics, and improve to some extent data pattern recognition that is currently subject to observer error and artefactual noise.

Applications of machine learning in familial hypercholesterolemia.

Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.

Using Artificial Intelligence to Predict and Prevent Future Food Insecurity

Abstract: The article explores the role and prospects of artificial intelligence (AI) in addressing global food insecurity. It provides an overview of machine learning (ML) techniques—the core learning component of AI—used to predict food security outcomes and discusses real-world examples as well as recent applications of ML. It further examines the challenges and limitations of ML, including concerns related to data quality and ethical considerations, followed by policy recommendations in crucial areas such as funding, cross-sector collaboration, education, and data standards. Finally, it underscores the importance of recognizing AI as a complementary tool, rather than a standalone solution, in the pursuit of the ultimate goal of achieving a world without hunger.

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada’s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent years due to climate change. The most significant flood occurred in 2019, surpassing a 100-year flood event, and serves as a stark reminder of how climate change impacts our environment. Considering the limitations of machine learning (ML) models, which heavily rely on historical data used during training, they may struggle to accurately predict such “non-experienced” or “unseen” floods that were not encountered during the training process. To tackle this challenge, our study has utilized a combination of numerical modeling and ML to create an integrated methodology. Indeed, a comprehensive dataset of river flow discharge was generated using a numerical model, encompassing a wide range of potential future floods. This significantly improved the ML training process to generalize the accuracy of results. Utilizing this dataset, a novel ML model called the Expanded Framework of Group Method of Data Handling (EFGMDH) has been developed. Its purpose is to provide decision-makers with explicit equations for estimating three crucial hydrodynamic characteristics of the Ottawa River: floodplain width, flow velocity, and river flow depth. These predictions rely on various inputs, including the location of the desired cross-section, river slope, Manning roughness coefficient at different river sections (right, left, and middle), and river flow discharge. To establish practical models for each of the aforementioned hydrodynamic characteristics of the Ottawa River, different input combinations were tested to identify the most optimal ones. The EFGMDH model demonstrated high accuracy throughout the training and testing stages, achieving an R2 value exceeding 0.99. The proposed model’s exceptional performance demonstrates its reliability and practical applications for the study area.

Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid disease predictions. A systematic literature review (SLR) strategy is used in this study to give a comprehensive overview of the existing literature on forecasting data on thyroid disease diagnosed using ML. This study includes 168 articles published between 2013 and 2022, gathered from high-quality journals and applied meta-analysis. The thyroid disease diagnoses (TDD) category, techniques, applications, and solutions were among the many elements considered and researched when reviewing the 41 articles of cited literature used in this research. According to our SLR, the current technique's actual application and efficacy are constrained by several outstanding issues associated with imbalance. In TDD, the technique of ML increases data-driven decision-making. In the Meta-analysis, 168 documents have been processed, and 41 documents on TDD are included for observation analysis. The limits of ML that are discussed in the discussion sections may guide the direction of future research. Regardless, this study predicts that ML-based thyroid disease detection with imbalanced data and other novel approaches may reveal numerous unrealised possibilities in the future

Autonomous experimental systems in materials science

The emergence of autonomous experimental systems integrating machine learning and robots is ushering in a paradigm shift in materials science. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses on discovering unexpected materials and theories with unconventional research approaches. This article reviews the latest achievements and discusses the impact of autonomous experimental systems, which will fundamentally change the way we understand research. Moreover, as autonomous experimental systems continue to develop, the need to think about the role of human researchers becomes more pressing. While machine learning and robotics can free us from the repetitive aspects of research, we need to understand the strengths and limitations of machine learning and robots and focus on how humans can perform higher creativity. In addition, we also discuss inventorship and authorship in the era of autonomous systems.

A Smart Gas Sensor Using Machine Learning Algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review

Over the past decade, Machine Learning and Artificial Intelligence have attracted great interest in research and various practical applications. Currently, smart, fast, highly sensitive with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigitated electrodes (IDE), such as electrochemical, resistive, capacitive and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, their advantages and limitations are presented. In addition, a comparison between different machine learning and artificial intelligence algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart machine learning algorithms, which provide efficient data processing methods, for the design of smart gas sensors which are highly selective. In addition, the challenges and limitations of machine learning in gas sensor applications are critically discussed. The study shows the importance of machine learning with the need for structural optimization to develop and improve smart, sensitive and selective sensors.

A Practical Fault Location Algorithm With Increased Adaptability for Active Low Voltage Grids

Quick power restoration has always been one of the grid operators' major goals and could be facilitated by the application of automatized fault location methods. The need for such methods is particularly visible in the low voltage (LV) grids which have been overlooked until now. Hence, this paper presents a novel turnkey solution to the fault location problem in LV grids. The proposed algorithm takes advantage of the growing data availability and employs a machine learning (ML) model for the prediction of the faulted point. In order to ensure the applicability of the method all the steps of its implementation are analyzed. The study begins by addressing the algorithm's data collection requirements and proposing a smart storage strategy. It continues with the implementation of advanced data processing and minimization techniques, such as the feature selection, and addresses the generalizability limitations of ML–based algorithms by proposing a retraining scheme. Finally, the algorithm's performance during load changes and data contamination is studied. For the validation of the algorithm a modified version of the CIGRE European LV benchmark was simulated. The results verify the algorithm's robustness against the main influencing parameters and reveal low mean absolute errors throughout the tests; the default study case error was 0.5 m.

More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA.

To examine the real-world safety problems involving machine learning (ML)-enabled medical devices. We analyzed 266 safety events involving approved ML medical devices reported to the US FDA's MAUDE program between 2015 and October 2021. Events were reviewed against an existing framework for safety problems with Health IT to identify whether a reported problem was due to the ML device (device problem) or its use, and key contributors to the problem. Consequences of events were also classified. Events described hazards with potential to harm (66%), actual harm (16%), consequences for healthcare delivery (9%), near misses that would have led to harm if not for intervention (4%), no harm or consequences (3%), and complaints (2%). While most events involved device problems (93%), use problems (7%) were 4 times more likely to harm (relative risk 4.2; 95% CI 2.5-7). Problems with data input to ML devices were the top contributor to events (82%). Much of what is known about ML safety comes from case studies and the theoretical limitations of ML. We contribute a systematic analysis of ML safety problems captured as part of the FDA's routine post-market surveillance. Most problems involved devices and concerned the acquisition of data for processing by algorithms. However, problems with the use of devices were more likely to harm. Safety problems with ML devices involve more than algorithms, highlighting the need for a whole-of-system approach to safe implementation with a special focus on how users interact with devices.

Machine learning for scattering data: strategies, perspectives and applications to surface scattering.

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.

Machine learning for nanoplasmonics.

Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.

The Use of Artificial Intelligence in Orthopedics: Applications and Limitations of Machine Learning in Diagnosis and Prediction

Over the last several years, the impact of Artificial Intelligence on the world and on society has been undeniable. More specifically, a subfield, known as Machine Learning (ML), is driving innovation in a vast variety of fields as it denotes the ability of a machine to identify relationships between data without explicit criteria, emulating a human-like type of learning. Over the last decade, research efforts have also been focused on orthopedics in order to provide help and assistance to surgeons and clinicians in their daily tasks. The purpose of this paper is to serve as a guide by presenting the most recent research and achievements in orthopedics concerning these new technologies, by exposing the main concepts and limitations of different applications, and tackling the main problems concerning both the field and the technology itself. The main ML techniques will be introduced and qualitatively explored, by considering the indexes that better identify the performance of the models; then, the main two applications will be addressed: diagnosis and prediction. Finally, a discussion about the limitations of the studies and technologies will be proposed.

Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future?

The use of machine learning (ML) in infectious diseases is expanding. This review aims to provide an overview of the literature on ML for clinical decision support in antimicrobial stewardship in the particular context of solid organ transplantation (SOT). References for this review were identified through searches of MEDLINE/PubMed and Google Scholar databases up to July 2022. ML may improve the prediction of infectious complications and the diagnosis and treatment of infectious diseases in SOT recipients. One of the most studied applications for antimicrobial stewardship is the individual prediction of antimicrobial resistance that could guide the empiric use of anti-infective treatments. ML may also guide the choice of antimicrobial dose taking into account the interactions with immunosuppressive drugs. The main challenge to the development of ML clinical decision support systems (CDSSs) in SOT is the development of large clinical databases, accessible to all, with good quality, comprehensive, and diversified data. ML-driven CDSSs are still at an experimental stage, and the education of clinicians about the benefits and limits of ML is essential. ML could improve antimicrobial stewardship for SOT, but literature on that specific topic is scarce. Future studies are needed to design ML-CDSS in the particular population of solid organ recipients and report clinical outcomes following use in routine practice.