Top Research Papers on Machine Learning

This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifiers, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language.

This survey paper attempts to create a self-contained guide that gives an in-depth overview of the field of Differential Privacy ML, and proposes a set of specific best practices for stating guarantees.

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning applicat...

A detailed review of challenges in ML-based resource management in current research, as well as current approaches to resolve these challenges, as well as their advantages and limitations are provided.

A comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on structural magnetic resonance image (MRI), functional MRI and hybrid imaging techniques over the past decade concludes that further large-scale studies are needed.

This survey adopts a structured approach to describe the various Wi-Fi areas where ML is applied and identifies specific open challenges and general future research directions, providing readers with an overview of the main trends.

PerSpect-based machine learning models can significantly improve prediction accuracy for protein-ligand binding affinity prediction and are better than all existing models, as far as the authors know.

The best performing model, Vaxign-ML, was compared to three publicly available RV programs with a high-quality benchmark dataset and showed superior performance in predicting bacterial protective antigens.

Clinicians should be motivated to actively embrace the opportunity to contribute to the development and implementation of new health technologies and digital tools that assist all health care professionals to identify missed care, before it occurs as a matter of importance for public safety and ethical implementation.

A systematic review of today’s applications of ML techniques in the factory environment and an overview of ML training concepts in learning factories is given.

Overall, it is found that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries, however, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption ofAI technology in India.

This work delves into the individual segments in the AutoML pipeline and cover their approaches in brief, and provides a case study on the industrial use and impact of AutoML with a focus on practical applicability in a business context.

A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydın offers a concise and...

This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics, such as supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and learning theory.

This article provides a conceptual distinction between relevant terms and concepts, explains the process of automated analytical model building through machine learning and deep learning, and discusses the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business.

The interactions between resilient CPS using ML and resilient ML when applied in CPS are surveyed to have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

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 a...

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (dee...

The primary task of this tutorial is to introduce interested students into the principles of Machine Learning and expose the most typical and illustrative approaches.

This article investigates different opportunities for using ML into metaheuristics and defines uniformly the various ways synergies that might be achieved and identifies some open research issues in this topic that need further in-depth investigations.

The field of IML (interpretable machine learning) grew out of concerns about people's inability to understand the reasoning of increasingly complex models to empower various stakeholders to tackle use cases, such as building trust in models, performing model debugging, and generally informing real human decision-making.

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various disciplines in quantitative finance, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making.

Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc. The key to study combustion is to improve the combustion efficiency with minimum emission of pollutants. Machine learning facilitates data-driven techniques for handling large amounts of combustion data, either obtained through experiments or simulations under multiple spatiotemporal scales, there...

This Review examines the present state of machine-learning-driven alloy research, discusses the approaches and applications in the field and summarizes theoretical predictions and experimental validations, and foresee that the partnership between machine learning and alloys will lead to the design of new and improved systems.

It is argued that the hard open problems of machine learning and AI are intrinsically related to causality, and how the field is beginning to understand them is explained.

The foundational ideas of ML are introduced to this community such that readers obtain the essential tools they need to understand publications on the topic and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

This work synthesizes foundational work on IML methods and evaluation into an actionable taxonomy that serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components.

The challenges of understanding how a complex ML system has reached its output are examined, and some of the technical approaches to making ML easier to interpret are examined.

It is concluded that ML is potentially transformative, especially if the community curates and shares its big data, when adopting machine learning approaches for nanoplasmonic research.

This work provides the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities and provides them with a set of tools essential to apply machine learning in microbiology research.

An overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples are provided and the application of ML in several healthcare fields are discussed, including radiology, genetics, electronic health records, and neuroimaging.

The different methods of ML, mathematics behind ML, its application in day to day life and future aspects are reviewed.

Different machine learning algorithms, especially variations of the decision tree (’DecisionTree’, ’RandomForest’), are investigated for their ability to predict the energy demand of CNC machining operations based on real production data, without the need for dedicated experiments.

An insight survey for machine learning along with deep learning applications in various domains is provided and some applications with new normal COVID-19 blues are exemplified.

It is concluded that ML and DL are powerful new tools for predictive modelling and data analysis, comparable to other traditional statistical tools.

A conceptual framework to specify the role of machine learning in building (artificial) intelligent agents is developed and a consistent typology for AI-based information systems is proposed.

The results uncover the notable vulnerability of quantum machine learning systems to adversarial perturbations, which not only reveals a novel perspective in bridging machine learning and quantum physics in theory but also provides valuable guidance for practical applications of quantum classifiers based on both near-term and future quantum technologies.

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase...

The most important applications of the Shapley value in machine learning: feature selection, explainability, multi-agent reinforcement learning, ensemble pruning, and data valuation are given.

An overview of the main concepts of identifying, measuring, and improving algorithmic fairness when using ML algorithms, focusing primarily on classification tasks is presented.