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.