Top Research Papers on Machine Learning Projects

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.

It is shown that the IllustrisTNG and SIMBA suites produce roughly similar distributions of galaxy properties over the full parameter space but significantly different halo baryon fractions and baryonic effects on the matter power spectrum, emphasizing the need for marginalizing over bariesonic effects to extract the maximum amount of information from cosmological surveys.

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.

Predictive analytics is used by proposing a machine learning approach to predict individuals' project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude and shows that the best method for predicting project performance is lasso.

The success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation and a neural network method based on ensemble machine learning and dropout methods for preventing the problem of overfitting.

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.

Machine learning and Materials Project are used to investigate stable and metastable perovskite materials based on a dataset of 397 ABO3 compounds. The best performance classification model Gradient Boosting Decision Tree (GBDT) can classify 397 compounds into 143 non-perovskites and 254 perovskites with a 94.6% accuracy over 10-fold cross-validation, which indicates that 9 descriptors are outstanding features for formability of perovskite: tolerance factor, octahedral factor, radius ratio of A to O, A-O and B-O bond length, electronegativity difference for A-O (B-O) multiplied by the radius r...

This paper presents analysis and studied manuscripts that proposed for cost estimation with machine learning techniques for the last 30 years and applied an analysis methodology in cost estimation based on direct cost and indirect cost of construction projects, which consists of two parts.

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.

The construction industry faces many challenges, including schedule and cost overruns, productivity constraints, and workforce shortages. Compared to other sectors, it lags in digitalization in every project phase. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies revolutionizing the construction sector. However, a discernible gap persists in systematically categorizing the applications of these technologies throughout the various phases of the construction project life cycle. In response to this gap, this research aims to present a thorough ass...

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.