From Conventional to Machine Learning Methods for Maritime Risk Assessment

Ergonomic Assessment of Posture Risk Factors Among Iranian Workers: An Alternative to Conventional Methods

Objectives: Work-related musculoskeletal disorders are a global problem which evolves at different workplaces such as industries, administrative, and agriculture sectors. In various studies, such disorders were assessed through multiple methods. It is necessary to evaluate different tools to use them in diverse communities. The aim of this study was to assess the validity of the new ergonomic e...

Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods

The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer's Disease...

A Machine Learning Approach to No-Reference Objective Video Quality Assessment for High Definition Resources

The video quality assessment must be adapted to the human visual system, which is why researchers have performed subjective viewing experiments in order to obtain the conditions of encoding of video systems to provide the best quality to the user. The objective of this study is to assess the video quality using image features extraction without using reference video. RMSE values and processing ...

Conventional Machine Learning for Social Choice

Deciding the outcome of an election when voters have provided only partial orderings over their preferences requires voting rules that accommodate missing data. While existing techniques, including considerable recent work, address missingness through circumvention, we propose the novel application of conventional machine learning techniques to predict the missing components of ballots via late...

Comparing Machine Learning Approaches for Fall Risk Assessment