INTELLIGENT ROAD MAINTENANCE: A MACHINE LEARNING APPROACH FOR SURFACE DEFECT DETECTION

The emergence of increased sources for Big Data through consumer recording devices gives rise to a new basis for the management and governance of public infrastructures and policy de-sign. Road maintenance and detection of road surface defects, such as cracks, have traditionally been a time consuming and manual process. Lately, increased automation using easily acquirable front-view digital natural scene images is seen to be an alternative for taking timely maintenance decisions; reducing accidents and operating cost and increasing public safety. In this paper, we propose a machine learning based approach to handle the challenge of crack and related defect detection on road surfaces using front-view images captured from driver’s viewpoint under diverse conditions. We use a superpixel based method to first process the road images into smaller coherent image regions. These superpixels are then classified into crack and non-crack regions. Various texture-based features are combined for the classification mod-el. Classifiers such as Gradient Boosting, Artificial Neural Network, Random Forest and Linear Support Vector Machines are evaluated for the task. Evaluations on real datasets show that the approach successfully handles different road surface conditions and crack-types, while locating the defective regions in the scene images

Advancing Recommendations on Two-Sided Platforms: A Machine Learning Approach to Context-Aware Profiling

Digitally enabled two-sided platforms rely on mediating different actors to evoke transactions. Here, the core value-generating mechanisms of these platforms relate to the recommendations that persuade users to make future transactions, thereby driving sales, customer satisfaction, efficiency, and trust. To generate effective recommendations, accurate user profiling is fundamental. Ubiquitous computing provides valuable data to enhance user profiling by uncovering behavioral patterns of individual users. Specifically, through machine learning methods, recommendation systems are able to understand users better by considering both past individual behaviors and their respective contexts. However, state-of-the-art recommendation systems rely either on collaborative or content-based approaches, thereby neglecting a user’s time-varying contexts and the dynamics that influence these contexts. We address this shortcoming by developing a context-aware and user-specific hybrid recommendation system using transfer-learning techniques based on (recurrent) neural networks. Evaluating our approach on Expedia’s hotel booking data, we demonstrate its enhanced performance compared to common recommendation approaches

Distributed Cognitive Expert Systems in Cancer Data Analytics: A Decision Support System for Oral and Maxillofacial Surgery

Although researchers have uncovered potential positive impacts of digital technologies in healthcare and medical centers have been increasingly making use of technology to digitally store their data, the use of healthcare analytics in clinical practice remains limited. In particular, the application of machine learning (ML) approaches, although holding the potential of providing valuable insights, is mainly restricted to descriptive ML, due to the approximate nature of ML, the impact of inaccuracies, and the perceived potential additional efforts in clinical workflows. Taking into account these barriers to healthcare analytics adoption, in this multidisciplinary study, we obtained and jointly analyzed cancer data on 799 cases of cranio-maxillofacial and oral-maxillofacial surgery. We developed a real-time decision support system that predicts optimal treatments and communicates its prediction confidence along with patient attributes that are significant to decision making, thereby providing potentials simultaneously for improving quality of care and for increasing process efficiency for physicians

Accelerating the Front End of Medicine: Three Digital Use Cases and HCI Implications

Digital applications in health care are a concurrent research and management question, where implementation experiences are a core field of information systems research. It also contributes to fighting pandemic crises like COVID-19 because contactless information flow and speed of diagnostics are improved. This paper presents three digital application case studies from emergency medicine, administration management, and cancer diagnosis with AI support from the University Medical Centers of Münster and Göttingen in Germany. All cases highlight the potential of digitalization to increase speed and efficiency within the front end of medicine as the crucial phase before patient treatment starts. General challenges for health care project implementations and human-computer interaction (HCI) concepts in health care are derived and discussed, including the importance of specific processes together with user analysis and adaption. A derived concept for HCI includes the criteria speed, accuracy, modularity, and individuality to achieve sustainable improvements within the front end of medicine