Engineering problems in machine learning systems

Application of a human-centered design for embedded machine learning model to develop data labeling software with nurses: Human-to-Artificial Intelligence (H2AI)

BACKGROUND

Nurses are essential for assessing and managing acute pain in hospitalized patients, especially those who are unable to self-report pain. Given their role and subject matter expertise (SME), nurses are also essential for the design and development of a supervised machine learning (ML) model for pain detection and clinical decision support software (CDSS) in a pain recognition automated monitoring system (PRAMS). Our first step for developing PRAMS with nurses was to create SME-friendly data labeling software.

PURPOSE

To develop an intuitive and efficient data labeling software solution, Human-to-Artificial Intelligence (H2AI).

METHOD

The Human-centered Design for Embedded Machine Learning Solutions (HCDe-MLS) model was used to engage nurses. In this paper, HCDe-MLS will be explained using H2AI and PRAMS as illustrative cases.

FINDINGS

Using HCDe-MLS, H2AI was developed and facilitated labeling of 139 videos (mean = 29.83 min) with 3189 images labeled (mean = 75 s) by 6 nurses. OpenCV was used for video-to-image pre-processing; and MobileFaceNet was used for default landmark placement on images. H2AI randomly assigned videos to nurses for data labeling, tracked labelers' inter-rater reliability, and stored labeled data to train ML models.

CONCLUSIONS

Nurses' engagement in CDSS development was critical for ensuring the end-product addressed nurses' priorities, reflected nurses' cognitive and decision-making processes, and garnered nurses' trust for technology adoption.

Ghost imaging of blurred object based on deep-learning

In this paper, a new, to the best of our knowledge, neural network combining a new residual neural network (ResNetV2), the residual dense block (RDB), and eHoloNet is proposed to reconstruct a blurred object. With the theory of ghost imaging, only the bucket signal that passes through the blurred object is necessary for reconstruction. The training sets are ENMNIST, which is used for simulation, and the blurred object is designed by Airy convolution. To test the generalization of the neural network, we use multi-slit as the testing sets. Both simulated and experimental results show that the trained neural network is superior in a generalized reconstruction of the blurred object. In addition, the limitation of the reconstruction is also explained in this work.

IoT Technology Applications-Based Smart Cities: Research Analysis

The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of this study is to analyze global research on smart cities based on IoT technology applications. For this, bibliometric techniques were applied to 1232 documents on this topic, corresponding to the period 2011–2019, to obtain findings on scientific activity and the main thematic areas. Scientific production has increased annually, so that the last triennium has accumulated 83.23% of the publications. The most outstanding thematic areas were Computer Science and Engineering. Seven lines have been identified in the development of research on smart cities based on IoT applications. In addition, the study has detected seven new future research directions. The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications. This study contributes to the academic, scientific, and institutional discussion to improve decision making based on the available information.