Optimization of vehicle classification model using genetic algorithm

This paper focuses on classifying vehicle types into car, van, motorcycle, bus, light truck, multi-axle truck and determine its class based on the Philippine Toll Regulatory Board\u27s vehicle classification. This study utilized DEvol, an open-source tool that uses genetic algorithm for evolving number of filters and nodes, optimizer, activation, dropout rate. The model attained the best accuracy with 78.53% using 9000 images from MIO-TCD dataset. © 2019 IEEE

Philippine license plate character recognition using faster R-CNN with inceptionV2

This research proposes a method for automatic license plate recognition (ALPR) using the Faster R-CNN with InceptionV2 feature extractor that works in the Philippines. While there exist character recognition systems, there still remains difficulty in recognition due to different variations of Philippine license plates. By training a deep neural network in the extraction of the features in images of the different types of Philippine license plates - 1981, 2003, 2014, and others - our proposed multi-class detection system can recognize the alphanumeric characters in the license plate images. The system was tested on actual traffic images in the Philippines that contains different types of license plates, and achieved the detection rate of 90.011%, recognition rate of 93.21% and an overall accuracy of 83.895%. © 2019 IEEE

Impact of the COVID-19 pandemic on patients with paediatric cancer in low-income, middle-income and high-income countries: a multicentre, international, observational cohort study

OBJECTIVES: Paediatric cancer is a leading cause of death for children. Children in low-income and middle-income countries (LMICs) were four times more likely to die than children in high-income countries (HICs). This study aimed to test the hypothesis that the COVID-19 pandemic had affected the delivery of healthcare services worldwide, and exacerbated the disparity in paediatric cancer outcomes between LMICs and HICs. DESIGN: A multicentre, international, collaborative cohort study. SETTING: 91 hospitals and cancer centres in 39 countries providing cancer treatment to paediatric patients between March and December 2020. PARTICIPANTS: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, Wilms' tumour, sarcoma, retinoblastoma, gliomas, medulloblastomas or neuroblastomas, in keeping with the WHO Global Initiative for Childhood Cancer. MAIN OUTCOME MEASURE: All-cause mortality at 30 days and 90 days. RESULTS: 1660 patients were recruited. 219 children had changes to their treatment due to the pandemic. Patients in LMICs were primarily affected (n=182/219, 83.1%). Relative to patients with paediatric cancer in HICs, patients with paediatric cancer in LMICs had 12.1 (95% CI 2.93 to 50.3) and 7.9 (95% CI 3.2 to 19.7) times the odds of death at 30 days and 90 days, respectively, after presentation during the COVID-19 pandemic (p<0.001). After adjusting for confounders, patients with paediatric cancer in LMICs had 15.6 (95% CI 3.7 to 65.8) times the odds of death at 30 days (p<0.001). CONCLUSIONS: The COVID-19 pandemic has affected paediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, many patients with paediatric cancer continued to receive their normal standard of care. This speaks to the adaptability and resilience of healthcare systems and healthcare workers globally

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present