Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

2021 ◽  
Vol 193 (7) ◽  
Author(s):  
Yong Jie Wong ◽  
Yoshihisa Shimizu ◽  
Akinori Kamiya ◽  
Luksanaree Maneechot ◽  
Khagendra Pralhad Bharambe ◽  
...  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format


Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 133
Author(s):  
Jérémie Sublime

The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.


2021 ◽  
pp. 175791392097933
Author(s):  
SW Flint ◽  
A Piotrkowicz ◽  
K Watts

Aims: The outbreak of severe acute respiratory syndrome coronavirus 2 (COVID-19) is a global pandemic that has had substantial impact across societies. An attempt to reduce infection and spread of the disease, for most nations, has led to a lockdown period, where people’s movement has been restricted resulting in a consequential impact on employment, lifestyle behaviours and wellbeing. As such, this study aimed to explore adults’ thoughts and behaviours in response to the outbreak and resulting lockdown measures. Methods: Using an online survey, 1126 adults responded to invitations to participate in the study. Participants, all aged 18 years or older, were recruited using social media, email distribution lists, website advertisement and word of mouth. Sentiment and personality features extracted from free-text responses using Artificial Intelligence methods were used to cluster participants. Results: Findings demonstrated that there was varied knowledge of the symptoms of COVID-19 and high concern about infection, severe illness and death, spread to others, the impact on the health service and on the economy. Higher concerns about infection, illness and death were reported by people identified at high risk of severe illness from COVID-19. Behavioural clusters, identified using Artificial Intelligence methods, differed significantly in sentiment and personality traits, as well as concerns about COVID-19, actions, lifestyle behaviours and wellbeing during the COVID-19 lockdown. Conclusions: This time-sensitive study provides important insights into adults’ perceptions and behaviours in response to the COVID-19 pandemic and associated lockdown. The use of Artificial Intelligence has identified that there are two behavioural clusters that can predict people’s responses during the COVID-19 pandemic, which goes beyond simple demographic groupings. Considering these insights may improve the effectiveness of communication, actions to reduce the direct and indirect impact of the COVID-19 pandemic and to support community recovery.


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