Tropical Cyclones Affecting Tokyo and Changing Storm Surge Hazard since 1980

This study investigated tidal records and landfall tropical cyclone (TC) best tracks in Japan from 1980 to 2019 to determine changes in storm surge heights in coastal regions of eastern Japan, including Tokyo. The results indicate that annual mean storm surge heights have increased in the last 20 years (2000–2019) compared to those in 1980–1999, and that these changes are noteworthy, particularly in Tokyo Bay. The storm surge hazard potential index (SSHPI), proposed by Islam et al. (2021), is positively correlated with surge height. The temporal change analysis of SSHPI suggests that TC wind intensity and size during landfall time frame have become stronger and larger, respectively, corresponding to increasing storm surge magnitudes from 1980 to 2019. The increased occurrence frequency of TCs with more northeastward tracks is another factor that may have contributed to the increased surge hazards around Tokyo. Tokyo area is likely to experience increasing numbers of extreme storm surge events in the future, if, the current increasing tendency continues.

Temporal Change Analysis Based Recommender System for Alzheimer Disease Classification

The development of recommender systems gathered momentum due to its relevance and application in providing a personalized recommendation on a product or a service for customer relations management. It has proliferated into medicine and its allied domains for the recommendations on disease prediction/detection, medicine, treatment, and other medical services. This chapter describes a new composite and comprehensive recommender system named Temporal Change Analysis based Recommender System for Alzheimer Disease Classification (TCA-RS-AD) using a deep learning model. Its performance is evaluated on the dataset with T1-weighted MRI clinical temporal data of OASIS and the results were recorded in terms of Precision, Recall, F1-Score and Accuracy, Hamming Loss, Cohens Kappa Coefficient, and Matthews Correlation Coefficient. The improved accuracy of this recommendation model endorses its suitability for its application in the classification of AD