Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.
To promote effective tax administration, the United States Congress set a goal of 80% of all tax returns filed electronically by 2007. The deadline was extended to 2012, which saw 69% accomplished. Using time series analysis from 2005 to 2016, this study analyses the advancements of electronic tax filings in the USA. Several interesting observations are reported. First, individual e-filing has reached the goal in 2012. Second, overall trends indicate continuous and steady progress both by volume and by share. Third, the total e-filing ratio shows monotonical increase over the years but is still shy of the target in 2016. Fourth, individual e-filing constitutes the majority while business and tax-exempt e-file comprises 15% of the total. Fifth, employment tax e-file, while constituting over half of business e-file, ranks the lowest due to few mandates. Finally, several strategies and recommendations are proposed to reach the composite target.
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