Artificial Intelligence (AI) is an important part of our everyday lives. We use it in self-driving cars and smartphone assistants. People often call it a"black box" because its complex systems, especially de...
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Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets share...
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Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets shared by the people on social media can be mined to infer the valuable ‘mindset’ of the people which is much required for any ruling government in the world. The objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. The idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. The pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). The larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. The non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries’ sentiment and their corresponding reliance on Russian oil is performed.
Loan approval is one of the most important processes that any banking organization owns. The acceptance or rejection of any loan application has a direct impact on the bank revenue and the profitability in quarterly i...
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ISBN:
(数字)9781728196756
ISBN:
(纸本)9781728196763
Loan approval is one of the most important processes that any banking organization owns. The acceptance or rejection of any loan application has a direct impact on the bank revenue and the profitability in quarterly issued financial statements. Though loan approval is a critical process, the actual decision made is not a straightforward procedure and comes with a lot of uncertainties. Recently, statisticians and data scientists have tried to automate this process to minimize risk and increase profitability by applying different statistical learning methods. In this work we explore a framework with an application by applying tree-based methods on publicly available dataset. This work aimed at developing a high performance predictive model for loan approval prediction using decision trees. Experiments were made in different varieties of tree methods ranging from the most simplified and comprehensible decision tree reaching up to the most complex random forests. Results yielded inadequate performance with respect to simplified decision trees due to the highlight correlated and complex feature space, majority of critical parameters affecting loan approval was not reflected upon and yielded an impractically over-simplified tree. However, boosting came in superior in terms of performance, relevance and interpretation via the importance chart scoring accuracy on testing dataset [98.75%] specificity [100%], Minority class prediction accuracy [92.85%], and classification efficiency of [97.0%]. Therefore, boosting-based decision-tree predictive model was recommended to facilitate decision making regarding the eligibility of loan applicants based on their characteristics.
We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared the...
作者：
Svetla SlavovaJennifer VillaniDaniel J FeasterAustin BoothJaNae L HollowayPeter J RockLindsey R HammerslagAimee MackCharles E KnottJohn V McCarthyJeffery TalbertMarc R LaRochelleBridget FreisthlerBrent J GibbonsGregory PattsMatthew J BullardSharon L WalshDepartment of Biostatistics
University of Kentucky Healthy Kentucky Research Building Suite 260 760 Press Avenue Lexington KY 40536 USA. Electronic address: ssslav2@email.uky.edu. National Institutes of Health
National Institute on Drug Abuse 3WFN MSC 6025 301 North Stonestreet Avenue Bethesda MD 20892 USA. Electronic address: jennifer.villani@nih.gov. Department of Public Health Sciences
University of Miami Miller School of Medicine 1425 NW 10th Avenue Room 1059 Miami FL 33136 USA. Electronic address: dfeaster@med.miami.edu. Social
Statistical and Environment Sciences Health Practice Area RTI International 6110 Executive Blvd Suite 900 Rockville MD 20852 USA. Electronic address: abooth@***. Center for Clinical Research
Analytics Practice Area RTI International 3040 E Cornwallis Rd Research Triangle Park NC 27709 USA. Electronic address: jlholloway@***. Institute for Biomedical Informatics
University of Kentucky Healthy Kentucky Research Building Suite 260 760 Press Avenue Lexington KY 40536 USA. Electronic address: peter.rock@uky.edu. Division for Biomedical Informatics
College of Medicine University of Kentucky Healthy Kentucky Research Building Suite 260 760 Press Avenue Lexington KY 40536 USA. Electronic address: l.hammerslag@uky.edu. The Ohio Colleges of Medicine Government Resource Center
The Ohio State University Wexner Medical Center 150 Pressey Hall 1070 Carmack Road Columbus OH 43210 USA. Electronic address: Aimee.mack@osumc.edu. Social
Statistical and Environment Sciences Data Practice Area RTI International 3040 E. Cornwallis Road Research Triangle Park NC 27709 USA. Electronic address: cknott@***. Social
Statistical and Environment Sciences Analytics Practice Area RTI International 3040 E. Cornwallis Road Research Triangle Park NC 27709 USA. Electronic address: jmccarthy@***. Division of Biomedical Informatics
University of Kentucky College of Medicine 267 Healthy Kentucky Research Building 760 Press Avenue Lexington KY 40536
INTRODUCTION:The HEALing Communities Study (HCS) tested a community-based intervention in 67 communities across Kentucky, Massachusetts, New York, and Ohio to reduce opioid overdose deaths. This paper introduces the H...
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INTRODUCTION:The HEALing Communities Study (HCS) tested a community-based intervention in 67 communities across Kentucky, Massachusetts, New York, and Ohio to reduce opioid overdose deaths. This paper introduces the HCS measures for monitoring the intervention uptake, reports crude rates for benchmarking, and highlights the importance of interpreting jurisdictional trends in the context of state policies.
METHODS:We present technical specifications for the HCS measures and the common data model. Crude rates for the evaluation period (July 2021- June 2022) are reported by state and study arm (intervention/Wave 1 or wait-listed/Wave 2 communities), along with longitudinal trends from 2017 to 2023. Year 2023 serves as a post-intervention period for Wave 1 communities and an intervention year for Wave 2 communities.
RESULTS:After unprecedented increases in 2020-2021, the HCS crude opioid overdose death rates declined in 2023, but remained higher than the 2019 pre-pandemic rates. Opioid overdose death rates exceeded 100/100,000 adults among Non-Hispanic Black individuals in several states. In response to the rapid increase in opioid overdose deaths in Kentucky, the HCS team expanded the naloxone distribution in Kentucky intervention communities, reaching a 10-fold increase in Quarter 3 of 2021 (1498.2 units/100,000 residents). The methadone medication for opioid use disorder (MOUD) treatment rate for Medicaid enrollees with opioid use disorder during the evaluation period was highest in Massachusetts intervention communities (274/1000), while the buprenorphine MOUD treatment rate was highest in Kentucky (441/1000).
CONCLUSIONS:The HCS measures support comprehensive planning and evaluation of population-level opioid overdose prevention interventions and policies.
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