1,720,954 research outputs found
ENHANCING AIRPORT OPERATIONS WITH AI FOR A SEAMLESS PASSENGER EXPERIENCE
No full textThe paper is research exploring the importance of Artificial Intelligence (AI) and Data Analytics to optimize airport operations and the passenger experience in the environment of the expanding air traffic in the world and the smart city movement. With increasing tasks that airports currently experience, congestion, flight delays, mishandling of baggage, and limited capacity, nowadays AI-based technologies integration is crucial to the operational efficiency, sustainability, and customer satisfaction. This study discusses the use of predictive analytics, machine learning, and clustering models to enhance passenger flows, resource allocation, and performance in general at airports. The research is based on the working efficiency of operations and Smart Airport 4.0 that focus on the use of data to make decisions and the combination of AI, IoT, and analytics used in transport infrastructure. The context of the research is based on the boom in air travels in the period 2005-2018 when passenger numbers are continuously growing and there is need to be smarter in operational approaches. The study will be informed by four following questions: How can artificial intelligence be used effectively to optimize the daily procedures of the airport? What quantifiable changes have been induced by the introduction of AI in terms of elimination of flight delays and enhancement of throughput? What AI solutions can best be used to improve the efficiency and predict demand? Which are the major paradoxes to the introduction of AI into the complicated airport systems? The data collection and analysis strategy involved these questions using secondary data derived out of the Federal Aviation Administration (FAA) and TartanAviation, which contains 18,885 records of air traffic and passenger statistics across 13 years. The study is based on a quantitative data-focused approach that corresponds to a positivist paradigm. They prepared data by cleaning, transformation, and feature engineering to be able to perform advanced analysis. The models such as Linear Regression, random forests, and a Gradient Boosting predictive model have been created to predict passenger volumes, and the K-Means clustering algorithm has been used to identify trends of airline activities. Moving averages and time-series decomposition have also been used to bring out the seasonal variation and growth trends in the long-term. The strength and accuracy of the models were proved with the help of such evaluation metrics as R 2, R MSE, and Silhouette scores. The results show that AI has a greater impact on the capacity to anticipate the demand in passengers, minimize congestion, and foresee operations choke points. Random Forest model (R2 = 0.995) was found to have better predictive performance and hence it is worth considering it in real-time predictive passenger forecasting. Clustering also based on the operational categories of airlines showed four key categories of airlines, whereas the time-series analysis provided some ideas of peaks in relation to the season around July and August. This research comes to the conclusion that AI is not only capable of increasing the predictive capacity but also helps to make proactive decisions and optimize resources
Feasibility of Twitter sentiment analysis in predicting crime in the UAE
Get PDFIn this study, we demonstrate how the information provided by individuals on social media can represent some aspects of their behavior to predict criminal activities and intentions in societies. The problem discussed in this study is finding a model that can analyze social media posts to infer the intentions and feelings of the publisher behind those posts to predict the probability of committing a crime. This is a preventive technique that can be used to monitor individuals or organizations who have a behavioral pattern that can be inferred as criminal intent. To help detect and predict criminal activities, we observe the use of data mining followed by sentiment analysis on Twitter. This well-known online social network enables users to post small texts, aka tweets, that are up to 280 characters in length each. In the U.S. was the main That of the study and data collection. First, the targeted tweets were collected according to geographical and keyword-based filters. Then a sentiment analysis was applied to analyze the crime intensity in specific locations. A correlation was found between the collected tweets related to criminal activities and the crime rates in the corresponding cities. Furthermore, another analysis study that we based in the United Arab Emirates found out that the quality of tweets is lower than the tweets in the United States due to the number of spam tweets. The lack of coloration between tweets and crimes committed on the ground due to laws prohibiting sharing information of crimes from police departments
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
