1,720,954 research outputs found
Prediksi Supply Demand Pangan Untuk Mengatasi Ketahanan Pangan di ASEAN Menggunakan Model Machine Learning Dengan Data Panel
No full textPenelitian ini mengembangkan pendekatan prediksi supply demand pangan berbasis deep learning menggunakan data panel, dengan fokus pada ketahanan pangan di kawasan ASEAN. Tiga aspek utama dianalisis dalam penelitian ini, yaitu metode imputasi data hilang berbasis Generative Adversarial Network (GAIN), regresi data panel, dan model prediksi. Hasil penelitian menunjukkan bahwa metode GAIN mampu meningkatkan kualitas dataset dengan nilai RMSE, MSE, dan MAE yang lebih rendah, terutama pada tingkat kehilangan data (miss rate) yang rendah. Selain itu, analisis data panel menggunakan Fixed Effects Model (FEM) mengidentifikasi hubungan yang signifikan antara produksi, harga, ekspor, dan impor, yang berperan penting dalam memahami dinamika ketahanan pangan di kawasan ASEAN. Dalam prediksi supply demand pangan, model GRU menunjukkan performa unggul untuk sebagian besar variabel, sementara model LSTM lebih baik dalam memprediksi harga. Temuan ini menegaskan bahwa model deep learning berbasis recurrent lebih efektif dalam menangkap pola deret waktu dalam data panel pangan. Penelitian ini memberikan kontribusi dalam mengatasi tantangan data hilang, memahami hubungan antarvariabel supply demand pangan, serta menghasilkan prediksi yang lebih akurat untuk perencanaan kebijakan pangan di ASEAN. Penelitian lanjutan dapat memperluas cakupan dengan memasukkan faktor eksternal seperti perubahan iklim, penggunaan data waktu nyata, serta pengembangan model hybrid guna meningkatkan efisiensi dan generalisasi metode prediksi
OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES
Get PDFYouTube has evolved into a globally influential platform, engaging over 2.1 billion users worldwide and serving as a prominent medium for sharing, consuming, and creating diverse video content. Particularly popular among younger demographics, YouTube stands as a multifaceted hub spanning various genres and has significantly impacted education by providing extensive educational materials, fostering independent learning, and supporting a wealth of educational resources. This research conducts an in-depth investigation into sentiment analysis specifically within the context of educational YouTube videos. Leveraging advanced machine learning techniques, notably RoBERTa, this research conducts a comparative analysis with Multinomial Naive Bayes (MNB). The primary focus is on exploring RoBERTa\u27s adaptability and performance across a spectrum of educational video content, revealing its commendable accuracy of 91.21%, surpassing MNB\u27s accuracy of 79.59%. However, it is observed that RoBERTa\u27s performance is notably affected by smaller datasets, highlighting the critical importance of ample and diverse training data for achieving optimal results. These findings highlight the pivotal role of dataset characteristics and size in developing robust sentiment analysis models, especially with advanced natural language processing methods like RoBERTa
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
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