1,720,954 research outputs found
Seleksi Fitur Berbasis Metode Filter dan Random Forest untuk Deteksi Muslihat pada Data Audio
No full textMenurut Kamus Besar Bahasa Indonesia (KBBI), "muslihat" adalah siasat atau taktik untuk menjebak seseorang. Dalam hal ini, merupakan sebuah perbuatan atau pernyataan yang menyembunyikan kebenaran terhadap sesuatu informasi yang belum tentu benar. Untuk membedakan adanya tipu muslihat atau tidak, diperlukan sistem yang dapat mendeteksi pernyataan yang menyesatkan itu sendiri. Salah satu alat pendeteksi adalah poligraf yang mengukur berdasarkan fisiologi manusia, seperti denyut nadi dan tekanan darah. Namun, poligraf memiliki masalah karena tidak dapat mengukur berdasarkan psikologi manusia seperti ucapan dan intonasi.
Untuk itu, diperlukan deteksi muslihat berbasis audio yang dapat diukur berdasarkan psikologi manusia. Penelitian ini akan mengekstraksi fitur-fitur audio seperti Mel Frequency Cepstral Coefficient (MFCC), Jitter, Fundamental Frequency (F0), dan Perceptual Linear Prediction (PLP) menggunakan library openSMILE dan Shennong dari bahasa pemrograman python pada dataset privat berbahasa Indonesia sebanyak 420 data audio yang terdiri dari 210 data audio yang dilabeli sebagai data yang berbohong dan 210 data audio yang dilabeli sebagai data yang jujur. Dari hasil ekstraksi yang berupa data numerik berjumlah 6386 fitur, akan dilakukan seleksi fitur menggunakan seleksi fitur berbasis metode filter yang bertujuan untuk menyeleksi fitur-fitur terbaik. Setelah dilakukan seleksi fitur, fitur yang dipilih akan menjadi input untuk pengujian model machine learning seperti Random Forest. Setelah dilakukan pengujian model, dilakukan evaluasi metrik seperti akurasi, presisi, recall, dan f1-score. Hasil dari eksperimen ini menunjukkan jika model deteksi muslihat mengalami peningkatan setelah dilakukan proses seleksi fitur untuk mengurangi fitur-fitur tidak relevan. Penggunaan seleksi fitur berbasis metode filter berhasil menyeleksi fitur sebanyak 319 fitur. Selain itu evaluasi metrik pada deteksi muslihat menggunakan seleksi fitur berbasis metode filter menghasilkan 63,49% untuk akurasi beserta recall, 63,43% untuk presisi, dan 63,45% untuk f1-score.
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According to the Kamus Besar Bahasa Indonesia (KBBI), the term "deception" refers to a cunning plan or scheme to trick or catch someone off guard, often utilizing a deliberate tactic. In this scenario, it is an action or assertion that conceals the truth about information that is not necessarily factual. A system capable of detecting false or misleading statements is essential to determine whether deception is present or not. One detection tool is the polygraph, which measures physiological responses, including heart rate and blood pressure. Polygraphs face the challenge of not being able to measure various aspects of human psychology, particularly speech and intonation. Consequently, audio-based deception detection methods are required which can be evaluated in relation to human psychology. The research aims to extract audio features including the Mel Frequency Cepstral Coefficient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP) via openSMILE and Shennong libraries in Python, utilizing a private Indonesian dataset comprising 420 audio files, with 210 files categorized as lying data and 210 files categorized as honest data. Feature selection will be performed on 6386 extracted features, employing a filter-based approach to select the most relevant features. Following feature selection, the chosen features will serve as input for training various machine learning models including the random forest algorithm. Following model testing, evaluation of metrics including accuracy, precision, recall, and f1-score took place. A feature selection process has led to an improvement in the deception detection model by reducing the number of irrelevant features. Feature selection using the filter method effectively isolated 319 key features. Using the filter method for feature selection in deception detection metric evaluation yielded 63.49% accuracy combined with recall, 63.43% precision, and 63.45% f1-score
Audio Feature Analysis and Selection for Deception Detection in Court Proceedings
Full text linkDeception detection is a method to determine whether a person is lying or not. One lie detector is a polygraph that measures human physiology, such as pulse and blood pressure. However, polygraphs have a problem in that they cannot be measured based on human psychology, such as speech and intonation. Therefore, audio deception detection is required, and this can be measured based on human psychology. This research will extract audio features, such as the Mel Frequency Cepstral Coeffi-cient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP), from the Real-Life Trial dataset, which comprises 121 audio data. From the extraction results in the form of numerical data totaling 6387 features, various feature-selection methods are employed, such as Feature Importance (FI), Principal Component Analysis (PCA), Information Gain, Chi-Square, and Recursive Feature Elimination (RFE). After feature selection, the selected features are input to machine learning models, such as random forest and support vector machine (SVM). After model testing, metrics such as accuracy, precision, recall, and F1 score were evaluated, as well as statistical evaluation, to assess the developed model. Results from this experiment show that the deception detection model is improved after a feature selection process to reduce irrelevant features. Comparing the accuracy, Chi-Square achieves a significantly higher result, reaching up to 92% with an improvement of 24.32%, surpassing the SVM model\u27s accuracy of 67.57% before feature selection. In contrast, the RFE technique yielded the best accuracy of 86%, with an increase of 13.52%, building upon its baseline accuracy of 72.97%
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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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