1,720,957 research outputs found
Hybrid Deep VGG-NET Convolutional Classifier for Video Smoke Detection
No full textReal-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced. The proposed real-time algorithm uses some pre-processing for the image enhancement and next to the image enhancement processing; foreground and background regions are separated with Otsu thresholding. Here, to regulate the linear combination of foreground and background components alpha channel is applied to the image components. Here, Farneback optical flow evaluation technique diminishes the false finding rate and finally smoke particles are classified with the VGG-Net CNN classifier. In the end, the investigational outcome shows better statistical stability and performance regarding classification accuracy. The algorithm has better smoke detection performance among various video scenes
Maximum temperature forecasting using deep learning algorithm by hyperparameter optimization
No full textThe prediction of the daily temperature, an important meteorological variable, has been a topic of interest among researchers currently. The adverse impact of climate change on the livelihood of human beings makes it a contentious issue, hence the importance of accurate temperature predictions. In this paper, a global temperature change prediction model that adopts deep learning (DL) algorithms was presented which preprocess the Extreme-Weather Temperature Prediction Time Series Data by removing outliers using the standard deviation and normalizing the data. Statistical feature techniques are used for the extraction of characteristics, and forecasting is conducted using the Deep Belief Network (DBN) classifier. The proposed Egret Swarm Optimisation (ESO) method was used in training the multilayer perceptron (MLP) layer of the DBN. The success of the forecast is evaluated using mean absolute error (MAE), squared coefficient of correlation (R2), and root mean square error (RMSE). The results prove that the proposed model is better than as it has the lowest MAE (0.827), RMSE (0.892), the highest correlation (0.988), and the lowest Mean Absolute Relative Error (MARE) (0.126), showing a good linear relationship between the predicted and observed values, and low relative error (MARE). This makes it a significant advancement in temperature prediction
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
No full text“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
No full textWe 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
No full textWe 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
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