1,720,969 research outputs found
Forecasting Employees' Promotion Based on the Personal Indicators by Using a Machine Learning Algorithm
No full textJob promotion is considered one of the most important issues of importance in any organization, as it is vital for administrative development, and a means of motivating the worker for self-development and willingness to bear the burden and responsibility of work and the position attached to it, and thus it contributes to providing the necessary needs of the forces of mankind to occupy positions higher on the career ladder. Thus, this study aims to set up a sufficient framework to predict the promotion of an employee in an organization based on a variety of characteristics such as, but not limited to, the number of training, previous year rating, duration of service, awards earned, and average training score. Hence, this framework can be used and generalized to all prediction problems, not just our problem of predicting employee promotion. In this study, we used promotion data provided by Analytics Vidhya Data to test and prove the success of the framework. Our methodology is mainly composed of five phases: Input data, Data Pre processing, Data Manipulation, Data Modeling, and finally Data Evaluation. We constructed a new number of features in this study. Then we used several features including creating features and providing insights into the promotion and commitment of employees and using supervised learning techniques, namely XGBoost, Random Forest, Decision Tree, Logistic Regression, AdaBoost, and Gradient Boosting. Experimental results show that the XGBoost model has a higher accuracy of 94%, proving to be the most efficient. The result is accentuated by the high validation score similar to accuracy and efficiency. It is a very important and valuable study as it is the first study to predict employee promotion using the XGBoost classifier method
Real-Time Localization Methodology with RFID Technology in Closed Area
No full textIn recent years, many companies want to keep track of personnel, sources and working machines due to various reasons. This is sometimes achieved for security purposes and sometimes for coordination and performance purposes. The purpose and requirements are the main factors that determine the shape of tracking. Such developed systems for this purpose have different technologies according to their requirements. The real-time tracking can be determined with high precision in open areas with the global positioning system (GPS). However, previous research and developments for indoor tracking have mostly focused on infrared, wireless LAN and ultrasonic. In this study, the infrastructure of the system was designed by using RFID technology. The use of open source Geographical Information Systems (GIS) as a substructure also provided spatial display and analysis possibilities for this study. The open source t database is also integrated into this system. The tracking algorithm is completely unique and original and is encoded in the Java programming language. In the algorithm, the accuracy of locating the proximity, direction and whether the RFID reader is on the right and left of the last point received is increased. The methodology used was tested in an underground salt mine and proved to study successfully
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
Rssi-Based Hybrid Algorithm for Real-Time Tracking in Underground Mining by Using Rfid Technology
No full textKnowing the precise and real-time location of underground mining workers is essential for their health and safety in any emergency. However, the standard Global Positioning System (GPS) is insufficient for such indoor environments as it requires new infrastructure based on different technologies and algorithms. Instead, Radio Frequency Identification (RFID)-based real-time indoor localization systems and a hybrid algorithm are developed. The received-signal-strength (RSS) based positioning techniques are investigated and applied in indoor environments. A unique hybrid approach based on fingerprinting is proposed and developed to solve the disadvantages of the existing techniques. Consequently, the accuracy of this one-of-a-kind algorithm is found to be 2.52 m in an office and 3.13 m in an underground mine. We also compared the proposed hybrid algorithm to the Weighted K-Nearest Neighbor (WKNN). WKNN, on the other hand, has an accuracy of 4.01 m in the office and 4.33 m in underground mining environments. (C) 2022 Elsevier B.V. All rights reserved
Development of a supervised classification method to construct 2D mineral maps on backscattered electron images
No full textThe Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section
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
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