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1. |
Record Nr. |
UNINA9910645890303321 |
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Autore |
Kaya Muhammed Fatih |
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Titolo |
Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations / / by Muhammed Fatih Kaya |
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Pubbl/distr/stampa |
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Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Gabler, , 2023 |
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ISBN |
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9783658405342 |
9783658405335 |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (178 pages) |
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Collana |
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Gabler Theses, , 2731-3239 |
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Disciplina |
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Soggetti |
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Technological innovations |
Economics - Psychological aspects |
Innovation and Technology Management |
Behavioral Economics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- Application of Data Mining Methods for Pattern Recognition in Negotiation Support Systems -- Advanced Maintenance of Data Richness in Business Communication Data – An Evaluation of Dimensionality Reduction Techniques -- Analytical Comparison of Clustering Techniques for the Recognition of Communication Patterns -- Pattern Labelling of Business Communication Data -- Discussion and Outlook -- References. |
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Sommario/riassunto |
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The world of digitalisation is changing the way how people and business companies communicate with each other. Electronic negotiations represent one of the most important forms of business communication and can influence the successes and failures of companies in a significant way, whether in interorganisational or intraorganisational processes. Analysing negotiation interactions to determine pattern-based peculiarities in the communication offers new value-adding information concerning the management of optimised communication processes, even though the machine-based processing of communication data bears a series of challenges. The present book develops a new approach to analyse the automated pattern recognition |
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potential of Machine Learning methods in unstructured negotiation communication. It presents holistic research frameworks for the effective detection of structural patterns and reveals the pattern labelling potential in high-dimensional communication data by analytically implementing a series of Machine Learning methods. About the author Dr. Muhammed Fatih Kaya is a postdoctoral researcher at the Information Systems 1 department (Institute of Interorganisational Management and Performance) at the University of Hohenheim in Germany. His research interests include Machine Learning, Natural Language Processing, Text Mining, Recommender Systems and Electronic Negotiations. |
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2. |
Record Nr. |
UNINA9910557781303321 |
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Autore |
Carvajal-Ramírez Fernando |
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Titolo |
UAV Photogrammetry and Remote Sensing |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
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Descrizione fisica |
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1 online resource (257 p.) |
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Soggetti |
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Technology: general issues |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites.The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and |
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temporal resolution of the products obtained.More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products.This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry. |
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