The selection of proper field survey parameters of electrical resistivity can significantly provide efficient results within a reasonable time and cost. Four electrode arrays of 2D Electric Resistivity Imaging (ERI) surveys were applied to characterize and detect subsurface archaeological bodies and to determine the appropriate array type that should be applied in the field survey. This research is to identify the subsurface features of the Borsippa archaeological site, Babylon Governorate, Middle Iraq. Synthetic modeling studies were conducted to determine the proper array and parameters for imaging the shallow subsurface features or targets. The efficiency of many array types has been tested for the detection the buried archaeological artifacts by enhancing the data coverage and sensitivity with minimizing ambiguity, from the observations. The applied arrays are Wenner, Wenner-Schlumberger, Pole-Dipole, and Dipole-Dipole. The simulated synthetic model consists of five shallow artifacts or walls embedded in the proposed silt clayey soil deposits. The models were constructed using the RES2DMOD program, and the Inversion approach was conducted using the RES2DINV program. Data of subsurface resistivity variation were inverted using the robust (i.e., L1-norm) inversion algorithm. The results reflect that the Dipole-Dipole array is recommended for shallow depths investigations, while for greater depths, the Wenner-Schlumberger array is proper to apply. The concluded results were applied in real case studies, to effectively image archaeological bodies, and successfully detecting low resistivity zones at superficial and greater depths. The relatively high resistivity features have been imaged which is probably related to the archaeological features. The results of the investigation provide archaeologists with proper insights for assessing and excavating properly the surveyed part of the Borsippa and any archaeological sites in future work.
Autorías: Jehan Faris Yousif, Read Idrees Mahmood. Localización: Utopía y praxis latinoamericana: revista internacional de filosofía iberoamericana y teoría social. Nº. 1, 2020. Artículo de Revista en Dialnet.
Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection
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