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.
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreIn this study, copper was removed from an aqueous solution through electromembrane extraction (EME), a new approach that utilises a two-chamber electrochemical cell. It consists of two electrodes (stainless steel cathode and graphite anode) and a solid liquid membrane (SLM). SLM is composed of supporting polypropylene membrane impregnated with1-octanol as an organic solvent and bis(2-ethylhexyl) phosphate (DEHP) as a carrier. The effects of process parameters such as applied voltage, pH and copper concentration on the removal of copper were investigated. Response surface methodology (RSM) was applied to optimise these parameters and their interactions. Results indicated that pH has the major effect on the removal of copper, followed
... Show MoreThis study investigated three aims for the extent of effectiveness of the two systems in educational development of educators. To achieve this, statistical analysis was performed between the two groups that consisted of (26) participants of the electronic teaching method and (38) participants who underwent teaching by the conventional electronic lecture. The results indicated the effectiveness of the “electronic teaching method” and the “electronic lecture method” for learning of the participants in educational development. Also, it indicated the level of equivalence from the aspect of effectiveness of the two methods and at a confidence level of (0.05). This study reached several conclusions, recommendations, and suggestio
... Show MoreBackground: The quantity and the quality of available bone, influence the clinical success of dental implants surgery. Cone beam Computed tomography is an established method for acquiring bone images before performing dental implant. Cone beam computed tomography is an essential tool for treatment planning and post-surgical procedure monitoring, by providing highly accurate 3-D images of the patient's anatomy from a single, low-radiation scan which yields high resolution images with favorable accuracy. The aim of study is the Measurement of alveolar bone (height and buccolingual width) and density in the mandible among Iraqi adult subject using CBCT for assessment of dental implant site dimensions. Material and method: The study sample in
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