To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multiple RS datasets to overcome limitations and produce comparatively detailed outcomes. However, there are still knowledge gaps in examining the effectiveness of these RS approaches in enhancing the detection of archaeological remains/areas. Thus, this review paper is likely to deliver valuable comprehension for archaeological studies to fill knowledge gaps and further advance exploration of archaeological areas/features using RS along with DL approaches.
In the current paradigms of information technology, cloud computing is the most essential kind of computer service. It satisfies the need for high-volume customers, flexible computing capabilities for a range of applications like as database archiving and business analytics, and the requirement for extra computer resources to provide a financial value for cloud providers. The purpose of this investigation is to assess the viability of doing data audits remotely inside a cloud computing setting. There includes discussion of the theory behind cloud computing and distributed storage systems, as well as the method of remote data auditing. In this research, it is mentioned to safeguard the data that is outsourced and stored in cloud serv
... Show MoreFace recognition, emotion recognition represent the important bases for the human machine interaction. To recognize the person’s emotion and face, different algorithms are developed and tested. In this paper, an enhancement face and emotion recognition algorithm is implemented based on deep learning neural networks. Universal database and personal image had been used to test the proposed algorithm. Python language programming had been used to implement the proposed algorithm.
Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id
... Show MoreThere is a natural problem raised by the issue of media performance. As a separate activity and express its own capabilities. This problem can be framed in the form of a question: Is media performance merely a reflection of the activity of other sectors of society, especially political and economic, and what links them to other societal sectors of interrelated relations? Is the media limited to mere transfer, or is it an industry with its own mechanisms and rules? The answer may seem somewhat complicated if we handle media with research and study in general, but the issue may be less complicated when it comes to Arab media, because its data may add another setback to the overall Arab setbacks.
The present study was conducted to investigate the relationship between critical thinking, epistemological beliefs, and learning strategies with the academic performance of high school first-grade male and female students in Yazd. For this purpose, from among all first-grade students, as many as 250 students (130 females and 120 males) were selected by using multistage cluster sampling. The data needed were then collected through using California Critical Thinking Skills Test, Schommer's Epistemological Beliefs Questionnaire, Biggs’ Revised Two Factor Study Process Questionnaire. The findings indicated that there is a positive significant relationship between critical thinking and academic performance and achievement. Moreover, four fa
... Show MoreThe Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone
... Show MoreClinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b