Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

The ultimate goal of any sale contract is to maximize the combined returns of the parties, knowing that these returns are not realized (in long-term contracts) except in the final stages of the contract. Therefore, this requires the parties to the contract to leave some elements open, including the price, because the adoption of a fixed price and inflexible will not be appropriate to meet their desires when contracting, especially with ignorance of matters beyond their will and may affect the market conditions, and the possibility of modifying the fixed price through The elimination is very limited, especially when the parties to the contract are equally in terms of economic strength. Hence, in order to respond to market uncertainties, the

n this study, 25 clinical isolates of Proteus spp. were collected from urine, wounds and burns specimens from different hospitals in Baghdad city, all isolates were identified by using different bacteriological media, biochemical assays and Vitek-2 system. It was found that 15 (60%) isolates were identifies as Proteus mirabilis and 10 (40 %) isolates were Proteus vulgaris. The susceptibility of P. mirabilis and P. vulgaris isolates towards cefotaxime was (66.6 %) and (44.4 %) respectively; while the susceptibility of P. mirabilis and P. vulgaris isolates towards ceftazidime was (20%). Extended spectrum β-lactamses producing Proteus was (30.7 %). DNA of 10 isolates of P. mirabilis and 4 isolates of P. vulgaris were extracted and de

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa

In this study, we have created a new Arabic dataset annotated according to Ekman’s basic emotions (Anger, Disgust, Fear, Happiness, Sadness and Surprise). This dataset is composed from Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. Then we explored six different supervised machine learning methods to test the new dataset. We used Weka standard classifiers ZeroR, J48, Naïve Bayes, Multinomial Naïve Bayes for Text, and SMO. We also used a further compression-based classifier called PPM not included in Weka. Our study reveals that the PPM classifier significantly outperforms other classifiers such as SVM and N

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

The article characterizes metaphors and comparisons as a means of rethinking the phenomena of the surrounding world; two directions are named within which metaphors and comparisons are studied: 1) the linguo-classification direction combines several classifications of metaphors and comparisons, which are basically focused on comparing two denotations through the meanings of word forms that are part of the trope; 2) the theoretical and conceptual direction is associated with the concept of conceptual displacement and is the basis of the theoretical and methodological study of metaphor and comparison, i.e. the modern theory of metaphor is being formed; The main attention is focused on the comparative concept of Aristotle, which postul

In this study, the thermal buckling behavior of composite laminate plates cross-ply and angle-ply all edged simply supported subjected to a uniform temperature field is investigated, using a simple trigonometric shear deformation theory. Four unknown variables are involved in the theory, and satisfied the zero traction boundary condition on the surface without using shear correction factors, Hamilton's principle is used to derive equations of  motion depending on a Simple Four Variable Plate Theory for cross-ply and angle-ply, and then solved through Navier's double trigonometric sequence, to obtain critical buckling temperature for laminated composite plates. Effect of changing some design parameters such as, ortho

Globally, buildings use about 40% of energy. Many elements, such as the physical properties of the structure, the efficiency of the cooling and heating systems, the activity of the occupants, and the building’s sustainability, affect the energy consumption of a building. It is really difficult to predict how much energy a building will need. To improve the building’s sustainability and create sustainable energy sources to reduce carbon dioxide emissions from fossil fuel combustion, estimating the building's energy use is necessary. This paper explains the energy consumed in the lecture building of the Al-Khwarizmi College of Engineering, University of Baghdad (UOB), Baghdad, Iraq. The weather data and the building construction informati