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 classifiers. A hybrid supervised learning system that takes advantage of rich intermediate features extracted from deep learning compared to traditional feature extraction to boost classification accuracy and parameters is suggested. They provide the same set of characteristics to discover and verify which classifier yields the best classification with our new proposed approach of “hybrid learning.” To achieve this, the performance of classifiers was assessed depending on a genuine dataset that was taken by our camera system. The simulation results show that the support vector machine (SVM) has a mean square error of 0.011, a total accuracy ratio of 98.80%, and an F1 score of 0.99. Moreover, the results show that the LR classifier has a mean square error of 0.035 and a total ratio of 96.42%, and an F1 score of 0.96 comes in the second place. The ANN classifier has a mean square error of 0.047 and a total ratio of 95.23%, and an F1 score of 0.94 comes in the third place. Furthermore, RF, WKNN, DT, and NB with a mean square error and an F1 score advance to the next stage with accuracy ratios of 91.66%, 90.47%, 79.76%, and 75%, respectively. As a result, the main contribution is the enhancement of the classification performance parameters with images of varying brightness and clarity using the proposed hybrid learning approach.
The blade pitch angle (BPA) controller is key factor to improve the power generation of wind turbine (WT). Due to the aerodynamic structural behavior of the rotor blades, wind turbine system performance is influenced by pitch angle and environmental conditions such as wind speed, which fluctuate throughout the day. Therefore, to overcome the pitch angle control (PAC) problem, high wind speed conditions, and due to type-1 and type-2 fuzzy logic limitations for handling high levels of uncertainty, the newly proposed optimal hybrid type-3 fuzzy logic controller has been applied and compared since type-3 fuzzy controllers utilize three-dimensional membership functions, unlike type-2 and type-1 fuzzy logic controllers. In this paper six differen
... Show MorePolycystic ovary syndrome (PCOS) referring to a syndrome that is recognized as a life-course disease and has both metabolic and reproductive signs; main pathophysiological cause includes insulin resistance, hyperandrogenism, and oxidative stress state. The study aimed to assess the impact of combining Myoinositol and Metformin, the main insulin-sensitizing drugs, on improving clinical, metabolic, and hormonal parameters in females with PCOS. A clinical trial that was prospective, randomized, and comparative on 54 patients (aged 18-40 y) are divided into three groups: group1 patients allocated to receive Myo-inositol(4g), group2 patients assigned to receive Metformin(1g) and group3 patients assigned to receive Myo-inositol(4g) + Metformin
... Show MoreThis 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
... Show MoreThe tight gas is one of the main types of the unconventional gas. Typically the tight gas reservoirs consist of highly heterogeneous low permeability reservoir. The economic evaluation for the production from tight gas production is very challenging task because of prevailing uncertainties associated with key reservoir properties, such as porosity, permeability as well as drainage boundary. However one of the important parameters requiring in this economic evaluation is the equivalent drainage area of the well, which relates the actual volume of fluids (e.g gas) produced or withdrawn from the reservoir at a certain moment that changes with time. It is difficult to predict this equival
Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp
... Show MoreA novel analytical method is developed for the determination of azithromycin. The method utilizes continuous flow injection analysis to enhance the chemiluminescence system of luminol, H2O2, and Cr(III). The method demonstrated a linear dynamic range of 0.001–100 mmol L-1 with a high correlation coefficient (r) of 0.9978, and 0.001–150 mmol L-1 with a correlation coefficient (r) of 0.9769 for the chemiluminescence emission versus azithromycin concentration. The limit of detection (L.O.D.) of the method was found to be 18.725 ng.50 µL−1 based on the stepwise dilution method for the lowest concentration within the linear dynamic range of the calibration graph. The relative standard deviation (R.S.D. %) for n = 6 was less than 1.2%
... Show MoreThe research includes the preparation of a new Schiff base(4-methyl-2-((2-phenyl hydrazineylidene)methyl)naphthalen-1-ol), which was subsequently, used to prepare a series of complexes using chlorides of Mn2+, Co2+, Cu2+, Cr3+, and Fe3+ ions. The synthesized compounds were characterized using various techniques such as elemental microanalysis (C.H.N), chloride content determination using Mohr’s method, FT-IR spectroscopy, UV-Visible, mass spectra, conductivity, DSC (Differential Scanning Calorimetry), and thermogravimetric analysis. Overall, the decay of the ligand and its metal complexes was recorded to determine their thermal stability and weight-loss profiles. The results indicated that ligand acts as a bidentate doner, coordinating wi
... Show MoreThis growing interest of the international scientific specialized commissions is due to the role that the audit committee can play, as one of companies’ governance tools, to increase the accuracy and transparency of the financial information disclosed by the companies, through its oversight role on the process of preparing financial reports, its supervision on the internal audit function within the companies, and supporting its independency, as well as coordinating the efforts between the internal control unites and the external auditor represented by the (Board of Supreme Audit) to clear the observations and irregularities in order to reduce the fraud cases.
This research was built on an applied sample of audit committee works
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