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 necessities of steganography methods for hiding secret message into images have been ascend. Thereby, this study is to generate a practical steganography procedure to hide text into image. This operation allows the user to provide the system with both text and cover image, and to find a resulting image that comprises the hidden text inside. The suggested technique is to hide a text inside the header formats of a digital image. Least Significant Bit (LSB) method to hide the message or text, in order to keep the features and characteristics of the original image are used. A new method is applied via using the whole image (header formats) to hide the image. From the experimental results, suggested technique that gives a higher embe
... Show MoreThe proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.
In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete
... Show MoreIn current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi
... Show MoreProblem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
... Show MoreThe emergence of COVID-19 has resulted in an unprecedented escalation in different aspects of human activities, including medical education. Students and educators across academic institutions have confronted various challenges in following the guidelines of protection against the disease on one hand and accomplishing learning curricula on the other hand. In this short view, we presented our experience in implementing e-learning to the undergraduate nursing students during the present COVID-19 pandemic emphasizing the learning content, barriers, and feedback of students and educators. We hope that this view will trigger the preparedness of nursing faculties in Iraq to deal with this new modality of learning and improve it should t
... Show MoreThis study aimed to determine the optimal conditions for extracting basil seed gum in addition to determine the chemical components of basil seeds. Additionally, the study aimed to investigate the effect of the mixing ratio of gum to ethanol when deposited on the basis of the gum yield which was1:1, 1:2, 1:3 (v/v) respectively. The best mixing ratio was one size of gum to two sizes of ethanol, which recorded the highest yield. Based on the earlier, the optimal conditions for extracting basil seed gum in different levels which included pH, temperature, mixing ratio seeds: water and the soaking duration were studied. The optimal conditions were: pH 8, temperature of 60°C, mixing ratio seeds: water 1:65 (w/v) and soaking duration of 30 min
... Show MoreHuman skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measu
... Show MoreThis paper deals with constructing mixed probability distribution from mixing exponential
Coronavirus disease (COVID-19) is an acute disease that affects the respiratory system which initially appeared in Wuhan, China. In Feb 2019 the sickness began to spread swiftly throughout the entire planet, causing significant health, social, and economic problems. Time series is an important statistical method used to study and analyze a particular phenomenon, identify its pattern and factors, and use it to predict future values. The main focus of the research is to shed light on the study of SARIMA, NARNN, and hybrid models, expecting that the series comprises both linear and non-linear compounds, and that the ARIMA model can deal with the linear component and the NARNN model can deal with the non-linear component. The models
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