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 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 archaeolog
... Show More<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. T
... Show MoreRegarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
... Show MoreThe deliberative system of communicative studies is exposed to cultural openness, according to modern cultural studies, to establish a verbal language system that achieves reciprocal and cross-cultural relations. This research has examined the concept of deliberative approach in the contemporary Iraqi theater text by studying the deliberative system of text and its interrelationship with The stage of the verbal embodiment of the dramatic event, because the dramatic text achieves in its construction of racism, a deliberative approach between the verbal event and the language, and what later leads to the completion of the dramatic action. The research included four chapters. The first chapter was the systematic framework of the research, T
... Show MoreThis study includes adescription of Human serum Albumin by amodified using ion- exchange chromatography with manipulated comparison with cold ethanol precipitation method , It has been nticed that this procedure is superior orer the classical method . The Final yield by the new method 69.32% with purity of 83.42% compared with cohn which yield 60.30 % with purity of 80.7 % . The new method prored that it suitable for the pusi Fication of such material because it yield no precipitation material and it increases the Final yield of albumin solutions . • Human serum Albumin . • Albumin purification . • Ion – exchange chromatography . • Human plasma . • Albumin extraction .
This study investigates the treatment of used lubricating oils from AL-Mussaib Gas Power Station Company-Iraq, which was treated with different extractive solvents (heptane and 2-propanol). The performance activity of these solvents in the extraction process was examined and evaluated experimentally. Operating parameters were solvent to oil ratios of (1:2, 1:4, 1:6, and 1:8), mixing time (20, 35, 50, and 65 min), temperatures (30, 40, 50, and 60 ºC), and mixing speed (500 rpm). These parameters were studied and analyzed. The quality is determined by the measuring and assessment of important characteristics specially viscosity, viscosity index, specific gravity, pour point, flash point, and ash content. The results confirm that the
... Show MorePolyphenol oxidase (PPO) is an enzyme containing copper, presents in various fruits and vegetables. It is responsible for the browning reactions when the cells are damaged during handling. The best conditions for extraction of polyphenol oxidase from banana peel was by using an extraction buffer containing phosphate buffer (0.05 M, pH 7), 0.01 M ascorbic acid and 0.5% polyethylene glycol, with extraction ratio 1:4 (w:v) for one minute by using blender. The enzyme activity was measured spectrophotometrically at 425 nm. PPO was studied to prevent the browning of banana peel which results in the loss of their marketability. The aim of this study was to determine the optimum conditions for polyphenol oxidase extraction from banana peel.