Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.

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 class

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

The problem of the research is focused on importance limited of Iraq industrial companies in application of scientific measurements of supply chains performance,  The research sought to achieve a group of goals, the most important are , identifying the strengths and weaknesses in the reality of supply chain in General Company for Cotton Industries, The data and information required are gathered from the dependence company, records through the field observations and personal interviews, the research used some quantitative indicators to measure of supply chain performance, The research reached to many conclusions , the most outstanding among them is the existence of a strong inverse correlatio

In this study, the preparation and characterization of hyacinth plant /chitosan composite, as a heavy metal removal, were done. Water hyacinth plant (Eichhorniacrasspes) was collected from Tigris river in Baghdad. The root and shoot parts of plant were ground to powder. Composite materials were prepared at different ratios of plant part (from 2.9% to 30.3%, wt /wt) which corresponds to (30-500mg) of hyacinth plant (root and shoot) and chitosan. The results showed that all examined ratios of plant parts have an excellent absorption to copper (Cu (II)). Moreover, it was observed that 2.9% corresponds (30mg) of plant root revealed highest removal (82.7%) of Pb (II), while 20.23% of shoot removed 61% of Cd (II) within 24 hr

The research aims to identify the extent to which Iraqi private banks practice profit management motivated by reducing the taxable base by increasing the provision for loan losses by relying on the LLP it model, which consists of a main independent variable (net profit before tax) and independent sub-variables (bank size, total debts to total equity, loans granted to total obligations) under the name of the variables governing the banking business. (Colmgrove-Smirnov) was used to test the normal distribution of data for all banks during the period 2017-2020, and then find the correlation between the main independent variable sub and the dependent variable by means of the correlation coefficient person, and then using the multiple

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a