Seroprevalence and Molecular Detection of Influenza A Virus (H1N1) in Sulaimani Governorate-Iraq

Influenza A (H1N1) virus is now rapidly scattering across the world. Early detection is one of the most effective measures to stop the further spread of the virus. The current study was aimed to detect influenza A (H1N1) serologically and by polymerase chain reaction (PCR) techniques. From September 2020 to June 2021, three hundred nasopharyngeal swabs and blood samples were collected from Hiwa and Shahid Tahir Hospitals in Sulaimani city. Obtained results revealed that 23.3% of the tested patients were seropositive anti-IgG for Influenza A, while 13.3% showed anti-IgM seropositive results although 10% of the tested cases were with both anti-IgG and anti-IgM seropositive results. Gender, residency, and flu symptoms showed no significant relations with seropositive results (p<0.05) whereas valuable relations were found between seropositive observations and smoking, the previous history of chronic diseases as well as employment status (p<0.05). It was concluded that hematologic investigations (CBC) were not dependable if H1N1 diagnosis and detection. Only 1% of the tested samples showed positive results for influenza A (H1N1) RNA using reverse transcription-PCR.

Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Thoracic surgery refers to the information gathered for the patients who have to suffer from lung cancer. Various machine learning techniques were employed in post-operative life expectancy to predict lung cancer patients. In this study, we have used the most famous and influential supervised machine learning algorithms, which are J48, Naïve Bayes, Multilayer Perceptron, and Random Forest (RF). Then, two ranker feature selections, information gain and gain ratio, were used on the thoracic surgery dataset to examine and explore the effect of used ranker feature selections on the machine learning classifiers. The dataset was collected from the Wroclaw University in UCI repository website. We have done two experiments to show the performances of the supervised classifiers on the dataset with and without employing the ranker feature selection. The obtained results with the ranker feature selections showed that J48, NB, and MLP’s accuracy improved, whereas RF accuracy decreased and support vector machine remained stable.

Characterization of European Medieval Silver Bars Using Micro X-ray Fluorescence, Conductivity Meter and Scanning Electron Microscopy

The objective of this paper is to use μ-X-ray fluorescence (XRF) analysis to evaluate the fineness and components of European Medieval Silver Bars samples. Conductivity measurements were used to assess the fineness and localization of the faults found in the samples. Because unevenness causes a change in conductivity, the tests were performed on the flattest areas of the Bars. Some rods, such as B3 and B9, have greater conductivity than others. All bars were subjected to the segregation test. In the instance of certain bars, it was not always practicable to categorically state that segregation had happened. There is no diminishing conductivity curve as one moves away from the zero height, as there is for bars B1, B8, and B9. As a result, there may be no solidification on these bars from Obverse to Reverse. A scanning electron microscope was used to record the following bars at various positions on the bars, and quantitative determinations were achieved using energy-dispersed XRF analysis through intensity measurements of the element-specific wavelength.

Knowledge Management Functions Applied in Jordanian Industrial Companies: Study the Impact of Regulatory Overload

This research aims to study the impact of electronic information overload on knowledge management functions in Jordanian industrial companies. The research population included all Jordanian industrial companies listed on the Amman Stock Exchange. A simple random sample of 30% of the research population of 1242 seniors and middle managers in the research population was done to 373 individuals. 206 questionnaires are successfully retrieved to be analyzed. Descriptive and heuristic statistical methods such as simple and multiple regression analysis were applied using SPSS.16 program. The obtained result indicated that there is a statistically significant impact of the electronic information overload (organizational overload) on the knowledge management functions (acquisition, generation, transmission, sharing, and application of knowledge) in Jordanian industrial companies. In the scope of the results, this work made a number of recommendations, including: Adopting an organizational aspect that suits the nature of the tasks that the industrial companies operate in Jordan, in addition to providing technical capabilities to reduce the electronic information overload faced by the industrial companies in Jordan while practicing their tasks.

A Review of Database Security Concepts, Risks, and Problems

Currently, data production is as quick as possible; however, databases are collections of well-organized data that can be accessed, maintained, and updated quickly. Database systems are critical to your company because they convey data about sales transactions, product inventories, customer profiles, and marketing activities. To accomplish data manipulation and maintenance activities the Database Management System considered. Databases differ because their conclusions based on countless rules about what an invulnerable database constitutes. As a result, database protection seekers encounter difficulties in terms of a fantastic figure selection to maintain their database security. The main goal of this study is to identify the risk and how we can secure databases, encrypt sensitive data, modify system databases, and update database systems, as well as to evaluate some of the methods to handle these problems in security databases. However, because information plays such an important role in any organization, understanding the security risk and preventing it from occurring in any database system require a high level of knowledge. As a result, through this paper, all necessary information for any organization has been explained; in addition, also a new technological tool that plays an essential role in database security was discussed.

Face Recognition Use Local Image Dataset and Correlation Technique

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

Face Recognition Use Local Image Dataset and Correlation Technique

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

Comparison of Different Ensemble Methods in Credit Card Default Prediction

Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance

Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

The Luminosity Function of Galaxies in Some Nearby Clusters

In the present work, the galaxy luminosity function (LF) has been studied for a sample of seven clusters in the redshift range (0.0 ≲ z ≲ 0.1), within Abell radius (1.5 h−1 Mpc), in the five SDSS passbands ugriz. In each case, the absolute magnitude distribution is found and then fitted with a Schechter function. The fitting is done, using the χ2 – minimization method to find the best values of Schechter parameters Ф* (normalization constant), M* (characteristic absolute magnitude), and α (faint-end slope). No remarkable changes are found in the values of M* and α, for any cluster, in any passband. Furthermore, the LF does not seem to vary with such cluster parameters as richness, velocity dispersion, and Bautz–Morgan morphology. Finally, it is found that M* becomes brighter toward redder bands, whereas almost no variation is seen in the value of α with passband, being around (−1.00).