Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject

Association rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Objective: To assess the role of tumour necrosis factor alpha level and genotyping in susceptibility to leishmaniasis.Method: The case-control study was conducted from March to July 2021 at Baqubah Teaching Hospital, Diyala, Iraq,and comprised patients of cutaneous leishmaniasis in group A and healthy controls in group B. The serum level andsingle nucleotide polymorphisms of tumour necrosis factor-alpha rs41297589 and rs1800629 were compared betweenthe groups. Data was analysed using SPSS 28.Results: Of the 150 subjects, there were 75(50%) in group A; 39(52%) males and 36(48%) females with mean age23.91±13.14 years. The remaining 75(50%) subjects were in group B; 38(50.7%) males and 37(49.3%) females withmean age 22.84±4.35 years.

Aim of the present study is Identification of specific gene for GPCR using specific primers .and identification of difference in PCR analysis in patients with heart thrombosis and compared with healthy, Sequencing of PCR product regarding GPCR compared for all three subject, Identification the similarity of human GPCR with local strain of yeast fifty healthy control and fifty patients with thrombosis which diagnosed medically with cardiac specific troponin t, troponin 1 levels and electro myocardiogram ECG. The aged for all subjects ranged (39-75) years patients were lying in cardiac care unit at Ibn- al- Nafees teaching hospital and Sheikh Zayed teaching hospital. Genomic DNA of whole blood was extracted from buffy coat and cell cu

Background: Multiple sclerosis (MS) is a chronic neurodegenerative autoimmune disease mediated by autoreactive T cells against myelin-basic proteins. Cytokines are suggested to play a role in the etiopathogenesis of the disease. Among these cytokines is interleukin-2 (IL-2). Aim of the study: To investigate the association between IL2+166 G/T single nucleotide polymorphism (SNP: rs2069763) and MS in Iraqi patients. Serum level of IL-2 was also detected. Anti-rubella IgG antibody was further determined in the sera of patients. Patients and methods: Eighty MS patients (28 males and 52 females; age mean ± SD: 39.2 ± 16.1 years) and 80 healthy control matched patients for age (32.15 ± 16.13 years) and gender (28 males and 52 females) were en

Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization

Bigheaded carps (bighead carp, Hypophthalmichthys molitrix, and silver carp, Hypophthalmichthys nobilis) and their hybrids play an important ecological and economic role in their original habitat, while their introduced stocks may pose serious ecological risks. To address questions about the persistence and invasiveness of these fish, we need to better understand their population structures. The genetic structures of bigheaded carp populations inhabiting Lake Balaton and the Tisza River were examined with ten microsatellite markers and a mitochondrial DNA marker (COI). The Lake Balaton stock showed higher genetic diversity compared with the Tisza River stock. Based on hierarchical clustering, the Tisza population was characterized o