Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-ba

Diabetes is a disease caused by high sugar levels. Currently, diabetes is one of the most common diseases in the number of people with diabetes worldwide. The increase in diabetes is caused by the delay in establishing the diagnosis of the disease. Therefore, an initial action is needed as a solution that requires the most appropriate and accurate data mining to manage diabetes mellitus. The algorithms used are artificial neural network algorithms, namely Restricted Boltzmann Machine and Backpropagation. This research aims to compare the two algorithms to find which algorithm can produce high accuracy, and determine which algorithm is more accurate in detecting diabetes mellitus. Several stages were involved in this research, including d

     The healthcare sector has traditionally been an early adopter of technological progress, gaining significant advantages, particularly in machine learning applications such as disease prediction. One of the most important diseases is stroke. Early detection of a brain stroke is exceptionally critical to saving human lives. A brain stroke is a condition that happens when the blood flow to the brain is disturbed or reduced, leading brain cells to die and resulting in impairment or death. Furthermore, the World Health Organization (WHO) classifies brain stroke as the world's second-deadliest disease. Brain stroke is still an essential factor in the healthcare sector. Controlling the risk of a brain stroke is important for the surviv

The aim of this study was to identify the effectiveness of using generative learning model in learning kinetic series on rings and horizontal bar in artistic gymnastics for men ,Also, the two groups were better in learning the two series of movements on the rings and horizontal bar . The experimental method was used to design two parallel groups with pretested and posttest .The sample included third graders at the College of Physical Education and Sports Sciences - University of Baghdad ,The third class (d) was chosen to represent the control group that applied the curriculum in the college, with (12) students per group. After conducting the tribal tests, the main experiment was carried out for (8) weeks at the rate of two units per week di

Background: Colorectal carcinoma is common in Northwest Europe, North America, and other Anglo-Saxon areas, while it decreases in number in Africa, Asia, and some parts of South America, There are many immunohistochemical markers react to colonic tissue, the large majority of colorectal carcinomas are positive for mucin stains. Colorectal adenocarcinomas are invariably positive for cytokeratin (CK), Reactivity for CEA is also the rule; as a matter of fact, failure to detect CEA in an adenocarcinoma of makes a colo-rectal site of origin seems to be unlikely, and many other markers that could claimed in colorectal tumors, a one marker that may has a role in staining colorectal tumors is HepPar-1 which is a monoclonal antibody that reacts t

     Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers.  The experiment’s

Background: the morphological features of the brest undergoes substantial change between early adolescene and menopause which may result in fibrocystic changes.

Background: Toll-like receptor -2 (TLR-2)play important roles in tumor biology; by activation and promotion of tumor cell proliferation, resistance to apoptosis andalso, enhancement of tumor cell invasion and metastasis by regulating metalloproteinase and integrin’s.As toll-like receptors are widely expressed on tumor cells and participatein the initiation and progression of cancer, they may thus serve an important target and have an effective perspective on breast cancer treatment.
Objectives:The aims of the present study was to determine the levels of TLR-2 in the sera of healthy people and patients with benign and malignant breast tumors and also to investigate the validity of using TLR-2 as specific diagnostic markers of breast

Growth of Penicillium expansum, an ubiquitous mould found in stored fruit globallyt, was significantly restricted by exposure to 48 h cell-free supernatant of two strains of Lactobacillus plantarum (p < 0.001). In addition, the biotransformation of patulin, a toxic secondary metabolite formed by P. expansum, on exposure to L. plantarum cells and cell-free supernatant highlights the potential of this GRAS microbe as a biocontrol agent. Up to 80% of patulin was biotransformed following a 4 h incubation with 1010 cells ml−1 (37 °C) forming E- and Z-ascladiol. The formation of these products was more pronounced at elevated pH and cell density. Exposure to cell free supernatant or sonicated cells resulted in complete patulin biotransformation

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select