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

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

In this paper, various aspects of smart grids are described. These aspects include the components of smart grids, the detailed functions of the smart energy meters within the smart grids and their effects on increasing the awareness, the advantages and disadvantages of smart grids, and the requirements of utilizing smart grids. To put some light on the difference between smart grids and traditional utility grids, some aspects of the traditional utility grids are covered in this paper as well.

A recurrent condition that affects up to 10% of people worldwide is gastric ulceration illness. The existence of gastric juice pH with the lowering of mucous defences is prerequisites for the development of chronic ulcers. The main variables affecting the mucosa susceptibility to damage include Helicobacter pylori (H. pylori) infections or non-steroidal anti-inflammatory medicines (NSAIDs). Proton pump inhibitors (PPIs) including histamine-2 (H2) receptor inhibitors, two common therapies for peptic ulcers, have been linked to side impacts, recurrence or a variety of pharmacological combinations. Conversely, therapeutic herbs or the chemicals they contain may be used to cure or eliminate a wide range of illnesses. Therefore, prominent pharma

CD40 is a type 1 transmembrane protein composed of 277 amino acids, and it belongs to the tumor necrosis factor receptor (TNFR) superfamily. It is expressed in a variety of cell types, including normal B cells, macrophages, dendritic cells, and endothelial cells, as a costimulatory molecule. This study aims to summarize the CD40 polymorphism effect and its susceptibility to immune-related disorders. The CD40 gene polymorphisms showed a significant association with different immune-related disorders and act as a risk factor for increased susceptibility to these diseases.

Introduction. The coexistence of non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is commonly observed, primarily due to overlapping risk factors like smoking and environmental exposures. This dual diagnosis introduces complex clinical scenarios, often associated with worsened outcomes and heightened vulnerability to treatment-related side effects. Immune checkpoint inhibitors, especially atezolizumab, have emerged as pivotal agents in enhancing clinical outcomes for individuals diagnosed with NSCLC. Recent evidence suggests that atezolizumab remains effective and well-tolerated in NSCLC patients with coexisting COPD. This review evaluates the efficacy and safety of atezolizumab in NSCLC patients with coex

Self-repairing technology based on micro-capsules is an efficient solution for repairing cracked cementitious composites. Self-repairing based on microcapsules begins with the occurrence of cracks and develops by releasing self-repairing factors in the cracks located in concrete. Based on previous comprehensive studies, this paper provides an overview of various repairing factors and investigative methodologies. There has recently been a lack of consensus on the most efficient criteria for assessing self-repairing based on microcapsules and the smart solutions for improving capsule survival ratios during mixing. The most commonly utilized self-repairing efficiency assessment indicators are mechanical resistance and durab