Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E

Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

Urinary tract infections (UTIs) mean microbial pathogens in the urethra or bladder (lower urinary tract). Important risk factors for recurrent UTI include obstruction of the urinary tract, use of a bladder catheter or a suppressed immune system. This study aims to isolate and identify bacteria from patients with TCC-bladder cancer or patients with a negative cystoscope and estimate antibiotic susceptibility patterns and evaluate some of the virulence factors. From a total of 62 patients with TCC-BC or negative cystoscope, only 35 favorable bacterial growths were obtained, including Escherichia coli (UPEC), a significant bacterial isolate, and Stenotrophomonas maltophilia. The percentage of multi drug-resistance bacteria

To determine the important pathogenic role of celiac disease in triggering several autoimmune disease, thirty patients with Multiple Sclerosis of ages (22-55) years have been investigated and compared with 25 healthy individuals. All the studied groups were carried out to measure anti-tissue transglutaminase antibodies IgA IgG by ELISA test, anti-reticulin antibodies IgA and IgG, and anti-endomysial antibodies IgA and IgG by IFAT. There was a significant elevation in the concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to control groups (P≤0.05), there was 4(13.33%) positive results for anti-reticulin antibodies IgA and IgG , 3(10%) positive results for anti-endomysial antibodies IgA and IgG . There were 4 pos

To determine the important pathogenic role of celiac disease in triggering several autoimmune disease, thirty patients with Multiple Sclerosis of ages (22-55) years have been investigated and compared with 25 healthy individuals. All the studied groups were carried out to measure anti-tissue transglutaminase antibodies IgA IgG by ELISA test, anti-reticulin antibodies IgA and IgG, and anti-endomysial antibodies IgA and IgG by IFAT. There was a significant elevation in the concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to control groups (P≤0.05), there was 4(13.33%) positive results for anti-reticulin antibodies IgA and IgG , 3(10%) positive results for anti-endomysial antibodies IgA and IgG . There were 4 pos

G-system composed of three isolates G3 ( Bacillus),G12 ( Arthrobacter )and G27 ( Brevibacterium) was used to detect the mutagenicity of the anticancer drug, cyclophosphamide (CP) under conditions similar to that used for standard mutagen, Nitrosoguanidine (NTG). The CP effected the survival fraction of isolates after treatment for 15 mins using gradual increasing concentrations, but at less extent comparing to NTG. The mutagenic effect of CP was at higher level than that of NTG when using streptomycin as a genetic marker, but the situation was reversed when using rifampicin resistant as a report marker. The latter effect appeared upon recording the mutagen efficiency (ie., number of induced mutants/microgram of mutagen). Measuring the R