Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks

Introduction of modern and diverse applications in telecommunication field has raised challenges in networking area regarding efficient use of network resources and with optimizing performance. Therefore MPLS/GMPLS (Generalized multiprotocol label switching) networks were introduced to provide a better quality of service to meet users’ requirements as well as to optimize network resources. GMPLS networks use traffic engineering techniques for more efficient communication within the network and help to optimize network resources. This paper proposes BAT inspired metaheuristic algorithm for selecting an efficient route in MPLS/ GMPLS networks. In our investigation we considered routing costs as an objective function with goal to minimize it. The paper uses BAT algorithm with various levels of loudness parameter. The simulation results show performance improvements in MPLS/GMPLS networks of different size

Detection and localization enhancement for satellite images with small forgeries using modified GAN-based CNN structure

The image forgery process can be simply defined as inserting some objects of different sizes to vanish some structures or scenes. Satellite images can be forged in many ways, such as copy-paste, copy-move, and splicing processes. Recent approaches present a generative adversarial network (GAN) as an effective method for identifying the presence of spliced forgeries and identifying their locations with a higher detection accuracy of large- and medium-sized forgeries. However, such recent approaches clearly show limited detection accuracy of small-sized forgeries. Accordingly, the localization step of such small-sized forgeries is negatively impacted. In this paper, two different approaches for detecting and localizing small-sized forgeries in satellite images are proposed. The first approach is inspired by a recently presented GAN-based approach and is modified to an enhanced version. The experimental results manifest that the detection accuracy of the first proposed approach noticeably increased to 86% compared to its inspiring one with 79% for the small-sized forgeries. Whereas, the second proposed approach uses a different design of a CNN-based discriminator to significantly enhance the detection accuracy to 94%, using the same dataset obtained from NASA and the US Geological Survey (USGS) for validation and testing. Furthermore, the results show a comparable detection accuracy in large- and medium-sized forgeries using the two proposed approaches compared to the competing ones. This study can be applied in the forensic field, with clear discrimination between the forged and pristine images

Energy-aware sink node localization algorithm for wireless sensor networks

Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime.Web of Scienceart. ID 81035

Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization

Multiprotocol label switched (MPLS) networks were introduced to enhance the network`s service provisioning and optimize its performance using multiple protocols along with label switched based networking technique. With the addition of traffic engineering entity in MPLS domain, there is a massive increase in the networks resource management capability with better quality of services (QoS) provisioning for end users. Routing protocols play an important role in MPLS networks for network traffic management, which uses exact and approximate algorithms. There are number of artificial intelligence-based optimization algorithms which can be used for the optimization of traffic engineering in MPLS networks. The paper presents an optimization model for MPLS networks and proposed dolphin-echolocation algorithm (DEA) for optimal path computation. For Network with different nodes, both algorithms performance has been investigated to study their convergence towards the production of optimal solutions. Furthermore, the DEA algorithm will be compared with the bat algorithm to examine their performance in MPLS network optimization. Various parameters such as mean, minimum /optimal fitness function values and standard deviation

Pareto based bat algorithm for multi objectives multiple constraints optimization in GMPLS networks

Modern communication networks offer advance and diverse applications, which require huge usage of network resources while providing quality of services to the users. Advance communication is based on multiple switched networks that cannot be handle by traditional IP (internet protocol) networks. GMPLS (Generalized multiprotocol label switched) networks, an advance version of MPLS (multiprotocol label switched networks), are introduced for multiple switched networks. Traffic engineering in GMPLS networks ensures traffic movement on multiple paths. Optimal path(s) computation can be dependent on multiple objectives with multiple constraints. From optimization prospective, it is an NP (non-deterministic polynomial-time) hard optimization problem, to compute optimal paths based on multiple objectives having multiple constraints. The paper proposed a metaheuristic Pareto based Bat algorithm, which uses two objective functions; routing costs and load balancing costs to compute the optimal path(s) as an optimal solution for traffic engineering in MPLS/GMPLS networks. The proposed algorithm has implemented on different number of nodes in MPLS/GMPLS networks, to analysis the algorithm performance

Sagitol C, a new cytotoxic pyridoacridine alkaloid from the sponge Oceanapia sp.

AbstractA new pyridoacridine alkaloid named sagitol C (2) together with two known compounds; kuanoniamine C (1) and sagitol (3) were isolated from the EtOAc fraction of the Indonesian sponge Oceanapia sp. Their chemical structures were established on the basis of physical and spectroscopic methods 1D and 2D NMR, in addition to mass spectrometry and comparison with literature data. Sagitol C was found to exhibit cytotoxic activity when tested against different cancer cell lines

Energy efficient software defined networking algorithm for wireless sensor networks

The real-time properties and operational constraints of Wireless Sensor Networks (WSNs) have emerged the need for designing energy efficient routing protocols. Recently, software defined network based WSN (SDN-WSN) emerging technology has offered a significant development by untying control logic plane from the low power sensor nodes. This centralized programmable control still suffers from several configuration challenges in distributed sensors environment. Meta-heuristic based SDN approaches had been proposed for the efficient path selection in WSN but they still suffer from both, exploration and exploitation problems. Therefore, this paper addresses these shortcomings by proposing a meta-heuristic based dolphin echolocation algorithm (DEA) for optimizing route selection in WSNs. Objective function of the DEA algorithm is to consider the residual energy of the nodes for selecting energy efficient routes. The proposed algorithm performance is compared with several meta-heuristic algorithms in terms of energy-consumption, and network throughput parameters

A novel MapReduce Lift association rule mining algorithm (MRLAR) for Big Data

Big Data mining is an analytic process used to dis-cover the hidden knowledge and patterns from a massive, com-plex, and multi-dimensional dataset. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance ineffective. Recently, there has been renewed inter-est in using association rule mining (ARM) in Big Data to uncov-er relationships between what seems to be unrelated. However, the traditional discovery ARM techniques are unable to handle this huge amount of data. Therefore, there is a vital need to scal-able and parallel strategies for ARM based on Big Data ap-proaches. This paper develops a novel MapReduce framework for an association rule algorithm based on Lift interestingness measurement (MRLAR) which can handle massive datasets with a large number of nodes. The experimental result shows the effi-ciency of the proposed algorithm to measure the correlations between itemsets through integrating the uses of MapReduce and LIM instead of depending on confidence.Web of Science7315715

Role of nanoparticles in diagnosis and management of parasitic diseases: Review article

Background: An extensive class of materials, nanoparticles (NPs) include particulate compounds with a minimum diameter of 100 nanometers (nm). This is because of their tiny size and huge surface area, which allows them to traverse the blood-brain barrier, enter the respiratory system and be adsorbable through endothelial cells. Today, nanoparticles for drug administration are being studied to increase their sustained release, intracellular penetrability as well as bioavailability, due to the constant development and innovation of nanomedicine.Objective: To determine how nanoparticles can help diagnose and treat parasitic diseases.Conclusion: Nanoparticles could be conjugated with proteins and immunoglobulins that could help in specific diagnosis of several parasitic diseases, in addition, improved efficacy and reduced harmful side effects can be achieved by immobilizing antiparasitic medicines on or inside nanomaterials

A modified whale optimization algorithm for enhancing the features selection process in machine learning

In recent years, when there is an abundance of large datasets in various fields, the importance of feature selection problem has become critical for researchers. The real world applications rely on large datasets, which implies that datasets have hundreds of instances and attributes. Finding a better way of optimum feature selection could significantly improve the machine learning predictions. Recently, metaheuristics have gained momentous popularity for solving feature selection problem. Whale Optimization Algorithm has gained significant attention by the researcher community searching to solve the feature selection problem. However, the exploration problem in whale optimization algorithm still exists and remains to be researched as various parameters within the whale algorithm have been ignored and not introduced into machine learning models. This paper proposes a new and improved version of the whale algorithm entitled Modified Whale Optimization Algorithm (MWOA) that hybrid with the machine learning models such as logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine, naïve Bayes model. To test this new approach and the performance, the breast cancer datasets were used for MWOA evaluation. The test results revealed the superiority of this model when compared to the results obtained by machine learning models