Metaheuristic Optimization of Time Series Models for Predicting NetworksTraffic

Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm is employed to optimize the hyper-parameters of long short-term memory (LSTM) network as an efficient time series modeling approach which is widely used in sequence prediction tasks. To prove the superiority of the proposed algorithm, four other optimization algorithms were employed to optimize LSTM, and the results were compared. The evaluation results confirmed the effectiveness of the proposed approach in predicting the traffic of wireless networks accurately. On the other hand, a statistical analysis is performed to emphasize the stability of the proposed approach.     

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection (FS) techniques to increase classification accuracy by minimizing the number of features selected. On the other hand, metaheuristic optimization algorithms have been widely used in feature selection in recent decades. In this paper, we proposed a hybrid optimization algorithm for feature selection in IDS. The proposed algorithm is based on grey wolf (GW), and dipper throated optimization (DTO) algorithms and is referred to as GWDTO. The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance. On the employed IoT-IDS dataset, the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in the literature to validate its superiority. In addition, a statistical analysis is performed to assess the stability and effectiveness of the proposed approach. Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.     

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).     

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Networking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.     

Optimization-based control of a reactive simulated moving bed process for glucose isomerization

In this paper, we investigate the continuous production of high-fructose corn syrup in a reactive simulated moving bed (RSMB) process which combines a quasi-continuous chromatographic separation with the enzymatic biochemical conversion of glucose to fructose. Such an integration of reaction and separation in one unit operation is advantageous for the equilibrium limited glucose isomerization. However, it complicates process design and process control. The continuous operating parameters and the discrete distribution of the columns over the different zones of the RSMB process are determined using a rigorous model-based optimization strategy. In order to maintain the product purity in the presence of disturbances while injecting a minimal additional amount of eluent, a nonlinear model predictive controller was developed which can deal with the complex hybrid (continuous/discrete) dynamics of the RSMB plant and takes hard process constraints (e.g. the maximal allowable pressure drop) into account. The efficiency of the control concept is proven in experimental studies using a 6-column RSMB plant of pharmaceutical scale.     

Experimental and numerical investigation on single specimen methods of determination of J in rubber materials

In this paper, we have verified the validity of some formulations allowing the determination of the fracture surface energy in the case of rubber-like materials. The J-integral is chosen as a fracture characterizing parameter which is experimentally determined by considering a multiplying form that; numerically evaluated using a finite element method. The numerical results are compared to the experimental data and a good agreement has been pointed out for the deeply cracked specimen (a/w0.5). Below this limit, a significant divergence is observed which is attributed to a lack of accuracy of the experimental data processing.     

Canopy bidirectional reflectance calculation based on Adding method and SAIL formalism: AddingS/AddingSD

The SAIL model (proposed by Verhoef) is largely used in the remote sensing community to calculate the canopy Bidirectional Reflectance Distribution Function. The simulation results appear acceptable compared to observations especially for not very dense planophile vegetation. However, for erectophile dense crops (e.g. corn) the simulations appear less accurate. This inadequacy is due to the assumption that the multiple scattered fluxes are isotropically distributed. The SAIL parameters are interpretable at the level of elementary layer components. Now, the Adding method (initially proposed by Van de Hulst) provides a good framework to model the radiative transfer inside a vegetation layer, but its parameter estimation lies on very simple geometric modeling of the canopy. In this paper, we first propose an adaptation of the Adding method using the SAIL model canopy representation in the turbid case: it is called AddingS model. Such an approach allows to overcome the isotropy assumption. Second, AddingS is extended to the Discrete case: defining the AddingSDmodel. It allows to take into account the multi hot spot effect. Moreover, the AddingS and AddingSD models allow to check the energy conservation in respectively turbid and discrete cases. Finally, in order to keep reasonable time performance, a fast computation method was developed.     

Data Association in Multi-target Tracking Using Belief Function

In this paper, we present a new method for data association in multi-target tracking. The representation and the fusion of the information in our method are based on the use of belief function. The proposal generates the basic belief mass assignment using a modified Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses. Our method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases. The results demonstrate the feasibility of the proposal and show improved performance compared to the aforementioned alternative commonly used methods.     

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ??circulatory error in class/property hierarchy??, ??common class between disjoint classes/properties??, ??redundancy of subclass/subproperty of relations?? and other types of ??semantic inconsistency?? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system??s performance by producing only consistent accurate mappings, and reduce the users?? dependability for ensuring the satisfiability of merged ontology.