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The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled.

Data mining integrates statistical analysis, machine learning and database technology to extract hidden patterns and relationships from data. The presence of irrelevant, redundant and inconsistent attributes in the data ushers poor classification accuracy. In this paper, a novel bio-inspired heuristic swarm optimization algorithm for feature selection, namely Constructive Lazy Wolf Search Algorithm is proposed based on the backbone of the Wolf Search Algorithm. It is based on the behavior of the real wolves, which search for their food and consequently survive the attacks of the threats by avoiding them. Based on the study conducted on the behavior of wolves two natural factors, namely laziness and health are introduced for attaining highest efficiency. Restricting and controlling the wolves’ behavior by allowing only healthy and constructive lazy wolves to take part in the search reduces the search time and complexity required to search for the best fitness. The proposed algorithm is then applied on a prisoner dataset for crime propensity prediction along with a few benchmark datasets to prove the stability in the improved performance compared with other bio-inspired optimization algorithms. The accuracy achieved by fine-tuning the proposed algorithm was 98.19% providing accurate crime prevention.

Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection’s efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets.

Software-defined networking (SDN) is a networking paradigm of subsequent generation where various network components are used by a centralized controller that allows reliability in network system configuration, execution of policy decisions, and management via a primary programmable network infrastructure unit. SDN is known to deny DDoS attacks despite the default security protocols. State-of-the-art researches have shown that SDN intrusion is possible in diverse layers of its generalized architecture. Addressing this problem, this work presents an optimized intrusion detection system for SDN to mitigate the effect of DDoS attacks. This article’s main contribution comprises the development of a voting strategy-based ensemble classifier, which is established based on bio-inspired particle swarm optimization and salp swarm optimization in the context of optimized classification of DDoS attack-prone traffic SDN. Experimental analysis of the proposed SDN-IDS depicts that the proposed strategy outperforms existing classifiers in terms of accuracy.

A kind of bio-inspired lightweight sandwich structure was designed based on the microstructure of the cross section of the beetle elytra. The traditional lightweight honeycomb sandwich structure was used to compare with the new structure. Samples of the two structures were manufactured by 3D printing technology. The mechanical properties of the lightweight structures were analyzed by finite element method (FEM). Besides, the three points bending tests of the samples were carried out with universal testing machine. Comparing the results of FEM analysis and experiments, it turned out that the results were consistent with each other and the effectiveness of the FEM analysis was proved. Also, the superiorities of the bio-inspired sandwich structure were showed through the analysis. Additionally, the unit cell of the lightweight bio-inspired sandwich structure was optimized based on response surface analysis, which eventually reduced weight of the unit cell of the structure further on basis of meeting the requirements of strength, realized lightweight design and established base for applications.

This paper presents the leg design of the HexaQuaBip robot, namely a bio-inspired polymorphic robot intended to be able to reconfigure its morphology in most of hexapod, quadruped and biped animals. We first review segmentation and kinematics of their legs and define the HQB's reconfigurable kinematics. The resulting leg model is four-segmented and entails seven degrees of freedom. Then, we analyze the diversity in limb proportions of animal legs and determine where to put sliding joints on HQB's legs. We found that one sliding joint on the third segment of each leg is enough to approximate limb proportions of most of hexapod, quadruped and biped animals. Finally, we conclude by considering hardware implementations of the complex theoretically-designed HQB's morphology.