A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate

Robust Data Hiding in Multimedia for Authentication and Ownership Protection

Establishing robust and blind data hiding techniques in multimedia is very importantfor authentication, ownership protection and security. The multimedia being usedmay include images, videos and 3D mesh objects.A hybrid pyramid Discrete-Wavelet-Transform (DWT) Singular-Value-Decomposition(SVD) data hiding scheme for video authentication and ownership protection is proposed.The data being hidden will be in the shape of a main color logo image watermarkand another secondary Black and White (B&W) logo image. The colorwatermark will be decomposed to Bit-Slices. A pyramid transform is performed onthe Y-frames of a video stream resulting in error images; then, a Discrete WaveletTransform (DWT) process is implemented using orthonormal lter banks on theseerror images, and the Bit-Slices watermarks are inserted in one or more of the resultingsubbands in a way that is fully controlled by the owner; then, the watermarkedvideo is reconstructed. SVD will be performed on the color watermark Bit-Slices.A secondary B&W watermark will be inserted in the main color watermark usinganother SVD process. The reconstruction was perfect without attacks, while the averageBit-Error-Rates (BER's) achieved under attacks are in the limits of 2% for thecolor watermark and 5% for the secondary watermark; meanwhile, the mean PeakSignal-to-Noise Ratio (PSNR) is 57 dB. Furthermore, a selective denoising lter toeliminate the noise in video frames is proposed; and the performance with data hidingis evaluated.Moreover, a 3D mesh blind optimized watermarking technique is proposed in thisresearch. The technique relies on the displacement process of the vertices locationsdepending on the modication of the variances of the vertices's norms. Statisticalanalysis were performed to establish the proper distributions that best t each mesh,and hence establishing the bins sizes. Experimental results showed that the approachis robust in terms of both the perceptual and the quantitative qualities.In conclusion, the degree of robustness and security of the proposed techniques areshown. Also the schemes that can be adopted to further enhance the performance,and the future work that can be done in the eld are introduced

Robust Data Hiding in Multimedia for Authentication and Ownership Protection

Establishing robust and blind data hiding techniques in multimedia is very importantfor authentication, ownership protection and security. The multimedia being usedmay include images, videos and 3D mesh objects.A hybrid pyramid Discrete-Wavelet-Transform (DWT) Singular-Value-Decomposition(SVD) data hiding scheme for video authentication and ownership protection is proposed.The data being hidden will be in the shape of a main color logo image watermarkand another secondary Black and White (B&W) logo image. The colorwatermark will be decomposed to Bit-Slices. A pyramid transform is performed onthe Y-frames of a video stream resulting in error images; then, a Discrete WaveletTransform (DWT) process is implemented using orthonormal lter banks on theseerror images, and the Bit-Slices watermarks are inserted in one or more of the resultingsubbands in a way that is fully controlled by the owner; then, the watermarkedvideo is reconstructed. SVD will be performed on the color watermark Bit-Slices.A secondary B&W watermark will be inserted in the main color watermark usinganother SVD process. The reconstruction was perfect without attacks, while the averageBit-Error-Rates (BER's) achieved under attacks are in the limits of 2% for thecolor watermark and 5% for the secondary watermark; meanwhile, the mean PeakSignal-to-Noise Ratio (PSNR) is 57 dB. Furthermore, a selective denoising lter toeliminate the noise in video frames is proposed; and the performance with data hidingis evaluated.Moreover, a 3D mesh blind optimized watermarking technique is proposed in thisresearch. The technique relies on the displacement process of the vertices locationsdepending on the modication of the variances of the vertices's norms. Statisticalanalysis were performed to establish the proper distributions that best t each mesh,and hence establishing the bins sizes. Experimental results showed that the approachis robust in terms of both the perceptual and the quantitative qualities.In conclusion, the degree of robustness and security of the proposed techniques areshown. Also the schemes that can be adopted to further enhance the performance,and the future work that can be done in the eld are introduced

Optimal Adaptive Super-Twisting Sliding-Mode Control Using Online Actor-Critic Neural Networks for Permanent-Magnet Synchronous Motor Drives

In this paper, a novel optimal adaptive-gains super-twisting sliding-mode control (OAGSTSMC) using actor-critic approach is proposed for a high-speed permanent-magnet synchronous motor (PMSM) drive system. First, the super-twisting sliding-mode controller (STSMC) is adopted for reducing the chattering phenomenon and stabilizing the PMSM drive system. However, the control performance may be destroyed due external disturbances and parameter variations of the drive system. In addition, the conservative selection of the STSMC gains may affect the control performance. Therefore, for enhancing the standard super-twisting approach performance via avoiding the constraints on knowing the disturbances as well as uncertainties upper bounds, and to achieve the drive system robustness, the direct heuristic dynamic programming (HDP) is utilized for optimal tuning of STSMC gains. Consequently, an online actor-critic algorithm with HDP is designed for facilitating the online solution of the Hamilton-Jacobi-Bellman (HJB) equation via a critic neural network while pursuing an optimal control via an actor neural network at the same time. Furthermore, based on Lyapunov approach, the stability of the closed-loop control system is assured. A real-time implementation is performed for verifying the proposed OAGSTSMC efficacy. The experimental results endorse that the proposed OAGSTSMC control approach achieves the PMSM superior dynamic performance regardless of unknown uncertainties as well as exterior disturbances

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptors for revealing the underlying structure of a texture. Recently several variants of local binary descriptors have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently provide a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper, a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as a benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of the descriptor

A novel fuzzy trust-based secure routing scheme in flying ad hoc networks

Today, many studies assess vulnerabilities, threats, and attacks in flying ad hoc networks (FANETs) to provide solutions for countermeasures. Protecting FANETs against attackers and coordinating connections are challenging. The purpose of this study is to increase and maintain communication security. In this paper, a fuzzy trust-based secure routing scheme (FTSR) is presented in FANETs. FTSR utilizes two trust assessment mechanisms, namely local trust and path trust. Local trust strategy is a distributed process for finding reliable neighboring nodes and isolating hostile nodes on the network. In this regard, only reliable nodes are allowed to contribute to the path discovery procedure. This lowers the risk of forming fake paths in FANETs. Path trust strategy is responsible for identifying hostile nodes that are not identified in the local trust process. This strategy shows a general view of the trust status of the desired path. To design this mechanism, the source node runs a fuzzy system to select the safest path between source and the destination. Finally, network simulator 2 (NS2) implements FTSR, and the results such as malicious detection rate, packet delivery ratio, packet loss, accuracy, and delay are obtained from the simulation process. These results indicate that FTSR presents better performance compared to TOPCM, MNRiRIP, and MNDA. However, FTSR takes more time to find paths compared to TOPCM

A model for skin cancer using combination of ensemble learning and deep learning

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively