A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures

Due to the drastic increase in the number of critical infrastructures like nuclear plants,industrial control systems(ICS),transportation,it becomes highly vulnerable to several attacks.They become the major targets of cyberattacks due to the increase in number of interconnections with other networks.Several research works have focused on the design of intrusion detection systems(IDS)using machine learning(ML)and deep learning(DL)models.At the same time,Blockchain(BC)technology can be applied to improve the security level.In order to resolve the security issues that exist in the critical infrastructures and ICS,this study designs a novel BC with deep learning empowered cyber-attack detection(BDLE-CAD)in critical infrastructures and ICS.The proposed BDLE-CAD technique aims to identify the existence of intrusions in the network.In addition,the presented enhanced chimp optimization based feature selection(ECOA-FS)technique is applied for the selection of optimal subset of features.Moreover,the optimal deep neural network(DNN)with search and rescue(SAR)optimizer is applied for the detection and classification of intrusions.Furthermore,a BC enabled integrity checking scheme(BEICS)has been presented to defend against the misrouting attacks.The experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying aspects.The simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the accuy of 92.63%.

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.

Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological systems.Due to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)models.At the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier stage.The existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of diseases.In this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics applications.The proposed IDL-CCDC technique aims to detect and classify different classes of CC.In addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal process.Moreover,water wave optimization(WWO)based EfficientNet model is employed for feature extraction process.Furthermore,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or malignant.The design of WWO and CGSO algorithms helps to increase the overall classification accuracy.The performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.