IoT-based healthcare monitoring systems often lack context-awareness, hindering their ability to provide personalised and accurate healthcare services. The proposed architecture addresses this challenge by incorporating auto-metric graph neural network with Archimedes optimisation algorithm is proposed in this paper for Health Care Monitoring in IoT-based Context-Aware Architecture (ANGNN-AOA-IoT-CA). The proposed work contains four phases: IoT phase, data preprocessing phase, context-aware phase and decision-making phase. In internet of things (IoT) phase, the sensor nodes are used for identifying the health status. Then, the amassed data are stored in storage layer. Hence, in the data preprocessing phase, the data redundancy, recurrence/repetition data are deleted. In context-aware phase, the preprocessed information is presented in the cloud and fog layer. As a result, the context-aware phase minimises the search space activities. In decision-making phase, the data are extracted and given to the ANGNN for classifying as normal and critical condition. Then Archimedes optimisation algorithm is utilised to acquire the better solution. The acquired outcomes of the proposed technique are analysed with existing models. Finally, the proposed method attains 2.37%, 2.95% and 1.17% higher accuracy, 1.09%, 1.47% and 1.53% higher sensitivity, 1.17%, 0.73% and 1.22% higher specificity compared with existing methods.

In this manuscript, Evolutionary Gravitational Neocognitron Neural-Network-based Block chain Technology is proposed for a Secured Dynamic Optimal Routing in Wireless Sensor Networks (BT-SDOR-WSN-EGNNN). The purpose of block chain (BC) technology is to distribute the distributed routing information management depending on secure BC token transactions. The Proof-of-Authority (PoA)-based consensus process is selected for effectual transaction in BC wireless sensor networks. The Evolutionary Gravitational Neocognitron Neural Network (EGNNN) is considered to select salient nodes denote the features of node-based validators. The Evolutionary Gravitational Neocognitron Neural Network model enhances the collection of validators by the attributes corresponding to every node. Then, Trust-Based Secure Intelligent Opportunistic Routing Protocol (TBSIOP) is employed to assist routing nodes and makes more informed routing decisions and choose the dependable routing links. The proposed approach is simulated in Network Simulator (NS-2) tool. The performance metrics, such as average latency, average energy consumption, and throughput of blockchain token transactions, is examined. From the simulation, the proposed BT-SDOR-WSN-EGNNN method attains 76.26%, 65.57%, 48.99%, 42.9% lesser delay during 25% malicious routing environment, 73.06%, 63.82%, 59.25%, 38.84% lesser delay during 50% malicious routing environment analysed to the existing BT-SDOR-WSN-DCNN, BT-SDOR-WSN-MDP, BT-SDOR-WSN-RL and BT-SDOR-WSN-WL-DCNN methods.

Summary Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm (AOA) is proposed in this manuscript for kidney stone detection. Initially, the input kidney stone images are gathered via CT kidney dataset. Then, the input image is preprocessed by utilizing APPDRC filtering approach. Also, the preprocessed images are given to the multi‐level thresholding segmentation technique for segmented the image. Then the segmented images are given to the TF‐IDF feature extraction method for extracting the features. Then the extracted features are fed to feature selection using Weibull distributive generalized multidimensional scaling methods for selecting the features. Then the selected features are SPGGAN classification method for kidney stone detection. Generally, SPGGAN not reveal any adoption of optimization methods compute optimum parameters for assuring correct kidney stone detection. Thus, AOA is used for optimizing the weight parameters of SPGGAN and it is implemented in python, its performance is examined under certain performances metrics, such as accuracy, precision, sensitivity, specificity, F‐measure, computational time, and ROC. The proposed SPGGAN‐AOA‐KSD method attains 54.78%, 34.89%, and 20.96% higher accuracy and 3.45%, 4.08%, and 5.06% greater AUC compared with existing methods, such as a ANN‐OGGA‐KSD, HMANN‐BPA‐KSD, and ANN‐CSOA‐KSD respectively.

SUMMARY In this article, the detection of COVID‐19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra‐low‐dose CT (ULDCT) images is proposed. Here, the ultra‐low‐dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto‐encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI‐Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID‐19 ULDCT images classification as COVID‐19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN‐AOA‐ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%; precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet‐HHO‐ULDCT, ELM‐DNN‐ULDCT, EDL‐ULDCT, ResNet 50‐ULDCT, SDL‐ULDCT, CNN‐ULDCT, and DRNN‐ULDCT, respectively.

Summary Magnetic resonance imaging (MRI) is a practical tool for diagnosing tumors in human brain. In this work, MRI images are analyzed to find tumor‐containing regions and classify these regions into three different types of tumor: meningioma, glioma, and pituitary. Several methods were utilized previously for brain tumor detection, but no one method classify the brain tumor accurately and also it takes high computation time. To overwhelm these issues, a support vector machine optimized with seagull optimization algorithm (SOA) is proposed for brain tumor classification (SVM‐SOA‐BTC). The brain MRI images are gathered via Brats image dataset. Then the images are preprocessed using Savitzky–Golay denoising method. Then residual exemplars local binary pattern based feature extraction is utilized for extracting radiomic features. Then, the extracted features are fed to SVM classifier for classifying the brain tumors, like normal and abnormal. Then the weight parameters of the SVM are optimized using the SOA. The simulation is implemented on MATLAB. Then the proposed SVM‐SOA‐BTC method achieves 33.78%, 19.69%, and 11.62% higher accuracy; 30.62%, 25.05%, and 9.10% higher F‐score compared with existing methods, like Deep‐CNN‐DSCA‐BTC, CNN‐WHHO‐BTC, and AFDNN‐FLA‐BTC respectively.

Summary Intrusion detection systems (IDSs) are the major component of safe network. Due to the high volume of network data, the false alarm report of intrusion to the network and intrusion detection accuracy is the problem of these security systems. The reliability of Internet of Things (IoT) connected devices based on security model is employed to protect user data and preventing devices from engaging in malicious activity. In this article, intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT Network (IDF‐AGNN‐HYB‐WMA‐CSOA‐ IoT) is proposed. Initially the attacks affected in the IoT data is taken from the dataset such as CSIC 2010 dataset, ISCXIDS2012 dataset, then these data are preprocessed and the features are extracted to remove the redundant information using improved random forest with local least squares. Then the malicious attacks and the normal attacks are classified using the auto‐metric graph neural network. At last hybrid woodpecker mating and capuchin search optimization algorithm (Hyb‐WMA‐CSOA) is utilized to optimize the weight parameters of AGNN. The performance of ISCXIDS2012 dataset of the proposed method shows higher accuracy 25.37%, 29.57%, and 18.67%, compared with existing methods, such as IDF‐ANN‐IoT, IDF‐BMM‐IoT and IDF‐DNN‐IoT respectively.