Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.

Recently, Telehealth connects patients to vital healthcare services via remote monitoring, wireless communications, videoconferencing, and electronic consults. By increasing access to specialists and physicians, telehealth assists in ensuring patients receive the proper care at the right time and right place. Teleophthalmology is a study of telemedicine that provides services for eye care using digital medical equipment and telecommunication technologies. Multimedia computing with Explainable Artificial Intelligence (XAI) for telehealth has the potential to revolutionize various aspects of our society, but several technical challenges should be resolved before this potential can be realized. Advances in artificial intelligence methods and tools reduce waste and wait times, provide service efficiency and better insights, and increase speed, the level of accuracy, and productivity in medicine and telehealth. Therefore, this study develops an XAI-enabled teleophthalmology for diabetic retinopathy grading and classification (XAITO-DRGC) model. The proposed XAITO-DRGC model utilizes OphthoAI IoMT headsets to enable remote monitoring of diabetic retinopathy (DR) disease. To accomplish this, the XAITO-DRGC model applies median filtering (MF) and contrast enhancement as a pre-processing step. In addition, the XAITO-DRGC model applies U-Net-based image segmentation and SqueezeNet-based feature extractor. Moreover, Archimedes optimization algorithm (AOA) with a bidirectional gated recurrent convolutional unit (BGRCU) is exploited for DR detection and classification. The experimental validation of the XAITO-DRGC method can be tested using a benchmark dataset and the outcomes are assessed under distinct prospects. Extensive comparison studies stated the enhancements of the XAITO-DRGC model over recent approaches.

The internet of things (ransomware refers to a type of malware) is the concept of connecting devices and objects of all types on the internet. IoT cybersecurity is the task of protecting ecosystems and IoT gadgets from cyber threats. Currently, ransomware is a serious threat challenging the computing environment, which needs instant attention to avoid moral and financial blackmail. Thus, there comes a real need for a novel technique that can identify and stop this kind of attack. Several earlier detection techniques followed a dynamic analysis method including a complex process. However, this analysis takes a long period of time for processing and analysis, during which the malicious payload is often sent. This study presents a new model of dwarf mongoose optimization with machine-learning-driven ransomware detection (DWOML-RWD). The presented DWOML-RWD model was mainly developed for the recognition and classification of goodware/ransomware. In the presented DWOML-RWD technique, the feature selection process is initially carried out using an enhanced krill herd optimization (EKHO) algorithm by the use of dynamic oppositional-based learning (QOBL). For ransomware detection, DWO with an extreme learning machine (ELM) classifier can be utilized. The design of the DWO algorithm aids in the optimal parameter selection of the ELM model. The experimental validation of the DWOML-RWD method can be examined on a benchmark dataset. The experimental results highlight the superiority of the DWOML-RWD model over other approaches.

As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification.

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques.