The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

Cybersecurity in the Internet of Things (IoT) is the practice of implementing measures to secure networks and connected devices from data breaches, cyber threats, and unauthorized access. It is essential owing to the increasing interconnectivity of devices, ranging from smart home appliances to industrial sensors. The potential attack surface expands, necessitating strong cybersecurity measures to protect sensitive data, ensure privacy, and prevent disruptions to critical services with these increasing number of IoT devices. Artificial intelligence (AI) technologies, particularly deep learning (DL) and machine learning (ML) approaches, hold the potential to mitigate and identify cyberattacks on IoT networks. DL demonstrates promise for effectively preventing and detecting security threats within IoT devices. Despite the importance of Intrusion Detection Systems (IDS) in maintaining confidentiality by detecting suspicious activities, classical IDS solutions might face difficulties in the IoT platform. Therefore, this study presents an Artificial Orca Algorithm with Ensemble Learning cyberattack detection and classification (AOAEL-CDC) methodology in an environment of IoT. The presented AOAEL-CDC technique exploited the feature selection (FS) approach with an ensemble learning approach for cyberattack recognition and identification in the IoT atmosphere. In the developed AOAEL-CDC model, the feature selection takes place using the AOA technique. For the cyberattack detection process, the ensemble learning process is carried out by the use of three models such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and extreme learning machine (ELM). Finally, the hyperparameter range of the DL techniques takes place using the marine predator’s algorithm (MPA). To examine the performance analysis of the AOAEL-CDC methodology, a series of simulations take place using a benchmark dataset. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.31%.

The conventional e-commerce business chain is undergoing a transformation centered on short videos and live streams, giving rise to interest-based e-commerce as a burgeoning trend in the industry. Varied content stimulates the fast growth of interest in e-commerce. By employing wireless sensor networks (WSNs) to collect real-time data on user behavior, preferences, and contextual factors, businesses employ high-tech analytics and predictive modeling systems to evaluate individual purchasing power. This new integration supports E-commerce platforms to offer personalized and targeted product recommendations, pricing strategies, and promotional campaigns, thus optimizing the customer shopping experience. The WSN-assisted predictive abilities not only allow businesses to tailor their offerings to particular user segments for contributing to the overall performances and effectiveness of E-commerce ecosystems in a gradually dynamic market. This study develops a WSN-Assisted Consumer Purchasing Power Prediction via Barracuda Swarm Optimization Algorithm Driven Deep Learning (CP3-BSOADL) for E-Commerce Systems. The major aim of the CP3-BSOADL technique is to precisely forecast the procuring power level with the customer content preferences to offer new concepts for interest e-commerce systems. In the CP3-BSOADL technique, two major processes are involved. For the prediction process, the CP3-BSOADL technique utilizes a stacked auto-encoder (SAE) model which effectually forecasts the purchasing power of the consumers for e-commerce systems. Besides, the BSO algorithm can be applied to effectually fine-tune the hyperparameters related to the SAE model which leads to accomplishing enhanced predictive results. The performance analysis of the CP3-BSOADL technique is tested using an e-commerce dataset. The extensive result analysis stated that the CP3-BSOADL technique gains better performance over other recent state-of-the-art approaches in terms of distinct measures.

Intracranial haemorrhage (ICH) detection is a critical task in radiology and neurology, as timely recognition of haemorrhages in the brain can assist in rapid intervention and treatment. Several imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI), are widely utilized to detect and classify ICH. Traditional methods for intracranial haemorrhage detection relied on manual inspection of CT images by radiologists. However, with advancements in machine learning (ML) and deep learning (DL) techniques, automated and computer-aided systems have been developed to assist radiologists in detecting and diagnosing ICH efficiently. DL models, particularly convolutional neural network (CNN), has shown promising results in ICH detection on CT images. With this motivation, this study focuses on the development of a Political Optimizer with Deep Learning based Intracranial Haemorrhage Diagnosis on Healthcare Management (PODL-ICHDHM) technique. The presented PODL-ICHDHM technique majorly concentrates on the recognition and classification of ICH on CT images. In this study, bilateral filtering (BF) is initially applied to pre-process the CT images. For feature extraction purposes, the Faster SqueezeNet approach is utilized in this study. At last, the PO algorithm with denoising autoencoder (DAE) model is utilized for the classification of ICH accurately. The experimental result analysis of the PODL-ICHDHM approach was validated on a benchmark dataset. The outcomes emphasized the improved performance of the PODL-ICHDHM algorithm over other recent approaches with a maximum detection accuracy of 98.43%.

Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques.