Abstract Numerous techniques have been proposed in the literature to improve the performance of high‐gain observers with noisy measurements. One such technique is the linear extended state observer, which is used to estimate the system's states and to account for the impact of internal uncertainties, undesirable nonlinearities, and external disturbances. This observer's primary purpose is to eliminate these disturbances from the input channel in real‐time. This enables the observer to precisely track the system states while compensating for the various sources of uncertainty that can influence the system's behaviour. So, in this paper, a novel nonlinear higher‐order extended state observer (NHOESO) is introduced to enhance the performance of high‐gain observers under noisy measurement conditions. The NHOESO is designed to observe the system states and total disturbance while eliminating the latter in real time from the input channel. It is capable of handling disturbances of higher‐order derivatives, including internal uncertainties, undesirable nonlinearities, and external disturbances. The paper also presents two innovative schemes for parametrizing the NHOESO parameters in the presence of measurement noise. These schemes are named time‐varying bandwidth NHOESO (TVB‐NHOESO) and online adaptive rule update NHOESO (OARU‐NHOESO). Numerical simulations are conducted to validate the effectiveness of the proposed schemes, using a nonlinear uncertain system as a test case. The results demonstrate that the OARU technique outperforms the TVB technique in terms of its ability to sense the presence of noise components in the output and respond accordingly. However, it is noted that the OARU technique is slower than the TVB technique and requires more complex parameter tuning to adaptively account for the measurement noise.

Abstract Generally, the existing Intrusion Detection Systems (IDS) solutions suffer from low detection accuracy for some attack types compared with the overall detection accuracy of attacks. The data imbalance technically affects the ratio of detection accuracy of low frequent attacks class (e.g. zero‐day attack) compared to attacks with more instances. Therefore, IDS‐based machine learning algorithms potentially suffer from high false‐positive rates. To overcome the limitation of existing solutions, a hyper‐clustering model is proposed for dynamic intrusion detection based on the Density‐Based Spatial Clustering of Applications with Noise (DBSCAN) and cosine similarity. The proposed solution develops the standard DBSCAN by adding a new evolving process based on distance measures between the clusters to overcome the imbalance dataset. Moreover, a new classifier is proposed based on cosine similarity to predict the labelling of abnormal behaviour. The experimental results show that the proposed model outperformed the original DBCAN and the related works. The mean silhouette of the proposed DBSCAN achieves a high score of 0.87 compared to other solutions. Furthermore, the proposed DBSCAN reduces the mean square error from 0.66 to 0.13 and achieves 86.82%, 79.10% and 90.03% in general accuracy on KDDTest+, KDDTest‐21 NSL‐KDD and UNSW‐NB15 benchmark datasets, respectively.

Business intelligence is becoming more essential for supply chain administrators to make good decisions. The globalization of supply chains makes their management and control more challenging. Blockchain is a distributed digital ledger technology that guarantees traceability, transparency, and security and promises to ease global supply chain management issues. This paper proposes the Blockchain-assisted Secure Data Management Framework (BSDMF) for financial data handling for supply chain integrated business intelligence models. Analyzing, collecting, and demonstrating data could be important to a business, its supply chain performance, and sustainability. The blockchain can interrupt supply chain processes for improved finance handling, distributed management, and process automation. The study’s experimental result will help organizations deploy blockchain applications with intelligent business strategies to support supply chain management effectively. The simulation outcome has been implemented, and the recommended method achieves a computation time of fewer than 2 hours, an efficiency ratio of 97.4%, an error ratio of 94.1%, data authentication of 92.1%, and a data management ratio of 98.7%.

Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results.

Electronic health records are essential and sensitive since they include vital information and are routinely exchanged across several parties, such as hospitals and private clinics. These data must remain accurate, current, secret, and available only to authorized parties. Integrating these data improves the accuracy and cost-effectiveness of the present health data administration framework. Electronic Medical Records (EMRs) are now kept utilizing the structure of the client/server via whom patient data information is maintained in the hospital. Multiple hospitals use the same database to track a single patient. These limitations prevent a custom health system from providing various associated experts and patients with a cohesive, integrated, secure, and confidential medical history. Modern healthcare systems are distinguished by their complexity and expense. However, this may be mitigated by enhanced health record management and Blockchain technology. The Blockchain’s data availability, confidence, and security characteristics have a bright future in healthcare services, giving solutions to the issues of the traditional customer/server architecture EMR management platform: intricacy, confidence, dependability, compatibility, and anonymity. An e-health record management based on Internet of Medical Things (EHRM-IoMT) is proposed in this paper. This paper explores and analyzes Blockchain efficiency and customer/server paradigms. The findings show that a patient-centred strategy may achieve remarkable success utilizing Blockchain. Moreover, the immutable and accurate data of persons in Blockchain may enable healthcare practitioners to better forecast and aid with diagnosis utilizing the IoMT via machine learning and artificial intelligence.

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.

The advancement of healthcare technology is impossible without machine learning (ML). There have been numerous advances in ML to analyze, predict, and diagnose medical data. Integrating a centralized scheme and therapy for classifying and diagnosing illnesses and disorders is a major obstacle in modern healthcare. To standardize all medical data into a single repository, researchers have proposed using ML using the centralized artificial neural network model (ML-CANNM). Random tree, support vector machine, and gradient booster are just a few proposed ML classifiers. Artificial neural networks (ANNs) have been trained using a variety of medical datasets to predict and analyze outcomes. ML-CANNM collects patient data from various studies and uses ML and ANNs to determine the results. Three layers make up an ANN. ML is used to classify the given patients’ data in the input layer. In the hidden layer, classification data are compared to a training dataset. The output layer’s job is to identify, classify, and diagnose diseases. As a result, disease diagnosis and detection are integrated into a single healthcare database. The proposed framework has proven that ML-CANNM works with more accuracy and lesser execution time. Thus, the numerical outcome suggested ML-CANNM increased accuracy ratio of 99.2% and a prediction ratio of 97.5%. The findings further show that the execution time is enhanced by less than 2[Formula: see text]h, decision table using ML and results in an efficiency ratio of 97.5%.

The main aim of recent research effort based on 5G mobile technology is to increase the bandwidth for all users, large bandwidth, more efficient and easily manageable and uninterrupted uniform connectivity. A key aspect to this movement has been the development of novel signal transmission techniques and advanced signal processing receiver that allow significant increases in wireless capacity without attendant increases in bandwidth or power requirements. In order to achieve this goal, several transmission techniques are tested like FBMC, F-OFDM, UFMC and WOLA. In this paper, we simulate the performance of the transmission techniques listed above against OFDM technology used for 4G in terms of BER vs SNR. Simulations result show that FBMC technique has a good performance against other techniques.