Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.

Cyber-physical systems (CPS) combine computational and physical elements to enable effective and intelligent control of several applications. However, the increasing connectivity and complexity of CPS introduce new security challenges, making intrusion detection a critical aspect for maintaining the integrity and reliability of these systems. The rise in artificial intelligence (AI) techniques assists in addressing security problems related to CPS environments. Therefore, this study proposes a Quantum Dwarf Mongoose Optimization with Ensemble Deep Learning Based Intrusion Detection (QDMO-EDLID) technique in the CPS environment. The presented QDMO-EDLID technique aims to recognize the presence of intrusions by the feature selection (FS) and ensemble learning process. For feature subset selection purposes, the QDMO-EDLID technique employs the QDMO algorithm. Moreover, an ensemble of Convolution Residual Networks (CRN), Deep Belief Networks (DBN), and Deep Autoencoder (DAE) models are applied for the intrusion classification process. The experimental outcome of the QDMO-EDLID technique was tested employing benchmark intrusion databases. The simulation results highlighted the improved efficiency of the QDMO-EDLID approach concerning different performance measures.

Present-day queuing inventory systems (QIS) do not utilize two multi-server service channels. We proposed two multi-server service channels referred to as $T1S$ (Type 1 n-identical multi-server) and $T2S$ (Type 2 m-identical multi-server). It includes an optional interconnected service connection between $T1S$ and $T2S$, which has a finite queue of size N. An arriving customer either uses the inventory (basic service or main service) for their demand, whom we call $T1$, or simply uses the service only, whom we call $T2$. Customer $T1$ will utilize the server $T1S$, while customer $T2$ will utilize the server $T2S$, and $T1$ can also get the second optional service after completing their main service. If there is a free server with a positive inventory, there is a chance that $T1$ customers may go to an infinite orbit whenever they find that either all the servers are busy or no sufficient stock. The orbital customer can request for $T1S$ service under the classical retrial policy. $Q(=S-s)$ items are replaced into the inventory whenever it falls into the reorder level s such that the inequality always holds $n<s$. We use the standard $(s,Q)$ ordering policy to replace items into the inventory. By varying S and s, we investigate to find the optimal cost value using stationary probability vector $\varphi$. We used the Neuts Matrix geometric approach to derive the stability condition and steady-state analysis with R-matrix to find $\varphi$. Then, we perform the waiting time analysis for both $T1$ and $T2$ customers using Laplace transform technique. Further, we computed the necessary system characteristics and presented sufficient numerical results.

Financial risk assessment (FRA) is an essential process in financial institutions determining a company’s creditworthiness. This paper introduces a new wrapper feature selection with a clustering-based FRA model to assess the financial status. This study involves three different phases of operations such as feature selection, clustering, and classification. The proposed model initially designs an Information Gain Directed Feature Selection algorithm that offers to rank to the features utilising the information gain. In addition, the proposed model also involves an improved K-means clustering technique to cluster the data. Finally, the gradient boosting tree classifier model is executed to perform the classification process. The proposed model tested using two benchmark datasets. The simulation results indicate that the projected FRA model obtains maximum accuracy values of 95.68% and 94.76% on the applied datasets.

Healthcare and medical advances have prolonged human life and thus have led to increasing numbers of elderly individuals. To make their lives more convenient, several ubiquitous technologies have been considered, including the RFID system, which can play a vital role in elderly care by caregivers as well as by elderly individuals themselves. Caregivers can take advantage of the RFID system by recording and tracking elderly individuals' belongings and assisting these individuals in healthcare provision by accessing their relevant information, among others. Similarly, the RFID system can help manage elderly individuals' daily lives by reminding them of their daily schedules (e.g., reminding them to take medicine on time) and tracking their personal belongings, among others. In addition, the RFID system can mitigate human errors such as medical mistakes, delays in service provision, and hassles in tracking and identifying patients and objects. This study provides a survey of solutions proposed in the literature and discusses the potential benefits of integrating the RFID system with sensors and applying the integrated system. In addition, the study addresses the opportunities, technological challenges, and research directions for the integrated RFID system in the context of smart solutions in elderly care facilities.