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Cybersecurity is a vital technology and measures intended to protect networks, computers, information, and programs from threats and illegal access, modification, or damage. A security model covers a network and a computer safety method. Each system has antivirus software, firewalls, and an intrusion detection system (IDS). IDS helps in discovering and identifying illegal system behavior such as usage, copying, alteration, and damage. By estimating network traffic anomalies and patterns, deep learning (DL) models can enhance the detection abilities of IDS when compared to traditional rule-based methods. These models learn complex representations from data, authorizing them to recognize subtle and developing attack patterns. Techniques like recurrent neural network (RNN) and convolutional neural network (CNN) can be applied to progress consecutive or spatial features in network data, correspondingly. This manuscript empowers Cybersecurity by utilizing an Enhanced Rat Swarm Optimizer with a Deep Stack-Based Ensemble Learning (ERSO-DSEL) model. The ERSO-DSEL approach leverages feature selection (FS) with EL strategies to boost cybersecurity. In the ERSO-DSEL system, Z-score normalization is employed to measure the input data. Besides, an improved equilibrium optimizer (IEO) based FS approach is applied to choose a set of features. For cyberattack recognition, the ERSO-DSBEL approach uses the DSEL approach comprising three models namely deep neural network (DNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Furthermore, the hyperparameter selection of these DL models takes place using the ERSO system. The solution result of the ERSO-DSBEL model is executed on a benchmark IDS database. A wide-contrast study reported that the ERSO-DSBEL model accomplishes an enhanced accuracy outcome of 99.67% over other models of cybersecurity.

Autonomous Intelligent Systems (AIS) states to a class of intelligent devices that manage and create decisions independently without human interference. These techniques use numerous models contains artificial intelligence (AI), robotics, machine learning (ML), and sensor fusion, in order to identify its environment, reflect that data, and execute accordingly to achieve specific goals. Facial emotion detection in AIS applies to the capability of AI-driven autonomous machines to detect and interpret human emotions reliant on facial expressions. This technology permits AIS to identify and reply to the emotional conditions of individuals they interconnect with, foremost to more normal and empathetic human-machine communications. So, this study develops an Oppositional Brain Storm Optimizer with Deep Learning based Facial Emotion Recognition (OBSODL-FER) system for AIS. The foremost goal of the OBSODL-FER system is to identify and organize dissimilar classes of facial emotions of the drives in autonomous vehicles. To achieve, the OBSODL-FER approach mainly employs an Xception-based deep convolutional neural networks (CNNs) for feature extractor. Also, the developed OBSODL-FER approach exploits the OBSO system for the hyperparameter selection of the Xception method. Besides, an improved LSTM model (ILSTM) is applied to the classification procedure. Furthermore, a jellyfish search (JFS) optimizer is employed for the optimum hyperparameter selection of the ILSTM technique. The simulation results of the OBSODL-FER approach are verified on a benchmark facial emotion dataset. The experimental results inferred the enhancement of the OBSODL-FER system over other DL algorithms.

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.

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%.

This study investigates a multi-server queueing-inventory system in a random environment. The system has an unlimited waiting space and an inventory capacity of $S$ units. The customer arrives at the system according to the Markovian arrival process. Whenever all the servers are busy, an arriving customer either goes to an unlimited waiting space or leaves the system. And if there is a free server with positive inventory, then the arriving customer gets service immediately. Inventories are filled under the instantaneous replenishment policy. We consider that the service time follows a phase-type distribution, and we assess the joint probability distribution of the inventory level and the number of customers in the steady-state case. On top of that, we extract the sojourn time distributions of arbitrary customers using the Laplace-Stieltjes transform. Finally, a few numerical examples are provided to illustrate our mathematical model.

Our study was designed to screen the antibacterial potency of Princepia utilis leaf and seed extract and to measure their antioxidant effects, total phenol content, total flavonoid content, and total carbohydrate content. Collected samples were extracted by cold maceration. Hexane, ethyl acetate, methanol, and distilled water were used as extraction solvents. In the disc diffusion method, P. utilis ethyl acetate leaf extract was most prominent against Staphylococcus epidermis with a zone of inhibition (ZOI) of 13.83 mm. Similarly, methanolic leaf extract was most prominent against Staphylococcus aureus (ZOI-12.33 mm). Furthermore, the methanolic seed extract was most sensitive against Klebsiella pneumoniaee (ZOI-11.66 mm) Escherichia coli (ZOI-9.0 mm). The lowest minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values of 0.5 mg/mL and 0.6 mg/mL, respectively, were shown by ethyl acetate leaf extract against S. epidermis. Similarly, the highest values of MIC and MBC, i.e., 20.8 mg/mL and 33.3 mg/mL, respectively, were shown by hexane leaf extract against S. epidermidis. On the other hand, evaluation of antioxidant capacity revealed that ethyl acetate leaf extract showed the maximum antioxidant effect (IC50: 66.69 μg/mL). The total flavonoid contents of different extracts were measured in the range of 37 ± 0.74 μg QE/mg dry extract weight (methanolic seed extract) to 321.84 ± 4.82 μg QE/mg dry extract weight (hexane seed extract). Likewise, the total polyphenol content ranged from the hexane leaf extract (17.33 ± 0.642 μg GAE/mg dry extract weight) to ethyl acetate leaf extract (62.56 ± 1.284 μg GAE/mg dry extract weight). We found a variation in total carbohydrate content in the range of 23.55 ± 1.125 μg glucose/mg dry extract weight (hexane leaf extract) to 96.63 ± 2.253 μg glucose/mg dry extract weight (aqueous leaf extract). Overall, this study revealed that leaf and seed extract of P. utilis exhibited noteworthy antibacterial effects against diverse pathogenic microorganisms.

In this study, we consider a perishable inventory system that has an (s, Q) ordering policy, along with a finite waiting hall. The single server, which provides an item to the customer after completing the required service performance for that item, only begins serving after N customers have arrived. Impatient demand is assumed in that the customers waiting to be served lose patience and leave the system if the server’s idle time overextends or if the arriving customers find the system to be full and will not enter the system. This article analyzes the impatient demands caused by the N-policy server to an inventory system. In the steadystate, we obtain the joint probability distribution of the level of inventory and the number of customers in the system. We analyze some measures of system performance and get the total expected cost rate in the steadystate. We present a beneficial cost function and confer the numerical illustration that describes the impact of impatient customers caused by N-policy on the inventory system’s total expected cost rate.

We present a decentralized medium access control protocol for cognitive radio wireless sensor networks. The proposed protocol allows secondary wireless sensors nodes to recognize spectrum opportunities and transmits data based on the licensed users’ arrival prediction on the channel. It estimates the number of active cognitive wireless sensor nodes and it also adjusts the sleep cycle in order to conserve energy. The proposed protocol does not need a dedicated common control channel to negotiate for the data channels. We evaluate delay, energy consumption, and goodput, which are the three important qualities of service parameters through simulation. The simulation results show that the proposed approach achieves higher energy conservation with a small cost of delay and adequate aggregated goodput.

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.