A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

Despite advances in intelligent medical care,difficulties remain.Due to its complicated governance,designing,planning,improving,and managing the cardiac system remains difficult.Oversight,including intelligent monitoring,feedback systems,and management practises,is unsuccessful.Current platforms cannot deliver lifelong personal health management services.Insufficient accuracy in patient crisis warning programmes.No frequent,direct interaction between healthcare workers and patients is visible.Physical medical systems and intelligent information systems are not integrated.This study introduces the Advanced Cardiac Twin(ACT)model integrated with Artificial Neural Network(ANN)to handle real-time monitoring,decision-making,and crisis prediction.THINGSPEAK is used to create an IoT platform that accepts patient sensor data.Importing these data sets into MATLAB allows display and analysis.A myocardial ischemia research examined Health Condition Tracking’s(HCT’s)potential.In the case study,75%of the training sets(Xt),15%of the verified data,and 10%of the test data were used.Training set feature values(Xt)were given with the data.Training,Validation,and Testing accuracy rates were 99.9%,99.9%,and 99.9%,respectively.General research accuracy was 99.9%.The proposed HCT system and Artificial Neural Network(ANN)model gather historical and real-time data to manage and anticipate cardiac issues.

Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

Class Title:Radiological imaging method a comprehensive overview purpose.This GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the field.Materials and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed tomography.Additionally recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis systems.Results:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical conditions.Conclusion:Radiological imaging is an extremely important tool in modern medicine to assist in medical diagnosis.This work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence.Considering the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper perspect-ive.In a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm intelli-gence.In a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different perspectives.Single-population swarm intel-ligence is inspired by biological intelligence.To further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm intelligence.However,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human cognition.Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence.In addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper.We also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other fields.Finally,we discuss future research directions and key issues to be studied in swarm intelligence.

Steps toward Parallel Intelligence

The origin of artificial intelligence is investigated,based on which the concepts of hybrid intelligence and parallel intelligence are presented.The paradigm shift in Intelligence indicates the "new normal" of cyber-social-physical systems(CPSS),in which the system behaviors are guided by Merton's Laws.Thus,the ACP-based parallel intelligence consisting of Artificial societies,Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS.

Human-Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine(SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization(GA-PSO)method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

Mathematical Model and Its Hybrid Dynamic Mechanism in Intelligent Control of Ironmaking

A hybrid dynamic model was proposed, which considered both the hydrokinetic and the chaotic properties of the blast furnace ironmaking process; and great emphasis was put on its mechanism. The new model took the high complexity of the blast furnace as well as the effects of main parameters of the model into account, and the predicted results were in very good agreement with actual data.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

Internet of Lamps for Future Ubiquitous Communications:Integrated Sensing,Hybrid Interconnection,and Intelligent Illumination

The ubiquitous deployment and restricted consumption are the requirements restricting the development of Internet of Things.Thus,a promising technology named Internet of Lamps(Io L)is discussed in this paper to address these challenges.Compared with other communication networks,the remarkable advantage of Io L is that it can make full use of the existing lighting networks with sufficient power supply.The lamps can be connected to the Internet through wired power line communication and/or wireless communication,while the integration of integrated sensing,hybrid interconnection,and intelligent illumination is realized.In this paper,the Io L is discussed from three aspects including sensing layer,network layer,and application layer,realizing the comprehensive upgrade based on the conventional communication and illumination systems.Meanwhile,several novel technologies of Io L are discussed based on the requirements of sensing,communication,and control,which have put forward practical solutions to the issues faced by Io L.Moreover,the challenges and opportunities for Io L are highlighted from various parts of the system structure,so as to provide future insights and potential trends for researchers in this field.

Intelligent Control Scheme of Engineering Machinery of Cluster Hybrid System

In a hybrid system, the subsystems with discrete dynamics play a central role in a hybrid system. In the course of engineering machinery of cluster construction, the discrete control law is hard to obtain because the construction environment is complex and there exist many affecting factors. In this paper, hierarchically intelligent control, expert control and fuzzy control are introduced into the discrete subsystems of engineering machinery of cluster hybrid system, so as to rebuild the hybrid system and make the discrete control law easily and effectively obtained. The structures, reasoning mechanism and arithmetic of intelligent control are replanted to discrete dynamic, conti- nuous process and the interface of the hybrid system. The structures of three types of intelligent hybrid system are presented and the human experiences summarized from engineering machinery of cluster are taken into account.

Wireless Hybrid QoS Architecture with an Enhancement of Fair Intelligent Congestion Control

More subtle and explicit QoS control mechanisms are required at the radio access level, even though the simple and scalable Differentiated Services (DiffServ) QoS control model is acceptable for the core of the network. At the radio access level, available resources are severely limited and the degree of traffic aggregation is not significant, thus rendering the DiffServ principles less effective. In this paper we present a suitable hybrid QoS architecture framework to address the problem. At the wireless access end, the local QoS mechanism is designed in the context of IEEE 802.11 WLAN with 802.11e QoS extensions;so streams of those session-based applications are admitted, established according to the traffic profile they require, and guaranteed. As the core in the Admission Control of the hybrid QoS architecture, the Fair Intelligent Congestion Control (FICC) algorithm is applied to provide fairness among traffic aggregates and control congestion at the bottleneck interface between the wireless link and the network core via mechanisms of packet scheduling, buffer management, feedback and adjustments. It manages effectively the overloading scenario by preventing traffic violation from uncontrolled traffic, and providing guarantee to the priority traffic in terms of guaranteed bandwidth allocation and specified delay.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

The Role of Computational Intelligence in Sensory Evaluation

Sensory evaluation is the evaluation of signals that a human receives via its senses of sight, smell, taste, touch and hearing. In today’s industrial companies, sensory evaluation is widely used in quality inspection of products, in marketing study and in many other fields such as risk evaluation, investment evaluation and safety evaluation. In practice, setting up a suitable mathematical formulation, an efficient working procedure and a pertinent computing method for sensory evaluation is quite difficult because of uncertainty and imprecision in sensory panels and their results involving linguistic expressions, non normalized data, data reliability, etc. At the present a prime problem of the practitioner is not the lack of useful methods but the lack of transparency in this area. In this tutorial lecture, we briefly describe some of the technology in the computational intelligence (CI) areas that has been developed for application to sensory evaluation and related fields. Moreover, we will illustrate the role of CI in sensory evaluation related applications from some recent publications.