Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Review of Key Technologies and Applications in Intelligent Transportation Systems

The development of Intelligent Transportation Systems(ITS)is closely intertwined with the growth of every city,serving as a critical component of smart city construction.This paper provides a concise overview of the concept and overall framework of smart transportation.It emphasizes the application of key technologies,including Traffic Element Identification and Perception,data mining,and Smart Transportation System Integration Technology,in the field.Furthermore,the paper elucidates the current practical applications of smart transportation,showcasing its advancements and implementations in real-world scenarios.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System

Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.

Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

MigroGrid(MG)has emerged to resolve the growing demand for energy.But because of its inconsistent output,it can result in various power quality(PQ)issues.PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources.Similarly,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system.Thus,this research focuses on power quality enhancement in the MG using hybrid shunt filters.However,the performance of the filter mainly depends upon the design,and stability of the controller.The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)controller.The performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this approach.Further,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in MG.From the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

Analyzing the Impact of Blockchain Models for Securing Intelligent Logistics through Unified Computational Techniques

Blockchain technology has revolutionized conventional trade.The success of blockchain can be attributed to its distributed ledger characteristic,which secures every record inside the ledger using cryptography rules,making it more reliable,secure,and tamper-proof.This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context.Furthermore,it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure.To realize the full potential of the accurate and efficacious use of blockchain in the transportation sector,it is essential to understand the most effective mechanisms of this technology and identify the most useful one.As a result,the present work offers a priority-based methodology that would be a useful reference for security experts in managing blockchain technology and its models.The study uses the hesitant fuzzy analytical hierarchy process for prioritizing the different blockchain models.Based on the findings of actual performance,alternative solution A1 which is Private Blockchain model has an extremely high level of security satisfaction.The accuracy of the results has been tested using the hesitant fuzzy technique for order of preference by similarity to the ideal solution procedure.The study also uses guidelines from security researchers working in this domain.

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

The concept of smart healthcare has seen a gradual increase with the expansion of information technology.Smart healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more convenient.This work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare applications.The presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare data.In the presented HBO-DQNN model,three stages of operations were performed.Data normalization is applied to pre-process the input data at the initial stage.Next,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare data.At last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN model.The extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional approach.The accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems.This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs.To attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature vectors.In addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 model.Moreover,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of COVID19.For ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive aspects.The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule‑based decision‑making model

A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis.This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies,which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment.To obtain this goal and inspired by a model ensemble,we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing,fuzzy set theory,and a multi-attribute decision making algorithm.The results display that the order of priority in improvement—(A)AI application strategy,(B)AI governance,(D)the human factor,and(C)data infrastructure and data quality—is based on the magnitude of their impact.This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.Highlights Artificial intelligence(AI)promotes the sustainability development of audit tasks.A fuzzy MRDM model extracts key factors from large amounts of data.Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors.An effective framework of AI-driven business audit is proposed in which“AI cognition of senior executives”is the most important criterion.

A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology,smart grids(SGs)are being employed in many fields,such as smart homes and smart cities.Moreover,the application of artificial intelligence(AI)in SGs has promoted the development of the power industry.However,as users’demands for electricity increase,traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities.This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation,electrical security,and other aspects.Accordingly,this study proposes a distributed power trading scheme based on blockchain and AI.To protect the legitimate rights and interests of consumers and producers,credibility is used as an indicator to restrict untrustworthy behavior.Simultaneously,the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency,and a weighted communication tree construction algorithm is designed to achieve superior data forwarding.Finally,AI sensors are set up in power equipment to detect electricity generation and transmission,which alert users when security hazards occur,such as thunderstorms or typhoons.The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

Design of an Intelligent Self-Healing Smart Grid using a Hybrid Multi-Agent Framework

This paper discusses the applications of a hybrid multi-agent framework for self-healing applications in an intelligent smart grid system following catastrophic disturbances such as loss of generators or during system fault.The proposed hybrid multi-agent framework is a hybrid of both centralized and decentralized scheme to allow distributed intelligent agent in the smart grid system to make fast local decision while allowing the slower central controller to judge the effectiveness of the decision made by the local agents and to suggest more optimal solutions.

Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual agriculture.Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture.Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques.Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist.With this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent agriculture.The MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination purposes.Besides,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not needed.The MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

A Review on Intelligent Detection and Classification of Power Quality Disturbances:Trends,Methodologies,and Prospects

With increasing global concerns about clean energy in smart grids,the detection of power quality disturbances(PQDs)caused by energy instability is becoming more and more prominent.It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous,which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids.In order to ensure safe and reliable equipment implementation,appropriate PQDdetection technologiesmust be adopted to avoid such adverse effects.This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field,where specific scenarios and events for which each technique is applicable are also clearly presented.Finally,comments on the future evolution of PQD detection techniques are given.Unlike the published review articles,this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. Healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

Current status and construction scheme of smart geothermal field technology

To address the key problems in the application of intelligent technology in geothermal development,smart application scenarios for geothermal development are constructed.The research status and existing challenges of intelligent technology in each scenario are analyzed,and the construction scheme of smart geothermal field system is proposed.The smart geothermal field is an organic integration of geothermal development engineering and advanced technologies such as the artificial intelligence.At present,the technology of smart geothermal field is still in the exploratory stage.It has been tested for application in scenarios such as intelligent characterization of geothermal reservoirs,dynamic intelligent simulation of geothermal reservoirs,intelligent optimization of development schemes and smart management of geothermal development.However,it still faces many problems,including the high computational cost,difficult real-time response,multiple solutions and strong model dependence,difficult real-time optimization of dynamic multi-constraints,and deep integration of multi-source data.The construction scheme of smart geothermal field system is proposed,which consists of modules including the full database,intelligent characterization,intelligent simulation and intelligent optimization control.The connection between modules is established through the data transmission and the model interaction.In the next stage,it is necessary to focus on the basic theories and key technologies in each module of the smart geothermal field system,to accelerate the lifecycle intelligent transformation of the geothermal development and utilization,and to promote the intelligent,stable,long-term,optimal and safe production of geothermal resources.

FISH’s new technology makes the management of textile enterprises smarter

Scientific and technological innovation has brought new experiences such as model changes,marketing changes and management changes to the textile and garment industry.In intertextile Shanghai Apparel Fabrics–Autumn Edition,FISH Technology Co.,Ltd.brought an upgraded version of intelligent application-the world’s first intelligent search platform for materials,its two ace products made a wonderful appearance,committed to the textile industry’s full product line system and information integration research and development and intelligent application.