Multimodal artificial intelligence system for detecting a small esophageal high-grade squamous intraepithelial neoplasia: A case report

BACKGROUND Recent advancements in artificial intelligence(AI)have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases.AI has shown great promise in clinical practice,particularly for diagnostic support,offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.CASE SUMMARY In this study,we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy,highlighting its potential for early detection of malignancies.The lesion was confirmed as high-grade squamous intraepithelial neoplasia,with pathology results supporting the AI system’s accuracy.The multimodal AI system offers an integrated solution that provides real-time,accurate diagnostic information directly within the endoscopic device interface,allowing for single-monitor use without disrupting endoscopist’s workflow.CONCLUSION This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier,more accurate interventions.

Recent progress on artificial intelligence-enhanced multimodal sensors integrated devices and systems

Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.

Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors

This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.

A review of artificial intelligence applications in high-speed railway systems

In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.

Automation 5.0: The Key to Systems Intelligence and Industry 5.0

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.

Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review

Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

Evaluating the role of transportation system in community seismic resilience

The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments,proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery.Focusing on optimizing urban population capacity in the aftermath of earthquakes,the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity.The methodology is demonstrated through an analysis of Beijing’s transportation sys-tem,elucidating how enhancements to transportation infrastructure fortify the resilience of built environments.Additionally,the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake.This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources,indicating a need for retrofitting.Through sensitivity analysis,this study explores how investments in the built environment or logistical systems can increase the resource supply rate,thereby contributing to more resilient urban areas in the face of seismic challenges.

Hub-and-Spoke System in Air Transportation and Its Implications to Regional Economic Development——A Case Study of United States

Considerable changes have taken place in commercial passenger air transport since the enactment of the 1978 Airline Deregulation Act in the US and the deregulation of airline networks that has occurred elsewhere. The commercial and operational freedoms have led most of the larger carriers to develop hub-and-spoke networks, within which certain cities or metropolitan areas emerge as key nodes possessing tremendous advantages over other locations in the air transport system. This paper examines the nature of hub-and-spoke operations in air transportation services, and the benefits that accrue to a city or geographical region that is host to an airline hub. In particular, it looks into linkages between the air service hub and local economic development. Four potential types of impact of airports on the regional economy are defined and discussed. As an example, the assessment of the economic impacts of Cincinnati-Northern Kentucky International Airport (CVG), a major Delta Airlines hub, is introduced.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022

AIM:To conduct a bibliometric analysis of research on artificial intelligence(AI)in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies.METHODS:Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved,covering the period from January 1,2013,to December 31,2022.In order to assess the contributions and co-occurrence relationships among different countries/regions,institutions,authors,and journals,CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified.RESULTS:A total of 750 English articles published between 2013 and 2022 were collected,and the number of publications exhibited an overall increasing trend.The majority of the articles were from China,followed by the United States and India.National University of Singapore,Chinese Academy of Sciences,and Sun Yat-sen University made significant contributions to the published works.Weinreb RN and Fu HZ ranked first among authors and cited authors.American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma.The disciplinary scope of this field includes ophthalmology,computer science,mathematics,molecular biology,genetics,and other related disciplines.The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma.Initially,the hot topics in this field were primarily“segmentation”,“classification”and“diagnosis”.However,in recent years,the focus has shifted to“deep learning”,“convolutional neural network”and“artificial intelligence”.CONCLUSION:With the rapid development of AI technology,scholars have shown increasing interest in its application in the field of glaucoma.Moreover,the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot.However,the reliability and interpretability of AI data remain pressing issues that require resolution.

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

Artificial intelligence for the detection of glaucoma with SD-OCT images:a systematic review and Meta-analysis

●AIM:To quantify the performance of artificial intelligence(AI)in detecting glaucoma with spectral-domain optical coherence tomography(SD-OCT)images.●METHODS:Electronic databases including PubMed,Embase,Scopus,ScienceDirect,ProQuest and Cochrane Library were searched before May 31,2023 which adopted AI for glaucoma detection with SD-OCT images.All pieces of the literature were screened and extracted by two investigators.Meta-analysis,Meta-regression,subgroup,and publication of bias were conducted by Stata16.0.The risk of bias assessment was performed in Revman5.4 using the QUADAS-2 tool.●RESULTS:Twenty studies and 51 models were selected for systematic review and Meta-analysis.The pooled sensitivity and specificity were 0.91(95%CI:0.86–0.94,I2=94.67%),0.90(95%CI:0.87–0.92,I2=89.24%).The pooled positive likelihood ratio(PLR)and negative likelihood ratio(NLR)were 8.79(95%CI:6.93–11.15,I2=89.31%)and 0.11(95%CI:0.07–0.16,I2=95.25%).The pooled diagnostic odds ratio(DOR)and area under curve(AUC)were 83.58(95%CI:47.15–148.15,I2=100%)and 0.95(95%CI:0.93–0.97).There was no threshold effect(Spearman correlation coefficient=0.22,P>0.05).●CONCLUSION:There is a high accuracy for the detection of glaucoma with AI with SD-OCT images.The application of AI-based algorithms allows together with“doctor+artificial intelligence”to improve the diagnosis of glaucoma.

Research on Intelligent Transportation System and Its Key Technology based on IOT

This paper presents a design scheme of intelligent transportation system based on the Internet of things. First, the paper elaborated the related technical and functional demand of intelligent traffic system, designed the gateway level model and the overall project. Then, we design gateway hardware circuit according to the overall plan, and design the gateway application software according to the functional requirements. Through the experiment and simulation results show that, the intelligent transportation system gateway based on Internet of things is ability to create ZigBee network through the way of wireless access, GPRS network, Ethernet access based on the wired way, to realizes multimode access, multi-protocol conversion gateway, ad hoc network functions.

Battlefield target intelligence system architecture modeling and system optimization

To address the current problems of poor generality,low real-time,and imperfect information transmission of the battlefield target intelligence system,this paper studies the battlefield target intelligence system from the top-level perspective of multi-service joint warfare.First,an overall planning and analysis method of architecture modeling is proposed with the idea of a bionic analogy for battlefield target intelligence system architecture modeling,which reduces the difficulty of the planning and design process.The method introduces the Department of Defense architecture framework(DoDAF)modeling method,the multi-living agent(MLA)theory modeling method,and other combinations for planning and modeling.A set of rapid planning methods that can be applied to model the architecture of various types of complex systems is formed.Further,the liveness analysis of the battlefield target intelligence system is carried out,and the problems of the existing system are presented from several aspects.And the technical prediction of the development and construction is given,which provides directional ideas for the subsequent research and development of the battlefield target intelligence system.In the end,the proposed architecture model of the battlefield target intelligence system is simulated and verified by applying the colored Petri nets(CPN)simulation software.The analysis demonstrates the reasonable integrity of its logic.

Analysis of the Application of Artificial Intelligence in Transportation

With the advancement of the information age,the transportation industry has experienced rapid growth,leading to an expansion in the scale and number of highway constructions.However,this development has also given rise to numerous traffic issues,including frequent vehicle congestion and traffic accidents.To address these problems,it is essential to leverage modern technology for real-time information collection and analysis,providing robust technical support for intelligent transportation systems.This paper focuses on artificial intelligence(AI)technology,explaining its concept and its role in intelligent transportation.It reviews the various application areas and analyzes the use of AI in intelligent transportation.Finally,it proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.