A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

基于WEB的智能学习帮助系统ILAS的系统设计

本文提出并设计了一个基于Web、面向师生、以实现适应性学习与交互式群体协同学习相结合的、并具有初步智能的学习帮助系统——I LAS。这一系统集学习论坛、FAQ系统、电子邮件、搜索引擎、聊天室、呼叫中心、智能助手、热线电话等多种有效的学习帮助手段于一体,在网络教学系统中使用时,能够向学生提供全面周到、功能强大的学习帮助。

Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy

Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful tools for computer assisted diagnosis(CAD) systems. However, training and assessing a computer's ability to diagnose like a human are complex tasks, and successful outcomes depend on various factors. We have focused our work on gastrointestinal(GI) endoscopy because it is a cornerstone for diagnosis and treatment of diseases of the GI tract. About 2.8 million luminal GI(esophageal, stomach, colorectal) cancers are detected globally every year, and although substantial technical improvements in endoscopes have been made over the last 10-15 years, a major limitation of endoscopic examinations remains operator variation. This translates into a substantial inter-observer variation in the detection and assessment of mucosal lesions, causing among other things an average polyp miss-rate of 20% in the colon and thus the subsequent development of a number of post-colonoscopy colorectal cancers. CAD systems might eliminate this variation and lead to more accurate diagnoses. In this editorial, we point out some of the current challenges in the development of efficient computer-based digital assistants. We give examples of proposed tools using various techniques, identify current challenges, and give suggestions for the development and assessment of future CAD systems.

基于深度学习神经网络技术的脊柱椎弓根螺钉自动规划研究

目的:针对骨科手术机器人螺钉手工规划效率低下的问题,实现基于CT的脊柱椎弓根螺钉自动、高效、高质量规划。方法:采用深度学习神经网络对标注分割和螺钉的CT图像进行有监督的机器学习,实现脊柱椎弓根螺钉的自动规划;本实验使用44例腰椎CT共440枚螺钉作为训练集,使用11例CT生成110枚螺钉作为测试集,以手工规划作为对照组,通过盲法专家评价评估螺钉规划效果,并通过记录规划时间评估规划效率。结果:该自动规划方法生成的螺钉规划临床可用率为95.4%,自动规划时间与平均手工规划时间分别为68.8 s和177.6 s。结论:该自动规划方法可初步实现高效、高质量的脊柱椎弓根螺钉自动规划,但仍需临床医生监督复核。

基于卷积神经网络的胸部CT肺炎辅助筛查系统研发及应用

新型冠状病毒感染疫情发生以来,胸部CT的检测量急速提升,各医院和基层医疗机构都面临着医疗资源不足、诊疗服务能力不足等问题。为了在医学影像领域开展辅助筛查或诊断等医疗信息化研究,众多医疗信息化人员引进计算机视觉等相关技术,打造了胸部CT肺炎辅助筛查系统。以人工智能(AI)胸片智能辅助筛查应用为例,研究了AI辅助诊断在包括新型冠状病毒感染在内的肺炎大流行期间的应用模式和效果。凸显出系统提升临床和放射工作人员影像阅片工作效率的有利性,并结合该AI辅助诊断模型的准确率和应用效率,阐明该类应用定性判断肺炎类型以及定量估计病灶面积的功效。最后结合临床科研实际工作,提出了应用的局限性和未来展望。

生成式AI时代大学生智能学习助手:框架、挑战与应对

生成式AI时代人工智能技术的快速发展,为大学生智能学习助手的产生和发展提供了坚实的技术支撑。研究以联通主义学习教学交互与认知参与模型为指导,针对生成式AI时代大学生智能学习助手的设计框架、现实挑战与建设进路展开。研究发现,生成式AI时代大学生智能学习助手的设计框架包括三大功能:作为知识获取的智慧平台,为学习者提供友好的学习交互体验的基础功能;作为知识交流的智能座舱,支持学习者的深度学习和研究活动的进阶功能;作为知识生产的创新引擎,促进学生全面持续性发展的高级功能。生成式AI时代大学生智能学习助手的现实挑战包括技术内容可靠性困境、互动与创生困难以及数据的伦理难题。为此,研究提出了大学生智能学习助手的建设进路:一是整合多元资源,提升智能学习助手的问答准确性;二是提升智能素养,促进师生与智能学习助手良性互动;三是完善政策标准,保障智能学习助手的顺利运行。

ASSISTments平台:一款优秀的智能导学系统

ASSISTments平台是一款优秀的基于计算机环境的智能导学系统,它以开放性的应用环境、适应性的学习支架、多样化的诊断报告和自动化的再评补救等特征,为智能导学系统的研究和开发提供了良好的范式。基于此,文章首先介绍了ASSISTments平台,随后详细分析了该平台的特征,最后从研发系统、应用环境、运行机制、诊断功能等方面,探讨了该平台对中国智能导学系统建设带来的启示。文章对ASSISTments平台的介绍和分析,有利于推动智能导学系统的建设及其研究的进一步深入。

基于图特征的组织病理学图像分析方法的最新发展情况与展望

【目的】本文旨在综述最近五年人工智能在辅助组织病理学分析方面的研究进展,主要是图特征方法的应用、当前面临的问题以及未来的挑战。【方法】文章回顾了图论在组织病理学图像分析中的应用,包括图像分割、检测和分类。探讨了图像拓扑结构特征提取的各种图构建算法,例如经典的最小生成树算法及其衍生创新算法等,并分析了图卷积神经网络等网络结构的性能。【结果】通过结构图提取的图特征能够有效表示组织病理学图像中的拓扑信息,有助于实现精确的肿瘤分割、检测以及分类、分级等任务。此外,图特征方法综合全局与局部特征,提供了一种系统化的分析方式,促进了对复杂病理学图像的理解。【结论】图特征与先进的机器学习技术相结合在组织病理学图像分析中展现出强大的潜力,未来这些方法将被优化以提高临床诊断的准确性和效率。

基于文献计量学探讨人工智能在代谢相关脂肪性肝病中的应用及展望

目的基于文献计量学探讨人工智能(AI)在代谢相关脂肪性肝病(MAFLD)中的应用及展望。方法检索Web of Science核心合集数据库(WoSCC)中AI技术应用于MAFLD中的相关文献。运用CiteSpace、VOSviewer、R包“bibliometrix”以及文献计量在线分析平台分析该领域的应用热点及趋势。结果共获得303篇符合要求的文献。从2017年开始,该领域论文数量呈爆发式增长。美国在AI应用于MAFLD领域的研究中处于领先地位,并且是参与国际合作最频繁的国家。加州大学圣地亚哥分校是发文量最高的机构。LOOMBA R是发文量最高的作者,发表了14篇文章。共被引关键词聚类标签显示了10个主要聚类:digital image analysis,machine learning,computer-aided diagnosis,fibrosis stage,automated quantitative analysis,metaproteomics,non-invasive diagnosis,ultrasonography,electronic health records,knowledge representation。AI在MAFLD领域的相关研究目前主要集中在MAFLD的诊断、鉴别诊断以及分期中的应用。图像识别与分析、智能辅助诊断、AI算法和监测疾病进展将是AI在MAFLD领域的重要研究方向。结论AI应用于MAFLD的相关研究呈指数级增长,考虑到该领域的巨大潜力和临床应用前景,AI在MAFLD相关领域的应用仍将是未来的研究热点。

人工智能在教学中的应用与效果评估

分析了人工智能在教学中的基本应用与优劣性,针对典型的个性化学习、自动评估和虚拟助教等人工智能技术的案例,进行了基于案例的数据分析与效果量化评价,评估了人工智能在提高教学效果方面的潜在影响,并提出了人工智能教学应用中可能遇到的难题和挑战,给出了对于未来教学发展的建议。

基于多实例学习的消化道病理辅助筛查模型研究

目的:构建基于多实例学习的消化道病理辅助筛查模型,帮助病理医生在消化道远程会诊场景下快速准确地进行诊断。方法:从郑州大学第一附属医院国家远程医疗中心的远程病理诊断平台收集从2018年至2023年的病理切片图像,经关键词筛选得到6228张消化道切片图像,经数据清洗最终得到5658张切片图像用于模型的训练与测试。该模型由多个基于深度学习的算法模块构成,包括前景提取、图像块分类和切片分类。模型使用消化道阳性切片679张和阴性切片1304张进行训练,在2353张阳性切片和295张阴性切片上进行测试。该模型输入为消化道数字病理切片,输出为该数字切片为阳性切片的概率值。选取具有基层医院医生诊断的切片1027张作为独立数据集比较本文模型和基层医生的分类性能。结果:本模型切片分类的特异度、敏感度和ROC曲线下面积(AUC)分别为0.902、0.955和0.978。选取1027张切片进行人机结果对比,结果显示,本模型的敏感度为0.96,特异度0.95;与基层医院医生诊断结果相比,敏感度提升6.7%,特异度提升26.7%。结论:基于多实例学习的消化道病理辅助筛查模型准确性较高,可提升基层医疗机构医生诊断水平,为临床诊疗提供有效的决策支持。

基于实验和准实验的智能技术提升学习效果元分析

针对智能技术教育对学生学习效果的影响,选取27篇国内外相关文献进行元分析,并对其调节变量进行深入分析。从整体上看,智能技术在对学习效果上有较大的提升作用,但由于调节变量的不同会有明显的差异。基于研究结果,对如何更好地发挥智能技术在学习中的作用提出了实践性建议。

CVTD: A Robust Car-Mounted Video Text Detector

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.

基于机器学习的既有住宅改造辅助诊断智能模型构建

我国既有住宅存量巨大,近些年来,既有住宅改造的研究与实践不断深入。已有的研究经验表明,住宅改造是一个体系化的完整流程,其中诊断评估是改造取得成功的基础和关键。针对数量庞大的存量住宅进行调查与诊断,信息化与智能化技术为其效率的提高带来了新的机遇和挑战。梳理既有住宅诊断评估的阶段与内涵,提出精细化诊断评估的流程与方法,基于机器学习技术构建既有住宅改造辅助诊断的智能评估模型,实现将主观经验转换为可量化的客观评估预测,从而探讨智能化技术辅助既有住宅改造诊断提高客观性和效率的可能性。

基于深度学习的车道线检测算法的研究

近年来,随着软硬件设备的快速升级,智能设备得到了快速发展,智能辅助驾驶技术逐渐落地。而智能辅助驾驶要解决的关键问题之一是车道线、车辆以及行人三者之间的矛盾,因此对智能辅助驾驶系统中的车道线检测展开研究。针对车道线检测算法,采用了不规则编码器-解码器的网络结构以及多任务学习的思想,对车道线检测算法LaneNet进行改进以及对卷积模块进行优化,解决了复杂场景难以检测以及模型精度低的问题。经过大量数据训练和实测数据集的验证,车道线检测在Tusimple数据集上的准确率为96.42%,参数量为5.14 M。测试结果表明:车道线检测效果较好,满足车道线检测的研究目标。

Application of artificial intelligence in gastroenterology

Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.

6G Visions:Mobile Ultra-Broadband,Super Internet-of-Things,and Artificial Intelligence

With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation(6G) mobile communications be on the eve of the fifth-generation(5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things(IoT), and artificial intelligence(AI). Then, we review key technologies to realize each aspect. In particular, teraherz(THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.

Implications of Implementation of Artificial Intelligence in the Banking Business with Correlation to the Human Factor

Banks daily interact with a vast number of customers and are still depending on a legacy system. With today’s advances in technology, regarding lifting almost all processes to automation, from start of production to finish, there is a need for revolution in archaic monetary management institutes. By not being in tune with the contemporary trends and times, banks are losing on an opportunity to transform some of their business models and relieve humans of repetitive work, prevent frauds, make better decisions and consequently gain losses. Banks can engage in implementation of new Virtual Assistants and Artificial Intelligence (A.I.) machine learning technologies, just as the other industries have engaged in modernizing i.e. medical checks, medical reports and evaluations, and this research paper will elaborate and emphasize the impact of artificial intelligence implementation on the banking sector processes. This research is based on both quantitative and model-based proofs of system performance by using several analytical tools, such as SPSS. The automation process helps institutions to enhance profitability, performance and to reduce human dependency. In a nutshell, Virtual Assistants powered with Artificial Intelligence improve the business process performance in every sector of business, especially the banking sector making it fast, reliable and not human dependent.

Artificial intelligence in gastrointestinal endoscopy:The future is almost here

Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.