AI技术在全真互联实现过程中的作用分析

全真互联代表数字创新的一个新维度,提供沉浸式和互动式虚拟体验的潜力,模糊物理世界和数字世界之间的界限。而AI在这个过程中扮演至关重要的角色,其可以处理和分析大量的数据,从而提供更加精准的感知和决策支持。该文讨论人工智能在全真互联中的关键作用,从全真互联的介绍出发,论述全真互联所需的四大人工智能技术——机器学习、自然语言处理、计算机视觉技术与数字孪生,既而引发对人工智能在智慧城市、全真互联游戏、数字虚拟人、医疗服务及其他潜在应用如电子商务、岗位招聘中所发挥的作用的分析,并对人工智能技术对未来全真互联的发展进行展望。

无线通信系统中AI/ML技术在物理层的运用

无线通信系统物理层作为无线通信系统中的核心,结合当前无线通信系统物理层方面存在的一些不足以及人工智能(AI)/机器学习(ML)的技术优势,可为无线通信系统中物理层中部分问题的解决提供思路.本文通过对AI/ML相关内容的解释,进一步分析AI/ML在无线通信系统物理层中的应用优势以及需要解决的问题,并通过案例分析的方式展示AI/ML在无线通信系统物理层中的具体应用,旨在为AI/ML在无线通信系统物理层中的应用提供参考.

基于机器学习的法医微生物研究现状

微生物广泛存在于自然界和人体,其群落分布差异性为解决法医学相关问题提供了很多新的可能性。作为实现人工智能的主要方法之一,机器学习拥有强大的识别、处理和分析数据能力,为法医微生物研究提供了新思路和新方法。通过介绍近年来在法医微生物研究中使用较多的无监督学习、监督学习和深度学习等机器学习算法,并对其在个体识别、死亡时间推断、地理位置溯源等领域的探索性研究进行总结。此外,还系统剖析了机器学习算法在法医微生物研究中标准化流程、数据库建立和证据解释等方面存在的问题,并对其在法医微生物中的应用进行展望。

Evaluating Pharmacological and Rehabilitation Strategies for Effective Management of Bipolar Disorder: A Comprehensive Clinical Study

Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy of pharmacological interventions and rehabilitation strategies to improve patient outcomes and quality of life. Utilizing a randomized controlled trial with multiple treatment arms, participants will receive pharmacotherapy, polypharmacotherapy, rehabilitation interventions, or combination treatments. Outcome measures will be assessed using standardized scales, including the Hamilton Depression Scale, Yale-Brown Obsessive Compulsive Scale (Y-BOCS), and Mania Scale. Preliminary data suggest improvements in symptom severity and functional outcomes with combination treatments. This research aims to inform clinical practice, guide treatment decisions, and ultimately enhance the quality of care for individuals living with bipolar disorder. Findings will be disseminated through peer-reviewed journals and scientific conferences to advance knowledge in this field.

基于人工神经网络方法的FRP增强混凝土断裂研究新思路

纤维增强复合材料(FRP)作为一种新型的增强加固材料,由于其强度高、质量轻、防腐蚀、耐疲劳、与混凝土粘结性能好以及便于施工等诸多优点,在混凝土结构修复加固领域得到了广泛的应用。近年来,随着人工智能(AI)的逐渐兴起,机器学习(ML)作为实现AI的一种途径,在水利、建筑等各行各业也得到了长足的发展。首先简单介绍了ML的基本原理,并通过对ML在混凝土结构工程中应用的系统回顾与总结,指出了传统试验和数值模拟分析中FRP增强混凝土断裂研究存在的一些难点和局限性,阐述了基于ML的人工神经网络(ANN)方法在处理混凝土结构问题中的优越性,认为采用ANN方法能够有效解决FRP增强混凝土断裂研究中难以解决的问题;其次,对ANN方法应用于FRP增强混凝土断裂韧度预测中的新思路进行了详细介绍,给出了ANN方法应用于FRP增强混凝土断裂韧度预测的具体流程,并对其流程中的一些步骤给出了建议;最后,对ML应用于FRP增强混凝土断裂方向的深入研究进行了展望,提出了ML应用于FRP增强混凝土断裂方向深入研究的相关问题。

强化学习可解释性基础问题探索和方法综述

强化学习是一种从试错过程中发现最优行为策略的技术,已经成为解决环境交互问题的通用方法.然而,作为一类机器学习算法,强化学习也面临着机器学习领域的公共难题,即难以被人理解.缺乏可解释性限制了强化学习在安全敏感领域中的应用,如医疗、驾驶等,并导致强化学习在环境仿真、任务泛化等问题中缺乏普遍适用的解决方案.为了克服强化学习的这一弱点,涌现了大量强化学习可解释性(explainable reinforcement learning,XRL)的研究.然而,学术界对XRL尚缺乏一致认识.因此,探索XRL的基础性问题,并对现有工作进行综述.具体而言,首先探讨父问题——人工智能可解释性,对人工智能可解释性的已有定义进行了汇总;其次,构建一套可解释性领域的理论体系,从而描述XRL与人工智能可解释性的共同问题,包括界定智能算法和机械算法、定义解释的含义、讨论影响可解释性的因素、划分解释的直观性;然后,根据强化学习本身的特征,定义XRL的3个独有问题,即环境解释、任务解释、策略解释;之后,对现有方法进行系统地归类,并对XRL的最新进展进行综述;最后,展望XRL领域的潜在研究方向.

人工智能在美国药物警戒中的应用现状及启示

目的 论述人工智能在美国药物警戒中的应用现状,以期为我国的药品上市后监管工作提供借鉴。方法 通过查阅文献,从药品上市后监管视角,系统介绍人工智能相关定义和性能指标,美国食品药品监督管理局(FDA)应用人工智能的背景,FDA在人工智能的实践进展和考量,以及在药物警戒工作中的挑战等。结果 近10余年,FDA已广泛探索人工智能在药物警戒中的应用,现阶段FDA主要专注于人工智能处理和评价提交至FDA不良事件报告系统中的个例安全性报告(ICSRs),并在ICSRs关键信息提取和分析、ICSRs关联性评价等方面取得了主要进展。结论 借鉴FDA经验,建议我国加快引入人工智能技术、加强相关人员培训和人才培养与引进、建立人工智能社会技术与管理体系、促进人工智能领域国际领域交流和合作。

A Survey of the Machine Learning Models for Forest Fire Prediction and Detection

Forest fires are a significant threat to the environment, causing ecological damage, economic losses, and posing a threat to human life. Hence, timely detection and prevention of forest fires are critical to minimizing their impact. In this paper, we review the current state-of-the-art methods in forest fire detection and prevention using predictions based on weather conditions and predictions based on forest fire history. In particular, we discuss different Machine Learning (ML) models that have been used for forest fire detection. Further, we present the challenges faced when implementing the ML-based forest fire detection and prevention systems, such as data availability, model prediction errors and processing speed. Finally, we discuss how recent advances in Deep Learning (DL) can be utilized to improve the performance of current fire detection systems.

人工智能在肿瘤三级预防中的应用机遇与挑战

随着人工智能(artificial intelligence,AI)技术的快速发展,其在处理高通量、多维度信息方面的优势逐渐显现,为肿瘤防控带来新的机遇。将AI技术与影像学、病理学、电子健康数据和组学资料结合,将有效促进恶性肿瘤病因和危险因素识别以推动一级预防,更早且更准确地发现和诊断恶性肿瘤而增进二级预防,并对患者进行风险评估和预后预测以指导临床用药和治疗使三级预防受益。然而,AI的应用仍受限于数据库的系统完整性和可及性,在模型鲁棒性、泛化性和结果解读等方面仍面临挑战,因此限制了其在真实世界肿瘤防控中的应用。本文阐述近年来AI技术在肿瘤三级预防领域的研究进展和应用现况,介绍当前AI应用于肿瘤防控面临的挑战和进展,并对其前景进行展望。

基于特征值分布和人工智能的网络入侵检测系统的研究与实现

为维护操作系统的安全性和可靠性,提出了一个具备泛用能力、基于人工智能模型的网络入侵检测系统框架,其主要功能是针对来自互联网里各种形式的网络流量进行检测,并嗅探出可能的入侵攻击及恶意网络连接并将其归类。该框架首先使用采样、独热编码、特征选择和归一化将网络流量实例进行预处理,获取基本信息和筛选重要特征;然后利用网络连接实例的特征值分布建立评分机制,对数据进行信息再提取;最后针对不同的网络流量形式,利用不同的基于机器学习或深度学习的模型进行结果判断。实验中使用三个公开基准数据集KDDCup99、UNSW-NB15和CICIDS2017进行训练和测试。通过与相关文献比较,发现提出方法在三个数据集的正确率和F 1得分上均有着优异的表现。

人工智能在肿瘤研究和临床中的应用

人工智能(AI)的快速发展为肿瘤防控带来新的机遇。本综述首先简要概述了AI的起源和基本要素,接着梳理了AI在肿瘤领域的获批产品,并围绕基于图像的肿瘤筛查和诊断,列举了AI在几大肿瘤中的重要研究进展。最后,从数据、算法、应用和伦理角度出发,总结了AI在肿瘤应用中的问题和展望,为未来肿瘤领域的AI研究提供更多思路。

Artificial Intelligence-Empowered Resource Management for Future Wireless Communications: A Survey

How to explore and exploit the full potential of artificial intelligence(AI)technologies in future wireless communications such as beyond 5G(B5G)and 6G is an extremely hot inter-disciplinary research topic around the world.On the one hand,AI empowers intelligent resource management for wireless communications through powerful learning and automatic adaptation capabilities.On the other hand,embracing AI in wireless communication resource management calls for new network architecture and system models as well as standardized interfaces/protocols/data formats to facilitate the large-scale deployment of AI in future B5G/6G networks.This paper reviews the state-of-art AI-empowered resource management from the framework perspective down to the methodology perspective,not only considering the radio resource(e.g.,spectrum)management but also other types of resources such as computing and caching.We also discuss the challenges and opportunities for AI-based resource management to widely deploy AI in future wireless communication networks.

人工智能及其在中医心血管疾病中的应用

心血管疾病是全球主要的高致死性疾病,早期预测心血管事件的风险可以降低急性心肌梗死的发生率,降低心血管疾病患者的死亡率。由于心血管疾病复杂的病理机制和多因素参与心血管病的发生和发展过程,传统的数据分析无法有效处理心血管疾病和从多维数据层面预测心血管疾病风险、解读医学影像、提出治疗决策和预测疾病预后。另一方面,中医药已被广泛用于治疗心血管疾病,中医药在心血管疾病的诊治方面具有独特的理论和实践体系。多学科技术探索中医诊断方法产生的大数据难以用传统方法进行分析,同时,中医配方含有多种草药成分,阐明活性化合物和网络调节的复杂相互作用也需要先进的数据分析能力。人工智能技术的最新进展为解决这些挑战提供了强大的工具,极大地促进了发展中西医结合诊断和治疗策略以及理解中药复方治疗心血管病的科学原理。我们简要介绍了人工智能和机器学习技术的基本概念和最新进展,并总结了先进的人工智能和机器学习在心血管疾病诊断和治疗中的应用。此外,我们还回顾了利用综合人工智能和机器学习技术研究心血管疾病中医诊治科学依据的主要进展。我们预期人工智能和机器学习技术的应用将促进中西医对话以及为发展现代中西医结合诊治心血管疾病创造巨大机遇。

机器学习在创新药物研发中的应用进展

介绍人工智能和药物设计基本概念、主要算法、技术和实际应用,探讨机器学习在创新药物研究中的应用,从分类回归、靶蛋白结构预测、活性位点识别和结合亲和力角度,详述基于机器学习策略的虚拟筛选技术在创新药物研发中的应用和挑战,对该技术发展进行展望。

Prediction of Wine Quality Using Machine Learning Algorithms

As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aims to understand the structure of the data and fit it into models, which later can be used in unseen data to achieve the desired task. ML has been widely used in various sectors such as in Businesses, Medicine, Astrophysics, and many other scientific problems. Inspired by the success of ML in different sectors, here, we use it to predict the wine quality based on the various parameters. Among various ML models, we compare the performance of Ridge Regression (RR), Support Vector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer Artificial Neural Network (ANN) to predict the wine quality. Multiple parameters that determine the wine quality are analyzed. Our analysis shows that GBR surpasses all other models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873 respectively. This work demonstrates, how statistical analysis can be used to identify the components that mainly control the wine quality prior to the production. This will help wine manufacturer to control the quality prior to the wine production.

2020国外石油物探技术进展与趋势

为了解2020年国外石油物探业务和技术的主要进展及发展趋势,对EAGE(欧洲地质学家与工程师学会)和SEG(国际勘探地球物理学家学会)年会与展会情况,及国外主要石油物探技术服务公司的业务发展与技术研发动态进行跟踪。2020年新冠肺炎疫情叠加低油价冲击,对石油物探技术服务需求和地震作业活动产生重大负面影响,尚未完全复苏的石油物探行业再次面临巨大挑战和压力。石油物探业务发展更加依赖轻资产业务,石油物探技术更加依赖低成本、高效、环保型技术。基于人工智能的地震数据处理解释技术快速发展,软件产品不断完善,基于云的石油物探综合数据管理方案是行业发展热点。

Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFASt^TM)

Using the latest available artificial intelligence (AI) technology, an advanced algorithm LIVERFAStTM has been used to evaluate the diagnostic accuracy of machine learning (ML) biomarker algorithms to assess liver damage. Prevalence of NAFLD (Nonalcoholic fatty liver disease) and resulting NASH (nonalcoholic steatohepatitis) are constantly increasing worldwide, creating challenges for screening as the diagnosis for NASH requires invasive liver biopsy. Key issues in NAFLD patients are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. In this prospective study, the staging of three different lesions of the liver to diagnose fatty liver was analyzed using a proprietary ML algorithm LIVERFAStTM developed with a database of 2862 unique medical assessments of biomarkers, where 1027 assessments were used to train the algorithm and 1835 constituted the validation set. Data of 13,068 patients who underwent the LIVERFAStTM test for evaluation of fatty liver disease were analysed. Data evaluation revealed 11% of the patients exhibited significant fibrosis with fibrosis scores 0.6 - 1.00. Approximately 7% of the population had severe hepatic inflammation. Steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and Fibrosis) scores obtained using the LIVERFAStTM algorithm, NAFLD was detected in 13.41% of the patients (Sx > 0, Ay 0). Approximately 1.91% (Sx > 0, Ay = 2, Fz > 0) of the patients showed NAFLD or NASH scorings while 1.08% had confirmed NASH (Sx > 0, Ay > 2, Fz = 1 - 2) and 1.49% had advanced NASH (Sx > 0, Ay > 2, Fz = 3 - 4). The modified SAF scoring system generated by LIVERFAStTM provides a simple and convenient evaluation of NAFLD and NASH in a cohort of Southeast Asians. This system may lead to the use of noninvasive liver tests in extended populations for more accurate diagnosis of liver pathology, prediction of clinical path of individuals at all stages of liver diseases, and provision of an efficient system for therapeutic interventions.

The Future of Tax Administration

This article analyzes the historical progression of tax administration in the utilization of computer technology since the mid-20th century.The digitalization of tax administration is divided into three main stages which are elaborated respectively.The most precise analysis is performed for today's practice with using of machine learning and artificial intelligence(ML&AI),and the key areas for ML&AI implementation in tax administration are identified.Some examples and results of applying ML&AI tools in Armenian Tax Administration are also presented.Plans for future activities in the area of digitalization are also presented.The article also tries to predict in which direction progress will go,what will be the most important change in Tax Officer vs.Taxpayer Cooperation.

核电站数字化升级与转型研究

云计算、大数据分析、物联网(IoT)、人工智能(AI)、机器学习(ML)和数字孪生等技术的兴起和快速发展,为核电站等重要基础设施的数字化转型提供了强大的技术支持和广阔的应用前景。本文旨在探讨核电站数字化现状、数字化升级与转型的关键技术及关键问题,为迎接数字化转型浪潮提供参考。

人工智能在检验医学的应用与展望

人工智能(AI)、大数据分析、网络通讯等技术飞速发展,给检验医学的发展带来了巨大的机遇与挑战。检验大数据应与AI相融合,优化检验系统的管理,协助临床医生进行决策。检验AI在四方面具有发展前景:以患者为中心的检验AI、个性化健康预防保健体系的构建、慢性疾病的个体化管理、改善医疗不平衡。但目前检验AI研发还有一些困难和挑战。