Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition

Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare.Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine(TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects:(1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation,(2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice,(3) the current progress of AI technology in TCM pulse diagnosis,(4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Artificial intelligence as a future in cancer surgery

Artificial intelligence(AI)is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence,such as visual perception,speech recognition,and decision-making.Machine learning and deep learning(DL)are subfields of AI that are able to learn from experience in order to complete tasks.AI and its subfields,in particular DL,have been applied in numerous fields of medicine,especially in the cure of cancer.Computer vision(CV)system has improved diagnostic accuracy both in histopathology analyses and radiology.In surgery,CV has been used to design navigation system and robotic-assisted surgical tools that increased the safety and efficiency of oncological surgery by minimizing human error.By learning the basis of AI,surgeons can take part in this revolution to optimize surgical care of oncologic disease.

Artificial Intelligence & Machine Learning in the Earth Sciences

1 Introduction In the same way mathematics is regarded by many mathematicians as the study of patterns in numbers(Hardy,1940),the earth sciences can be thought of usefully as the study of patterns in the physical,chemical and biotic constituents of the Earth in both time and space.The documentation,definition and,ultimately。

Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals.One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high accuracies.MFCCs represents a sequence of voice signal-specific features.This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings.Since the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D array.Several learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the speaker.The highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation function.The most important output of this study is the inclusion of human voice as a new feature set.

Artificial Neural Network for Websites Classification with Phishing Characteristics

Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.

人工智能技术造纸化学品实验室人员安全行为识别方法分析

在造纸行业的全流程中涉及众多细致入微的分析测试项目,包括但不限于原料准备、蒸煮、漂白、打浆以及造纸等阶段的参数和性质指标,其分析方法和流程繁复且多样。鉴于此,聚焦于人工智能技术,针对造纸化学品实验室人员可能出现的不安全操作行为,提出一种创新的安全管理行为识别策略。该策略旨在通过智能化手段,提升对实验室人员安全行为的识别效率,进而为造纸企业的安全稳健发展提供科技支撑。

基于迁移学习与智能模式识别的城市地标可视性研究——以南京紫峰大厦为例

城市地标可视性研究是城市设计及景观风貌保护等相关规划的重点内容。目前,传统的“眺望”视线控制方法在可视域划定、可视度分析上存在缺陷,后续结合数字化手段改进的三维可视域模拟分析方法部分解决了可视域划定的问题,但数据精度要求高,且无法满足可视度分析要求。针对此问题,本文提出了一种基于迁移学习与智能模式识别的城市地标可视性分析方法,以南京紫峰大厦为例,利用自主采集的街景数据,结合地标数据集,训练出结合迁移学习、深度神经网络的人工智能体,完成对不同尺寸下,符合紫峰大厦特征的地标识别。通过改进的智能模式识别方法,可以实现地标的可视域及可视度识别。经验证,分析结果较过去的“眺望”视线控制方法,三维可视域模拟分析方法更为精准、真实。弥补现有方法在可视域、可视度分析上的不足。

基于人工智能算法的网络安全自动预警系统设计

随着网络技术的快速发展,网络安全问题日益突出。为了提高网络安全预警的准确性和实时性,提出一种基于人工智能算法的网络安全自动预警系统。该系统通过对网络流量和安全日志进行实时监测,利用人工智能算法进行数据分析和模式识别,及时发现潜在的安全威胁,并生成预警信息。实验结果表明,该系统能够有效提高网络安全预警的准确性和实时性,为网络安全防护提供有力支持。

An Approach to Checking 3D Model with Related Engineering Drawings

For some reasons, engineers build their product 3D mo del according to a set of related engineering drawings. The problem is how we ca n know the 3D model is correct. The manual checking is very boring and time cons uming, and still could not avoid mistakes. Thus, we could not confirm the model, maybe try checking again. It will effect the production preparing cycle greatly , and should be solved in a intelligent way. The difficulties are quite obvious, unlike word checking in a word processing package, the checking described above is not a comparison between same items. One is 2D drawing, the another is 3D mo del, they are not in the same dimension. So, we should make a change for compari son in the same dimension. If we can rebuild a 3D model through related 2D drawi ngs automatically, that’s great. We can not only compare two 3D models to check and correct, but also omit the manual process itself completely. Unfortunately, we can not build such a 3D model automatically right now. So only one way left: compare two 2D drawings, one is the original, the another is processed from tha t manual built one.The method is to select a drawing as a background, rotate th e 3D model and make projections, compare projection with the background automati cally to find a case which they meet each other in certain amount of error ( tolerance), otherwise alarm. This process can be repeated many times if needed t o fulfil the checking task. Also, this is a man-machine system, computer does h ard working, man keeps final decision. The project involved in CAD, VRML, patter n recognition, image capture and comparison, artificial intelligence.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

A multi-agent system for itinerary suggestion in smart environments

Modem smart environments pose several challenges,among which the design of intelligent algorithms aimed to assist the users.When a variety of points of interest are available,for instance,trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints.Unfortunately in many cases,these interests must be explicitly requested and their lack causes the so-called cold-start problem.Moreover,lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult.To address these aspects,a multi-agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed.A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic,spatial and temporal features;then,a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi-Armed Bandit policy;Framing the trajectory recommendation as a reinforcement learning problem permits to provide high-quality suggestions while avoiding both cold-start and preference elicitation issues.The effectiveness of the approach is demonstrated by some deployments in real-life scenarios,such as smart campuses and theme parks.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

粒子群优化算法在电力系统中的应用

粒子群优化方法是一种基于群体智能的新型演化计算技术。它在函数优化、神经网络设计、分类、模式识别、信号处理、机器人技术等许多领域已取得了成功应用,但在电力系统中应用的研究起步较晚,关于它实际应用的报道尚不多见。文章较为全面地详述了粒子群优化方法在配电网扩展规划、检修计划、机组组合、负荷经济分配、最优潮流计算与无功优化控制、谐波分析与电容器配置、配电网状态估计、参数辨识、优化设计等方面应用的主要研究成果。随着粒子群优化理论研究的深入,它还将在电力市场竞价交易、投标策略以及电力市场仿真等领域发挥巨大的应用潜力。

人体运动非监督聚类分析

提出了一种基于非监督学习的人体运动分析方法.该方法通过使用MDL准则约束下的HMM模型对连续运动序列进行分割和聚类,并实现对运动序列的自动分割和标记.该方法由两步组成,首先通过聚类将连续运动离散化,并按照最小描述长度准则在离散域得到初始解.在此基础上,返回到连续域训练MDL准则约束下的HMM模型.使用HMM模型可以进一步利用原始序列中的动态信息获得更精确的最终结果.通过对实际人体运动序列进行的实验验证了方法的有效性.

ART-2神经网络的研究与改进

ART-2神经网络可以很好地应用于模式识别中的聚类问题,但是由于其算法结构中固有的归一化环节,在处理数据过程中丢失了非常重要的幅度信息。在分析这一不足的基础上,提出两种改进算法,同时给出了相应的实验结果。

人工神经网络和机械故障诊断

智能化诊断是现代故障诊断技术发展的主要趋势,人工神经网络技术的出现为这种智能化提供了一个全新的途径。本文首先简单介绍了人工神经网络的基本性能及几个重要模型,着重探讨了人工神经网络技术在机械故障诊断领域中预测与控制、工况监测与故障分类诊断、模糊诊断和基于专家系统的故障诊断等几个主要方面的应用,指出人工神经网络技术与现有的信号处理、模式识别、模糊逻辑、专家系统等技术相结合,以解决故障信号分析与处理、故障模式识别以及故障论域专家知识的组织和推理等问题,必将加快智能化诊断发展的进程。可以预料:基于人工神经网络的故障诊断技术将具有广阔的发展与应用前景,并且随着VLsI 技术的发展,这一新技术必将广泛地应用于各种诊断实例。最后讨论了进一步值得研究的方向。

人工免疫系统:原理、模型、分析及展望

目前 ,受生物免疫系统启发而产生的人工免疫系统 (Artificial Im mune System,AIS)正在兴起 ,它作为计算智能研究的新领域 ,提供了一种强大的信息处理和问题求解范式 .该文侧重以 AIS的基本原理框架为线索 ,对其研究状况加以系统综述 .首先从 AIS的生物原型入手 ,归纳提炼出其仿生机理 ,主要包括免疫识别、免疫学习、免疫记忆、克隆选择、个体多样性、分布式和自适应等 ,进而对几种典型的 AIS模型和算法分门别类地进行了细致讨论 ,随后介绍了 AIS在若干具有代表性的领域中的应用情况 .最后通过对 AIS的特性和存在问题的分析 ,展望了今后的研究重点和发展趋势 .

面向变异短文本的快速聚类算法

本文主要针对近些年来大量出现在聊天语言中和手机短信中的短文本，提出了一种快速有效的聚类算法。这些短文本由于具有不规范性和大量相似性等特点，我们称其为变异短文本。本文在原有的网页去重算法的基础上，根据变异短文本的特点，采取了特定的特征串抽取方法，并融合了压缩编码的思想，从而加快了处理速度。实验表明，基于该算法的聚类系统对于大量的变异短文本处理速度可以达到每小时百万级以上，并且有比较高的准确率。

质量控制图在线智能诊断分析系统

在计算机集成制造系统环境下,为了有效实现工序质量控制,提出了质量控制图的在线智能诊断分析系统框架,它由控制图模式识别、参数估计、专家诊断分析系统和加工参数调整系统四个模块组成。在该系统中,采用了一种适用于模式识别与分类的新型神经网络模型———局部有监督特征映射网络,将其应用于该系统的控制图模式识别和参数估计中。仿真实验和应用实例表明,识别和分类结果与实际相符,并可以保证实时性。