A machine learning approach for predictings stroke

Background:Stroke is one of the most dangerous and life-threatening disease as it can cause lasting brain damage,long-term disability,or even death.The early detection of warning signs of a stroke can help save the life of a patient.In this paper,we adopted machine learning approaches to predict strokes and identify the three most important factors that are associated with strokes.Methods:This study used an open-access stroke prediction dataset.We developed 11 machine learning models and compare the results to those found in prior studies.Results:The accuracy,recall and area under the curve for the random forest model in our study is significantly higher than those of other studies.Machine learning models,particularly the random forest algorithm,can accurately predict the risk of stroke and support medical decision making.Conclusion:Our findings can be applied to design clinical prediction systems at the point of care.

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives: minimizing makespan, total flow time, and total number of tardy jobs. The decision making method consists of three phases. In the first phase, a mathematical model of a single machine scheduling problem, of which the objective is a weighted sum of the three objectives, is constructed. Such a model will be repeatedly solved by the CPLEX in the proposed Multi-Objective Simulated Annealing (MOSA) algorithm. In the second phase, the MOSA that integrates job clustering method, job group scheduling method, and job group – machine assignment method, is employed to obtain a set of non-dominated group schedules. During this phase, CPLEX software and the bipartite weighted matching algorithm are used repeatedly as parts of the MOSA algorithm. In the last phase, the technique of data envelopment analysis is applied to determine the most preferable schedule. A practical example is then presented in order to demonstrate the applicability of the proposed decision making method.

Medical Diagnosis Using Machine Learning:A Statistical Review

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate diagnosis.Machine learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis.Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published.Hence,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this study.To carry out the review,six different databases are selected.Inclusion and exclusion criteria are employed to limit the research.Further,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and results.Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis.The ndings of this study show the most used ML methods and the most common diseases that are focused on by researchers.It also shows the increase in use of machine learning for disease diagnosis over the years.These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

An Expert System Based on Multi-reasoning Mechanism for Port Machine Diagnosis

Expert system plays an important role in port machine diagnosis, which aims at automatic equipment test for higher availability and efficiency of port operations. In this study, a port machine diagnosis expert system is proposed based on multi-reasoning mechanism. Relying on the knowledge acquired from the experienced experts in the port machine engineering, the system builds a library of relative experience and a set of rules of reasoning and estimating. Multi-reasoning mechanism that simulates the decision-making process of domain experts is employed to achieve reliable diagnosis results. The reasoning machine integrates artificial neural network, uncertain decision making and decision tree, which complements each other by sustainable growing voting mechanism. The effect of this multi-reasoning mechanism is evaluated and validated by means of Matthew＇s Correlation Coefficient （MCC）. The system incorporating the mechanism is successfully designed, implemented and applied in Shanghai Port.

Machine Learning Based Depression,Anxiety,and Stress Predictive Model During COVID-19 Crisis

Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among people.Therefore,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and mortalities.With this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

The Time and Cost Prediction of Tunnel Boring Machine in Tunnelling

Making use of microsoft visual studio. net platform, the assistant decision-making system of tunnel boring machine in tunnelling has been built to predict the time and cost. Computation methods of the performance parameters have been discussed. New time and cost prediction models have been depicted. The multivariate linear regression has been used to make the parameters more precise, which are the key factor to affect the prediction near to the reality.

Geological adaptability matching design of disc cutter using multicriteria decision making approaches

Geological adaptability matching design of a disc cutter is the prerequisite of cutter head design for tunnel boring machines(TBMs)and plays an important role in improving the tunneling efficiency of TBMs.The main purpose of the cutter matching design is to evaluate the cutter performance and select the appropriate cutter size.In this paper,a novel evaluation method based on multicriteria decision making(MCDM)techniques was developed to help TBM designers in the process of determining the cutter size.The analytic hierarchy process(AHP)and matter element analysis were applied to obtaining the weights of the cutter evaluation criteria,and the fuzzy comprehensive evaluation and technique for order performance by similarity to ideal solution(TOPSIS)approaches were employed to determine the ranking of the cutters.A case application was offered to illustrate and validate the proposed method.The results of the project case demonstrate that this method is reasonable and feasible for disc cutter size selection in cutter head design.

Greedy Algorithm Based Deep Learning Strategy for User Behavior Prediction and Decision Making Support

In this paper, we suggest a deep learning strategy for decision support, based on a greedy algorithm. Decision making support by artificial intelligence is of the most challenging trends in modern computer science. Currently various strategies exist and are increasingly improved in order to meet practical needs of user-oriented platforms like Microsoft, Google, Amazon, etc.

Research on Web-GIS Based Intelligent Management Decision-Making System for Soybean

The design of Web-GIS based intelligent management decision-making system for soybean follows the life cycle standard of software engineering. Based on the analysis of the flow of soybean growth technology, the data flow chart was protracted and the function from chart was brought in the course of the designing and realizing the system. By making use of the directed program tool such as VC, Java and multi-media technique the fimctions of decision-making system were realized. It will do a lot of for the theoretical and practical development of intelligent technology of agricultural information of Heilongjiang Province.

Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding

Artificial Intelligence(AI)is a type of intelligence that comes from machines or computer systems that mimics human cognitive function.Recently,AI has been utilized in medicine and helped clinicians make clinical decisions.In gastroenterology,AI has assisted colon polyp detection,optical biopsy,and diagnosis of Helicobacter pylori infection.AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding.Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding.AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion,determining bleeding stigmata during endoscopy,and predicting recurrence of gastrointestinal bleeding.The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding,primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods.This review will also discuss challenges in implementing AI in routine practice.

基于驾驶场景与决策规则的智能汽车换道决策

复杂交通环境中,换道决策直接影响智能汽车自主换道效果,然而在换道决策过程中依旧存在着预测正确率低以及决策安全的问题。因此,针对这一问题,提出了基于驾驶场景和决策规则的换道决策模型。考虑换道后的交通行驶状况对换道决策的影响,引入换道后的期望速度和换道前后与前车的距离作为新的特征变量,基于特征变量与换道决策的相关性建立了换道决策规则。建立了模拟真实驾驶环境的换道场景数据集,扩充了NGSIM换道场景数据集,并对其进行了有效性验证。针对换道决策的多参数和非线性问题,提出了基于贝叶斯优化核函数的支持向量机模型,在换道场景数据集上进行测试验证。结果表明:新引入的决策特征变量对换道行为有积极作用,换道场景数据集能够模拟真实的换道场景,可进一步应用到换道决策和轨迹规划的研究中,支持向量机模型对换道行为的预测正确率达95.40%,高于其他机器学习分类器,提高了换道行为的安全性。

遥感卫星任务智能决策的机器学习方法研究

基于遥感卫星任务决策的特点,研究如何采用机器学习方法对执行任务时产生的大量动作、指令和遥测数据进行分析和训练。为了给遥感卫星任务建立机器学习方法,探索机器学习辅助遥感卫星任务智能决策的可行性,并探讨机器学习模型对卫星任务数据的适应性和处理效率。借鉴地面相关人工智能系统成熟的机器学习架构,研究建立遥感卫星任务相关智能决策的机器学习方法,并给出了机器学习的样例。研究结果表明:机器学习方法的适应性很强,初步实现了遥感卫星自主任务决策,并达到一定的准确率,对卫星任务智能决策技术进行了有益探索。

棉花育苗钵体制钵机设计与实验

针对市场上缺乏适用于个户棉农的小型、实用、经济的棉花钵体制钵农具,对现有制钵机器进行改进设计。在分析制钵机工作原理基础上,结合棉花钵体的物理参数和制钵工序,确定了关键零部件的结构设计参数、整机的动力参数及传动机构的传动比,并利用UG的运动分析模块对制钵机进行运动仿真与分析,研究了冲压机构和钵盘运动机构的运动规律。最后,对制钵机的性能进行试验分析,结果表明:制钵机运动平稳,结构参数设计合理,效率高,制钵完好率达95.54%。研究结果可为棉花育苗钵体制钵机的进一步发展和研究提供参考。

晚清国人对机器制茶的认知与接受

晚清时期,国人对机器制茶的认知经历了由浅入深的过程,对机器制茶的接受程度也由低到高。19世纪80年代后期,华茶贸易危机初现,国人开始了对机器制茶的探讨,此时国人对这一新的生产方式认识尚浅,意见存在分歧。19世纪末,报刊媒体的介绍使国人对机器制茶的认识有所深化,对机器制茶的倡导成为中国茶业改良思想的一个重要部分,政府和商人开始了机器制茶的尝试,但不少人仍对机器制茶存有疑虑。20世纪初的清朝新政时期,对国外的考察进一步增强了国人对机器制茶的认知,机器制茶被视为振兴茶业、挽回利权的必要手段。虽然出于种种原因,晚清机器制茶的实践十分有限,但国人对机器制茶的认知与接受过程反映了其对待新的外来科技积极开放与谨慎理性的双重态度,为中国茶业的近代化发展奠定了一定的基础。

基于中医古籍知识库的临床辅助决策系统建设

旨在探索中医古籍知识库在临床辅助决策系统中的应用与建设。中医古籍包含了丰富的临床经验和中医理论知识,然而由于古籍数量庞大且馆藏分散,保存状态参差不齐,使得医生在临床实践难以充分运用。因此,将中医古籍知识库与临床辅助决策系统相结合,逐渐成为提升中医临床诊疗水平的重要趋势之一。本研究收集岭南中医古籍共计278本,涵盖488种疾病,1198种证候,3907种症状,系统对中医古籍进行整理和分类,构建出一个结构化和标准化的中医古籍库,同时关联引入现代临床医学相关知识,形成了综合性的中医临床知识库。本文设计了一套基于规则的推理引擎和机器学习算法,根据患者的症状、体质等信息,从中医古籍知识库中检索出相应的诊断和治疗建议。通过实际应用和测试,验证了基于中医古籍知识库的临床辅助决策系统可提高中医诊疗的有效性,系统的建设不仅有助于传承和发扬中医古籍知识,也为现代中医临床实践提供了有力的支持。

Predictive Analytics for Project Risk Management Using Machine Learning

Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.

考虑样本多样性的转辙机故障诊断决策模型

为解决现有研究未充分考虑诊断模型在不同转辙机数据样本条件下的适用性,且单一诊断方法难以满足故障诊断精度与决策要求的问题,提出一种考虑样本多样性的故障诊断决策模型.首先,从ZYJ7电液转辙机8种故障模式和正常模式所对应的油压曲线中分别提取时域、频域、时频域特征量,采用基于核函数的主成分分析法对3个域内的特征量分别降维,得到每个域的特征矩阵,进而构成不同类型的数据样本.然后,基于PSO-KNN、SA-PSO-PNN、PSO-SVM算法构建决策模型.当样本是一般数据样本时,决策模型采用3种算法分别做同一域数据分类,并对同一域各算法诊断结果进行三取二表决,分别得到每一域诊断结果;当样本是大数据样本、不均衡数据样本时,决策模型根据不同样本特点采用3种算法中的推荐算法得到每一域诊断结果.最后,利用决策模型对各域诊断结果进行三取二表决得到最终诊断结果 .仿真结果表明:与单一诊断算法相比,决策模型在大数据样本下,平均准确率提升1.01%;在不均衡数据样本条件下,决策模型的平均准确率提升12.82%;在一般数据样本下,决策模型平均准确率提升6.18%.决策模型通过结合多域的多维特征与各算法特点提高了诊断精度,为集成学习在转辙机故障诊断领域应用提供了一种思路.

基于自动化技术的机床电气控制系统改造的设计分析

现代工业发展对工作设备的要求较高,包括机床在内,这为其电气控制系统改造、自动化技术的应用提供了空间。以基于自动化技术的机床电气控制系统改造优势为切入点,分析其设计思路、设计实现方法,包括通信系统、感知系统、决策系统等,并结合模拟实验对上述内容进行论证。最后就自动化技术下机床电气控制系统改造进行展望,服务后续有关工作。

深部金属矿山巷道TBM掘进技术应用现状及研究进展

未来10年,我国将有30%以上金属矿山开采深度达到或超过千米。深部金属矿山开采面临高地应力、高地温、地层多变、采掘强扰动等复杂地质环境,给巷道掘进带来严峻挑战。传统钻爆法巷道掘进非连续作业、效率低、灾害频发,难以满足建设需求,采用隧道掘进机(TBM)进行机械化连续掘进是深部金属矿山巷道建设的未来发展方向。然而,因深部金属矿山独有工程地质特点和巷道掘进需求,TBM技术在深部金属矿山巷道掘进中仍存在一些技术难题和挑战。本文首先总结了TBM技术在深部金属矿山巷道掘进中的三个应用难点:1)缺乏适用于深部金属矿山巷道的TBM适应性选型设计理论;2)缺乏适用于深部金属矿山巷道的TBM高效掘进技术;3)缺乏适用于深部金属矿山巷道的TBM掘进灾害监测预警与防控技术。然后,从六个方面介绍了深部金属矿山巷道TBM掘进技术应用的研究进展:适应性选型与设计技术、刀盘刀具高效破岩理论、掘进性能精准预测与评价方法、小半径转弯和倾斜巷道掘进技术、掘进灾害智能预警与防控技术、智能决策和辅助驾驶技术。上述研究成果为推动TBM掘进技术在深部金属矿山巷道中的广泛应用,实现深部金属矿山巷道快速掘进指明了发展方向。