Aviation Safety and Data Mining in Marketing Dimension

The aviation industry is a sector that is developing, changing and growing every day in terms of technological and legal framework. There are generally three factors that enable airlines to hold on to the market. These factors are safety, service quality and price. Airline companies can analyze the customers in the market with a focus on price and quality and develop a business model according to their expectations. For example, business class and economy class passenger expectations are different from each other, so the service and price to be offered to them will be different. However, all customers have one common expectation and that is safety. No matter how high quality the service is or how cheap the price is, no one wants to fly with an airline or plane that is not safe. From an airline company’s point of view, an accident or breakdown of one of the company’s aircraft can cause irreparable image loss and financial damage. If we look at past examples, we see that there are many airline companies or maintenance organizations that could not recover after an accident and went bankrupt. Safety is an indispensable factor. Therefore, there is a unit in the sector called the safety management system (SMS), which collects data by taking a proactive and reactive approach. The way and purpose of the safety management system is to take a proactive approach to recognize and prevent unsafe situations before they cause accidents or breakdowns, or to take a reactive approach to find the causes of accidents and breakdowns that have occurred as a result of certain factors and to take the necessary measures to prevent the same situations from happening again in the sector. The field of data mining, which is necessary to predict the future behavior of customers in the field of marketing, is an area that marketing also values. In this study, data mining studies to ensure safety in the aviation industry and the security of customer information in marketing will be emphasized, firstly, the concept and importance of data mining will be mentioned.

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

Data Mining in Customer Relationship Management in companies

More and more practice proves that focusing on customer needs is the key to business success.So studying on customer relationship management is very important for its implementation in enterprises.In recent years,data mining in customer relationship management(CRM) application has always been one of the hot spots.This paper shows the relevant methods of data mining application in CRM taking Telecom as an example.

The Research of Customer Loyalty Improvement in Telecom Industry Based on NPS Data Mining

In recent years, the telecommunications have used the concept of NPS(Net Promoter Score) for customer relationship management, but there is neither definite theory research nor instructive instance research. However, this paper summarizes an approach with instance case analysis to improve customer loyalty via NPS data mining, which has extensive and practical significance for tele-companies. First, this paper finds some driven forces of customer loyalty, which are relative to customer consumption such as the call duration, the usage of data, ARPU, etc., by using some innovative reasoning-analysis based on IG(Information Gain) and xg-boost decision-making tree model, so the tele-companies can predict the role of individual customer and form daily monitoring on big data, which will save a lot of NPS survey cost. Second, this paper summarizes how customer group feature impacts the relationship between NPS and financial performance. Taking ARPU value as the performance goals, we divide the sample customers into 6 groups and summarize their characteristics based on k-means clustering, and give targeted suggestion of each group.

Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data

Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.

气象敏感性疾病综合数据平台设计与实现

目的/意义设计综合数据平台,以满足气象敏感性疾病调查数据收集和共享的需要,提升我国对气象敏感性疾病的监测能力。方法/过程通过数据抽取、数据交换、数据导入、定制采集等多种方式,将疾病数据、气象数据、环境数据、诊疗数据等加工处理,纳入平台统一管理。结果/结论该平台实现了数据采集、汇交、可视化展现、数据共享功能,可为全国各基地的科研人员更好地管理和利用气象敏感性疾病调查数据提供支撑。

嵌入式处理器自定义指令迭代识别方法仿真

嵌入式系统中的硬件资源是有限的,并且自定义指令和原始指令之间可能存在冲突,导致指令代码识别精准度降低、运行功耗较高。为此,提出嵌入式处理器自定义指令迭代低功耗识别方法。对嵌入式处理器的指令代码展开可视化处理,将指令图像输入卷积神经网络中,检测指令代码中存在的恶意代码,采用开源编译器将代码转变为控制数据流图,枚举并选择子图,通过代码转换完成嵌入式处理器自定义指令识别。仿真结果表明,所提方法的恶意代码检测精度高、代码识别准确率高,始终保持在70%以上,平均能耗仅为89J。

基于特征提取的电力客户服务大数据溯源模型

为提高数据溯源算法的运行效率,基于特征提取方法提出电力客户服务大数据溯源模型。定义数据的基因组,查找数据库内的任意两个存在血缘关系的数据,以此建立数据染色体追溯模型;获取电力数据中的四类特征值,建立电力数据多次遗传的转移矩阵,基于特征提取构造电力大数据溯源路径;得出大数据溯源算法,构建电力客户服务大数据溯源模型。实验结果显示,特征提取算法在模型层数以及数据量相同时,溯源所需时间最短,算法运行速度最快。

数据驱动的鞋楦设计现状及发展综述

鞋楦是制鞋成型过程中的模具,是链接足与鞋的重要纽带,传统的鞋楦设计囿于楦师手工和经验,难以实现高效准确的大规模个性化定制。鉴于此,本文结合数字智能化技术的发展,对比国内外鞋楦设计的研究现状,明确当今鞋楦设计的趋势和我国在该领域面临的挑战;提出了数据驱动的鞋楦设计内涵、优势及新特点;介绍了数据驱动的足-楦一体式设计流程,为鞋楦领域研究人员提供参考与借鉴。

天然气站场特殊时段法分输控制逻辑设计与应用

针对天然气分输用户的数量不断增多,用户的供气需求日益复杂的状况,传统分输站的分输控制逻辑已难以满足天然气合理配置与高效利用的要求。采用STL分解法分析某站场实际运行数据,根据分析结果得出传统不均匀系数逻辑的不足;提出了新的分输控制逻辑一一特殊时段法,并对特殊时段法的可靠性进行了验证,证明了特殊时段法相比不均匀系数法的优化率可提升25%以上。

服装设计的个性化定制中运用人工智能技术的研究

随着人们对服装个性化需求的不断增长,传统的服装设计模式已难以满足市场需求。文章梳理了人工智能技术在服装设计中的应用,分析了人工智能在服装设计个性化定制中的机制与原理,认为目前服装个性化定制中人工智能技术尚存在的数据安全、客户接受度、技术复杂与成本的问题,并阐述了相应的解决对策。相信通过进一步探索和应用人工智能技术,个性化定制在服装设计领域会有更好的发展。

大数据杀熟是否可以避免——基于演化博弈和仿真的分析

以节约公共资源和支持市场自由发展为出发点,建立平台与用户之间的演化博弈模型,探讨用户在可以自由切换平台的情境下,不同均衡点的形成条件及其稳定性。根据理论推导的条件,使用Octave进行数值仿真,以分析影响平台算法杀熟定价的因素,并确定不使用杀熟定价的理想状态所需满足的条件。同时,研究旨在探索除监管外,哪些措施可以帮助实现这一理想状态。研究结果表明,避免大数据杀熟并不一定需要依赖实时监控或罚款等手段,以市场化手段发展多个平台和鼓励平台竞争可能是最直接且可行的方法。

算网融合DPU可定制化能力分层方法

社会数字化转型推动着行业提升“联接+计算”能力,促进网络向算网融合发展。数据处理单元(Data Processing Unit,DPU)是在海量数据高效传输和处理需求下快速发展起来的新技术形态,部署位置处于网络和计算的衔接点,是算网融合的关键技术。文章首先阐述DPU的典型应用场景和方案,其次分析DPU的组成架构和可编程资源,最后提出DPU的可定制化能力分层方法,用于DPU的软硬件资源和可定制化能力细分,有助于用户根据业务需求和研发能力进行产品评估和选型、二次开发和业务优化,有利于DPU在算网融合场景中发挥其灵活和高效的数据处理能力实现算网业深度协同,助力DPU产业生态发展和算网融合技术创新。

基于扩散模型生成数据重构的客户流失预测

在数据挖掘领域普遍存在数据不平衡影响到模型预测精度的问题,同时还存在未考虑用户隐私保护的问题.生成伪造数据是一种重要的解决方法,但在以结构化数据为主的场景中,由于存在数据特征维度多且不相关等特点,生成高质量的数据存在挑战.考虑到扩散模型在图像生成等任务中被成功应用,以客户流失预测为典型应用场景,尝试将扩散模型应用到客户流失预测任务中.针对该场景数据中的数值型特征和类别型特征,通过高斯扩散模型和多项式扩散模型获得生成数据,并对模型预测效果和数据隐私保护能力进行研究和分析.在多个领域的客户流失数据上进行了大量实验,探索应用生成数据对真实数据融合重构的可能性.实验结果表明基于扩散模型可生成高质量数据,且对多种预测方法均有一定提升,可实现缓解数据不平衡问题.同时,基于扩散模型生成的数据分布更接近真实数据,具有应用于用户隐私保护的潜在价值.

基于大数据分析的精准营销与客户关系深度维护策略

随着大数据技术的迅速发展,精准营销已成为提高企业市场竞争力的关键策略。大数据技术通过对体量庞大的数据进行深入分析,实现对目标客户的精准定位,进而优化个性化推荐和精确推广计划的实施,这种做法不仅显著提升了企业营销工作的效率,而且有效降低成本,增加客户的忠诚度。深度的客户关系维护对于企业的长期成长具有重要意义,因此,文章研究了基于大数据分析的精准营销与客户关系深度维护策略,旨在为相关人员的工作提供参考。

大数据背景下油品销售企业精准营销策略研究

针对油品销售企业精准营销存在的痛点,分析了大数据营销数据关联、客户画像、精准营销、辅助决策四方面的研究重点,提出了客户数据分析的主要内容:客户数据抽取、客户数据整合、客户画像分群,给出了智能数字营销客户需求识别预判、精准营销策略匹配、营销成效评估的方法。通过数据+智能+营销,赋能企业数字化转型,深度应用挖掘客户数据价值,形成客户多维画像,实现客户行为的深度洞察及营销措施的智能推送,最终实现数据驱动业务的有效增长。

新能源汽车装配产线模块化管理方法研究

针对新能源汽车装配产线零部件模块线下替换出错率高的问题,提出一种零部件模块化管理方法。通过阐述模块化中采用大批量定制设计技术和产品数据管理技术的原理,结合某新能源车企的实际数据,实现了零部件模块的线上化管理,解决了新能源汽车装配产线零部件模块替换容易出错的问题,为后续车型的设计与开发提供了可便捷使用的部件模块资源库。

实装对抗训练数据采集与实时裁评系统设计实现

该文提出一种创新的电子实装对抗训练数据采集与实时裁决评估系统,旨在解决传统实装对抗训练中数据采集和裁决评估的效率和准确性问题。系统设计和实现包括数据管理分系统、数据采集分系统、数据处理分系统和裁决评估分系统等模块。系统的主要创新之处在于其灵活自定义采集表单的能力,裁决评估指标体系模板化的能力,以及自动化裁决评估的能力。这些特性使得系统能够根据不同装备训练的需求进行灵活地调整和配置,快速地进行裁决评估,提高裁决评估的自动化和智能化水平。该文详细阐述系统的组成、系统架构设计、工作流程,并介绍该系统在实际电子实装对抗训练场景的应用。通过该文的研究,期望为实装对抗训练的数据采集、分析和裁决评估提供一种高效、准确且智能化的解决方案,为军事训练的数字化转型和智能决策提供有力支持。

人工智能大模型在我国教育领域的应用现状、问题挑战与实践路径

人工智能大模型的兴起对我国各领域产生了巨大影响,不仅推动了技术产业的进步,也促进了教育事业的繁荣。通过提供个性化学习方案、优化教学资源分配以及辅助教学决策,人工智能大模型展现出巨大潜力,但其在数据处理、技术创新和实践应用中仍面临着数据处理水平不足、用户隐私泄露风险、软硬件实力有待提升、模型优化和泛化能力不足、培训引导缺少等问题。因此,该研究旨在通过加强模型数据处理水平、构建安全保护框架,提升模型性能和稳定性、开发课程培训、完善智能评估系统等多个方面解决难题,以期人工智能大模型在个性化学习、智能辅导和定制化教学中发挥作用。为提升整体教育质量,构建包容、公平的“人工智能+教育”环境提供新思路和新视角,在优化人工智能大模型和深化教育应用的同时,减少智能技术对教育产生的不利影响。

基于客户行为数据的卷烟价值感FCD-KNN聚类评估方法

卷烟产品特征众多,如果将所有特征都纳入评估范围,不仅会增加评估的复杂度和工作量,还会因信息冗余而影响评估结果的分离性与准确性,对此研究了基于客户行为数据的卷烟价值感FCD-KNN聚类评估方法.首先标准化处理客户行为数据并量化部分非数值型数据,利用FCD方法(频繁项集挖掘,Frequent Combining Diging)对客户行为数据进行挖掘,提取出多个与卷烟价值感相关的特征;然后在KNN聚类算法(K近邻算法,K Nearest Neighbor)的作用下,通过计算特征相似度,基于高相似度选择与价值感关联性强、能够准确反映产品价值的特征,构建卷烟价值感评估体系.实验结果表明,该方法的Davies-Bouldin指数数值较小,而Calinski-Harabasz指数数值较高,评估结果的分离性与准确性得到保证.