Proceedings of the 2023 6th International Conference on Software Engineering and Information Management最新文献

CMSDPP: Cloud-native Multi-source Streaming Data Processing Platform CMSDPP:云原生多源流数据处理平台

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584894

Haoran Du, Yuxuan Wu, Hong-sheng Gan

引用次数: 0

Analysis of Smart City Construction Under the Trend of New Urbanization: Taking Xinyang City, Henan Province as an example 新型城镇化趋势下的智慧城市建设探析——以河南省信阳市为例

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584907

Bin Hu, Fang Pan, Shuqi Yao

引用次数: 0

The Performance of Trust in Knowledge Sharing Activities using the SECI Model in Online Teamwork 基于SECI模型的在线团队知识共享活动信任绩效研究

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584901

M. Pane, A. Benawa

引用次数: 0

CNDAS-WF: Cloud Native Data Analysis System Based On Workflow Engine CNDAS-WF:基于工作流引擎的云原生数据分析系统

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584891

Xinshi Zhou, Yuxuan Wu

引用次数: 0

An Extractive Text Summarization Based on Reinforcement Learning 基于强化学习的抽取文本摘要

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584874

Kai Du, Guoming Lu, Ke Qin

引用次数: 0

Mobile application for the possible detection of melanoma in the skin by digital photography: A preliminary feasibility study 通过数码摄影检测皮肤黑色素瘤的移动应用程序:初步可行性研究

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584887

Miguel Tarazona-Odar, Wendel Palomino-Davalos, W. Auccahuasi

引用次数: 0

Deep Learning techniques for stock market forecasting: Recent trends and challenges 股票市场预测的深度学习技术:最近的趋势和挑战

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584872

Manali Patel, K. Jariwala, C. Chattopadhyay

{"title":"Deep Learning techniques for stock market forecasting: Recent trends and challenges","authors":"Manali Patel, K. Jariwala, C. Chattopadhyay","doi":"10.1145/3584871.3584872","DOIUrl":"https://doi.org/10.1145/3584871.3584872","url":null,"abstract":"Stock market forecasting has been a very intensive area of research in recent years due to the highly uncertain and volatile nature of stock data which makes this task challenging. By accurately predicting a particular stock's price investors can gain maximum profit out of their investment. With the great success of Deep Learning methods in various domains, it has attracted the research community to apply these models for financial domain also. These DL methods have been proven to achieve better accuracy and predictions compared to econometric and traditional ML methods. This work reviews recent papers according to various Deep Learning models which included: Artificial Neural Networks, Convolution Neural Networks, Sequence to Sequence models, Generative Adversarial Networks, Graph Neural Networks and Transformers applied for stock market forecasting. Furthermore this work also reviews datasets, features, evaluation parameters and results of various methods. From the analysis done on various DL models we found that Graph Neural Networks and Transformer models have potential to interpret dynamic and non-linear patterns of financial time series data with greater accuracy. In addition to this, correlation among various stock indices and investors sentiment along with historical data has great influence on the prediction accuracy. We also identified the benchmark datasets for stock market forecasting based on market capitalization value of an economy. The aim of this paper is to provide insight into most recent work done in the finance domain and identify future directions for more accurate predictions.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132538752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Current Speech to Text Analysis to Help Programmers Dictate Code 当前语音到文本分析帮助程序员口述代码的综述

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584878

Issac G Tijerina, Soma Datta

引用次数: 0

Customer Value Evaluation Model for SMEs Based on K-means Clustering Algorithm 基于k均值聚类算法的中小企业客户价值评价模型

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584895

Y. Bu, Shanshan Liu

{"title":"Customer Value Evaluation Model for SMEs Based on K-means Clustering Algorithm","authors":"Y. Bu, Shanshan Liu","doi":"10.1145/3584871.3584895","DOIUrl":"https://doi.org/10.1145/3584871.3584895","url":null,"abstract":"With the acceleration of the process of global economic integration, the market competition has become increasingly fierce, and the competition among SMEs has changed from the competition and technologies to the competition of customer value. The purpose of this paper is to build a customer value evaluation model for SMEs based on k-means clustering algorithm. Based on customer value theory, it analyzes the functions and costs that customers care about. Explore ways to maximize customer value through analysis of customer functions and customer costs. The AHP is used to determine the weight coefficient. After the weight is determined, based on the customer's questionnaire, data analysis is performed on the questionnaire to determine the proportion of each level of customer function indicators in the indicator area, and then according to the fuzzy Comprehensive evaluation to determine the function coefficient. The survey results show that the factors that affect customer value can be attributed to two points, namely customer function and customer cost. In terms of improving customer function, corporate image accounts for 46% and has the largest weight; Currency costs accounted for 26 percentage points higher.","PeriodicalId":173315,"journal":{"name":"Proceedings of the 2023 6th International Conference on Software Engineering and Information Management","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127396879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Application of In-Memory Databases (IMDB) and Object-Oriented Programming (OOP) in material Micro Texture (MiTx) analysis from data collected by Energy Dispersive Laue Diffraction (EDLD) experiments using pnCCD cameras 内存数据库(IMDB)和面向对象编程(OOP)在pccd相机能量色散劳埃衍射(EDLD)实验数据材料微纹理(MiTx)分析中的应用

Proceedings of the 2023 6th International Conference on Software Engineering and Information Management Pub Date : 2023-01-31 DOI: 10.1145/3584871.3584890

A. Tosson, Ayush J. Sharma, M. Shokr, U. Pietsch

引用次数: 0